inverted u hypothesis theory was developed by

How it works

For Business

Join Mind Tools

Article • 10 min read

The Inverted-U Theory

Balancing performance and pressure with the yerkes-dodson law.

By the Mind Tools Content Team

Have you ever worked on a project with a tight-but-achievable deadline, where your unique knowledge and skills were vital for a successful result? Even though you found it challenging, you may well have done some of your very best work.

Or, think back to a task where you felt little pressure to deliver. The deadline may have been flexible, or perhaps the work wasn't challenging. Chances are, you did an average job at best.

There's a subtle relationship between pressure and performance. When people experience the right amount of pressure, they often perform brilliantly. However, if there's too much or too little pressure, performance can suffer.

In this article, you'll learn how the Inverted-U Theory – also known as the Yerkes-Dodson Law – can help you to understand the relationship between pressure and performance. The result will be that you'll get the best from a happy and engaged team!

Click here to watch our video on the Inverted-U Theory/Yerkes-Dodson Law.

What Is the Inverted-U Theory?

The Inverted-U Theory was created by psychologists Robert Yerkes and John Dodson in 1908. Despite its age, it's a model that has stood the test of time. [1]

The theory describes a clear relationship between pressure and performance. In the original research, pressure was exerted by electric shocks – to motivate rats to escape from a maze!

The Inverted-U Theory gets its name from the curve created when the correlation between pressure (or "arousal") and performance is shown on a graph. See figure 1, below.

Figure 1: The Inverted-U Curve.

inverted u hypothesis theory was developed by

From " The Relation of Strength of Stimulus to Rapidity of Habit‐Formation " by Robert Yerkes and John Dodson. Published in the Journal of Comparative Neurology (1908). Work now in the public domain.

According to Yerkes and Dodson, peak performance is achieved when the level of pressure we experience is appropriate for the work we're doing. When we're under too much or too little pressure, performance declines, sometimes severely.

Understanding the Inverted-U Curve

The left hand side of the graph, above, shows the situation where people aren't being challenged. Here, they see no reason to work hard at a task, or they're in danger of approaching their work in a "sloppy," unmotivated way.

The middle of the graph shows where people work at peak effectiveness. They're sufficiently motivated to work hard, but they're not so overloaded that they're starting to struggle. This is where people can experience "flow," the enjoyable and highly productive state in which they can do their best work. (For more on this, see our article, The Flow Model .)

The right hand side of the graph shows where they're starting to fall apart under pressure. They're overwhelmed by the volume and scale of competing demands on their attention, and feeling a serious lack of control over their situation. They may exhibit signs of hurry sickness , stress, or out-and-out panic.

In reality, the exact shape of the curve will depend on both the individual and their situation. It's also important to recognize that seemingly small changes in professional or personal life can lead to rapid repositioning on the curve.

What's the Difference Between Pressure and Stress?

The Inverted-U Theory shows that pressure can be positive – up to a point. Stress, however, is never positive, and it's important not to confuse the two ideas.

When the levels of pressure we're experiencing are right for the work we're doing, we're stimulated in a beneficial way: motivated, engaged, and excited about doing our best. But stress happens when people feel out of control, and it's a wholly negative thing.

The Inverted-U Theory is about using pressure wisely, always aware of where the benefits end and stress begins.

For more information about how to identify and manage stress, see our article, Minimizing Workplace Stress .

You can take steps to manage the way you experience pressure by using techniques such as Relaxation Imagery , Centering , and Deep Breathing . You can also use Affirmations to maintain a positive outlook and control. Consider teaching these techniques to your teams, too – though you'll also need to have the right organizational processes in place to ensure that pressure levels remain beneficial.

The Four Influencers of the Inverted-U Theory

The impact of pressure can be complex. But four key factors, or "influencers," affect how the Inverted-U Theory plays out in practice*:

Skill Level.
Personality.
Trait Anxiety.
Task Complexity.

1. Skill Level

Someone's level of skill with a given task will directly influence their performance, in terms of both their attitude and their results.

For a while, a new task is likely to be challenging enough. Later, if it starts to feel too easy, some form of extra pressure might be needed to help the person re-engage with their role.

Don't worry about people becoming too skilled or too confident. You can use the other influencers to balance this, so that they feel the optimum amount of positive pressure. Increased skill and confidence can only bring benefits to individuals and organizations.

2. Personality

A person's personality also affects how well they perform.

For instance, some psychologists believe that people who are extroverts are likely to perform better in high-pressure situations. People with an introverted personality, on the other hand, may perform better with less pressure.

The Inverted-U Theory prompts us to match our own personalities – and those of our people – to appropriate tasks. Observation, detailed knowledge of individuals, and open communication, are all important when we're allocating roles and responsibilities.

Although not addressed directly within the Inverted-U Theory, it's important to remember that people can experience various forms of personal pressure (from their family lives, for instance, or from underlying concerns about their role or organization). Try to bear these pressures in mind when setting deadlines and allocating tasks.

3. Trait Anxiety

Think of trait anxiety as the level of a person's "self-talk." People who are self-confident are more likely to perform better under pressure. This is because their self-talk is under control, which means that they can stay "in flow," and they can concentrate fully on the situation at hand.

By contrast, people who criticize or question themselves are likely to be distracted by their self-talk, which can cause them to lose focus in more challenging situations.

The more that people are able to lower their anxiety about a task (with practice, or with positive thinking, for example) the better they'll perform.

4. Task Complexity

Task complexity describes the level of attention and effort that people have to put into a task in order to complete it successfully. People can perform simple activities under quite high levels of pressure, while complex activities are better carried out in a calm, low-pressure environment.

But even when someone's skill levels are high, they may still benefit from a calm environment in which to carry out their most complex work. Conversely, people carrying out low-complexity tasks may need extra stimulation in order to feel motivated and achieve their potential.

Using the Inverted-U Theory

The simplest way to use the Inverted-U Theory is to be aware of it when you allocate tasks and projects to people on your team, and when you plan your own workload.

Start by thinking about existing pressures. If you're concerned that someone might be at risk of overload, see if you can take some of the pressure off them. This is a simple step to help them improve the quality of their work.

By contrast, if anyone is underworked, it may be in everyone's interest to shorten some deadlines, increase key targets, or add extra responsibilities – but only with clear communication and agreement.

From there, balance the factors that contribute to pressure, so that your people can perform at their best. Remember, too little pressure can be just as stressful as too much!

Try to provide team members with tasks and projects of an appropriate level of complexity, and work to build confidence in the people who need it.

Also, manage any negativity in your team, and train your people so that they have the skills they need to do the jobs they're given. Our article on Training Needs Assessment (TNA) will help you do this. Tools like the Four Dimensions of Relational Work can also help you match tasks to people's personalities and interpersonal skills.

However, bear in mind that you won't always be able to balance the "influencers." Motivate and empower your people so that they can make effective decisions for themselves.

The Inverted-U Theory illustrates the relationship between pressure and performance. Also known as the Yerkes-Dodson Law, it explains how to find the optimum level of positive pressure at which people perform at their best. Too much or too little pressure can lead to decreased performance.

Various factors affect how much people react to pressure in different situations. There are "four influencers" that can affect how much pressure people feel:

The Inverted-U Theory helps you to observe and manage these four factors, aiming for a balance that supports engagement, well-being, and peak performance.

You can use the model by managing these four influencers, and by being aware of how they can positively or negatively influence your people's performance.

*Originator unknown. If you know the originator of the "Four Influencers," please contact us.

[1] Yerkes, R.M. and Dodson, J.D. (1908). 'The Relation of Strength of Stimulus to Rapidity of Habit-Formation,' Journal of Comparative Neurology and Psychology, 18(5), 459-482. Available here .

You've accessed 1 of your 2 free resources.

Get unlimited access

Discover more content

Managing stress in your team.

How to Identify and Deal With Stress in Your People

Book Insights

Don't Just Do Something, Stand There! Ten Principles for Leading Meetings that Matter

Marvin Weisbord and Sandra Janoff

Add comment

Comments (0)

Be the first to comment!

Try Mind Tools for free

Get unlimited access to all our career-boosting content and member benefits with our 7-day free trial.

Sign-up to our newsletter

Subscribing to the Mind Tools newsletter will keep you up-to-date with our latest updates and newest resources.

Subscribe now

Business Skills

Personal Development

Leadership and Management

Discover something new today

Frederick taylor and scientific management.

Understanding Taylorism and Early Management Theory

What Is Frederick Taylor's Scientific Management Theory?

Applying science to management

How Emotionally Intelligent Are You?

Boosting Your People Skills

Self-Assessment

What's Your Leadership Style?

Learn About the Strengths and Weaknesses of the Way You Like to Lead

Recommended for you

Dealing with a wide span of control.

How to Cope With Managing a Big Team

Business Operations and Process Management

Strategy Tools

Customer Service

Business Ethics and Values

Handling Information and Data

Project Management

Knowledge Management

Self-Development and Goal Setting

Time Management

Presentation Skills

Learning Skills

Career Skills

Communication Skills

Negotiation, Persuasion and Influence

Working With Others

Difficult Conversations

Creativity Tools

Self-Management

Work-Life Balance

Stress Management and Wellbeing

Coaching and Mentoring

Change Management

Team Management

Managing Conflict

Delegation and Empowerment

Performance Management

Leadership Skills

Developing Your Team

Talent Management

Problem Solving

Decision Making

Inverted U Theory Explained

The inverted u theory describes the relationship between arousal and performance. The theory hypotheses that arousal levels that are either too high or too low can result in gradual decreases in performance. In between these high and low arousal levels, is an optimum level of arousal for performance, which can be seen in the inverted u curve below.

History of the Inverted u Theory

The inverted u theory may also be referred to as the Yerkes-Dodson law due to its creation by two researchers – Yerkes and Dodson. In 1908, these researchers were trying to understand the relationship between the strength of a stimulus and forming habits in mice. They found that there was a negative relationship between the two i.e. the harder it is to form a habit, the less strong the stimulus needs to be to make the habit stick. This study formed the foundation if the inverted u theory, which has stood the test of time.

Understanding the Inverted-U Curve

The inverted u theory takes its name from the shape of the curve. The peak of the curve highlights the arousal level needed for optimum performance. Either side of the peak, where arousal levels are either too high, or too low, suggests gradual decreases in performance.

Showing a graphic of arousal versus performance

What is arousal?

Arousal has been defined as the blend of physiological (i.e. heart rate, muscle tension) and psychological (i.e attention) levels of activation within an athlete, which varies from low (i.e deep sleep) to high arousal (i.e. extreme excitement) (Hackfort, Schinke & Strauss, 2019).

Factors Influencing the Curve

The peak of the inverted u curve, where the optimal levels of arousal are needed for optimal performance, may look different for every individual. There are many factors that might influence where the peak of the curve is, examples of these factors include (1) the individual athlete, (2) the sport, (3) difficulty of the task and (4) the skill level of the athlete – we’ll delve into a few of these factors below…

Task Difficulty

Tasks or sports that involve high levels of coordination may benefit from lower levels of arousal to ensure high focus and attention can be sustained. In contrast, sports or tasks that use major muscle groups may need higher levels of arousal than high-coordination tasks.

Skill Level

Similar to the high-coordination sports and tasks outlined above, beginners may also need lower levels of arousal to maintain focus and avoid distractions and performance declines. In contrast, an expert in a task or sport may not need the same levels of focus and attention as a beginner, and can complete the task with a higher arousal level.

Examples of the Inverted U Theory in Sport

An example of the Inverted u theory can be found in Snooker. This sport requires a high level of fine skill and focus of attention, and therefore players may benefit from a lower arousal level for optimal performance. There are many ways that a lower optimal arousal level can be achieved, such as listening to calm or relaxing music, or using visualisation or meditation to remain composed.

In contrast, sports like boxing and rugby naturally favour higher optimum arousal levels due to their physical nature. Even so, arousal levels that are too high can lead to mistakes and poor performance.

Why is the Inverted U Theory Important in Sport?

From an applied sport psychology perspective, the inverted u theory can help to understand the circumstances in which an athlete can perform at their best. As highlighted, this might look different even for athletes competing in the same sport, but this understanding can benefit athletes and their support staff to achieve and maintain their optimal performance zone – this can include arousal level, mindset, physical fitness and warm ups, and many other factors that influence performance.

How Can This Theory Help Athletes?

Achieving the optimum performance level is important for athletes. This theory can build an understanding of what ideal performance looks and feels like. Through this understanding, athletes can begin to tailor their preparation for competition.

Thinking specifically about how to achieve an optimum level of arousal, athletes can consider ways to increase or decrease their arousal level. Common strategies include listening to music (upbeat to increase arousal, calm and relaxing to lower arousal levels), meditation, and the use of psychological skills such as imagery and self-talk through mental skills training .

How can this theory help coaches?

Coaches also play a part in the preparation to perform. Often, coaches are present at competitions, so understanding how an athlete performs best can help the coach to support in managing the arousal levels. Training sessions can be tailored to replicate demands of competition, encouraging athletes to train under pressure, or perhaps exploring performing at different levels of arousal.

What is the Difference Between Pressure and Stress?

Whilst pressure can be thought of positively, stress is not. Excessive amounts of pressure naturally lead to stress, and excessive or chronic stress can lead to both mental and physical illnesses. It is important to understand the different pressures that we face in different situations in order to manage and use them to our advantage. This links back to athletes and coaches understanding their optimum performance zones.

Responding to stress and pressure

There are several variables that influence the way people respond to pressure and stress. Individual differences in optimal arousal levels, the level of pressure and stress the person is under, and the coping strategies employed can all influence our responses.

Stress can lead to feelings of being overwhelmed, or out of control, therefore it is important to manage and reduce stress as much as possible.

Coping Strategies

The effectiveness of coping strategies is dependent on the individual. Generally, coping strategies can be categorised into 3 types:

Avoidance coping – the problem/situation is avoided. For example, using tv or music to distract from the situation and avoid thinking about it.
Emotion-focused coping – the emotion attached to a problem or situation is dealt with, rather than the problem itself. Practicing meditation and mindfulness are examples of this.
Problem-focused coping – the problem or situation is addressed directly, such as through setting boundaries or seeking support.

There are no right or wrong answers as to which coping strategies to use. For example, avoidance coping might be effective in the short term, but ineffective in the long run, as the problem/situation will continue, whereas problem-focused coping may have more long-term effectiveness in managing a problem or situation.

Final Thoughts

In summary, the inverted u theory describes (but does not explain) the relationship between arousal level and performance. Each individual will have an arousal level that is optimal for peak performance. Arousal levels that are above or below the optimum can lead to gradual declines in performance.

Nicole Wells

Nicole Wells https://sportscienceinsider.com/author/nicole-wells/ Catastrophe Theory in Sport Explained
Nicole Wells https://sportscienceinsider.com/author/nicole-wells/ Arousal In Sport – What Does The Research Suggest?

Memberships

Inverted-U Theory of Stress (Yerkes & Dodson)

Inverted-U Theory: this article explains Inverted-U Theory , developed by Robert Yerkes and John Dodson in a practical way. Next to what it is, this article also highlights the interpreting of the model, the four influencing factors and responding to stress and pressure. After reading, you’ll understand the basics of this stress management theory . Enjoy reading!

What is Inverted-U Theory?

Inverted-U Theory is a theory that sheds light on the relation between performance and pressure or arousal. In the original study, rats were given electric shocks as motivation for escaping from a maze.

The Inverted-U Theory owes its name to the line, in the form of an inverted U, that appears when there is a correlation between pressure and performance. This is illustrated in the graph presented in this article.

Figure 1 – the nverted-U theory model

A quick look at the curve reveals that performance lags behind when there’s little pressure, and that performance is positively influenced when there’s some more pressure.

If even more pressure is added, performance is negatively influenced and efficiency decreases. The worker’s efficiency and performance can reach an optimal point if the pressure or arousal have reached an optimal point.

Inverted-U Theory was developed by psychologists Robert Yerkes and John Dodson in 1908. Despite the fact that the model was developed long ago, it continues to be relevant.

Interpreting the Model

When looking at the left-hand side of the graph, it’s notable that low pressure or low stress levels result in a stress response corresponding to ‘boredom or lack of challenge’.

Even if the task itself is a critical activity, the attention, concentration, and precision required to properly execute a task is absent in the absence of an appropriate level of pressure or stress.

On the right-hand side of the graph from the Inverted-U Theory, we can see that extreme pressure levels or high stress levels don’t automatically result in good performance.

The opposite is true: if pressure gets too high, or a too high stress level is activated, this results in a feeling of unhappiness, stressfulness, and anxiety. These are all results of overwhelming stress.

In the middle of the graph, however, is a region where the worker performs best. This area is where an optimal amount of pressure is applied. In this region, the moderate pressure leads to an optimal stress level, which is manageable as well. Eventually, this results in the highest performance level for the user.

The optimal level of pressure or arousal is influenced by a number of factors.

Four Influencing Factors of the Inverted-U Theory

It can be hard to determine how much impact pressure, and stress have because the desired amount of pressure is influenced by four factors. These factors are also known as influencers. Inverted-U Theory recognises the following four influencers:

Personality

Different personality types benefit from different levels of stress or pressure.

Generally, extraverted personalities are more resistant to stress and better able to keep their head above water when stressed than introverted personalities. Introverted people usually have a higher chance of performing well in environments with little stress or excitement.

There are also factors, of course, that can cause temporary pressure or stress. These may be professional matters or matters in private life. The duration of the period in which stress or pressure are present may differ as well.

Task Difficulty

The degree of complexity of a task relates to the level of attention and effort a person requires to successfully complete it. People are generally able to carry out simple activities even when pressure is high, but complex tasks are better taken care of in quiet surroundings.

A shop manager and an accountant have completely different jobs. Each has more knowledge of the work they do individually than of the other’s job.

If they would swap jobs, the challenge and the pressure would be so high in the beginning that it would strongly motivate them. After a while, when tasks get easier, they would have to use a new form of pressure to keep their performance up.

People who are better at this also perform better under pressure. People who are not good at it will enter into challenging situations more often.

Complexity and Motivation

In situations that require carrying out tasks with a high level of complexity, or solving complex problems, motivation plays an important role.

There have been various situations in which the relation between motivation and complex problem solving was studied. These have yielded several theories, such as McClelland’s motivation theory and Maslow’s hierarchy of needs .

Stress Management: 40+ easy ways to deal with stress Stress relief and burnout prevention. Don’t let stress control your life. Beat anxiety and worries. Live, Laugh, Love. >> More information

Inverted-U Theory: difference between pressure and stress

The terms ‘pressure’ and ‘stress’ are often used interchangeably, as if they refer to the same thing. ‘I work in a high-pressure environment’ , or ‘I have a stressful job’ . According to science, however, there definitely is a difference between pressure and stress. These two things reappear regularly in Inverted-U Theory.

‘Stress’ refers to situations that demand a lot from someone who has few resources such as money, time, energy, or manpower.

Pressure, on the other hand, is a situation in which someone notices that there are extensive consequences to the outcome of a certain action. It’s the feeling that something is at stake, depending on one’s performance. Stress can create several problems that can lead to feelings of overload and, in the most extreme case, even a occupational burnout .

Stress on the job is caused by things such as late meetings, long lists of emails that have to be answered, approaching deadlines, or emergency situations.

Pressure often becomes apparent from signals such as anxiety and the feeling that a situation is life-or-death.

A job interview, too, is an example of a situation where one’s actions may have far-reaching consequences, which is why it is not described as a stressful situation.

Inverted-U Theory: responding to stress and pressure

Different stress levels or feelings of pressure cause different reactions and approaches in people. In an extraordinarily stressful situation, a person’s goal is to feel less overwhelmed. In a situation of pressure, the goal is to perform well.

There are various things that can be done to reduce stress. Going for a walk after a long day in the office is good for you, and spending time on hobbies on the weekend also lowers stress.

In a high-pressure situation, these same options are often unavailable. A special forces soldier who is involved in a rescue mission has no time to relax and settle down, but may have to react instinctively and adequately without changing their stress levels.

Now It’s Your Turn

What do you think? Do you recognise the explanation about Inverted-U Theory? What do you think are important points when it comes to using this tool? How do you experience the influence of pressure on your performance? Are you facing excessive stress, or rather little or no pressure? Do you have any tips or additional comments?

Share your experience and knowledge in the comments box below.

More information

Broadhurst, P. L. (1957). Emotionality and the Yerkes-Dodson law . Journal of experimental psychology, 54(5), 345.
Broadhurst, P. L. (1959). The interaction of task difficulty and motivation: The Yerkes Dodson law revived . Acta Psychologica, Amsterdam.
Cohen, R. A. (2011). Yerkes–Dodson Law . Encyclopedia of clinical neuropsychology, 2737-2738.
Teigen, K. H. (1994). Yerkes-Dodson: A law for all seasons . Theory & Psychology, 4(4), 525-547.

How to cite this article: Janse, B. (2019). Inverted-U Theory of Stress (Yerkes & Dodson) . Retrieved [insert date] from Toolshero: https://www.toolshero.com/human-resources/inverted-u-theory/

Original publication date: 09/18/2019 | Last update: 12/25/2023

Add a link to this page on your website: <a href=”https://www.toolshero.com/human-resources/inverted-u-theory/”>Toolshero: Inverted-U Theory of Stress (Yerkes & Dodson)</a>

Did you find this article interesting?

Your rating is more than welcome or share this article via Social media!

Average rating 4.3 / 5. Vote count: 11

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

Stress and Happiness at Work

4 Types of Stress by Karl Albrecht

Social Defeat: a Stress Theory

Holmes and Rahe Stress Scale - Toolshero

Holmes and Rahe Stress Scale: Theory and Test

Cognitive Restructuring: Worksheet and Theory

PAEI Model by Ichak Adizes

Also interesting.

occupational safety and health osh - Toolshero

Occupational Safety and Health (OSH) explained

Job Characteristics Model (Hackman and Oldham)

HR Analytics: the basics explained

BOOST YOUR SKILLS

Toolshero supports people worldwide ( 10+ million visitors from 100+ countries ) to empower themselves through an easily accessible and high-quality learning platform for personal and professional development.

By making access to scientific knowledge simple and affordable, self-development becomes attainable for everyone, including you! Join our learning platform and boost your skills with Toolshero.

ABOUT TOOLSHERO

Free Toolshero e-book
Memberships & Pricing

Subscriber Services
For Authors
Publications
Archaeology
Art & Architecture
Bilingual dictionaries
Classical studies
Encyclopedias
English Dictionaries and Thesauri
Language reference
Linguistics
Media studies
Medicine and health
Names studies
Performing arts
Science and technology
Social sciences
Society and culture
Overview Pages
Subject Reference
English Dictionaries
Bilingual Dictionaries

inverted U hypothesis

Quick reference.

Hypothesis that states that performance improves with increasing levels of arousal up to an optimal point beyond which further increases in arousal produce a detrimental effect on performance. Therefore, athletes may perform badly because they are over- or under-aroused. The hypothesis is qualitative, and does not attempt to quantify the relationship between arousal and performance. The optima vary between people doing the same task and one person doing different tasks. A basic assumption in the hypothesis is that arousal is unidimensional and that there is, consequently, a very close correlation between indicators of arousal; this is not the case. See also catastrophe theory.

inverted-U hypothesis

From: inverted-U hypothesis in The Oxford Dictionary of Sports Science & Medicine »

Subjects: Medicine and health — Clinical Medicine

Inverted U Theory in Sport – What is it and why is it important?

The Inverted U Theory in Sport

In this post we will discuss the Inverted U theory in sport:

Why is it important?
What is involved?
What is the Inverted U Theory in Sport
Sporting examples of the Inverted U Theory
How can this theory help athletes and coaches

This post is part of our series discussing the relationship between arousal and performance. You may also want to check out our other articles below:

Drive Theory in Sport
Catastrophe Theory in Sport
Personality Types

Why is the Inverted U Theory in Sports Important?

All those involved in sports should understand the principles and purpose of the Inverted U theory.

The Inverted U theory in sports aims to explain the relationship between arousal levels and performance. The theory also suggests how different levels of arousal can lead to either an increase or decrease in performance.

In 1908, researchers Yerkes and Dodson published a study that forms the foundation of the Inverted U theory. The Inverted U theory began to explain the relationship between performance and arousal different to that of the drive theory . The Inverted U theory differs by suggesting that too much arousal can lead to a decrease in performance.

Researchers have then developed the Inverted U Theory further resulting in the Catastrophe Theory by Fazey and Hardy (1988) . You can read our article on the Catastrophe theory here .

Sports coaches and athletes need to understand the impact on arousal and performance and how an athlete’s performance can potentially increase and/or decrease with different levels of anxiety . You may also want to check out out article on personality types .

If sports coaches understand the link between arousal and performance, this could result in better performances and reduce the risk of a decline in performance. The inverted U Theory is still taught as part of many sports qualifications and coaching qualifications , helping demonstrate the importance of the theory.

All those involved in sports should understand the principles and purpose of the Inverted U theory.

What is involved in the Inverted U theory?

The two factors involved in the Inverted U theory in sport are:

An athlete’s arousal or anxiety level
Performance level

What is the Inverted U Theory in Sport?

The Inverted U theory in sport suggests that if an athlete’s arousal is low/none existent then this will result in a low-performance level. As an athlete’s arousal level increases, the performance will gradually increase up to a point of maximum performance. The point of peak performance in the Inverted U theory is called the optimum point.

If arousal continues to increase after the optimum point, The Inverted U Theory suggests performance will decrease gradually.

Sporting Examples of the Inverted U Theory in Sport

A sporting example to help explain the Inverted U Theory would be a boxer who is just about to enter a boxing match.

A low arousal level at the start of the match would result in the boxer’s performance level being low. The low arousal level could lead to a slower reaction time or lack of concentration levels.

Alternatively, too much arousal could lead to loss of strategy or increase the risk of foul play and potentially being penalised.

Whereas, if the boxer had the optimum level of arousal at the start of the boxing match, they would perform at their best.

For the Inverted U Theory, it is important to note that each sport requires different optimum performance levels. Therefore, the Inverted U theory can be described as being on a continuum of arousal and the arousal level for peak performance for one sport may be different to another. For example, a boxer would have a different peak performance arousal level compared to a snooker player.

How Can This Theory Help Athletes and Coaches?

The Inverted U Theory builds on the drive theory (you can read our article on the drive theory here) and further explains the importance for coaches to understand the relationship between arousal and performance.

Sports coaches need to be aware that performances can drop due to an increase or decrease in arousal levels.

However, there are criticisms among researchers as the Inverted U theory does not explain sudden drops in performance. The Inverted U theory suggests performance gradually improves or declines. However, this is not always the case and researchers have created a new theory called the Catastrophe Theory.

The Inverted U theory in sports links both arousal levels and performance levels. The Inverted U theory states that performance will gradually increase if arousal increases to an optimum point. Too much arousal after this point will then lead to a gradual decline in performance. Read our next article in this series here .

Learn how to identify development opportunities and implement strategies with our training newsletters to help you improve even further. All you need to do to sign up is to enter your email address below.

Get every new post delivered to your inbox.

Email address:

New to sports coaching? Then you may want to check out our guide to sports coaching. Here you will be able to learn techniques and strategies that will excel your coaching career. Topics include Stages of Learning , Methods of Training and Leadership Styles .

You may also be interested in the following articles:

Stages of Learning
The Best GPS Sports Vests
Downloadable Sports Session Planner Template
What are the different Leadership Styles used in sports
Learn The Different Methods of Practice

References:

Yerkes, R.M. , & Dodson, J.D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18, 459-482.

Understanding the Inverted U Theory: An Introduction

The Theory of Inverted U , also known as the Yerkes-Dodson Law , is a critical psychological concept that explores the complex relationship between arousal, stress, and performance. Introduced by psychologists Robert Yerkes and John Dodson in 1908, the law suggests that a certain level of stress can enhance performance, but there's a threshold beyond which performance deteriorates.

This theory is a fundamental framework to understand productivity, stress management, decision making, and even motivation. This article aims to present an in-depth exploration of this mental model with relatable examples and practical applications.

Unpacking the Inverted U Model

The Inverted U model visualizes the correlation between pressure (stress or arousal) and performance. This correlation is divided into three primary phases:

Ascending Phase (Increasing Returns) : At this stage, as stress or stimulation increases, performance also improves. The pressure can act as a catalyst to drive focus and energy.

Peak Point (Optimal Performance) : This is the ideal stress-performance equilibrium. At this point, an individual or system is at their peak performance—the right amount of stress fuels motivation and focus without causing overwhelm.

Descending Phase (Decreasing Returns) : Past the optimal point, any additional stress results in deteriorating performance. Here, stress outweighs the individual's coping mechanisms, leading to errors, decreased productivity, or even burnout.

A Day in the Life: The Inverted U Model in Action

To better grasp this theory, imagine a regular workday. In the morning, as you sip your coffee, your arousal levels gradually increase. You start working and as the pressure mildly intensifies, you find yourself becoming more efficient - this is the ascending phase of the model.

Come mid-day, you're entirely engrossed in your work, handling tasks effectively - you're at the peak point of the inverted U, experiencing optimal stress levels and showcasing your best performance.

As the day progresses, if the workload continues to pile up, you might start feeling overwhelmed. The excessive stress leads to fatigue and mistakes - you've entered the descending phase of the model, where increased stress leads to decreased performance.

Practical Applications of the Inverted U Theory

Understanding the theory of Inverted U allows us to optimize performance and well-being in various contexts, from personal growth to professional environments, education, and even sports training.

Workplace Productivity

Effective stress management is crucial in the workplace. Leaders and managers can utilize this model to ensure employees aren't overloaded with work and to prevent burnout. For instance, setting realistic deadlines, promoting a healthy work-life balance, and recognizing employees' efforts can help maintain an optimal stress-performance balance.

Education and Learning

The Yerkes-Dodson law is equally applicable in the realm of education. It helps teachers, parents, and students understand the impact of stress on academic performance. Moderate pressure can encourage students to study and prepare well for exams. However, excess stress might impair focus, memory recall, and overall learning.

Sports and Performance Psychology

In sports, the right amount of arousal can boost performance. Athletes often perform their best when they're mildly stressed - it enhances focus and adrenaline flow. However, too much anxiety can lead to poor performance. Coaches and athletes can use this model to devise optimal training strategies, taking care to avoid overtraining and promoting proper rest and recovery.

Conclusion: Harnessing the Power of the Inverted U Theory

The Theory of Inverted U or the Yerkes-Dodson Law offers vital insights into the intricate interplay of stress and performance. By understanding this relationship, we can strive for balance, optimizing productivity without compromising well-being.

Whether you're a professional trying to maximize your work output, a student seeking to optimize study habits, or a sports coach aiming to improve team performance, this mental model offers a powerful framework to inform your strategy.

Remember, the goal isn't to eliminate stress, but to harness it - striking the right balance is the key to unlocking peak performance.

Arousal, anxiety, and performance: a reexamination of the Inverted-U hypothesis

Affiliation.

1 Department of Exercise Science and Sport Studies, Rutgers, the State University of New Jersey, USA.
PMID: 14768844
DOI: 10.1080/02701367.2003.10609113

Until recently, the traditional Inverted-U hypothesis had been the primary model used by sport psychologists to describe the arousal-performance relationship. However, many sport psychology researchers have challenged this relationship, and the current trend is a shift toward a more "multidimensional" view of arousal-anxiety and its effects on performance. In the current study, 104 college-age participants performed a simple response time task while riding a bicycle ergometer. Participants were randomly assigned to one of eight arousal groups (between 20 and 90% of heart rate reserve) and were told they were competing for a cash prize. Prior to the task, the Competitive State Anxiety Inventory-2 and Sport Anxiety Scale (SAS) were administered to assess the influence of cognitive and somatic anxiety. As hypothesized, regression analysis revealed a significant quadratic trend for arousal and reaction time. This accounted for 13.2% of the variance, F change (1, 101) = 15.10, p < .001, in performance beyond that accounted for by the nonsignificant linear trend. As predicted by the Inverted-U hypothesis, optimal performance on the simple task was seen at 60 and 70% of maximum arousal. Furthermore, for the simple task used in this study, only somatic anxiety as measured by the SAS accounted for significant variance in performance beyond that accounted for by arousal alone. These findings support predictions of the Inverted-U hypothesis and raise doubts about the utility theories that rely on differentiation of cognitive and somatic anxiety to predict performance on simple tasks that are not cognitively loaded.

Publication types

Clinical Trial
Randomized Controlled Trial
Competitive Behavior*
Personality Inventory
Psychomotor Performance*
Sports / psychology*

Motivation and emotion/Book/2019/Zone of optimal functioning hypothesis

2 What is the difference between ZOFH and Flow Theory?
3 Five dimensions of ZOFH and the emotion-performance relationships
4.1 Psychological theory for anxiety and sports performance
4.2 Pre performance event zones of functioning
4.3 Positive psychology and during performance event zones of functioning
5.1 Methodology
5.2 Major finding
6.1 Assessment of Emotion
6.2 Assessment of Performance
8 Case study: Alex Honnold
9 Conclusion
10 See also
11 References
12 External links

Overview [ edit | edit source ]

In 1908 scientists Yerkes and Dodson created the theory known as the "Inverted U Hypothesis" (Yerkes & Dodson, 1908). This was the psychological foundation of the anxiety-athletic relationship known as the Yerkes-Dodson law . Over two decades past when, in 1927 Sigmund Freud identified the minimal psychoanalitics [ spelling? ] in drive and motivation research. As time past in 1943, Spence and Hull created what is to this day referred to as Drive Theory , the theory aiming to identify and describe the instinctual needs and behaviours behind behaviour (Hanin, 2000).

It was seventy years later when research into the emotion-performance relationship began with the work of Yuri Hanin in 1980 (Krane, 1993). He believed that there was an 'optimal balance' for each individual's state-trait anxiety and peak athletic performance (Woodman, Albinson & Hardy, 1997). He developed an idiographic model based on the subjective emotions of the individual and their performance outcome soon to be known as the Zone of Optimal Functioning Hypothesis (ZOFH) (Ruiz, Raglin & Hanin, 2015). The ZOFH or IZOF acronym was later introduced by Hanin in 1995. Hanin proposed five core dimensions (form, content, intensity, time, and context) which carried over into later studies (Hanin, 1997). There is still much to be explored in the world of ZOFH and Hanin's operational measures and the implementation in an athletic performance setting. Future directions point to methodological aspects such as the assessment of the multi-dimensional emotion and non-emotion and the assessment of performance (Ruiz, Raglin & Hanin, 2015).

What is the difference between ZOFH and Flow Theory? [ edit | edit source ]

To Sports Psychologists and Positive Psychologists ZOFH sound very similar to Csikszentmihalyi 's Flow Theory , and they focus on the same measures, however they are not in fact the same concept. The ZOFH centres on qualitatively and quantitatively measuring emotions such as anxiety and fear (can be seen as arousal in Figure 2. ) and how it predicts optimal performance (Kamata, Tenenbaum & Hanin, 2002). Kamata, Tenenbaum & Hanin (2002) believe it is a state of emotional intensity from which the athlete is able to reach optimal performance. Flow theory was coined in 1975 by Csikszentmihalyi when he developed a theory to understand what made physical activity enjoyable (Mandigo & Holt, 2000). Csikszentmihalyi focuses on the 'state of enjoyment' one experiences from optimal experience (Chan & Ahern, 1999). You may say this sounds like ZOFH ... and you would be correct, however the complexity of flow focuses on the motivation and emotional experience of optimal performance (why do we feel the way we do?) and the ZOFH focuses on the performance outcome itself (did the individual achieve optimal performance from a qualitative and/or quantitative perspective?) (Mandigo & Holt, 2000; Ruiz, Raglin & Hanin, 2015).

Five dimensions of ZOFH and the emotion-performance relationships [ edit | edit source ]

Hanin (1993) provided a comprehensive description of the elements of ZOFH and how they could be utilised to enhance the emotion-performance relationship in many ways. Hanin and future researchers use the global affect approach which uses emotional experience to the individuals emotional content in a hedonic tone (Hanin, 2000).

Anxiety and performance [ edit | edit source ]

Psychological theory for anxiety and sports performance [ edit | edit source ]

The importance of anxiety in influencing a athlete's performance is a well-known factor contributing to their success or failure (Ruiz, Raglin & Hanin, 2015). It wasn't until the early 1990's when researchers stated developing sports-specific and individualistic approaches to their anxiety-performance related theories (Krane 1992; Raglin 1992). This is where current research in the anxiety-performance relationship is growing in its findings (e.g. ZOFH and flow theory).

Pre performance event zones of functioning [ edit | edit source ]

Majority [ grammar? ] of the research has focused on the pre performance measures an athlete can take to ensure they understand the importance of the ZOF. Predictive performance zones are highlighted in Hanin & Syrjä's (1995) subjective emotion (happiness) and Bortoli & Robazza's (2002) physiological response (relaxation), [ grammar? ] these two biopsychosocial factors offer a stronger prediction of performance outcomes (Robazza et al., 2004). This has been a repeated measure of ZOFH and is now widely supported (Robazza et al., 2004).

Positive psychology and during performance event zones of functioning [ edit | edit source ]

Flow theorist Csikszentmihalyi was one of the primary psychologists that developed a modern branch in the science know as positive psychology. This is the area of psychology that focuses of personal fulfilment, making the most of one's life and achieving optimal performance. Hanin and other researchers have used this mindset when developing the ZOFH (Phan & Ngu, 2017). The approach of enhancing athlete's emotional regulation skills was used in a 2012 study in which a single-case study used a 19 year old female collage [ spelling? ] cross-country runner gave an immediate and delayed reflection and had a social validation interview at the end of a season long intervention (Woodcock, Cumming, Duda & Sharp, 2012). Woodlock and colleagues (2012) used the modified Borg CR-10 scale to measure if she was apprehensive, dispirited, doubtful, scared, worn-out, uncertain, comfortable, calm, confident, determined and motivated. Results found that three changes were presented in the participants [ grammar? ] emotional state, performance process and performance outcome. This study is one of the first to observe during event zones of functioning and suggests that their could be different zone profiles between elite and non-elite athletes (Woodcock, Cumming, Duda & Sharp, 2012).

ZOFH methodology and major finding [ grammar? ] [ edit | edit source ]

The primary objective in Hanin's theory is predicting performance outcomes and without understanding the arousal of the individual than this will not be achieved (Kamata, Tenenbaum & Hanin, 2002). From the development pf the ZOF model the following assumptions were made about the emotion-performance relationship;

Emotions are triggered by person’s appraisals of the probability of achieving relevant goals.
Since sport is a repetitive activity, situational emotional experiences gradually develop into emotional patterns.
Emotion patterns are specific to the individual, task, and setting.
Individual emotion–performance relationships are bi-directional.
The prediction of performance is based on interactions of optimal and dysfunctional emotions.
Meta-experiences are gradually developed because athletes often reflect on their experiences in successful and poor performances.

Methodology [ edit | edit source ]

When the ZOFH framework first began when Hannin suggested that optimal performance zones can be identified by empirical testing and retrospective recall (Thewell & Maynard, 1998). Hanin used the testing that was available for general psychology to test the " the individual's state anxiety (score) prior to ...optimal performance plus or minus half of a standard deviation (4 points) on Spielberger, Gorsuch, and Lushene's (1970) State-Trait Anxiety Inventory ." (Hanin, 1997). This was the primary measure of multidimensional anxiety (testing both cognitive and somatic anxiety). Other studies later developed to include POMS , Positive and Negative Affect Schedule (Watson, Clark, & Tellegen, 1988), the State-Trait Anger Expression Inventory-2 (Spielberger, 1999), and Affect Grid (Russell, Weiss, & Mendelsohn, 1989). One of the most utilised sport-specific scales is the Competitive State Anxiety Inventory-2 (Martens et al., 1990).

These tools are beginning to develop with the ZOFH developing into a more dynamic and multi-faceted framework of emotional regulation. Figure 4. identifies the research conducted by Syrjä and Hanin (1997) who collected data from an ice-hockey and Olympic level Football team to determine the inter-individual variability when given the choice to answer questions that they developed that were sports specific. The previous methodical approaches were shown to identify 85% variabilities were not captured using these scales.

Although there are a number of approaches to collecting personality of the individual there are still not enough sports-specific tools to suggest that ZOFH trends vary across sports. The positive and negative affect scales may be the step in future research as they currently have the most widespread forms of sport-specific material with pen, pencil and computer program testing (Hanin, 1993).

Major finding [ edit | edit source ]

Kamata, Tenenbaum & Hanin (2002) developed on Hanin's model and broke it down into a Monotonically Increasing Probability Model (MIPM) with two major categories; optimal performance (OP) and non-optimal performance (nOP). From here the research is widely debated on the best operational measure to use to identify ZOFH. It is known that the ZOF model has a lower boundary but not an upper one, so in order to identify nOP, this study aimed to identify the upper limit which would determine the zone of non-optimal performance, allowing the results to create a bell-shaped curve like the image in Figure 2. , resembling the inverted-U hypothesis. This finding helped give shape to the methodological approaches identified below as the items in the various tests, when sorted into nOP and OP reflected the MIPM (Kamata, Tenenbaum & Hanin, 2002).

The limitations identified in this model include using the traditional approaches to testing ZOF do not provide an accurate estimation of the zones of optimal functioning and the relationship between the zones needs further investigation (Kamata, Tenenbaum & Hanin, 2002). One other key finding that this study found was that the "action chain" was not developed, in this study Kamata, Tenenbaum and Hanin (2002) mention it as "the process by which IZOF is reached".

How can it be applied in future research? [ edit | edit source ]

Two of the major directions for future research centre on the development of the assessment of emotion-performance relationship (Ruiz, Raglin & Hanin, 2015).

Assessment of Emotion [ edit | edit source ]

Empirical evidence suggests that psychobiosocial states as described by Hanin (2000) (Table 1.) A model was developed recently that can potentially be used to identify the multimodal emotional profile of the athlete. This is know [ grammar? ] as the psychobiosocial states (PBS-S) scale (Ruiz, Raglin & Hanin, 2015). Further research is needed to develop the validity of this scale as well as how it performs on the athletic population, skill level, sport type and profiling of the emotion and non-emotion experience. Research as to each emotion associated with arousal starting with the seven basic emotions; fear, anger, disgust, happiness, sadness, surprise and contempt all need to be researched further to create a valid and reliable tool for identifying the ZOF (Ruiz, Raglin & Hanin, 2015).

Assessment of Performance [ edit | edit source ]

The direction towards ZOFH and performance in focus on the concept of the action-centered process. The is also conceptualised by Hanin and is used for the individual athletes task execution process (Ruiz, Raglin & Hanin, 2015). This task allows the individual to describe their action as part of a sequence or "action chain" (Hanin & Ekkekakis, 2014). Developed by the future development address by Kamata, Tenenbaum and Hanin just over a decade prior, Hanin and Ekkekakis created a step-wise recording of task achievement and optimal performance is then identified by the athlete themselves. This is a mjor [ spelling? ] area for future research with more scales needing to be developed for numerous sports with potentially varied ZOF ( Figure 4. )

Although from the research presented studies are moving towards a multidimensional, interactive and sports focused approach, ZOFH is still in its early stages and the need for further research on the emotional responses, or non-emotional responses as well as looking into the performance element itself and the pre, during and eventually post event zones of optimal functioning leave a lot to be done in the research space.

Quiz [ edit | edit source ]

Choose the correct answers and click "Submit":

1 Where were the poorest levels of performance in the zone of optimal functioning?

2 What is the ZOF measuring?

3 What is the earliest measure of the anxiety-athletic relationship?

Case study: Alex Honnold [ edit | edit source ]

Alex Honnold, 34, is a world famous rock climber who recently become the first and only person to Free Solo a 3000ft route of El Capitan (Approximately 914m high) at Yosemite National Park on June 6, 2018 in 1hr and 58mins. He is an advocate for 'optimal state of performance" and has spoken at Ted Talks and his Oscar Nominated Documentary Free Solo about the experience of Free Solo and how it differs from other forms of climbing. His psychological perspective is in line with positive psychology and the awareness with personal fulfilment and optimal performance. Honnald himself uses many of the anxiety techniques used to reduce state-trait anxiety in his climbing routine such as mental rehearsal e.g. move-by-move rehearsal of the route to the summit of El Capitan.

In the documentary, Honnold's doctor Jane E. Joseph mentions that he has an incredibly low activity level in his amygdala ( Figure 5. ). This means that his response to typically fearful images is almost non-responsive, she coined his as a "super sensation seeker ". An fMRI demonstrates Honnold's low reactivity in his Amygdala when asked questions relating to his fears and life decisions. This [ grammar? ] when compared to other sensation seekers highlights this very rare brain and his ability to process performance and fear in a way that has possibly developed over time through an extreme Pavlovian conditioning response or possible a genetic predisposition that has served as an advantage in his death-defying career. MacKinnon also displayed his personality score compared to other sensation seekers similar using a technique similar to the STAI, with the global affect approach to emotion, consistent with modern research in ZOFH Honnold reported a lower boredom score when compared to the high sensation seeker (HSS) population, lower neuroticism scores and higher urgency and perseverance. If Honnold is an atypical HSS he challenges the theory of ZOF and arousal and asks the question can the amygdala be conditioned to dismissing fear or is it a genetic pathway response?

In a study with nine musicians, scientists aimed to analyse music performance anxiety (MPA) and is identified by four traits, much like ZOFH; affect cognition, behaviour and physiology. The study corresponds with research that there is significant findings for two signal paths for performance anxiety. One through the thalamus and the amygdala to a vegetative, autonomous nervous system, resulting in the release of adrenaline and noradrenaline. The other is slower and exhibits conscious reaction, displaying recognition of the situation and is located in the hippocampus (Spahn, Echternach, Zander, Voltmer & Richter, 2010). Honnold could potentially be a candidate for a similar study on rock climbers, sensation seekers, or elite athletes.

Conclusion [ edit | edit source ]

The ZOFH is a theory that was developed to improve the awareness of the emotion-performance relationship in a sports-specific environment. Hanin's work has developed since the 1980s and continues to challenge how psychologists understand individual responses to arousal. Famous athletes such as Alex Honnold open up new discoveries into how the brain processes arousal and if it can be conditioned. The ZOFH is a step in the direction towards understanding optimal performance and pushing boundaries in sport excellence. It is an incredibly new area and as such any news is good news for this medium in sport psychology, and psychology in general. Prior to Hanin (1980), the relationship between emotion and performance had used general methodological techniques within psychology and now is moving towards a sport-specific way of research for both elite and non-elite athletes. Results from studies using the ZOFH can help the general population discover how to achieve their 'optimal performance' at any given task e.g. in the office, at home, with their diet. The future directions for this theory include studying the non-optimal performance measure in more depth, researching other biopsychosocial factors and fundamental emotions e.g. fear, sadness then developing into more complex emotions to predict future performance outcomes. This modern concept has a long way to go in the field of psychology but is an incredibly exciting one!

References [ edit | edit source ]

Chan, T., & Ahern, T. (1999). Targeting Motivation—Adapting Flow Theory to Instructional Design. Journal Of Educational Computing Research , 21 , 151-163. https://doi.org/10.2190/uj04-t5yb-yfxe-0bg2

Hanin, Y. L. (1995). Individual zones of optimal functioning (IZOF) model: An idiographic approach to performance anxiety. In K. Henschen & W. Straub (Eds.), Sport psychology: An analysis of athlete behavior (pp. 103–119). Longmeadow, MA: Movement Publications.

Hanin, Y. L. (1997). Emotions and athletic performance: Individual zones of optimal functioning model. European Yearbook of Sport Psychology, 1, 29–72.

Hanin, Y. L., & Stambulova, N. B. (2002). Athlete-generated metaphors as descriptors of performance states. Svensk Idrottspsykologi, 2, 7–9.

Hanin, Y. L., & Ekkekakis, P. (2014). Emotions in sport and exercise settings. In A. Papaioannou & D. Hackfort (Eds.), Routledge companion to sport and exercise psychology: Global perspectives and fundamental concepts (pp. 83–104). New York, NY: Routledge.

Kamata, A., Tenenbaum, G., & Hanin, Y. (2002). Individual Zone of Optimal Functioning (IZOF): A Probabilistic Estimation. Journal Of Sport And Exercise Psychology , 24 , 189-208. https://doi.org/10.1123/jsep.24.2.189

Krane, V. (1992). Conceptual and methodological considerations in sport anxiety research: From the inverted-U hypothesis to catastrophe theory. Quest, 44, 72–81.

Krane, V. (1993). A practical application of the anxiety-athletic performance relationship: The zone of optimal functioning hypothesis. The Sport Psychologist, 7, 113–126.

Mandigo, J., & Holt, N. (2000). Putting Theory into Practice: How Cognitive Evaluation Theory Can Help Us Motivate Children in Physical Activity Environments. Journal Of Physical Education, Recreation & Dance , 71 , 44-49. https://doi.org/10.1080/07303084.2000.10605984

Martens, R., Burton, D., Vealey, R. Bump, L., & Smith, D. (1990). Development and validation of the competitive state anxiety inventory-2. In R. Martens, R. S. Vealey, & D. Burton (Eds.), Competitive anxiety in sport (pp. 117–190). Champaign, IL: Human Kinetics

Phan, H., & Ngu, B. (2017). Positive psychology: The use of the Framework of Achievement Bests to facilitate personal flourishing (pp. 19-50). Trnava: Intech.

Raglin, J. S., & Hanin, Y. L. (2000) Competitive anxiety and athletic performance. In Y. L. Hanin (Ed.), Emotions in sport (pp. 93–112). Champaign, IL: Human Kinetics.

Robazza, C. (2006). Emotion in sport: An IZOF perspective. In S. Hanton & S. D. Mellalieu (Eds.), Literature reviews in sport psychology (pp. 127–158). Hauppauge, NY: Nova Science.

Ruiz, M., Raglin, J., & Hanin, Y. (2015). The individual zones of optimal functioning (IZOF) model (1978–2014): Historical overview of its development and use. International Journal Of Sport And Exercise Psychology , 15 , 41-63. https://doi.org/10.1080/1612197X.2015.1041545

Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57, 493–502. https://doi.org/10.1037/0022-3514.57.3.493

Spahn, C., Echternach, M., Zander, M., Voltmer, E., & Richter, B. (2010). Music performance anxiety in opera singers. Logopedics Phoniatrics Vocology, 35, 175-182. https://doi.org/10.3109/14015431003720600

Spielberger, C. (1999). State–Trait Anger Expression Inventory-2 (STAXI-2). Odessa, FL: Psychological Assessment Resource.

Syrjä, P., & Hanin, Y. L. (1997a). Individualised and group-oriented measures of emotion in sport: A comparative study. Annual congress of the European college of sports science. Book of abstracts. Part II, Copenhagen, Denmark: University of Copenhagen. pp. 641–642.

Thelwell, R., & Maynard, I. (1998). Anxiety-Performance Relationships in Cricketers: Testing the Zone of Optimal Functioning Hypothesis. Perceptual And Motor Skills, 87, 675-689. https://doi.org/10.2466/pms.1998.87.2.675

Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063

Woodcock, C., Cumming, J., Duda, J., & Sharp, L. (2012). Working within an Individual Zone of Optimal Functioning (IZOF) framework: Consultant practice and athlete reflections on refining emotion regulation skills. Psychology Of Sport And Exercise, 13, 291-302. https://doi.org/10.1016/j.psychsport.2011.11.011

Woodman, T., Albinson, J., & Hardy, L. (1997). An Investigation of the Zones of Optimal Functioning Hypothesis Within a Multidimensional Framework. Journal Of Sport And Exercise Psychology , 19 , 131-141. https://doi.org/10.1123/jsep.19.2.131

Yerkes, R., & Dodson, J. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal Of Comparative Neurology And Psychology, 18(5), 459-482. https://doi.org/10.1002/cne.920180503

External links [ edit | edit source ]

Flow, the secret to happiness (TED Talk)
How I climbed a 3,000 ft vertical cliff - without ropes! (TED Talk)
Free Solo Trailer (YouTube)
The Strange Brain of the World’s Greatest Solo Climber (Article)
Yosemite: Dark. Defiant. Free. (National Geographic Article)
How to handle things that terrify you, from a guy who climbs 2,500-foot cliffs without ropes (Business Insider Article)

Resources needing spell checking
Resources needing improved grammar
Motivation and emotion/Book/2019
Motivation and emotion/Book/Arousal
Motivation and emotion/Book/Flow
Motivation and emotion/Book/Performance
Motivation and emotion/Book/Positive psychology
Motivation and emotion/Book/Sport

Optimizing Performance: Understanding the Inverted U Theory

Inverted U Theory: Balancing Stress for Optimal Performance

Performing at an optimal level requires a high degree of concentration and precision. In sports, this can include fine tuning physical movements or using mental imagery.

Similarly, optimizing your PC’s performance can help you improve efficiency and speed up processing times. Simple steps such as limiting the number of programs that start at boot up or clearing out temporary and junk files can make a significant difference.

Introduction to the Inverted U Theory in Psychology

Analyzing the inverted u hypothesis in work environments, balancing stress and performance: practical applications, the peaks and valleys of arousal: managing workload, future predictions: evolving concepts of the inverted-u.

The Inverted U Theory or Yerkes-Dodson Law is an important psychological principle that explains how stress and performance interact. First developed by psychologists Robert Yerkes and John Dodson in 1908, this model sheds light on how to achieve optimal performance levels when performing a task. The Yerkes-Dodson law states that as the level of arousal increases, performance improves up to a point, beyond which additional arousal results in a decline in performance. This phenomenon is illustrated by an inverted U-shaped curve on a graph.

Athletes should understand this concept so that they can maximize their performance without causing themselves undue stress. It is also important for sports coaches to be aware of how different personality types react to varying levels of stress and arousal. If they do not, they may be putting their athletes in unnecessary danger by pushing them too hard or too fast.

This is why effective stress management is essential in the workplace. By providing healthy work-life balance, setting realistic deadlines, and recognizing employee accomplishments, managers can help employees achieve a desirable level of arousal that will enhance productivity and allow them to thrive.

While some people perform better under pressure, others find it difficult to handle stress. This can be due to a number of factors, including their genetics or their reaction to stress. In these cases, it is a good idea to seek professional help to reduce their anxiety and stress. If these strategies do not help, it might be a sign that the person is suffering from an underlying mental health condition that needs to be addressed. A psychiatrist can help evaluate the situation and recommend appropriate treatment.

The Inverted U Theory is a useful tool for sports coaches and athletes who need to understand the impact of stress on performance. It suggests that performance increases with increasing arousal levels, up to a certain point, when it begins to decline. It is important to recognize this optimum level of arousal, so that an athlete can perform at their peak and avoid over-training, which can lead to a decrease in performance.

This inverted-U shape was originally developed by researchers Yerkes and Dodson in 1908. They studied the relationship between stimulus strength and habit formation for a difficult discrimination learning task in mice, finding that mice learned which chamber to enter more quickly when the electric shock used to punish them was moderate rather than high or low. This became known as the Yerkes- Dodson curve.

More recently, Broadhurst experimented with a radial arm water maze in rats and found that variations in the intensity of the stress experienced by the animals (i.e., differences in water temperature) impacted the rate at which they made errors while performing the task. However, this relationship has not been replicated using other experimental conditions.

More recent research has explored the potential for this inverted-U relationship to also occur with human cognitive tasks and work engagement. Three studies – a two- wave time-lagged study and a pair of panel studies – have found that workload has an indirect, inverted-U-shaped relationship with innovative work behavior through work engagement. However, the results were less clear-cut when mindfulness was involved. In the final pair of studies, this finding was confirmed: when workers were mindful, the indirect relationship between workload and innovative work behavior through work engagement was stronger, whereas it was weaker when mindfulness was low.

The Inverted U theory explains that a moderate level of stress is best for optimal performance. It is difficult to pinpoint what amount of stress will work for you, as everyone may have different triggers and levels of stress they can cope with. However, you can probably agree that when you feel too much pressure, your performance will suffer as a result.

The inverted u theory has many practical applications for sports and other activities. For example, when an athlete is preparing for a big game or important presentation, they will likely seek out ways to stay calm and focused. This can include listening to music, using relaxation techniques, and visualizing their success in order to keep themselves in a peak performance state.

In addition, sports coaches can use the inverted u theory to help their athletes improve performance. This is because it can be helpful for an athlete to understand that they can perform better under a certain level of stress, but if their stress levels become too high, this will result in a gradual decrease in their performance.

For instance, if an athlete is performing well in a sporting event, they may feel motivated to continue to push themselves further to improve their performance even further. But if this continues, the athlete will begin to suffer from burnout and fatigue, which may lead them to take more time off from their sport. This can negatively impact team morale and lead to a loss of motivation, which can ultimately affect performance. This can also lead to a higher rate of staff turnover, which can put additional strain on an already stressed company. For this reason, it is beneficial for managers to find ways to reduce employee stress levels to ensure productivity is maximized.

Arousal is a necessary condition for human performance. However, it is important to understand that too much arousal can be detrimental to your performance. In fact, the optimal level of arousal depends on your task characteristics. This is why it is essential to assess your performance and arousal levels to ensure that you are in the right zone.

The Inverted U Theory was developed by psychologists Robert Yerkes and John Dodson more than a century ago in 1908. This theory shed light on the relationship between stress, or arousal, and performance. Yerkes and Dodson found that performance increases with physiological or mental arousal, but only up to a certain point. Once arousal reaches this threshold, it begins to decrease and performance will decline.

Researchers later expanded on this theory to include the concept of cognitive appraisal, or an individual’s interpretation and perception of a situation. They also began to recognize that the arousal-performance relationship is influenced by task complexity and familiarity. Simple tasks may require higher arousal levels to maintain attention and motivation, while more complex or novel tasks can be better performed with lower arousal levels to promote focused and deliberate processing.

The Inverted U Theory is relevant in sports because athletes need to know how to manage their arousal levels for optimal performance. If they are too low, they will struggle to stay engaged in a game, and if they are too high, they may become too anxious or stressed to perform well. For example, in sports such as snooker that require fine motor skills, a lower optimal arousal level can be achieved by listening to calm or relaxing music or by visualizing calming scenes before a game.

Athletes, coaches, and businesses that want to optimize performance can benefit from understanding this theory. By balancing arousal levels and striving for the optimum level of stress, it is possible to achieve peak performance. For example, a goalkeeper who feels under arousal and unmotivated will likely underperform. But if they were to enter the game feeling over aroused, they might choke under pressure and make crucial mistakes that could cost them the match.

The inverted U Theory or Yerkes-Dodson law illustrates that there is an optimal level of stress for performance, and it is important to identify this zone. As we move away from this peak point, we experience a gradual decrease in performance. This can be attributed to both psychological and physiological effects of stress that outweigh benefits.

For example, a person who is too tired, stressed out, or anxious to concentrate will likely perform worse than someone who feels motivated and well rested. These effects are also true of athletes competing in their sport. A boxer who starts a match with low arousal will struggle to keep up, but a boxer who enters the fight with high arousal levels will be more confident and able to handle the challenge.

Understanding this theory can help individuals and businesses strike a healthy balance between arousal and productivity. By identifying the ideal arousal levels, it is possible to increase productivity and efficiency without over-stressing or overwhelming employees. Similarly, athletes can use this theory to develop an understanding of what it takes to be at their best in competition. This can include factors like arousal level, mindset, physical fitness and warm ups, and other aspects that will determine their success.

Save my name, email, and website in this browser for the next time I comment.

Understanding Motivational Factors in the Workplace

Advances in Intrinsic Motivation and Aesthetics pp 39–70 Cite as

The Quest for the Inverted U

Edward L. Walker 2

317 Accesses

14 Citations

The quest for the inverted U, although not without historical precedents, received its major modern impetus with the publication of Daniel E. Berlyne’s Conflict, Arousal and Curiosity (1960). In this and in later volumes Berlyne developed a conception of motivation that was in sharp contrast to the prevailing formulations of psychiatry, psychology, and behavior theory.

Intrinsic Motivation
Experimental Subject
Stimulus Complexity
Experimental Stimulus
Semantic Space

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, log in via an institution .

Buying options

Available as PDF
Read on any device
Instant download
Own it forever
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Unable to display preview. Download preview PDF.

Aeschbach , S. Ratings of dichords along the continua of complexity-simplicity, consonance-dissonance, and pleasing-displeasing . Honors dissertation, University of Michigan, 1975.

Google Scholar

Arkes , H. R. The relationship between repetition and organization and the role of organization in psychological complexity . Unpublished doctoral dissertation, University of Michigan, 1971.

Ayres , T. Exploratory behavior in musical intervals as related to measures of consonance and dissonance . Research paper, Amherst College, 1974.

Ayres , T. Psychological and physiological factors in the consonance of musical intervals . Honors dissertation, Amherst College, 1975.

Berlyne , D. E. Conflict, arousal and curiosity . New York: McGraw-Hill, 1960.

Book Google Scholar

Berlyne , D. E. Psychobiology and aesthetics . New York: Appleton-Century-Crofts, 1971.

Boykin , A. W., Jr. Verbally expressed preference and complexity judgments as they relate to levels of performance in a problem-solving situation . Unpublished doctoral dissertation, University of Michigan, 1972.

Boykin , A. W., Jr. Verbally expressed preference and problem-solving proficiency. Journal of Experimental Psychology: Human Perception and Performance . 1977, 3 , 165–174.

Article Google Scholar

Coombs , C. H. Theory of data . New York: Wiley, 1964.

Coombs , C. H., Dawes , R. M., & Tversky , A. Mathematical psychology . Englewood Cliffs, N.J.: Prentice-Hall, 1970

Day , H. I., & Crawford , G. C. Developmental changes in attitudes toward complexity . Paper read at the meetings of The Society for Research in Child Development, Minneapolis, 1971.

Dember , W. N., & Earl , R. W. Analysis of exploratory, manipulatory, and curiosity behavior. Psychological Review , 1957, 64 , 91–96.

Engle , R. Experimentelle Untersuchungen über die Abhängigkeit der Lust and Unlust von der Reizstärke beim Geschmacksinn. Archis fuer die Gesamte Psychologie , 1928, 64 , 1–36. Cited in Woodworth, R. S., & Schlosberg, H. Experimental psychology . New York: Holt, 1954.

Hebb , D. O. The organization of behavior . New York: Wiley, 1949.

Helson , H. Adaptation-level theory. In S. Koch (Ed.), Psychology: A study of a science . New York: McGraw-Hill, 1959.

Heyduk , R. G. Static and dynamic aspects of rated and exploratory preference for musical compositions . Unpublished doctoral dissertation, University of Michigan, 1972.

Heyduk , R. G. Rated preference for musical compositions as it relates to complexity and exposure frequency. Perception and Psychophysics , 1975, 17 , 84–91.

Kreitler , H., & Kreitler , S. Psychology of the arts . Durham: Duke University Press, 1972.

Mcclelland , D. C., Atkinson , J. W., Clark , R. A., & Lowell , E. L. The achievement motive . New York: Appleton-Century-Crofts, 1953.

Olson , M. H. Complexity and preference and information processing rate . Unpublished doctoral dissertation, University of Michigan, 1977.

Pfaffman , C. Taste preference and reinforcement. In Tapp, J. T. (Ed.), Reinforcement and behavior . New York: Academic Press, 1969.

Sales , S. M. Stimulus complexity as a determinant of approach behavior and inspection time in the hooded rat. Canadian Journal of Psychology/Review of Canadian Psychology , 1968, 11 , 11–17.

Sinclair , K. Optimal complexity and aesthetic preference . Honors dissertation, University of Michigan, 1967.

Thomas , H. Discrepancy hypotheses: Methodological and theoretical considerations. Psychological Review , 1971, 78 , 249–259.

Walker , E. L. Psychological complexity as a basis for a theory of motivation and choice. In D. Levine (Ed.), Nebraska symposium on motivation . Lincoln: University of Nebraska Press, 1964.

Walker , E. L. Reinforcement: “The one ring.” In J. T. Tapp (Ed.), Reinforcement and behavior . New York: Academic Press, 1969.

Walker , E. L. Psychological complexity and preference: A Hedgehog theory of behavior . Monterey, Calif.: Brooks/Cole, 1980.

Wundt , W. Grundzüge der physiologischen Psychologie . Leipzig: Englemann, 1874.

Download references

Author information

Authors and affiliations.

3041 Lopez, Pebble Beach, California, 93953, USA

Edward L. Walker

You can also search for this author in PubMed Google Scholar

Editor information

Editors and affiliations.

York University, Downsview, Ontario, Canada

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Cite this chapter.

Walker, E.L. (1981). The Quest for the Inverted U. In: Day, H.I. (eds) Advances in Intrinsic Motivation and Aesthetics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-3195-7_3

Download citation

DOI : https://doi.org/10.1007/978-1-4613-3195-7_3

Publisher Name : Springer, Boston, MA

Print ISBN : 978-1-4613-3197-1

Online ISBN : 978-1-4613-3195-7

eBook Packages : Springer Book Archive

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Member Login

Get Listed Today
Sport Psychology
Sports Nutrition
Chiropractor
Physiotherapy
Sports Therapy
Strength and Conditioning
Sports Injury Clinics
How It Works
Member Articles
Member Videos
Testimonials
Sport Quotes
Sports Books
Digital Products
Our Partners

Inverted-U Theory in Sport

One major approach to explaining the arousal-performance relationship in sport is the Inverted U-Hypothesis. Arousal, defined as the level of neural excitation (Malmo, 1959) may be increased with activation of the autonomic nervous system during an emotion. The Inverted-U hypothesis proposes that performance is best at a moderate level of arousal. Both low and high levels of arousal are associated with similar decrements in performance.

The original work done on the Inverted-U hypothesis related to the strength of stimulus and habit-formation (learning) in mice (Yerkes and Dodson, 1908). It was discovered that mice learnt which chamber of two to enter quickest, when the punishment for choosing the wrong chamber was an electric shock of moderate intensity. This was supported by later work with rats (Broadhurst, 1957). From these rodent-based studies it is difficult to see how the Inverted-U hypothesis has become such a commonly used explanation for the arousal-performance link in humans. But it has. Perhaps this is because the idea that moderate levels of arousal are suitable for performance has an intuitive appeal.

There is some research evidence showing that anxiety (although anxiety and arousal are not the same) relate to performance in the manner of an inverted-U. Specifically, the best performances of 145 high school basketball players occurred under moderate levels of anxiety (Klavora, 1979) and the performance of university female basketball players was higher following medium levels of anxiety (Sonstrom & Bernado, 1982). However, despite this support there has been some criticism of the Inverted-U hypothesis (cf. Neiss, 1988; Raglin, 1991; Zaichkowsky & Baltzell, 2001). Specifically:

It describes but does not explain the relationship between arousal and performance
The symmetrical shape is not realistic of a competitive sport situation
Arousal itself is multidimensional (Lacey, 1967) and accordingly the inverted-U hypothesis may be simplistic.
Not all studies support the Inverted-U hypothesis

While the Inverted-U does have some (intuitive) appeal research moved towards exploring the relationship between anxiety (of which arousal is a component) and performance. Anxiety is characterized by feelings of apprehension and tension along with activation or arousal of the autonomic nervous system (Spielberger, 1966). Thus, anxiety may comprise cognitive (e.g., feelings of apprehension) and physiological (e.g., increased activation of the autonomic nervous system) changes.

Image

Image by Markus Bieck from Pixabay

Selected References

Broadhurst, P. L. (1957). Emotionality and the Yerkes-Dodson law. Journal of Experimental Psychology, 54, 345-352.

Klavora, P. (1979). Customary arousal for peak athletic performance. In P. Klavora & J. David (Eds.), Coach, athlete and the sport psychologist (pp. 155-163). Toronto, Canada: University of Toronto.

Neiss, R. (1988). Reconceptualising arousal: psychobiological states in motor performance. Psychological Bulletin, 103, 345-366.

Raglin, J. (1992) Anxiety and sport performance. In J. O. Holloszy (Ed.) Exercise & Sport Science Reviews , (Volume 20, pp. 243-274). New York: William & Wilkins.

Sonstroem, R. J., & Bernardo, P. (1982). Individual pregame state anxiety and basketball performance: A re-examination of the inverted-U curve. Journal of Sport Psychology, 4, 235-245.

Spielberger, C. D. (1966). Theory and research on anxiety. In C. D. Spielberger (Ed.), Anxiety and behavior (pp. 3-22). New York: Academic Press.

Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18, 459-482.

Zaichkowsky, L. D., & Baltzell, A. (2001). Arousal and Performance. In R. N. Singer, H. A. Hausenblas, C. M. Janelle (Eds.), Handbook of research on sport psychology, (pp. 319-339). New York: John Wiley and Sons.

Find Sport Performance Specialists

We are an online sports directory that lists sports performance specialists, clinics, and coaches. We are specialists in Brain and Body to help you get your Sporting Bounce!
Sports Massage
Physical Fitness
Sports injury
List Your Company
Password Retrieval
PMJ Sports Ltd, Colony, 5 Piccadilly Place, Manchester, M1 3BR, UK.

Join Our Website Today »
Not a Registered User? View Our Membership Deals
Are You a Local Business List Your Company Now

Join Our Newsletter

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
Advanced Search
Journal List
HHS Author Manuscripts

Quantifying the inverted U: A meta-analysis of prefrontal dopamine, D1-receptors, and working memory

Matthew a. weber.

1 Department of Neurology, University of Iowa, Iowa City, IA 52242.

Mackenzie M. Conlon

2 Medical Scientist Training Program, University of Iowa, Iowa City, IA 52242.

Hannah R. Stutt

3 Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, 52242

Linder Wendt

4 Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA 52242.

Patrick Ten Eyck

Nandakumar s. narayanan.

Dopamine in the prefrontal cortex can be disrupted in human disorders that affect cognitive function such as Parkinson’s disease (PD), attention-deficit hyperactivity disorder (ADHD), and schizophrenia. Dopamine has a powerful effect on prefrontal circuits via the D1-type dopamine receptor (D1DR). It has been proposed that prefrontal dopamine has “inverted U-shaped” dynamics, with optimal dopamine and D1DR signaling required for peak cognitive function. However, the quantitative relationship between prefrontal dopamine and cognitive function is not clear. Here, we conducted a meta-analysis of published manipulations of prefrontal dopamine and the effects on working memory, a high-level executive function in humans, primates, and rodents that involves maintaining and manipulating information over seconds to minutes. We reviewed 646 papers and found that 75 studies met criteria for inclusion. Our quantification of effect sizes for dopamine, D1DRs, and behavior revealed a negative quadratic slope. This is consistent with the proposed inverted U-shape of prefrontal dopamine and D1DRs and working memory performance, explaining 10% of the variance. Of note, the inverted quadratic fit was much stronger for prefrontal D1DRs alone, explaining 26% of the variance, compared to prefrontal dopamine alone, explaining 10% of the variance. Taken together, these data, derived from a variety of manipulations and systems, demonstrate that optimal prefrontal dopamine signalling is linked with higher cognitive function. Our results provide insight into the fundamental dynamics of prefrontal dopamine, which could be useful for pharmacological interventions targeting prefrontal dopaminergic circuits, and into the pathophysiology of human brain disease.

Introduction

Human diseases that affect high-level cognitive processes such as working memory, reasoning, and flexibility can disrupt prefrontal dopamine. For instance, in humans with Parkinson’s disease, hypo- and hyperdopaminergic states have been linked with impaired cognition ( Cools and D’Esposito, 2011 ; Mattay et al., 2002 ; Narayanan et al., 2013 ). In addition, dysfunctioning prefrontal dopaminergic systems may be related to the pathophysiology of attention-deficit hyperactivity disorder (ADHD) ( Bellgrove et al., 2005 ), and prefrontal dopamine has been critically implicated in the pathogenesis of schizophrenia ( Abi-Dargham et al., 2002 ; Goldman-Rakic et al., 2004 ; Okubo et al., 1997 ). Despite these data, the precise relationship between prefrontal dopamine and behavior is unclear. Understanding this relationship is relevant for pharmacological strategies that modulate prefrontal dopaminergic function to improve cognitive function in human disease ( Soriano et al., 2010 ).

Preclinical work in rodents and non-human primates has established that prefrontal dopamine is required for high-level cognitive behaviors ( Brozoski et al., 1979 ; Bubser and Schmidt, 1990 ; Kim et al., 2017 ). One of the most commonly studied cognitive behaviors is working memory, in which information is held for brief periods of time to guide future goal-directed behavior and has been studied extensively to show that decreased or increased prefrontal dopamine is linked with impaired behavioral performance ( Cools and D’Esposito, 2011 ; Floresco, 2013 ; Goldman-Rakic et al., 2004 ). Prefrontal dopamine acts on cortical circuits via D1-type dopamine receptors (D1DRs), which also has been linked with impaired working memory performance ( Floresco and Phillips, 2001 ; Goldman-Rakic et al., 2004 ; Seamans et al., 1998 ; Seamans and Yang, 2004 ). These findings lead to the hypothesis that working memory follows an inverted U-shaped function, in which optimal working memory performance is achieved with optimal levels of prefrontal dopamine and D1DR activation. While inverted U-shaped dynamics have substantial supporting evidence, the contours of this function are not clear. Further, it is not clear whether the inverted U-shape is more strongly dependent on either D1DR levels or overall prefrontal dopamine concentrations, or whether the curve is the same for both dopamine and D1DR manipulations. This is particularly relevant in predicting the degree of behavioral impairment that can be expected with prefrontal dopaminergic manipulations or for interventions that target D1DRs.

To formally quantify the relationship between prefrontal dopamine signaling and working memory, we conducted a meta-analysis of studies in which working memory and either prefrontal dopamine or D1DRs were measured. We report two major results: 1) there was a negative quadratic fit for the relationship between working memory and both prefrontal dopamine and prefrontal D1DR combined; and 2) the relationship was stronger for prefrontal D1DR manipulation and working memory, explaining 26% of the variance, compared to prefrontal dopamine and working memory that explained only 10% of the variance. We interpret these data in the context of prefrontal dopamine dynamics and their relevance for understanding prefrontal function in human disease.

Search strategy and inclusion/exclusion criteria

An electronic search of PubMed, PsychInfo, and Embase was performed on September 15, 2021 using the terms “frontal cortex,” “dopamine,” and “working memory”. Terms such as “human” and “dopamine D1” were also utilized to ensure a comprehensive search was completed. We restricted the search to peer-reviewed articles to ensure that only the most rigorous studies were included. Using functions in EndNote X9, we removed duplicates and literature reviews. resulting in 646 peer-reviewed articles. Two authors independently screened all of the abstracts (M.A.W and M.M.C) to determine appropriateness for this meta-analysis. We sought to synthesize data across multiple domains, including species of the model organism studied, working memory behavioral paradigms, and measure of prefrontal dopamine and D1DRs. Therefore, inclusion criteria were: 1) peer-reviewed original research in either rodents, non-human primates, or humans; that 2) measured prefrontal dopamine or D1DRs and 3) measured working memory performance. Exclusion criteria were: 1) non-original research; 2) case studies; 3) in vitro or computational studies; 4) non-dopamine or D1DR studies; 5) studies that examined executive functions other than working memory; 6) studies that lacked between-group comparisons, control groups, or baseline measures; 7) central or peripheral pharmacology without direct measure of dopamine or D1DRs; and 8) study of genetic polymorphisms without direct measure of dopamine or D1DRs. This screening process resulted in 75 peer-reviewed publications included in the final quantitative analysis. This study’s design and hypothesis were not preregistered.

Data extraction

Several variables were extracted from each study included in the final analysis. Broad characteristics of each study were: 1) article title; 2) authors; 3) publication year; 4) species; 5) experimental manipulation or comparison; 6) type of working memory task; and 7) type of prefrontal dopamine or D1DR measure. Quantitative variables for the measure of working memory and prefrontal dopamine or D1DRs were: 1) number of subjects for each experimental group; 2) group average; and 3) group standard deviation or standard error. Every effort was taken to extract quantitative variables directly from the methods, results, and/or figure captions to ensure exact values were reported. Primary data extraction was completed by M.A.W, but all qualitative and quantitative data was verified independently by two other authors (H.R.S and N.S.N).

When multiple versions of the same working memory task were reported (e.g., the length of the working memory delay period, see Abi-Dargham et al., 2002 ), we extracted the working memory behavior data points with the largest effect size. When multiple dopamine values were presented (e.g., at multiple time points during in vivo microdialysis, see Schmeichel et al., 2013 ), we extracted basal prefrontal dopamine values when available or data that matched the working memory time point as closely as possible when basal prefrontal levels were not reported. When the precise number of subjects in a group was not explicitly reported, we estimated group size based on the information available (e.g. Pietraszek et al., 2009 ). When group average, standard deviation, and standard error were not explicitly reported, we used plot digitizer software (Rohatgi, A., WebPlotDigitizer: Version 4.4, 2020, https://automeris.io/WebPlotDigitizer/ ) to extract relevant statistical data. Several publications contributed multiple data points to the final quantitative analysis because we were able to extract multiple values from these datasets. For example, Adams & Moghaddam, 1998 , tested working memory performance at three time points following peripheral drug injection and included three corresponding prefrontal dopamine measures. Other examples include Novick et al., 2013 (two working memory paradigms), Szczepanik et al., 2020 (multiple doses of the same drug with corresponding prefrontal dopamine values), and Kellendonk et al., 2006 (multiple different measures of prefrontal dopamine - i.e., TH variscosities, D1 mRNA, DA content, c-Fos expression). We compared measures of working memory with prefrontal dopamine concentrations and D1DR activation in control and experimental groups, regardless of the specific statistical analysis that was presented in the publication. Our statistical analysis of control vs. experimental groups was used to generate effect sizes for both 1) difference in working memory performance and 2) difference in prefrontal dopamine or D1DRs between control and experimental conditions.

Following data extraction, we calculated Cohen’s d effect sizes ( Cohen, 1969 ) for each measure of working memory and prefrontal dopamine or D1DRs. This standardized metric of effect size is calculated from differences between group means divided by the pooled standard deviation, and is widely used to compare effects across studies with diverse methodologies. For instance, if administration of a dopaminergic drug or external manipulation affected behavior, then the averages of behavioral performance with or without the experimental drug would be subtracted, divided by the variance. The same comparisons can be made for measures of prefrontal dopamine or D1DR levels by diverse methods. In general, a Cohen’s d value of ~0.1 is considered small, ~0.3 is considered medium, and greater than 0.5 is considered large. Effect sizes were adjusted so that enhanced working memory and increased prefrontal dopamine or D1DRs were reflected by positive values, and impaired working memory and dampened prefrontal dopamine or D1DRs were reflected by negative values. We then sorted effect sizes based on prefrontal dopamine or D1DRs and grouped data to facilitate analysis of working memory performance ( Tables 1 and and2 2 ).

Quadratic equations (aX 2 + bX + (Intercept)) derived from manipulations of prefrontal dopamine or D1DRs and measures of working memory performance.

Studies that reported comparisons of prefrontal cortex D1-type dopamine receptors (D1DRs) and working memory between control and experimental subjects.

Statistical analyses were completed using R software, version 4.1.1. All code and raw data are available at https://narayanan.lab.uiowa.edu . All statistical analyses were performed and verified independently by the Biostatistics, Epidemiology, and Research Design Core within the Institute for Clinical and Translational Science at the University of Iowa.

The primary goal of this meta-analysis was to identify polynomial models (up to order three) that explain changes in working memory performance with changes in prefrontal dopamine and/or D1DRs. We developed models based on the relationship between working memory effect sizes and prefrontal dopamine and D1DR effect sizes. We excluded values greater than or less than a Cohen’s d of +/− 4, as these could have an outsized effect on our models. First, we fit a model based on working memory performance and all prefrontal dopamine and D1DRs. This analysis was followed by stratifying the data set to develop a model fit based on working memory performance and prefrontal dopamine and a model fit based on working memory performance and prefrontal D1DRs. Several publications contributed multiple values to the final data set, and this was accounted for by including a random intercept for each publication. Model fits between different polynomial orders were compared via Akaike Information Criteria (AIC), with lower AICs indicating a better combination of parsimony and goodness of fit.

We used a bootstrap analysis approach to compare R 2 values for prefrontal dopamine and prefrontal D1DRs. This process began by simulating a new dataset for both prefrontal dopamine and prefrontal D1DRs; we resampled the original datasets with replacement to create new datasets the same size as the original. Then, a quadratic model was built on each resampled dataset, and the R 2 value of the dopamine model was subtracted from the R 2 values of the D1DR model. This process was repeated 10,000 times to obtain bootstrap-estimated intervals that reflect 95% confidence for the difference between the two models and that one model’s fit is superior to the other. Here, a positive confidence interval that does not contain zero would indicate that the prefrontal D1DR model provides a superior R 2 value compared to the prefrontal dopamine R 2 value.

Our literature search and screening procedures yielded 75 journal articles that fit our criteria, resulting in 165 data points ( Tables 1 and and2). 2 ). After extreme values (Cohen’s d >+4 and <− 4) were excluded, 156 data points remained. We found that a quadratic function provided the optimal model fit (2 nd order polynomial; p<0.001; AIC = 400.2 vs. linear AIC = 412.7). The R 2 value for the negative quadratic fit was 0.10. A higher order polynomial model did not decrease AIC values (3 rd order AIC = 408.8), suggesting that the 2 nd order model is optimal.

We then stratified our data based on type of prefrontal measure, with a sub-analysis focused on prefrontal dopamine (i.e., dopamine content or turnover, tyrosine hydroxylase, dopamine transporter, etc.). These could include direct manipulations of prefrontal dopamine (e.g., dopamine depletion via 6-hydroxydopamine) or indirect manipulation such as stress or peripheral drug administration. For this analysis, we found 61 studies and 119 data points. A negative quadratic function provided the strongest fit with AIC = 314.4 (p<0.001; vs. linear AIC = 317.2, 3 rd order AIC = 322.7). The R 2 value for our quadratic model was 0.10.

Prefrontal dopamine released from synaptic terminals can powerfully act on prefrontal D1DRs ( Goldman-Rakic et al., 2004 , p.; Seamans and Yang, 2004 ). We examined the role of prefrontal D1DR manipulations on working memory performance in 17 studies with 37 data points. In line with data on prefrontal dopamine, we found that a negative quadratic function again provided the best fit, with AIC = 102.6 (p<0.001; vs. linear AIC = 110.2; 3 rd order AIC = 106.3). The R 2 value for this model was 0.26. Increasing the polynomial order coincided with an increase in the AIC values, suggesting that the negative quadratic model again provided the best combination of parsimony and goodness of fit. Adding an effect for the species being studied did not notably enhance our model’s goodness of fit, possibly due to insufficient sample size to detect this effect. When a variable controlling for species was added to our negative quadratic model, our AIC worsened from 314.4 to 315.5 for the prefrontal dopamine model and from 102.6 to 103.0 for the prefrontal D1DR model.

We then built new quadratic models using the resampling bootstrapped analysis described above for both prefrontal dopamine and prefrontal D1DRs and determined the difference between the two newly-built models. The average difference between R 2 values for the 10,000 iterations was 0.14, where a positive value indicated that the prefrontal D1DR models had a greater R 2 value. The 95% confidence interval for this result was (−0.10, 0.38) and the bootstrapped two-sided p value was 0.31.

Our goal was to quantify the relationship of working memory performance with prefrontal dopamine and D1DRs. We conducted a meta-analysis of 75 studies spanning rodents, non-human primates, and humans. These data suggest that 10% of the variance in working memory behavior was explained by manipulations of prefrontal dopamine, and 26% of the variance was explained by prefrontal D1DR manipulations. These data provide insight into how prefrontal dopamine and D1DRs affects cognitive behaviors.

Our findings are broadly consistent with past work that has proposed an inverted U-shaped relationship between prefrontal dopaminergic dynamics and working memory performance ( Cools and D’Esposito, 2011 ; Floresco, 2013 ). We were able to demonstrate this idea by quantitatively fitting an inverted quadratic function, supporting the idea that there is an optimal regime for dopamine function in the prefrontal cortex that may facilitate a wide range of interacting synaptic and post-synaptic proteins ( Arnsten et al., 2012 ; Arnsten and Li, 2005 ). In establishing this function, we show that prefrontal dopamine has strikingly different signaling principles than striatal dopamine ( Kreitzer, 2009 ; Mohebi et al., 2019 ; Yahr et al., 1969 ), in which striatal dopamine depletion can impair movement ( Burns et al., 1983 ; Schultz et al., 1989 ; Kirik et al., 1998 ) . However, in the striatum there are important differences in that many principal neurons express largely either D1- or D2-type dopamine receptors, and these systems can work in tandem to coordinate a wide range of behaviors. For instance, increasing striatal dopamine or stimulating D1 medium spiny neurons can facilitate or hyperstimulate movement ( Fredriksson et al., 1990 ; Brannan et al., 1998 ; Carta et al., 2006 ; Kravitz et al., 2010). However, both decreasing and increasing striatal dopamine can impair motivation ( Bryce & Floresco, 2019 ; Filla et al., 2018 ; Fry et al., 2021 ; Kamada & Hata, 2020; Salamone et al., 2012 ). Thus, the details of the dopaminergic effects on a behavior depend not just on the complex pharmacodynamics of the dopamine receptor, but how neurons expressing these receptors are precisely integrated into circuits.

While this work supports the hypothesis that working memory performance follows an inverted U-shape function dependent on prefrontal dopamine and D1DRs, our results should be interpreted carefully. For example, the bootstrapped analysis for models of prefrontal D1DRs were not significantly different from models of prefrontal dopamine; however, we note that there were fewer studies for prefrontal D1DRs, which may have affected our statistical power in separating prefrontal D1DRs from prefrontal dopamine manipulations. We also note that there may be important sampling bias; for instance, there are more studies that disrupt prefrontal dopamine/D1DRs than increase dopamine or D1DRs, and very few studies describing increased prefrontal dopamine or D1DRs result in increased working memory function. This insight may suggest that it is challenging to consistently improve working memory with dopaminergic manipulations, at least in intact prefrontal circuits.

Another key constraint is that rodents do not have lateral prefrontal regions that are present in primates ( Laubach et al., 2018 ), although dopamine is strongly released in medial prefrontal regions, and dopamine in these circuits may function according to similar principles ( Floresco, 2013 ; Zahrt et al., 1997 ). It is also important to acknowledge that changes to working memory performance are not only impacted by manipulations of prefrontal dopamine and D1DRs. Other prefrontal dopamine receptors ( Druzin et al., 2000 ; Glickstein et al., 2002 ), neurotransmitter systems ( Monaco et al., 2015 ; Robbins and Arnsten, 2009 ), brain regions ( Bolkan et al., 2017 ; Hart et al., 2018 ), and behaviors (i.e. interval timing, behavioral flexibility – Kim et al., 2017 ; Ragozzino, 2002 ; Zhang et al., 2019 ) are critical for optimal working memory performance. Furthermore, there are other paradigms that can be used to study executive functions, and U-shaped dynamics may be relevant for some behavioral paradigms such as attention, reversal learning, and interval timing ( Floresco, 2013 ; Parker et al., 2015 ; Robbins, 2007 ). However, other behavioral paradigms such as set-shifting or risk-based decision making may have distinct prefrontal dopaminergic dynamics, suggesting that these cognitive paradigms may have distinct relationships between prefrontal dopamine and D1DRs ( Floresco, 2013 ). However, our literature search revealed among manipulations of prefrontal dopamine and cognition that working memory paradigms had the largest number of studies, making it a reasonable starting point for comparisons across metholodogies and species. This work also has limitations that derive from comparing a broad range of studies across several different methodologies and model systems. However, this diversity is also a strength in that we report effects that are consistent across a range of approaches. Finally, publication bias may have affected this analysis, meaning that non-reviewed and unpublished research could have influenced our conclusions. While there are many small effect sizes within our datasets, the wealth of unpublished research possibly reporting nonsignificant prefrontal dopamine, prefrontal D1DR, or working memory changes could alter our interpretation of the inverted U-shape function.

In summary, this study advances the approach of bringing together diverse studies to elucidate patterns in prefrontal dopamine. A key finding here is that, while not statistically significant, the prefrontal D1DRs explained more variance than prefrontal dopamine. Fascinatingly, the initial description of the inverted-U shaped working memory function is based largely on pharmacological activation or inhibition of prefrontal D1DRs. It is possible that working memory performance is more strongly dependent on dopamine receptor activation than specific levels of prefrontal dopamine. This pattern will be useful in designing and interpreting preclinical studies, as well as in designing and optimizing new therapies for diseases such as ADHD, schizophrenia, and PD, which involve profound disruptions in prefrontal dopamine signaling.

An external file that holds a picture, illustration, etc.
Object name is nihms-1825760-f0001.jpg

We included studies that measured both working memory performance and either prefrontal D1DRs or dopamine levels. We included studies from rodents, non-human primates, and humans, and expressed effect sizes in Cohen’s d . We found that studies that measured prefrontal D1DRs (red), prefrontal dopamine (blue) were best fit by a negative quadratic function. The model aggregating both prefrontal dopamine and D1DR measurements is shown in grey. Data from 75 studies and a total of 156 data points; 119 that measured prefrontal dopamine levels and 37 that measured prefrontal D1DR levels.

Studies that reported comparisons of prefrontal cortex dopamine and working memory between control and experimental subjects.

Acknowledgements and Author Note:

MAW and NSN designed the meta-analysis. MAW and MMC independently screened abstracts for appropriateness. MAW collected the data, which was independently checked by NSN and HRS. LW, PTE, and NSN wrote the code and checked the analysis. MAW and NSN wrote the manuscript. HRS, LW, and PTE reviewed the manuscript. All code and raw data are available at https://narayanan.lab.uiowa.edu . This work was funded by NIH R01s MH116043, NS120987 to NN, and UL1TR002537.

This work was funded by NIH R01s MH116043, NS120987 to NSN. This study was partially supported by NIH UL1TR002537 to the Institute for Clinical and Translational Science at the University of Iowa.

Conflict of Interest:

There are no conflicts of interest.

Abi-Dargham A, & Laruelle M (2005). Mechanisms of action of second generation antipsychotic drugs in schizophrenia: insights from brain imaging studies . Eur Psychiatry , 20 ( 1 ), 15–27. doi: 10.1016/j.eurpsy.2004.11.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Abi-Dargham A, Mawlawi O, Lombardo I, Gil R, Martinez D, Huang Y, . . . Laruelle M (2002). Prefrontal dopamine D1 receptors and working memory in schizophrenia . J Neurosci , 22 ( 9 ), 3708–3719. doi: 10.1523/jneurosci.22-09-03708.2002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Adams B, & Moghaddam B (1998). Corticolimbic dopamine neurotransmission is temporally dissociated from the cognitive and locomotor effects of phencyclidine . J Neurosci , 18 ( 14 ), 5545–5554. doi: 10.1523/jneurosci.18-14-05545.1998 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Areal LB, Herlinger AL, Pelição FS, Martins-Silva C, & Pires RGW (2017). Crack cocaine inhalation induces schizophrenia-like symptoms and molecular alterations in mice prefrontal cortex . J Psychiatr Res , 91 , 57–63. doi: 10.1016/j.jpsychires.2017.03.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Arnsten AF, & Li BM (2005). Neurobiology of executive functions: catecholamine influences on prefrontal cortical functions . Biol Psychiatry , 57 ( 11 ), 1377–1384. doi: 10.1016/j.biopsych.2004.08.019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Arnsten AF, Wang MJ, & Paspalas CD (2012). Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses . Neuron , 76 ( 1 ), 223–239. doi: 10.1016/j.neuron.2012.08.038 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bäckman L, Karlsson S, Fischer H, Karlsson P, Brehmer Y, Rieckmann A, . . . Nyberg L (2011). Dopamine D(1) receptors and age differences in brain activation during working memory . Neurobiol Aging , 32 ( 10 ), 1849–1856. doi: 10.1016/j.neurobiolaging.2009.10.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Baumgartner J, Smuts CM, Malan L, Arnold M, Yee BK, Bianco LE, . . . Zimmermann MB (2012). Combined deficiency of iron and (n-3) fatty acids in male rats disrupts brain monoamine metabolism and produces greater memory deficits than iron deficiency or (n-3) fatty acid deficiency alone . J Nutr , 142 ( 8 ), 1463–1471. doi: 10.3945/jn.111.156281 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Baumgartner J, Smuts CM, Malan L, Arnold M, Yee BK, Bianco LE, . . . Zimmermann MB (2012). In male rats with concurrent iron and (n-3) fatty acid deficiency, provision of either iron or (n-3) fatty acids alone alters monoamine metabolism and exacerbates the cognitive deficits associated with combined deficiency . J Nutr , 142 ( 8 ), 1472–1478. doi: 10.3945/jn.111.156299 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bellgrove MA, Hawi Z, Kirley A, Gill M, & Robertson IH (2005). Dissecting the attention deficit hyperactivity disorder (ADHD) phenotype: sustained attention, response variability and spatial attentional asymmetries in relation to dopamine transporter (DAT1) genotype . Neuropsychologia , 43 ( 13 ), 1847–1857. doi: 10.1016/j.neuropsychologia.2005.03.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Berridge CW, Devilbiss DM, Andrzejewski ME, Arnsten AF, Kelley AE, Schmeichel B, . . . Spencer RC (2006). Methylphenidate preferentially increases catecholamine neurotransmission within the prefrontal cortex at low doses that enhance cognitive function . Biol Psychiatry , 60 ( 10 ), 1111–1120. doi: 10.1016/j.biopsych.2006.04.022 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bertolino A, Di Giorgio A, Blasi G, Sambataro F, Caforio G, Sinibaldi L, . . . Dallapiccola B (2008). Epistasis between dopamine regulating genes identifies a nonlinear response of the human hippocampus during memory tasks . Biol Psychiatry , 64 ( 3 ), 226–234. doi: 10.1016/j.biopsych.2008.02.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bhagya V, Srikumar BN, Raju TR, & Shankaranarayana Rao BS (2011). Chronic escitalopram treatment restores spatial learning, monoamine levels, and hippocampal long-term potentiation in an animal model of depression . Psychopharmacology , 214 ( 2 ), 477–494. doi: 10.1007/s00213-010-2054-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bolkan SS, Stujenske JM, Parnaudeau S, Spellman TJ, Rauffenbart C, Abbas AI, . . . Kellendonk C (2017). Thalamic projections sustain prefrontal activity during working memory maintenance . Nat Neurosci , 20 ( 7 ), 987–996. doi: 10.1038/nn.4568 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Brannan T, Prikhojan A, & Yahr MD (1998). Effects of repeated administration of l-DOPA and apomorphine on circling behavior and striatal dopamine formation . Brain Res , 784 ( 1-2 ), 148–153. doi: 10.1016/s0006-8993(97)01191-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Brozoski TJ, Brown RM, Rosvold HE, & Goldman PS (1979). Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey . Science , 205 ( 4409 ), 929–932. doi: 10.1126/science.112679 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bryce CA, & Floresco SB (2019). Alterations in effort-related decision-making induced by stimulation of dopamine D1, D2, D3, and corticotropin-releasing factor receptors in nucleus accumbens subregions . Psychopharmacology (Berl) , 236 ( 9 ), 2699–2712. doi: 10.1007/s00213-019-05244-w [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bubser M, & Schmidt WJ (1990). 6-Hydroxydopamine lesion of the rat prefrontal cortex increases locomotor activity, impairs acquisition of delayed alternation tasks, but does not affect uninterrupted tasks in the radial maze . Behav Brain Res , 37 ( 2 ), 157–168. doi: 10.1016/0166-4328(90)90091-r [ PubMed ] [ CrossRef ] [ Google Scholar ]
Burns RS, Chiueh CC, Markey SP, Ebert MH, Jacobowitz DM, & Kopin IJ (1983). A primate model of parkinsonism: selective destruction of dopaminergic neurons in the pars compacta of the substantia nigra by N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine . Proc Natl Acad Sci U S A , 80 ( 14 ), 4546–4550. doi: 10.1073/pnas.80.14.4546 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Butts KA, Weinberg J, Young AH, & Phillips AG (2011). Glucocorticoid receptors in the prefrontal cortex regulate stress-evoked dopamine efflux and aspects of executive function . Proc Natl Acad Sci U S A , 108 ( 45 ), 18459–18464. doi: 10.1073/pnas.1111746108 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Carta M, Lindgren HS, Lundblad M, Stancampiano R, Fadda F, & Cenci MA (2006). Role of striatal L-DOPA in the production of dyskinesia in 6-hydroxydopamine lesioned rats . J Neurochem , 96 ( 6 ), 1718–1727. doi: 10.1111/j.1471-4159.2006.03696.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cassidy CM, Van Snellenberg JX, Benavides C, Slifstein M, Wang Z, Moore H, . . . Horga G (2016). Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia . J Neurosci , 36 ( 15 ), 4377–4388. doi: 10.1523/jneurosci.3296-15.2016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Castañé A, Santana N, & Artigas F (2015). PCP-based mice models of schizophrenia: differential behavioral, neurochemical and cellular effects of acute and subchronic treatments . Psychopharmacology (Berl) , 232 ( 21-22 ), 4085–4097. doi: 10.1007/s00213-015-3946-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Castner SA, Vosler PS, & Goldman-Rakic PS (2005). Amphetamine sensitization impairs cognition and reduces dopamine turnover in primate prefrontal cortex . Biol Psychiatry , 57 ( 7 ), 743–751. doi: 10.1016/j.biopsych.2004.12.019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Clinton SM, Sucharski IL, & Finlay JM (2006). Desipramine attenuates working memory impairments induced by partial loss of catecholamines in the rat medial prefrontal cortex . Psychopharmacology (Berl) , 183 ( 4 ), 404–412. doi: 10.1007/s00213-005-0221-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cohen J (1969). Statistical power analysis for the behavioral sciences . New York: Academic Press. [ Google Scholar ]
Collins P, Roberts AC, Dias R, Everitt BJ, & Robbins TW (1998). Perseveration and strategy in a novel spatial self-ordered sequencing task for nonhuman primates: effects of excitotoxic lesions and dopamine depletions of the prefrontal cortex . J Cogn Neurosci , 10 ( 3 ), 332–354. doi: 10.1162/089892998562771 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cools R, & D'Esposito M (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control . Biol Psychiatry , 69 ( 12 ), e113–125. doi: 10.1016/j.biopsych.2011.03.028 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
de Almeida GRL, Szczepanik JC, Selhorst I, Schmitz AE, Dos Santos B, Cunha MP, . . . Dafre AL (2021). Methylglyoxal-Mediated Dopamine Depletion, Working Memory Deficit, and Depression-Like Behavior Are Prevented by a Dopamine/Noradrenaline Reuptake Inhibitor . Mol Neurobiol , 58 ( 2 ), 735–749. doi: 10.1007/s12035-020-02146-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Del Arco A, Segovia G, de Blas M, Garrido P, Acuña-Castroviejo D, Pamplona R, & Mora F (2011). Prefrontal cortex, caloric restriction and stress during aging: studies on dopamine and acetylcholine release, BDNF and working memory . Behav Brain Res , 216 ( 1 ), 136–145. doi: 10.1016/j.bbr.2010.07.024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Del Arco A, Segovia G, Garrido P, de Blas M, & Mora F (2007). Stress, prefrontal cortex and environmental enrichment: studies on dopamine and acetylcholine release and working memory performance in rats . Behav Brain Res , 176 ( 2 ), 267–273. doi: 10.1016/j.bbr.2006.10.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Druzin MY, Kurzina NP, Malinina EP, & Kozlov AP (2000). The effects of local application of D2 selective dopaminergic drugs into the medial prefrontal cortex of rats in a delayed spatial choice task . Behav Brain Res , 109 ( 1 ), 99–111. doi: 10.1016/s0166-4328(99)00166-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Du CX, Liu J, Guo Y, Zhang L, & Zhang QJ (2018). Lesions of the lateral habenula improve working memory performance in hemiparkinsonian rats . Neurosci Lett , 662 , 162–166. doi: 10.1016/j.neulet.2017.10.027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Elsworth JD, Groman SM, Jentsch JD, Leranth C, Redmond DE Jr., Kim JD, . . . Roth RH (2014). Primate phencyclidine model of schizophrenia: sex-specific effects on cognition, brain derived neurotrophic factor, spine synapses, and dopamine turnover in prefrontal cortex . Int J Neuropsychopharmacol , 18 ( 6 ). doi: 10.1093/ijnp/pyu048 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fallon SJ, van der Schaaf ME, Ter Huurne N, & Cools R (2017). The Neurocognitive Cost of Enhancing Cognition with Methylphenidate: Improved Distractor Resistance but Impaired Updating . J Cogn Neurosci , 29 ( 4 ), 652–663. doi: 10.1162/jocn_a_01065 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Filla I, Bailey MR, Schipani E, Winiger V, Mezias C, Balsam PD, & Simpson EH (2018). Striatal dopamine D2 receptors regulate effort but not value-based decision making and alter the dopaminergic encoding of cost . Neuropsychopharmacology , 43 ( 11 ), 2180–2189. doi: 10.1038/s41386-018-0159-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Finney CA, Proschogo NW, Snepvangers A, Holmes NM, Westbrook RF, & Clemens KJ (2020). The effect of standard laboratory diets on estrogen signaling and spatial memory in male and female rats . Physiol Behav , 215 , 112787. doi: 10.1016/j.physbeh.2019.112787 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fischer H, Nyberg L, Karlsson S, Karlsson P, Brehmer Y, Rieckmann A, . . . Bäckman L (2010). Simulating Neurocognitive Aging: Effects of a Dopaminergic Antagonist on Brain Activity During Working Memory . Biological Psychiatry , 67 ( 6 ), 575–580. doi: 10.1016/j.biopsych.2009.12.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Floresco SB (2013). Prefrontal dopamine and behavioral flexibility: shifting from an "inverted-U" toward a family of functions . Front Neurosci , 7 , 62. doi: 10.3389/fnins.2013.00062 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Floresco SB, & Phillips AG (2001). Delay-dependent modulation of memory retrieval by infusion of a dopamine D1 agonist into the rat medial prefrontal cortex . Behav Neurosci , 115 ( 4 ), 934–939. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/11508732 [ PubMed ] [ Google Scholar ]
Fredriksson A, Plaznik A, Sundstrom E, Jonsson G, & Archer T (1990). MPTP-induced hypoactivity in mice: reversal by L-dopa . Pharmacol Toxicol , 67 ( 4 ), 295–301. doi: 10.1111/j.1600-0773.1990.tb00833.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fry BR, Pence NT, McLocklin A, & Johnson AW (2021). Disruptions in effort-based decision-making following acute optogenetic stimulation of ventral tegmental area dopamine cells . Learn Mem , 28 ( 4 ), 104–108. doi: 10.1101/lm.053082.120 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Garrido P, De Blas M, Ronzoni G, Cordero I, Antón M, Giné E, . . . Mora F (2013). Differential effects of environmental enrichment and isolation housing on the hormonal and neurochemical responses to stress in the prefrontal cortex of the adult rat: relationship to working and emotional memories . J Neural Transm (Vienna) , 120 ( 5 ), 829–843. doi: 10.1007/s00702-012-0935-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gibbs SE, & D'Esposito M (2005). Individual capacity differences predict working memory performance and prefrontal activity following dopamine receptor stimulation . Cogn Affect Behav Neurosci , 5 ( 2 ), 212–221. doi: 10.3758/cabn.5.2.212 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Glickstein SB, Hof PR, & Schmauss C (2002). Mice lacking dopamine D2 and D3 receptors have spatial working memory deficits . J Neurosci , 22 ( 13 ), 5619–5629. doi:20026543 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Goldman-Rakic PS, Castner SA, Svensson TH, Siever LJ, & Williams GV (2004). Targeting the dopamine D1 receptor in schizophrenia: insights for cognitive dysfunction . Psychopharmacology (Berl) , 174 ( 1 ), 3–16. doi: 10.1007/s00213-004-1793-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
Grégoire S, Rivalan M, Le Moine C, & Dellu-Hagedorn F (2012). The synergy of working memory and inhibitory control: behavioral, pharmacological and neural functional evidences . Neurobiol Learn Mem , 97 ( 2 ), 202–212. doi: 10.1016/j.nlm.2011.12.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hart G, Bradfield LA, & Balleine BW (2018). Prefrontal Corticostriatal Disconnection Blocks the Acquisition of Goal-Directed Action . J Neurosci , 38 ( 5 ), 1311–1322. doi: 10.1523/JNEUROSCI.2850-17.2017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Herian M, Skawski M, Wojtas A, Sobocińska MK, Noworyta K, & Gołembiowska K (2021). Tolerance to neurochemical and behavioral effects of the hallucinogen 25I-NBOMe . Psychopharmacology (Berl) , 238 ( 8 ), 2349–2364. doi: 10.1007/s00213-021-05860-5 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hussein AM, Aher YD, Kalaba P, Aher NY, Dragačević V, Radoman B, . . . Lubec G (2017). A novel heterocyclic compound improves working memory in the radial arm maze and modulates the dopamine receptor D1R in frontal cortex of the Sprague-Dawley rat . Behav Brain Res , 332 , 308–315. doi: 10.1016/j.bbr.2017.06.023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Jentsch JD, Andrusiak E, Tran A, Bowers MB Jr., & Roth RH (1997). Delta 9-tetrahydrocannabinol increases prefrontal cortical catecholaminergic utilization and impairs spatial working memory in the rat: blockade of dopaminergic effects with HA966 . Neuropsychopharmacology , 16 ( 6 ), 426–432. doi: 10.1016/s0893-133x(97)00018-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kadowaki Horita T, Kobayashi M, Mori A, Jenner P, & Kanda T (2013). Effects of the adenosine A2A antagonist istradefylline on cognitive performance in rats with a 6-OHDA lesion in prefrontal cortex . Psychopharmacology (Berl) , 230 ( 3 ), 345–352. doi: 10.1007/s00213-013-3158-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kamada T, & Hata T (2021). Striatal dopamine D1 receptors control motivation to respond, but not interval timing, during the timing task . Learn Mem , 28 ( 1 ), 24–29. doi: 10.1101/lm.052266.120 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kellendonk C, Simpson EH, Polan HJ, Malleret G, Vronskaya S, Winiger V, . . . Kandel ER (2006). Transient and selective overexpression of dopamine D2 receptors in the striatum causes persistent abnormalities in prefrontal cortex functioning . Neuron , 49 ( 4 ), 603–615. doi: 10.1016/j.neuron.2006.01.023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kheirandish L, Gozal D, Pequignot JM, Pequignot J, & Row BW (2005). Intermittent hypoxia during development induces long-term alterations in spatial working memory, monoamines, and dendritic branching in rat frontal cortex . Pediatr Res , 58 ( 3 ), 594–599. doi: 10.1203/01.pdr.0000176915.19287.e2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kim YC, Han SW, Alberico SL, Ruggiero RN, De Corte B, Chen KH, & Narayanan NS (2017). Optogenetic Stimulation of Frontal D1 Neurons Compensates for Impaired Temporal Control of Action in Dopamine-Depleted Mice . Curr Biol , 27 ( 1 ), 39–47. doi: 10.1016/j.cub.2016.11.029 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kirik D, Rosenblad C, & Bjorklund A (1998). Characterization of behavioral and neurodegenerative changes following partial lesions of the nigrostriatal dopamine system induced by intrastriatal 6-hydroxydopamine in the rat . Exp Neurol , 152 ( 2 ), 259–277. doi: 10.1006/exnr.1998.6848 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kobori N, & Dash PK (2006). Reversal of brain injury-induced prefrontal glutamic acid decarboxylase expression and working memory deficits by D1 receptor antagonism . J Neurosci , 26 ( 16 ), 4236–4246. doi: 10.1523/jneurosci.4687-05.2006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kreitzer AC (2009). Physiology and pharmacology of striatal neurons . Annu Rev Neurosci , 32 , 127–147. doi: 10.1146/annurev.neuro.051508.135422 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kumar AM, Fernandez JB, Singer EJ, Commins D, Waldrop-Valverde D, Ownby RL, & Kumar M (2009). Human immunodeficiency virus type 1 in the central nervous system leads to decreased dopamine in different regions of postmortem human brains . J Neurovirol , 15 ( 3 ), 257–274. doi: 10.1080/13550280902973952 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kumar AM, Ownby RL, Waldrop-Valverde D, Fernandez B, & Kumar M (2011). Human immunodeficiency virus infection in the CNS and decreased dopamine availability: relationship with neuropsychological performance . J Neurovirol , 17 ( 1 ), 26–40. doi: 10.1007/s13365-010-0003-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lambertsen KL, Gramsbergen JB, Sivasaravanaparan M, Ditzel N, Sevelsted-Møller LM, Oliván-Viguera A, . . . Köhler R (2012). Genetic KCa3.1-deficiency produces locomotor hyperactivity and alterations in cerebral monoamine levels . PLoS One , 7 ( 10 ), e47744. doi: 10.1371/journal.pone.0047744 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Laplante F, Zhang ZW, Huppé-Gourgues F, Dufresne MM, Vaucher E, & Sullivan RM (2012). Cholinergic depletion in nucleus accumbens impairs mesocortical dopamine activation and cognitive function in rats . Neuropharmacology , 63 ( 6 ), 1075–1084. doi: 10.1016/j.neuropharm.2012.07.033 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Laubach M, Amarante LM, Swanson K, & White SR (2018). What, If Anything, Is Rodent Prefrontal Cortex? eNeuro , 5 ( 5 ). doi: 10.1523/ENEURO.0315-18.2018 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Leffa DT, de Souza A, Scarabelot VL, Medeiros LF, de Oliveira C, Grevet EH, . . . Torres ILS (2016). Transcranial direct current stimulation improves short-term memory in an animal model of attention-deficit/hyperactivity disorder . Eur Neuropsychopharmacol , 26 ( 2 ), 368–377. doi: 10.1016/j.euroneuro.2015.11.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Levin ED, Torry D, Christopher NC, Yu X, Einstein G, & Schwartz-Bloom RD (1997). Is binding to nicotinic acetylcholine and dopamine receptors related to working memory in rats? Brain Res Bull , 43 ( 3 ), 295–304. doi: 10.1016/s0361-9230(97)00009-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Liu KC, Li JY, Xie W, Li LB, Zhang J, Du CX, . . . Zhang L (2016). Activation and blockade of serotonin(6) receptors in the dorsal hippocampus enhance T maze and hole-board performance in a unilateral 6-hydroxydopamine rat model of Parkinson's disease . Brain Res , 1650 , 184–195. doi: 10.1016/j.brainres.2016.09.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Liu Y, Liu J, Jiao SR, Liu X, Guo Y, Zhang J, . . . Zhang L (2019). Serotonin(1A) receptors in the dorsal hippocampus regulate working memory and long-term habituation in the hemiparkinsonian rats . Behav Brain Res , 376 , 112207. doi: 10.1016/j.bbr.2019.112207 [ PubMed ] [ CrossRef ] [ Google Scholar ]
López-Téllez JF, López-Aranda MF, Navarro-Lobato I, Masmudi-Martín M, Montañez EM, Calvo EB, & Khan ZU (2010). Prefrontal inositol triphosphate is molecular correlate of working memory in nonhuman primates . J Neurosci , 30 ( 8 ), 3067–3071. doi: 10.1523/jneurosci.4565-09.2010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Luine VN, Richards ST, Wu VY, & Beck KD (1998). Estradiol enhances learning and memory in a spatial memory task and effects levels of monoaminergic neurotransmitters . Hormones and Behavior , 34 ( 2 ), 149–162. doi: 10.1006/hbeh.1998.1473 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Matheus FC, Rial D, Real JI, Lemos C, Ben J, Guaita GO, . . . Prediger RD (2016). Decreased synaptic plasticity in the medial prefrontal cortex underlies short-term memory deficits in 6-OHDA-lesioned rats . Behav Brain Res , 301 , 43–54. doi: 10.1016/j.bbr.2015.12.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mattay VS, Tessitore A, Callicott JH, Bertolino A, Goldberg TE, Chase TN, . . . Weinberger DR (2002). Dopaminergic modulation of cortical function in patients with Parkinson's disease . Ann Neurol , 51 ( 2 ), 156–164. doi: 10.1002/ana.10078 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Milstein JA, Elnabawi A, Vinish M, Swanson T, Enos JK, Bailey AM, . . . Frost DO (2013). Olanzapine treatment of adolescent rats causes enduring specific memory impairments and alters cortical development and function . PLoS One , 8 ( 2 ), e57308. doi: 10.1371/journal.pone.0057308 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mizoguchi K, Ishige A, Takeda S, Aburada M, & Tabira T (2004). Endogenous glucocorticoids are essential for maintaining prefrontal cortical cognitive function . J Neurosci , 24 ( 24 ), 5492–5499. doi: 10.1523/jneurosci.0086-04.2004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mizoguchi K, Shoji H, Tanaka Y, Maruyama W, & Tabira T (2009). Age-related spatial working memory impairment is caused by prefrontal cortical dopaminergic dysfunction in rats . Neuroscience , 162 ( 4 ), 1192–1201. doi: 10.1016/j.neuroscience.2009.05.023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mizoguchi K, Yuzurihara M, Ishige A, Sasaki H, Chui DH, & Tabira T (2000). Chronic stress induces impairment of spatial working memory because of prefrontal dopaminergic dysfunction . J Neurosci , 20 ( 4 ), 1568–1574. doi: 10.1523/jneurosci.20-04-01568.2000 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Moghaddam B, Adams B, Verma A, & Daly D (1997). Activation of glutamatergic neurotransmission by ketamine: A novel step in the pathway from NMDA receptor blockade to dopaminergic and cognitive disruptions associated with the prefrontal cortex . Journal of Neuroscience , 17 ( 8 ), 2921–2927. doi: 10.1523/jneurosci.17-08-02921.1997 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Moghaddam B, & Adams BW (1998). Reversal of phencyclidine effects by a group II metabotropic glutamate receptor agonist in rats . Science , 281 ( 5381 ), 1349–1352. doi: 10.1126/science.281.5381.1349 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mohebi A, Pettibone JR, Hamid AA, Wong JT, Vinson LT, Patriarchi T, . . . Berke JD (2019). Dissociable dopamine dynamics for learning and motivation . Nature , 570 ( 7759 ), 65–70. doi: 10.1038/s41586-019-1235-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Monaco SA, Gulchina Y, & Gao WJ (2015). NR2B subunit in the prefrontal cortex: A double-edged sword for working memory function and psychiatric disorders . Neurosci Biobehav Rev , 56 , 127–138. doi: 10.1016/j.neubiorev.2015.06.022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Morrow BA, Roth RH, & Elsworth JD (2000). TMT, a predator odor, elevates mesoprefrontal dopamine metabolic activity and disrupts short-term working memory in the rat . Brain Res Bull , 52 ( 6 ), 519–523. doi: 10.1016/s0361-9230(00)00290-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Narayanan NS, Rodnitzky RL, & Uc EY (2013). Prefrontal dopamine signaling and cognitive symptoms of Parkinson's disease . Rev Neurosci , 24 ( 3 ), 267–278. doi: 10.1515/revneuro-2013-0004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Nasuti C, Carloni M, Fedeli D, Gabbianelli R, Di Stefano A, Serafina CL, . . . Ciccocioppo R (2013). Effects of early life permethrin exposure on spatial working memory and on monoamine levels in different brain areas of pre-senescent rats . Toxicology , 303 , 162–168. doi: 10.1016/j.tox.2012.09.016 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Neese SL, Wang VC, Doerge DR, Woodling KA, Andrade JE, Helferich WG, . . . Schantz SL (2010). Impact of dietary genistein and aging on executive function in rats . Neurotoxicol Teratol , 32 ( 2 ), 200–211. doi: 10.1016/j.ntt.2009.11.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Novick AM, Miiller LC, Forster GL, & Watt MJ (2013). Adolescent social defeat decreases spatial working memory performance in adulthood . Behav Brain Funct , 9 , 39. doi: 10.1186/1744-9081-9-39 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Okubo Y, Suhara T, Suzuki K, Kobayashi K, Inoue O, Terasaki O, . . . Toru M (1997). Decreased prefrontal dopamine D1 receptors in schizophrenia revealed by PET . Nature , 385 ( 6617 ), 634–636. doi: 10.1038/385634a0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Parker KL, Ruggiero RN, & Narayanan NS (2015). Infusion of D1 Dopamine Receptor Agonist into Medial Frontal Cortex Disrupts Neural Correlates of Interval Timing . Front Behav Neurosci , 9 , 294. doi: 10.3389/fnbeh.2015.00294 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pereira AG, Poli A, Matheus FC, de Bortoli da Silva L, Fadanni GP, Izídio GS, . . . Prediger RD (2020). Temporal development of neurochemical and cognitive impairments following reserpine administration in rats . Behav Brain Res , 383 , 112517. doi: 10.1016/j.bbr.2020.112517 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Petro A, Sexton HG, Miranda C, Rastogi A, Freedman JH, & Levin ED (2016). Persisting neurobehavioral effects of developmental copper exposure in wildtype and metallothionein 1 and 2 knockout mice . BMC Pharmacol Toxicol , 17 ( 1 ), 55. doi: 10.1186/s40360-016-0096-3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pietraszek M, Michaluk J, Romańska I, Wasik A, Gołembiowska K, & Antkiewicz-Michaluk L (2009). 1-Methyl-1,2,3,4-tetrahydroisoquinoline antagonizes a rise in brain dopamine metabolism, glutamate release in frontal cortex and locomotor hyperactivity produced by MK-801 but not the disruptions of prepulse inhibition, and impairment of working memory in rat . Neurotox Res , 16 ( 4 ), 390–407. doi: 10.1007/s12640-009-9097-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
R Core Team (2021). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ [ Google Scholar ]
Ragozzino ME (2002). The effects of dopamine D(1) receptor blockade in the prelimbic-infralimbic areas on behavioral flexibility . Learn Mem , 9 ( 1 ), 18–28. doi: 10.1101/lm.45802 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ramkumar K, Srikumar BN, Shankaranarayana Rao BS, & Raju TR (2008). Self-stimulation rewarding experience restores stress-induced CA3 dendritic atrophy, spatial memory deficits and alterations in the levels of neurotransmitters in the hippocampus . Neurochem Res , 33 ( 9 ), 1651–1662. doi: 10.1007/s11064-007-9511-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ramkumar K, Srikumar BN, Venkatasubramanian D, Siva R, Shankaranarayana Rao BS, & Raju TR (2012). Reversal of stress-induced dendritic atrophy in the prefrontal cortex by intracranial self-stimulation . J Neural Transm (Vienna) , 119 ( 5 ), 533–543. doi: 10.1007/s00702-011-0740-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ray A, Chitre NM, Daphney CM, Blough BE, Canal CE, & Murnane KS (2019). Effects of the second-generation "bath salt" cathinone alpha-pyrrolidinopropiophenone (α-PPP) on behavior and monoamine neurochemistry in male mice . Psychopharmacology (Berl) , 236 ( 3 ), 1107–1117. doi: 10.1007/s00213-018-5044-z [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Robbins TW, & Arnsten AF (2009). The neuropsychopharmacology of fronto-executive function: monoaminergic modulation . Annu Rev Neurosci , 32 , 267–287. doi: 10.1146/annurev.neuro.051508.135535 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Robbins TW, & Roberts AC (2007). Differential regulation of fronto-executive function by the monoamines and acetylcholine . Cereb Cortex , 17 Suppl 1 , i151–160. doi: 10.1093/cercor/bhm066 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Salamone JD, Correa M, Nunes EJ, Randall PA, & Pardo M (2012). The behavioral pharmacology of effort-related choice behavior: dopamine, adenosine and beyond . J Exp Anal Behav , 97 ( 1 ), 125–146. doi: 10.1901/jeab.2012.97-125 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Schmeichel BE, Zemlan FP, & Berridge CW (2013). A selective dopamine reuptake inhibitor improves prefrontal cortex-dependent cognitive function: potential relevance to attention deficit hyperactivity disorder . Neuropharmacology , 64 ( 1 ), 321–328. doi: 10.1016/j.neuropharm.2012.07.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Schultz W, Studer A, Romo R, Sundstrom E, Jonsson G, & Scarnati E (1989). Deficits in reaction times and movement times as correlates of hypokinesia in monkeys with MPTP-induced striatal dopamine depletion . J Neurophysiol , 61 ( 3 ), 651–668. doi: 10.1152/jn.1989.61.3.651 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Seamans JK, Floresco SB, & Phillips AG (1998). D1 receptor modulation of hippocampal-prefrontal cortical circuits integrating spatial memory with executive functions in the rat . J Neurosci , 18 ( 4 ), 1613–1621. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9454866 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Seamans JK, & Yang CR (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex . Prog Neurobiol , 74 ( 1 ), 1–58. doi: 10.1016/j.pneurobio.2004.05.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Segovia G, Del Arco A, de Blas M, Garrido P, & Mora F (2008). Effects of an enriched environment on the release of dopamine in the prefrontal cortex produced by stress and on working memory during aging in the awake rat . Behav Brain Res , 187 ( 2 ), 304–311. doi: 10.1016/j.bbr.2007.09.024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Skirzewski M, Karavanova I, Shamir A, Erben L, Garcia-Olivares J, Shin JH, . . . Buonanno A (2018). ErbB4 signaling in dopaminergic axonal projections increases extracellular dopamine levels and regulates spatial/working memory behaviors . Mol Psychiatry , 23 ( 11 ), 2227–2237. doi: 10.1038/mp.2017.132 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Slifstein M, van de Giessen E, Van Snellenberg J, Thompson JL, Narendran R, Gil R, . . . Abi-Dargham A (2015). Deficits in prefrontal cortical and extrastriatal dopamine release in schizophrenia: a positron emission tomographic functional magnetic resonance imaging study . JAMA Psychiatry , 72 ( 4 ), 316–324. doi: 10.1001/jamapsychiatry.2014.2414 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Soriano A, Vendrell M, Gonzalez S, Mallol J, Albericio F, Royo M, . . . Casado V (2010). A hybrid indoloquinolizidine peptide as allosteric modulator of dopamine D1 receptors . J Pharmacol Exp Ther , 332 ( 3 ), 876–885. doi: 10.1124/jpet.109.158824 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Szczepanik JC, de Almeida GRL, Cunha MP, & Dafre AL (2020). Repeated Methylglyoxal Treatment Depletes Dopamine in the Prefrontal Cortex, and Causes Memory Impairment and Depressive-Like Behavior in Mice . Neurochem Res , 45 ( 2 ), 354–370. doi: 10.1007/s11064-019-02921-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tomasi D, Goldstein RZ, Telang F, Maloney T, Alia-Klein N, Caparelli EC, & Volkow ND (2007). Widespread disruption in brain activation patterns to a working memory task during cocaine abstinence . Brain Res , 1171 , 83–92. doi: 10.1016/j.brainres.2007.06.102 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tomasi D, Volkow ND, Wang GJ, Wang R, Telang F, Caparelli EC, . . . Fowler JS (2011). Methylphenidate enhances brain activation and deactivation responses to visual attention and working memory tasks in healthy controls . Neuroimage , 54 ( 4 ), 3101–3110. doi: 10.1016/j.neuroimage.2010.10.060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tractenberg SG, Orso R, Creutzberg KC, Malcon LMC, Lumertz FS, Wearick-Silva LE, . . . Grassi-Oliveira R (2020). Vulnerable and resilient cognitive performance related to early life stress: The potential mediating role of dopaminergic receptors in the medial prefrontal cortex of adult mice . Int J Dev Neurosci , 80 ( 1 ), 13–27. doi: 10.1002/jdn.10004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tsukada H, Miyasato K, Nishiyama S, Fukumoto D, Kakiuchi T, & Domino EF (2005). Nicotine normalizes increased prefrontal cortical dopamine D1 receptor binding and decreased working memory performance produced by repeated pretreatment with MK-801: a PET study in conscious monkeys . Neuropsychopharmacology , 30 ( 12 ), 2144–2153. doi: 10.1038/sj.npp.1300745 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tsukada H, Nishiyama S, Fukumoto D, Sato K, Kakiuchi T, & Domino EF (2005). Chronic NMDA antagonism impairs working memory, decreases extracellular dopamine, and increases D1 receptor binding in prefrontal cortex of conscious monkeys . Neuropsychopharmacology , 30 ( 10 ), 1861–1869. doi: 10.1038/sj.npp.1300732 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Virdee K, Kentrop J, Jupp B, Venus B, Hensman D, McArthur S, . . . Dalley JW (2016). Counteractive effects of antenatal glucocorticoid treatment on D1 receptor modulation of spatial working memory . Psychopharmacology (Berl) , 233 ( 21-22 ), 3751–3761. doi: 10.1007/s00213-016-4405-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Walsh TJ, Herzog CD, Gandhi C, Stackman RW, & Wiley RG (1996). Injection of IgG 192-saporin into the medial septum produces cholinergic hypofunction and dose-dependent working memory deficits . Brain Res , 726 ( 1-2 ), 69–79. [ PubMed ] [ Google Scholar ]
Wang DC, Lin HT, Lee YJ, Yu HF, Wu SR, & Qamar MU (2020). Recovery of BDNF and CB1R in the Prefrontal Cortex Underlying Improvement of Working Memory in Prenatal DEHP-Exposed Male Rats after Aerobic Exercise . Int J Mol Sci , 21 ( 11 ). doi: 10.3390/ijms21113867 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Watt MJ, Roberts CL, Scholl JL, Meyer DL, Miiller LC, Barr JL, . . . Forster GL (2014). Decreased prefrontal cortex dopamine activity following adolescent social defeat in male rats: role of dopamine D2 receptors . Psychopharmacology (Berl) , 231 ( 8 ), 1627–1636. doi: 10.1007/s00213-013-3353-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Willig F, M'Harzi M, Bardelay C, Viet D, & Delacour J (1991). Roman strains as a psychogenetic model for the study of working memory: Behavioral and biochemical data . Pharmacology Biochemistry and Behavior , 40 ( 1 ), 7–16. Retrieved from https://www.embase.com/search/results?subaction=viewrecord&id=L21324338&from=export [ PubMed ] [ Google Scholar ]
Willig F, Van de Velde D, Laurent J, M'Harzi M, & Delacour J (1992). The Roman strains of rats as a psychogenetic tool for pharmacological investigation of working memory: example with RU 41656 . Psychopharmacology (Berl) , 107 ( 2-3 ), 415–424. doi: 10.1007/bf02245169 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Winterfeld KT, Teuchert-Noodt G, & Dawirs RR (1998). Social environment alters both ontogeny of dopamine innervation of the medial prefrontal cortex and maturation of working memory in gerbils (Meriones unguiculatus) . J Neurosci Res , 52 ( 2 ), 201–209. doi: 10.1002/(sici)1097-4547(19980415)52:2<201::Aid-jnr8>3.0.Co;2-e [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wojtas A, Herian M, Skawski M, Sobocińska M, González-Marín A, Noworyta-Sokołowska K, & Gołembiowska K (2021). Neurochemical and Behavioral Effects of a New Hallucinogenic Compound 25B-NBOMe in Rats . Neurotox Res , 39 ( 2 ), 305–326. doi: 10.1007/s12640-020-00297-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yahr MD, Duvoisin RC, Schear MJ, Barrett RE, & Hoehn MM (1969). Treatment of parkinsonism with levodopa . Arch Neurol , 21 ( 4 ), 343–354. doi: 10.1001/archneur.1969.00480160015001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zahrt J, Taylor JR, Mathew RG, & Arnsten AF (1997). Supranormal stimulation of D1 dopamine receptors in the rodent prefrontal cortex impairs spatial working memory performance . J Neurosci , 17 ( 21 ), 8528–8535. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9334425 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Zhang Q, Jung D, Larson T, Kim Y, & Narayanan NS (2019). Scopolamine and Medial Frontal Stimulus-Processing during Interval Timing . Neuroscience , 414 , 219–227. doi: 10.1016/j.neuroscience.2019.07.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Skip to main page content

Institution: Google Indexer
Sign In via User Name/Password

Learning under stress: The inverted-U-shape function revisited

Basira Salehi ,
M. Isabel Cordero and
Carmen Sandi 1
Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland

Although the relationship between stress intensity and memory function is generally believed to follow an inverted-U-shaped curve, strikingly this phenomenon has not been demonstrated under the same experimental conditions. We investigated this phenomenon for rats’ performance in a hippocampus-dependent learning task, the radial arm water maze (RAWM). Variations in stress intensity were induced using different water temperatures (25°C, 19°C, and 16°C), which elicited increased plasma corticosterone levels. During spatial training over three consecutive days, an inverted-U shape was found, with animals trained at 19°C making fewer errors than animals trained at either higher (16°C) or lower (25°C) stress conditions. Interestingly, this function was already observed by the last trial of day 1 and maintained on the first day trial of day 2. A long-term recall probe test administered under equal temperature conditions (20°C) revealed differences in performance according to the animals’ former training conditions; i.e., platform searching for rats trained at 25°C was less accurate than for rats trained at either 16°C or 19°C. In reversal learning, groups trained at both 19°C and 25°C showed better performance than the 16°C group. We also found an interaction between anxiety and exploration traits on how individuals were affected by stressors during spatial learning. In summary, our findings confirm, for the first time, the existence of an inverted-U-shape memory function according to stressor intensity during the early learning and memory phases in a hippocampus-dependent task, and indicate the existence of individual differences related to personality-like profiles for performance at either high or low stress conditions.

Physiological stress responses are used by organisms to adapt to changing, demanding circumstances, so these responses are of enormous adaptive value ( Lightman 2008 ). However, survival success depends not only on the immediate ability to respond to threat, but also on the integration of previously acquired knowledge and skills into effective strategies to facilitate coping with similar demands in the future. This view provides an evolutionary explanation for stress effects on learning and memory processes.

Understanding the nature of stress–memory interactions has attracted significant attention in recent years. Surprisingly, despite much investigation, it is still not known how stress severity affects memory function. It is generally believed that the relationship between stress intensity and memory function follows an inverted-U-shaped curve, with memory increasing with stress to an optimal point, above or below which memory decreases. However, this stress–memory relationship seems to not apply to classical (Pavlovian) conditioning processes (for review, see Sandi and Pinelo-Nava 2007 ). Rather, current evidence supports a linear relationship between stressor intensity and the strength of the fear-conditioned memory formed, with an asymptotic waveform for high-to-very-high stress intensities ( Fanselow and Bolles 1979 ; Shors and Servatius 1997 ; Beylin and Shors 1998 ; Cordero et al. 1998 ; Radulovic et al. 1998 ; Anagnostaras et al. 2000 ; Merino et al. 2000 ; Laxmi et al. 2003 ).

The inverted U-shape function was originally proposed by Yerkes and Dodson (1908) to explain the relationship between stimulus strength and the rapidity of habit formation for “difficult” discrimination learning tasks in mice. In their experimental conditions, as with those of Broadhurst (1957) , “easy” tasks followed a linear relationship, as discussed above for classical conditioning. Hence, the so-called Yerkes-Dodson law implies that cognitive performance in difficult tasks is best when an individual is under optimal stress; performance would be impaired under conditions above or below optimal stress levels ( Yerkes and Dodson 1908 ; Broadbent 1965 ; Mendl 1999 ). Despite the great popularity of the inverted-U curve, or the Yerkes-Dodson law, to describe the relationship between stress and performance ( Diamond 2005 ), the validity of the law has been criticized due to significant methodological problems in the study performed by Yerkes and Dodson (1908) and their data being judged insufficient to substantiate conclusions, among other reasons ( Brown 1965 ; Baumler and Lienert 1993 ; Baumler 1994 ; Teigen 1994 ; Hancock and Ganey 2003 ; Diamond et al. 2007 ). In 1957, Broadhurst provided further evidence for the inverted-U-shape function using more refined methods and a visual discrimination task similar to that used by Yerkes and Dodson (1908) . In Broadhurst's experiments, variations in stress levels were achieved by exposing rats to different lengths of air deprivation just before the start of each trial. Therefore, stress was applied within the learning context, but did not originate from elements related to the cognitive task, so the stress could be considered “extrinsic” to the learning task. In the field of animal learning, it is surprising to note that not a single report has described an inverted-U-shape function for the relationship between “intrinsic” stress (i.e., induced by elements related to the cognitive task) and learning under the same experimental conditions ( Morris 2006 ; Sandi and Pinelo-Nava 2007 ). For example, recent proposals of an inverted-U-shaped function during spatial learning in rodents are based on independent, composite observations from different experimental settings and laboratories examining different parts (ascending or descending) of the function ( Mendl 1999 ; Morris 2006 ; Park et al. 2006 ; Sandi and Pinelo-Nava 2007 ).

Here we aimed to evaluate, for the first time, the validity of the inverted-U-shape function to account for the impact of variations in intrinsic stressor intensity on memory processes in a spatial learning task. We used the radial six-arm water maze (RAWM), in which animals learn to find a hidden escape platform located at the end of one of the arms with the help of extramaze visual cues. This task was chosen because it was previously shown to be both hippocampus dependent and sensitive to modulation by stress ( Diamond et al. 1999 ). To evaluate the effect of stressor intensity on task learning, we trained rats at different water temperatures, which produce different plasma corticosterone levels, and explored the rats' performance throughout each memory phase (learning acquisition, long-term memory retention, and reversal learning).

Furthermore, in line with the pioneering work by Eysenck (1955) , who questioned the role of personality in stress-influenced performance during learning tasks, as well as our own work relating anxiety-like trait with differences in spatial learning abilities ( Herrero et al. 2006 ) and behavioral and neurobiological vulnerability to stress ( Jakobsson et al. 2008 ; Sandi et al. 2008 ; Luksys et al. 2009 ), we set a second goal of capturing individual differences in the relationship between intrinsic stress and learning based on rat's personality traits.

Plasma corticosterone levels induced by swimming at different water temperatures

Our first step was to select three water temperatures that represent a gradation of physical stressor intensities when rats are placed in a pool without concomitant spatial learning. Based on pilot and previous experiments ( Sandi et al. 1997 ; Akirav et al. 2001 , 2004 ), 16°C, 19°C, and 25°C were chosen, and plasma levels of the stress hormone corticosterone were evaluated after submitting animals to one cued training session in the RAWM (i.e., cued platform) at one of the three water temperatures ( n = 6 rats/temperature; Fig. 1 A). A one-way ANOVA indicated an effect of temperature ( F (2,15) = 5.29, P < 0.05), with corticosterone levels from animals trained at 16°C being significantly higher than levels from animals trained at 25°C ( P < 0.05), and levels from animals trained at 19°C falling between the other two levels. A linear regression analysis confirmed the existence of a significant negative relationship between water temperature and corticosterone levels ( P < 0.005; Fig. 1 B), indicating that plasma corticosterone levels increased linearly as water temperature decreased. No differences were found among the groups for the number of errors across all trials (data not shown). Furthermore, to ensure that the low water temperatures used in this study did not induce hypothermia, rectal temperatures were measured immediately after training in the water maze at different temperatures. No differences in body temperature were observed between the groups (Supplemental Fig. S1).

In this window
In a new window
Download as PowerPoint Slide

( A ) Corticosterone levels measured 45 min after the first trial in the RAWM. Overall ANOVA: P < 0.05; (*) P < 0.05 vs. 16°C. ( B ) Regression analysis confirmed the existence of a significant negative relationship between water temperature level and corticosterone level. Data are the mean ± SEM.

Learning in the RAWM at different water temperatures

Rats were trained in the spatial version of the RAWM under the three selected water temperatures (16°C, n = 35; 19°C, n = 50; or 25°C, n = 37) over three consecutive days ( Fig. 2 A). A repeated-measures ANOVA on the arm entry errors revealed an effect of training days ( F (2,238) = 26.78, P < 0.0001), confirming that animals progressively learned the spatial location of the platform across the training sessions. ANOVA also revealed an effect of water temperature ( F (2,119) = 12.38, P < 0.0001). This effect was further confirmed when performance across the three training days was averaged ( F (2,119) = 12.38, P < 0.0001; Fig. 2 B). Interestingly, post-hoc analyses indicated the existence of an inverted-U-shaped function, with animals trained at 19°C making less errors to find the platform than those trained at either 16°C ( P < 0.0001) or 25°C ( P < 0.0001). No difference in performance was found between rats trained at either 16°C or 25°C water temperature. A repeated-measures ANOVA on data represented in Figure 2 A yielded no interaction between water temperature and training day ( F (4,238) = 0.74, n.s). Separate ANOVAs for each training day indicated that on each training day, rats trained at 19°C outperformed those trained at either 16°C or 25°C ( P < 0.01). To examine possible differences in motor performance that may have been caused by the different water temperatures, the swimming speed of the different groups was compared. No significant differences were found between swimming speeds of groups trained at different temperatures ( F (2,119) = 0.12, n.s.; Supplemental Fig. S2). It is important to note that the different patterns of performance observed at the different water temperatures were already observed on the first training day, but not on the first training trial, in which no significant differences were found among the different groups (Supplemental Fig. S3). However, on the last trial (Trial 4) of day 1, animals trained at 19°C performed significantly better than animals trained at either 16°C or 25°C (Supplemental Fig. S3).

Effect of water temperature on performance, as indicated by the number of arm entry errors, in the RAWM during spatial learning ( A–C ) or cued training ( D–F ). ( A ) Spatial learning: When compared with the 19°C group, rats trained at either 16°C or 25°C showed delayed acquisition (**) P < 0.01 vs. 19°C for 16°C and 25°C in each training day. ( B ) Average error values collapsed for performance across the three training days. (***) P < 0.001 vs. 16°C and 25°C. ( C ) Number of errors incurred on the first training trial of day 2. (*) P < 0.05 vs. 16°C and 25°C. ( D ) Cued training: No differences were found in reaching the cued platform for groups trained at different water temperatures. ( E ) Average arm entry errors made to reach the platform collapsed over the three cued training days reveals no differences among groups. ( F ) No differences were found in performance on the first trial of the second cued training day. Data are the mean ± SEM.

Evaluation of motivational factors in animals trained at different water temperatures

A critical issue was whether the differences in path length observed in rats trained at different water temperatures were due to differential motivation to escape from the water (i.e., to find the platform) or to genuine differences in spatial learning capabilities. To address this issue, a similar experiment was carried out on a new set of animals ( n = 9/temperature group), although here the platform was visible (not submerged) and cued. A repeated-measures ANOVA indicated an effect of training days ( F (2,48) = 5, P < 0.05), confirming that rats progressively reached the platform after shorter navigation distances. In contrast, there was no effect for water temperature ( F (2,24) = 1.5, n.s) and no interaction between the two factors ( F (4,48) = 0.37, n.s; Fig. 2 D). Similar findings were obtained when data from the three training days were averaged, yielding no differences in the number of errors to find a cued platform among groups of rats trained at different temperatures ( F (2,24) = 1.5, n.s; Fig. 2 E).

Evaluation of long-term retention

Long-term retention was tested both during and after the initial spatial training by applying different testing opportunities. First, we analyzed data from the first trial of the second training day, because this trial can be considered the earliest long-term memory test ( Fig. 2 C) (note that this was not a probe trial, but a regular training trial). An ANOVA indicated an effect of water temperature ( F (2,119) = 3.3, P < 0.05), and post-hoc analyses confirmed that the data followed a U-shape function, with animals trained at 19°C making a significantly lower number of arm entry errors to find the platform than those trained at either 25°C or 16°C ( P < 0.05). When the same analysis was performed on data from the cued platform version, no differences in performance were found among animals trained at different temperatures on the first trial of day 2 ( F (2,24) = 0.65, n.s; Fig. 2 F).

On day 8 (i.e., 5 d after the last training session), rats were administered a probe test in which the platform was removed and the water temperature was kept equal (20°C ± 0.5°C) for all groups. Different parameters were analyzed for the duration of the probe trial (60 sec) and for the first and second half of the test trial. No significant differences were found in the arm entry errors to reach the virtual platform among animals that had been trained at the different water temperatures ( F (2,119) = 2.06, P = 0.13; Supplemental Fig. S4). However, an interesting finding arose when animals' strategies (i.e., whether they spent more time in the error arms, the target arm, or the center of the pool) were evaluated in a time-dependent fashion (i.e., the first and last 30 sec of the probe trail analyzed separately; Fig. 3 ). An ANOVA on the amount of time spent in the target arm over the first 30 sec indicated a significant difference among the groups ( F (2,119) = 3.24, P < 0.05). Post-hoc analyses indicated that rats trained at 25°C spent less time in the target arm than rats trained at 16°C ( P < 0.05) or 19°C ( P < 0.05; Fig. 3 A). Furthermore, an ANOVA for the amount of time spent in the error arms during the first 30 sec revealed a significant difference of the three groups ( F (2,119) = 4.1, P < 0.05; Fig. 3 C); animals trained at 16°C and 19°C spent significantly less time in the error arms than those trained at 25°C ( P < 0.05). Finally, an ANOVA of the time spent in the center of the pool during the last 30 sec indicated a difference among the three groups ( F (2,119) = 3.41, P < 0.05; Fig. 3 F). Specifically, rats trained at 19°C spent significantly more time in the center of the pool than animals trained at 25°C ( P < 0.05). All together, these data indicate that when tested under identical experimental conditions (i.e., the same water temperature), animals previously trained at different water temperatures differ in their behavioral pattern, with the most significant differences appearing between animals trained at 19°C and 25°C and the latter displaying a more erratic search pattern. In other words, the animals trained at 25°C spent more time in the error arms and less time in the target arm than 19°C-trained animals. However, performance of the animals trained at 16°C was similar to those trained at 19°C, showing performance significantly superior to animals trained at 25°C during the first 30 sec.

Performance of animals trained in the spatial RAWM version at different water temperatures when administered a probe trial on day 8 at identical temperature conditions (20°C ± 0.5°C). Results are shown for the first and last 30 sec of the probe trial and are represented as the percent time in the target arm ( A , B ), the error arms ( C , D ), and the center of the pool ( E , F ). Data are the mean ± SEM. (*) P < 0.05 vs. 25°C.

The impact of increasing psychogenic stress: Reversal learning

After a further training day (day 9; no differences in performance were found among the groups; data not shown), on which animals were retrained as on days 1–3, all groups were submitted to a reversal learning session on day 10 using water temperatures that matched temperatures from previous training days (16°C, 19°C, or 25°C) ( Fig. 4 ). An ANOVA on the arm entry errors from trials 2–4 (the first trial was novel to all animals and not indicative of learning processes, so it was excluded from analyses) indicated a significant effect of temperature ( F (2,119) = 3.55, P < 0.05), and post-hoc analyses indicated that animals trained at 16°C made significantly more errors to reach the platform than those trained at either 19°C ( P = 0.05) or 25°C ( P < 0.05).

The effect of water temperature on performance in the reversal learning session. Rats trained at 16°C showed poor performance compared with rats trained at either 19°C or 25°C. Data are the mean number of arm entry errors ± SEM. (*) P < 0.05 vs. 16°C.

Individual differences and the inverted-U shape

One of the goals set for this study was to evaluate whether animals showing different behavioral profiles differ in how stress affects their performance during learning and memory tests. Data from principal component analyses performed on tests administered for behavioral characterization were used to classify animals into dichotomized variables for each behavioral trait (i.e., locomotion, anxiety, and exploration) (see Materials and Methods for details on the principal component analyses performed and the extracted factors, and Supplemental Tables S1–S3). Thus, animals were classified according to whether their score was above or below the mean for each factor into groups of low (LL) or high (HL) locomotion, low (LA) or high (HA) anxiety, and low (LE) or high (HE) exploration. Mean comparisons on the scores for each variable confirmed that the dichotomized groups representing high and low scores for each trait differed significantly (all Student t -tests; P < 0.01; Supplemental Fig. S5). RAWM performance of these groups was then compared using parametric analyses on the learning and memory data.

A factorial ANOVA, with temperature and the three behavioral traits extracted from the principal component analyses as the factors, performed on data from the first three RAWM training days, revealed a lack of significant interaction (n.s.). There was also no effect of each personality factor (n.s.). Further factorial ANOVAs performed on combinations of two behavioral traits and water temperature as factors did not yield statistical significance, except when anxiety and exploration were combined ( F (2,110) = 4.64, P < 0.05). Next, simple main effect analyses were performed to evaluate the impact of water temperature on RAWM learning for each behavioral profile resulting from the dichotomized groups of anxiety and exploration (HA-HE, LA-HE, HA-LE, LA-LE; Fig. 5 ). These analyses confirmed a U-shape relationship for the HA-HE profile, with animals trained at 19°C performing significantly better than those trained at 16°C ( P < 0.05) or 25°C ( P < 0.01). Among both the LA-HE and the HA-LE profiles, the group trained at 16°C performed significantly worse than the 19°C group ( P < 0.01), whereas performance at 25°C did not differ significantly from either of the other groups (n.s). Interestingly, a different pattern was observed for the LA-LE profile; animals trained at 25°C performed significantly worse than those trained at 19°C ( P < 0.01) or 16°C ( P < 0.05). Furthermore, an analysis of performance at each water temperature for the different personality groups revealed an effect of personality profiles at 25°C, with superior performance being observed for the HA-LE and LA-HE groups relative to the HA-HE and LA-LE groups (Supplemental Fig. S6).

Performance in the RAWM during the first training day at different water temperatures according to the personality profiles of high anxiety-high exploration (HA-HE), low anxiety-high exploration (LA-HE), high anxiety-low exploration (HA-LE), and low anxiety-low exploration (LA-LE). Only the HA-HE group displayed a U-shape learning response at different water temperatures. (*) P < 0.05 and (**) P < 0.01 vs. 19°C, (#) P < 0.05 vs. 16°C.

We report here, for the first time, the existence of an inverted-U-shape relationship between intrinsic stress intensity and performance in a hippocampus-dependent learning task, the RAWM. Various stress intensities were achieved using water temperatures of 25°C, 19°C, and 16°C to elicit increasing plasma corticosterone levels. By submitting rats to different training and testing protocols, we confirmed the existence of an inverted-U-shape function for performance at training; animals trained at 19°C made less errors to find the platform than animals trained at either higher (16°C) or lower (25°C) stress conditions. However, a long-term memory (probe) test performed 1 wk after training under equal temperature conditions (20°C ± 0.5°C) revealed a different performance pattern ( Fig. 3 ). Although the groups did not significantly differ in the number of errors made before reaching the virtual platform, analysis of the behavioral profile displayed during the test revealed that rats trained at 25°C were less accurate in platform searching than rats trained at either 16°C or 19°C, while no difference in searching was found for the latter two groups. When a cognitive challenge was subsequently introduced by changing the platform location (reversal learning), the groups trained at both 19°C and 25°C showed better performance than rats trained at 16°C, suggesting that cognitive difficulty affects the cognitive impact of “physical” stress. Furthermore, we presented evidence supporting the view that stress does not affect spatial learning and memory uniformly in all individuals. Rather, performance at either the high- or the low-stress levels is differentially affected in individuals with different personality-like profiles.

Previous studies using the Morris water maze ( Morris 1984 ) showed that rats trained at 19°C perform better than rats trained at 25°C, and corticosterone levels after the first training session were higher in rats trained at the colder water temperature ( Sandi et al. 1997 ; Akirav et al. 2004 ). A similar association among water temperature, learning and memory rate, and post-training corticosterone levels was also recently described in mice ( Conboy and Sandi 2010 ). Selden et al. (1990) presented evidence for impaired training at lower water temperatures (12°C) in rats, and they implicated coeruleo-cortical noradrenergic projections in the impairing effects of high stress on spatial learning. Here, we confirm the ascending portion of the U-shaped curve for the RAWM acquisition phase at 25°C (low stress) and 19°C (optimal stress) water temperature. Moreover, we show evidence supporting the existence of the descending portion of the U-shaped curve in animals trained at 16°C (high stress, or physical conditions leading to highest corticosterone levels) under otherwise identical experimental conditions. Importantly, differences in performance between animals trained at different temperatures seem to be related to the spatial learning nature of the RAWM task, previously reported to be hippocampus dependent ( Diamond et al. 1999 ), since no differences were found when animals were trained in the nonhippocampus-dependent cued platform version. These results are in agreement with pioneering observations by Wever (1932) , who observed no differences in latency to escape from a nonspatial water task in rats trained at temperatures ranging from 10°C to 25°C. Therefore, we show evidence for the existence of an inverted-U-shape function between stressor intensity and performance, specifically for spatial learning and in the memory test administered 24 h after the first training session.

While this curvilinear function was not captured in earlier work using intrinsic stress approaches, studies involving manipulations of the noradrenergic ( Introini-Collison et al. 1994 ) and glucocorticoid ( Lupien and McEwen 1997 ; Conrad 2005 ; Joëls 2006 ) systems have successfully substantiated the inverted-U-shape relationship between increasing glucocorticoid levels/function and both learning and synaptic plasticity. Glucocorticoids are adrenal hormones released into the bloodstream that, due to their lipophilic nature, can enter the brain, where they can influence brain function and cognition through genomic and nongenomic effects ( de Kloet et al. 1999 , 2005 ). Glucocorticoid receptors [GR] (mineralocorticoid receptors [MR]) are expressed in different brain areas, including regions that are central to learning and memory formation (e.g., hippocampus, amygdala, and prefrontal cortex) ( Sandi 1998 ; de Kloet et al. 1999 ). In chicks ( Sandi and Rose 1997 ), ground squirrels ( Mateo 2008 ), rats ( Roozendaal et al. 1999 ; Okuda et al. 2004 ), and humans ( Andreano and Cahill 2006 ), either very low or high glucocorticoid levels were reported to be associated with poor performance in a variety of learning and memory tasks ( Park et al. 2006 ). Similarly, the magnitude of hippocampal primed burst potentiation (i.e., a physiological type of synaptic plasticity) was shown to follow an inverted-U function relative to serum corticosterone levels (manipulated through adrenalectomy and different corticosterone concentrations delivered through subcutaneous pellets) ( Diamond et al. 1992 ). In agreement with this finding, opposite effects were described for activation of MR and GR in hippocampal long-term potentiation ( Pavlides et al. 1995 , 1996 ) and in a spatial learning task in rats ( Conrad et al. 1997 ), with activation of the MR exerting facilitatory and GR exerting inhibitory effects. However, despite this congruent evidence supporting a U-shape relationship between glucocorticoids and cognitive function, the full story is likely more complex than presented here. For example, cognitive effects depend on many factors, such as surrounding context ( de Kloet et al. 1999 ; Joëls et al. 2006 ), memory phase ( Roozendaal 2003 ), sex ( Conrad et al. 2004 ; Andreano and Cahill 2006 ), estrus cycle in females ( Andreano et al. 2008 ), previous experience, and emotional state ( de Quervain 2008 ; de Quervain et al. 2009 ).

A key question arising from the observed differences in performance of learning and memory tasks when animals were trained under different stress levels is whether these differences translate into differences in the strength of the long-term memory developed. To address this question, all groups were administered a probe test under equal temperature conditions. Strikingly, despite the inferior performance shown during training by the 16°C-trained group (i.e., the high-stress group), their behavioral pattern during the long-term probe test was very similar to that of the 19°C-trained group, which performed optimally during training. However, the group that was trained at 25°C (i.e., the low-stress group) showed during the first 30 sec the most erratic search patterns of the three groups, being the group that spent the most time in the error arms and the least time in the target arm. These data suggest that the 16°C-trained rats formed a stronger memory for the platform location than the 25°C-trained rats. The contribution of state-dependent mechanisms in this latter group cannot be discarded, since this group was the only one that was tested in the probe trial at a lower temperature than in previous sessions, and this difference might have produced an additional stress contributing to the impairing effect during this testing session. These findings also suggest that the deficits observed during the training phase in the 16°C group were probably due not only to impaired learning but also to impaired performance, particularly toward the final phase of training. This possibility would agree with a proposal by de Kloet et al. (1999) and Joëls et al. (2006) , both of whom suggested that when individuals are confronted with high stress levels, their strategy switches from an information-processing mode to a more opportune response that is adapted to the actual condition. More specifically, animals tested at a lower temperature in the probe trial may have changed strategies to conserve energy at the expense of navigation. Although this interpretation is plausible, we were surprised to find no differences in the speed at which the different water temperature groups swam and, hence, find no evidence for a change in a metabolic-related behavioral strategy. Furthermore, we did not find differences among groups in body temperature following training at different temperatures. Moreover, the fact that no differences were observed in the cued platform version of the task further suggests that the training deficits found in the 16°C and 25°C groups were, at least in part, related to the spatial orientation learning nature of the task and not to nonspecific effects related, for example, to swimming ability, hypothermia, or tracking down the relevant cues. Importantly, these results also strongly support the current view that stress (and glucocorticoids) facilitates memory consolidation ( Oitzl and de Kloet 1992 ; Sandi and Rose 1994 ; Sandi 1998 ; Roozendaal et al. 1999 , 2008 ; Sandi and Pinelo-Nava 2007 ; de Quervain et al. 2009 ).

We also found that in a subsequent training session using temperatures matching initial training, all groups achieved similar performance levels, suggesting that the observed inverted-U-shape relationship might be related to the initial stages of learning acquisition. With overtraining, initial differences due to variations in stress level seem to disappear. Then, when a new cognitive challenge was introduced (i.e., change of platform location in the reversal learning session), the 16°C group became impaired not only relative to the 19°C group, but also relative to the 25°C group, and performance of the 25°C group resembled the 19°C optimally performing group. This result is in agreement with studies testing the prediction from the Yerkes-Dodson law that the optimal stress or arousal state decreases with increasing task difficulty ( Mendl 1999 ). Therefore, the increase in task difficulty produced by the platform change would have extended the level of optimal stress for this type of learning from 19°C to 25°C ( Hancock and Ganey 2003 ).

Finally, we examined whether all individuals equally displayed the U-shape effects that we observed during training and the 24-h memory test. Previously, we reported that certain behavioral traits, such as anxiety, render subjects more sensitive to the behavioral and neurobiological effects of stress ( Jakobsson et al. 2008 ; Sandi et al. 2008 ; Luksys et al. 2009 ) and influence spatial learning abilities ( Herrero et al. 2006 ). Here, we considered whether more than one personality trait could contribute to differential performance under stress. To do so, we first extracted personality traits by applying principal component analyses to a series of behavioral tests for spontaneous behavior of rats. While the factor “locomotion” did not contribute to defining individuals with different responsiveness, the combination of the factors “anxiety” and “exploration” resulted in a meaningful interaction, yielding four personality-like profiles (HA-HE, HA-LE, LA-HE, and LA-LE). Animals falling into each of these different profiles showed different patterns of “learning under stress.” Highly anxious and highly explorative animals (HA-HE) were the only animals whose learning under different stress levels exhibited the U-shape function. Interestingly, among the remaining three profiles, two (HA-LE and LA-HE) showed optimal performance in the low-stress condition (25°C water) and impaired performance in the high-stress condition (16°C water), but one profile (LA-LE) showed the opposite pattern (i.e., optimal performance in the high-stress condition and impaired performance in the low-stress condition). These opposite response patterns to the different stress levels explain why an inverted-U shape is observed at the population level. In addition, these findings raise many interesting questions and, given the lack of similar studies in humans, raise the interest of addressing similar questions in humans. One interesting implication to extract from this study is that different personality types may be differentially affected in their cognitive functioning under varying stress levels. Therefore, the precise shape of the inverted-U-shape curve may vary for different personality types from a narrow bell revealing that performance is maximal only within a limited range of stimulus intensities (as observed in the HA-HE group) to curves that show maximal performance at either low (LA-HE, HA-LE) or high (LA-LE) stress levels (Supplemental Fig. S7). Interestingly, a physiological treatment that results in decreased anxiety to novelty ( Vataeva et al. 2001 ) was found to improve learning in the water maze at a temperature of 16°C–17°C, whereas performance was impaired at 23°C–24°C ( Vataeva et al. 2005 ). Accordingly, our study supports the conclusion that stress effects on hippocampus-dependent learning tasks vary for different personality profiles. Furthermore, our findings provide an attractive behavioral model to characterize the neurobiological mechanisms involved in the differential impact of stress levels in cognitive performance as well as the intrinsic interactions among personality, stress, and cognitive processes.

Materials and Methods

Adult male Wistar rats (Charles River Laboratories, Lyon, France), weighing 200–225 g at the beginning of the experiments, were housed in groups of three per cage. They were maintained under light (12 h light/dark cycle; lights on at 7:00 am) and temperature (22°C ± 2°C)-controlled conditions. Food and water were available ad libitum. Animal care procedures were approved through a license issued by the Cantonal Veterinary Authorities (Vaud, Switzerland).

General procedure

All experiments were conducted between 9:00 and 14:00 h. Approximately 2 wk after arrival, each rat was handled for 3 d, 2 min per day, just before the behavioral characterization started. The behavioral characterization included, first testing in the elevated plus maze, and 4 d afterward testing in the open field and novel object reactivity test. One week afterward, animals were distributed into three groups that were balanced for behavioral traits and body weights, and each group was submitted to training in the RAWM at a different water temperature (16°C, 19°C, or 25°C). Water-maze training was performed using either a cued-platform version or a spatial learning version. In all behavioral tests, the behavior of each rat was monitored using a video camera located on the ceiling, and movements of the rats were automatically registered and analyzed with a computerized tracking system (Ethovision 3.1.16, Noldus IT).

Behavioral characterization

Elevated plus maze (epm).

The first behavioral test was the elevated plus-maze ( Herrero et al. 2006 ), which is widely used to evaluate animals' anxiety-related behaviors. The elevated plus maze consists of two opposing open arms (45 × 10 cm) and two closed arms (45 × 10 × 50 cm) that extend from a central platform (10 × 10 cm), elevated 65 cm above the floor. The rats were placed individually on the central platform, always facing the same enclosed arm, and were allowed to freely explore the maze for 5 min. Different parameters were evaluated with the video tracking system: total distance moved (centimeters), distance moved (centimeters), and time spent (seconds) in the open and closed arms, and number of times the animal entered each type of arm. The floor of the apparatus was washed after each testing with 1% acetic acid solution to remove odors left by previous subjects.

Open field (OF) and novel object reactivity (NOR) tests

Animals' behavior was also assessed in the open field test (OF), which involves placing the animals in a circular open arena (100-cm diameter, 32-cm high). For analysis, the floor was divided into three virtual concentric parts, with a center zone in the middle of the arena (20-cm diameter), an interior zone (60-cm diameter), and an exterior zone made up of the remaining area along the sidewalls. At the start of the test, animals were placed in the center of the arena, and their behavior monitored for 10 min using a video camera mounted on the ceiling above the center of the arena. Different parameters were evaluated with the video tracking system: distance moved (centimeter) and time spent (seconds) in each zone.

Immediately after the open field test, rats were submitted to the novel object reactivity (NOR) test. For this purpose, a small, white plastic bottle (3 × 1.5 × 5 cm) was placed into the center of the open field while the rat was inside. Rats were then given 5 min to freely explore the novel object. Different parameters were evaluated with the video tracking system: time spent (seconds) in the center and the periphery of the compartment, number and latency of entries to the center, total distance moved (centimeters) in the center and in the whole compartment. The time spent exploring (touching) the novel object and the freezing time were recorded manually from the video recordings ( Jakobsson et al. 2008 ).

The apparatus used for testing spatial memory was a round black Plexiglas tank that was filled with clear water. The tank had a diameter of 170 cm and a height of 45 cm. Within the tank were Plexiglas walls that extended from the floor to a height of 43 cm and had a length of 60 cm. The walls were positioned to produce six swim paths radiating out of an open central area. A black metal platform (11-cm diameter) positioned 1.5 cm below the surface of the water was located at the end of one of the swim paths (arms), and the platform edge was ∼8 cm from the tank wall. When the rats swam to the end of this arm (referred to as the “target” arm) they could climb onto the platform to get out of the water. The tank was located in the middle of a well-lit testing room. Visually distinct cues were attached to the walls adjacent to the tank. All animals from each home cage were tested under the same water temperature condition. They were taken individually from the adjacent housing room and directly tested in the water maze.

The following parameters were evaluated with the video tracking system: latency (seconds) to find the platform, distance (centimeters) traveled to find the platform, and swim speed. The number of arm entry errors was determined according to the criteria established by Diamond et al. (1999) as the number of arm entries that did not result in the rat finding the escape platform. An arm entry was defined as a rat having all four paws extended out of the center area into an arm. An error was committed if a rat entered an arm that did not contain the platform or if a rat entered the correct arm but did not find the platform. Since we observed a very high correlation for the parameters “distance” to find the platform and “arm entry errors” ( r = 0.92, P < 0.001) and confirmed that the data for “distance” and “latency” gave similar results to analyses on “arm entry errors,” we only present the data corresponding to the “arm entry errors” parameter.

Animals were trained in either a cued or a spatial version of the RAWM. In the cued version, the platform is elevated slightly over the surface of the water and signaled with a 10-cm flag. This version is not sensitive to hippocampal lesions, so it is considered a hippocampus-independent task. Performance in the spatial version (in which the escape platform is hidden) is sensitive to hippocampal damage, so it is considered a hippocampus-dependent task. In each of these tasks, three different groups of animals were trained at different water temperatures that were selected with the goal of representing different stressor intensities (i.e., low, moderate, high stress). The temperatures of 19°C and 25°C were previously shown to be appropriate temperatures to elicit different stress levels in rats. The temperature of 16°C was added to enhance stressor intensity. Two independent experiments were performed with the cued version paradigm, one designed to evaluate plasma corticosterone levels immediately after the first training session at the different water temperatures ( n = 6/group), and a second one to evaluate motivational factors and performance over consecutive days ( n = 9/group). As to the spatial version protocol, a higher number of animals per group than in conventional studies was required to perform analyses, taking into account the combination of personality traits (final number of animals per group: 16°C, n = 35; 19°C, n = 50; 25°C, n = 37). A total of five replication experiments were performed, three of them including all water temperatures ( n = 8–9/group for each replication) and two of them involving two water temperature conditions (19°C in both cases and either 16°C or 25°C in each of the replications; n = 12/group for each replication). In each replication experiment, all water-temperature groups included were sequentially tested on the same days in a single RAWM. The order at which the different groups were run on each training day was counterbalanced both for different training days within each replication experiment and for each of the training days across the different replication experiments following a semirandom schedule. Every day, water temperature was easily changed by either adding hot water (available through the tap water) or ice obtained from an ice-making machine located in the animal facilities.

The acquisition phase of the spatial task consisted of a block of four trials per day run on each of three consecutive days. Four different starting arms were equally chosen around the perimeter of the pool. On each day, all four start positions were used once in a random sequence that was held constant for all rats. A trial began by placing the rat into the water facing the center of the pool at one of the starting points. If the animal failed to escape within 90 sec, it was manually guided to the platform. The animal was allowed to remain on the platform for 15 sec and was then placed into a holding cage under a warming lamp for 30 sec until the start of the next trial. After a rest period, on day 8 rats received a 60-sec free-swim period, during which the platform was removed from the maze (probe trial). At the end of the probe trial, the platform was reinserted into the pool and rats remained on it for 15 sec. On day 9, rats were retrained with another four-trial training session under the same conditions as described above. One day later (day 10), a reversal learning session was conducted, in which the platform position was changed to a different arm. Similar to the first 3 d of the experiment, the reversal session included four trials. After the last trial on each day, the rats were carefully dried with a towel and placed in the heated waiting cage for 10 min. Rats were then returned to their home cages.

Corticosterone analysis

Trunk blood was collected by decapitation 45 min after the beginning of a 1-d cued training session. The experiment was performed between 10 am and 1 pm. Samples were centrifuged (4000 rpm for 20 min at 4°C), and the serum was extracted and stored at −20°C. Corticosterone levels were assayed by ELISA (Assay Design) according to the manufacturer's instructions.

Statistical analyses

Results are expressed as mean ± SEM. The SPSS 13.0 statistical package was used for the statistical analyses.

Parametric statistics

Mean comparisons were carried out with either one-way or factorial ANOVAs; simple main effects analyses or post-hoc comparisons were made with LSD tests when appropriate. Normality and homogeneity of variance was tested, and adjusted statistics were used if required. Unless otherwise indicated, analyses of behavioral parameters excluded the first training trial (when no learning has as yet taken place).

Principal component analyses (PCA)

PCAs were applied to characterize animals according to their behaviors from the EPM, OF, and NOR tests. For the factorial analysis, which was twofold, the number of extracted factors was not predefined. Rather, PCAs were applied separately to a range of extracted parameters from the EPM (Supplemental Table 1) and the OF-NOR tests (Supplemental Table 2). Then, an overall principal component analysis was performed on the extracted factors, which revealed three factors that were termed “locomotion,” “anxiety,” and “exploration” according to the parameters that defined them (Supplemental Table 3). A continuous, interval scale score was calculated for each factor using principal components as the extraction method and varimax rotation with Kaiser normalization rotation. Then, individual factor scores were calculated for each subject through the relative weight and orientation (eigen values) of the parameters for each factor. Scores were generated using a Z distribution, where the value 0 corresponds to the mean, and values are expressed in terms of standard deviations. Animals were matched for their scores in the different factors and classified into the different experimental groups to yield groups with similar personality traits. In addition, data from the factorial analyses were used to investigate the modulatory effect of factor score differences on the learning measures investigated. For this study, animals were classified into groups such that scores laid either above or below the mean for each of the factors.

Acknowledgments

This work has been supported by grants from the EU (FP7-HEALTH-F2M-2008-201600, MemStick), the Swiss National Science Foundation (310000-120791), and intramural funding from the EPFL. We thank Cristina Marquez and Coralie Siegmund for technical assistance and the Laboratory of Behavioral Genetics at the EPFL for helpful discussions.

↵ 1 Corresponding author.

E-mail carmen.sandi{at}epfl.ch ; fax 41-21-6939636.

[Supplemental material is available online at http://www.learnmem.org. ]

Received June 18, 2010.
Accepted July 27, 2010.
Richter-Levin G
Kozenicky M ,
Anagnostaras SG ,
Josselyn SA ,
Frankland PW ,
Andreano JM ,
Arjomandi H ,
Baumler G ,
Beylin AV ,
Broadbent DE
Broadhurst PL
Conrad CD ,
Lupien SJ ,
Thanasoulis LC ,
Jackson JL ,
Wieczorek L ,
Harman JS ,
Wright RL ,
Cordero MI ,
Merino JJ ,
de Kloet ER ,
de Quervain DJ
de Quervain DJF ,
Schelling G ,
Roozendaal B
Diamond DM ,
Bennett MC ,
Fleshner M ,
Campbell AM ,
Halonen J ,
Fanselow MS ,
Hancock PA ,
Herrero AI ,
Introini-Collison IB ,
Castellano C ,
Jakobsson J ,
Groner AC ,
Busskamp V ,
Bensadoun J-C ,
Mansuy IM ,
Wiegert O ,
Lightman SL
Gerstner W ,
de Kloet ER
Roozendaal B ,
Woodson JC ,
Pavlides C ,
Watanabe Y ,
Magarinos AM ,
Radulovic J ,
Kammermeier J ,
Nguyen BT ,
Barsegyan A ,
Pinelo-Nava MT
Loscertales M ,
Ugolini A ,
Caberlotto L ,
Selden NRW ,
Everitt BJ ,
Servatius RJ
Vataeva LA ,
Kostkin VB ,
Makukhina GV ,
Khozhai LI ,
Tyul'kova EI ,
Samoilov MO ,
Yerkes RM ,

What's this?

This Article

Abstract Free
» Full Text Free
Full Text (PDF) Free
Supplemental Material

Article Category

Alert me when this article is cited
Alert me if a correction is posted
Similar articles in this journal
Similar articles in Web of Science
Similar articles in PubMed
Download to citation manager
Permissions

Citing Articles

Load citing article information
Citing articles via Web of Science
Citing articles via Google Scholar

Google Scholar

Articles by Salehi, B.
Articles by Sandi, C.
Search for related content

PubMed/NCBI

PubMed citation

Navigate This Article

Current issue.

January/February 2024, 31 (1-2)
Alert me to new issues of Learning & Memory
Author Information
Subscriptions
E-mail Alerts & RSS Feeds

Print ISSN: 1072-0502
Online ISSN: 1549-5485

Remember me Not recommended on shared computers

Forgot your password?

Or sign in with one of these services

We ask and you answer! The best answer wins!

Inverted U Theory

inverted u theory
performance
team performance

Asked by Vishwadeep Khatri , February 5, 2021

Inverted U Theory (or Yerkes-Dodson Law) is a stress management tool that depicts the relationship between performance and pressure.

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Aritra Das Gupta, Jayanth Sura, Santosh Sharma, Subodh Tripathi and Sanjay Singh.

Applause for all the joint winners!!

Vishwadeep Khatri

Q 337. What is Inverted U Theory? How can a project leader use it to get the best performance from the team?

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/
Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday.
The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

Link to comment

Share on other sites, 6 answers to this question.

Sort by votes
Sort by date

Jayanth Sura

Inverted 'U' theory:- The inverted ' U ' theory speaks about the correlation between an employee's performance Vs pressure on employee. In simple terms this theory say the following.

--> Low or no pressure at work will leads to low employee's performance.

--> Appropriate pressure at work will optimize the employee's performance.

--> High and intolerable pressure will impact negatively on performance at time the negative impact is very

sever.

When the correlation of performance Vs pressure is shown on a graph, the graph will take the shape of inverted U as shown in the figure below, hence the theory is named as "inverted 'U' theory".

This theory is also called as " Yerkes - Dodson law" as the same is proposed by two famous psychologists "Robert Yerkes" & " John Dodson".

We can indeed correlate this theory to one of the Lean Six Sigma believes that any Goal should follow the concept of SMART, where the goal should be A - achievable and R - realistic.

A goal with too low in target and very high and unachievable targets will indeed impact employee's performance negatively.

There are many crucial projects for which deadlines are very important. For such projects with tight and achievable deadlines, project leader can use the "Inverted U theory" concept to motivate the team to achieve desired results.

One of the important prerequisite according to this theory is "Skill set of employees", if employees are not adequately skilled, then even realistic targets will burden employees and pressure will turn into stress.

So according to this "inverted U theory", project leaders should ensure the following

1. employees should be skilled

2. targets should be realistic

3. teams should be periodically motivated by various means to ensure pressure won't turn into stress.

Santosh SHARMA

What is Inverted-U Theory?

It is a theory that throws light on the relation between performance and pressure / arousal. In the original study, rats got electric shocks as motivation for escaping from a maze. The Inverted-U Theory owes its name to the line, in form of an inverted U, that appears when there is a correlation between pressure & performance.

image.png.483458ee371eef41719526d3081ef3a4.png

A quick look at the curve reveals that performance lags behind when there’s little pressure, and that performance is positively influenced when there’s some more pressure. If even more pressure is added, performance is influenced negatively and efficiency decreases. The worker’s efficiency and performance can reach an optimal point if the pressure or arousal have reached an optimal point.

Inverted-U Theory was developed by psychologists Robert Yerkes and John Dodson in 1908. Despite the fact that the model was developed long ago, it continues to be relevant.

Interpreting the Model

When looking at the left-hand side of the graph, it’s notable that low pressure or low stress levels result in a stress response corresponding to ‘boredom or lack of challenge’. Even if the task itself is a critical activity, the attention, concentration, and precision required to properly execute a task is absent in the absence of an appropriate level of pressure or stress.

On the right-hand side of the graph from the Inverted-U Theory, we can see that extreme pressure levels or high stress levels don’t automatically result in good performance. The opposite is true: if pressure gets too high, or a too high stress level is activated, this results in a feeling of unhappiness, stressfulness, and anxiety. These are all results of overwhelming stress.

Four Influencing Factors

Personality

Different personality types benefit from different levels of stress or pressure. Generally, extraverted personalities are more resistant to stress and better able to keep their head above water when stressed than introverted personalities. Introverted people usually have a higher chance of performing well in environments with little stress or excitement.

Task Difficulty

A shop manager and an accountant have completely different jobs. Each has more knowledge of the work they do individually than of the other’s job. If they would swap jobs, the challenge and the pressure would be so high in the beginning that it would strongly motivate them. After a while, when tasks get easier, they would have to use a new form of pressure to keep their performance up.

Inverted-U Theory shows that fear can also have an effect on performance. This mainly relates to the ability to set aside or ignore feelings of fear in order to be able to keep one’s focus on the situation and the tasks. People who are better at this also perform better under pressure. People who are not good at it will enter into challenging situations more often.

Complexity and Motivation

In situations that require carrying out tasks with a high level of complexity, or solving complex problems, motivation plays an important role. There have been various situations in which the relation between motivation and complex problem solving was studied. These have yielded several theories, such as McClelland’s motivation theory and Maslow’s hierarchy of needs.

Using the Inverted-U Theory, To get the best performance from the team…

The simplest way to use the Inverted-U Theory is to be aware of it when you allocate tasks and projects to people on your team, and when you plan your own workload.

By contrast, if anyone is under-worked, it may be in everyone's interest to shorten some deadlines, increase key targets, or add extra responsibilities – but only with clear communication and agreement.

From there, balance the factors that contribute to pressure, so that your people can perform at their best. Remember, too little pressure can be just as stressful as too much!

Try to provide team members with tasks and projects of an appropriate level of complexity, and work to build confidence in the people who need it.

However, bear in mind that you won't always be able to balance the "influencers." Motivate and empower your people so that they can make effective decisions for themselves.

subodh tripathi

Inverted Theory :

The Inverted-U Theory explains the relation between performance and pressure. It describes how to find the optimal degree of positive pressure at which people perform at their highest, also known as the Yerkes-Dodson Law. Too much or too little pressure can result in reduced efficiency. The left side of the graph, above, indicates the condition where individuals are not challenged. They see no need to work hard on a task here, or they are at risk of approaching their job in a careless, unmotivated manner.

The center of the graph illustrates where individuals work at peak productivity. They are driven enough to work hard, but they are not so overloaded that they are beginning to fail. This is where individuals can feel the "flow," the fun and incredibly efficient state in which they can do their best job.

The right hand side of the graph indicates that, under pressure, they begin to fall apart. The intensity and size of competing demands on their attention and a significant lack of control over their situation overwhelm them. They can display signs of hasty illness, anxiety, or out-and-out panic.

image.png.c549dd9305a954c54f29fc6801980ad2.png

Note : The exact shape of the curve, in fact, will depend on both the person and their situation. It is also important to note that obviously minor adjustments can lead to rapid repositioning on the curve in professional or personal life.

Difference between pressure and stress :

The Inverted-U Theory shows that, up to a degree, pressure can be positive. Stress, however, is never optimistic, and it's important not to confuse the two thoughts. We are activated in a beneficial way when the levels of pressure we feel are right for the work we do: inspired, committed, and enthusiastic about doing our best.

But when individuals feel out of control, tension happens, and it's a totally negative thing. The Inverted-U Principle is about wisely using pressure, always mindful of where the advantages end and tension starts.

Four influencers of the inverted u theory:

1. Skill level : The level of skill of someone with a specific task can directly affect their performance, both in terms of their attitude and their outcomes. A new job is likely to be difficult enough for a while. Later, if it begins to feel too convenient, it will require some sort of extra pressure to help the person re-engage with their role.

2. Personality : The personality of a person also determines how well they perform. Some psychologists, for example, assume that people who are extroverts are likely to do well in conditions of high pressure. On the other hand, individuals with an introverted personality may perform better with less pressure. The Inverted-U Theory prompts us to adapt our own personalities to suitable roles, and those of our people. When we assign roles and responsibilities, observation, thorough knowledge of individuals, and open communication are all necessary.

3. Trait Anxiety : Think of trait anxiety as the level of "self-talk" of a person. People who are positive of themselves are more likely to perform better under pressure. This is because they are under control of their self-talk, which ensures that they can remain "in flow," and can focus entirely on the situation at hand. By comparison, individuals who criticize or challenge themselves are likely to be overwhelmed by their self-talk, which in more difficult circumstances can cause them to lose concentration. The more individuals are able to reduce their anxiety about a task (for example, with practice or with positive thinking), the more they can perform.

4. Task Complexity : The difficulty of the task defines the amount of commitment and effort that individuals have to put into a task in order to effectively accomplish it. Under very high levels of pressure, people may perform simple activities, whereas complex activities are best done in a relaxed, low-pressure environment. But even though someone's ability levels are high, they can still benefit from a relaxed atmosphere in which their most complex work can be carried out. Conversely, in order to feel motivated and fulfill their potential, people carrying out low-complexity tasks will need extra stimulation. The Inverted-U Theory allows these four variables to be observed and controlled, striving for a balance that encourages dedication, well-being, and peak performance. Through controlling these four influencers, and by being mindful of how they can positively or negatively affect the success of your people, you can use the model.

How to use this theory? When you assign tasks and assignments to individuals in your squad, and when you organize your own workload, the best way to use the Inverted-U Principle is to be conscious of it. Start by focusing on current pressures. If you're worried that someone may be at risk of being overwhelmed, see if they can take away any of the burden. This is an easy step to help them improve their job quality. By comparison, if anyone is underworked, shortening those deadlines, increasing key priorities, or adding additional responsibilities could be in everyone's interest, but only with clear communication and agreement. From there, reconcile the variables that lead to strain, so that your people can work at their best. Don't forget, too little pressure can be as overwhelming as too much! Try to provide tasks and projects of an acceptable degree of complexity to team members, and strive to create trust in the people who need them. Also, in your team, handle any negativity and prepare your people so that they have the abilities they need to do the jobs they are given. Nevertheless, keep in mind that the "influencers." will not always be able to balance you. Inspire and encourage the people so they can make effective decisions for themselves.

Inverted U theory enables you to understand the relationship between pressure and performance. The result will be that you'll get the finest results from a happy and engaged team!

When people feels the right amount of pressure, they often perform brilliantly. But, if there's too much or too little pressure, performance can suffer.

inverted-u-theory.png.c53ec9060c0d2c07a35ec719aa3baedc.png

The left hand side of the graph, above, shows the situation where people aren't being challenged. Here, they see no motive to work hard at a task, or they're in danger of approaching their work in a "sloppy," unmotivated way.

The middle of the graph shows where people work at peak Productiveness. They're Satisfactorily motivated to work hard, but they're not so overloaded that they're starting to struggle. This is where people can experience "flow," the Pleasant and highly productive state in which they can do their best work.

The right hand side of the graph shows where they're starting to crumble under pressure. They're engulfed by the volume and scale of competing demands on their attention and feeling a serious lack of control over their situation. They may exhibit signs of hastiness , stress, or out-and-out panic.

The Project Leader can use the Inverted-U Theory while allocating tasks to the people in its team and planning his own task/work also.

Start by thinking about existing pressures. If you're feel that someone might be at risk of overload, see if you can take some of the pressure off them. This helps them to improve the standard of their tasks/work.

And, if anyone is underworked, it may be in everyone's interest to shorten some deadlines, increase key targets, or add extra responsibilities but only with clear communication and agreement.

Try to provide team members with tasks and projects of right level of complexity, and work to build confidence in the people who need it.

Also, manage any Pessimism in your team, and train your people so that they have the skills they need to do the jobs they're given

Mayank Gupta

Phew! All the answers are so well articulated that it is very difficult to distinguish the best vs the rest. Hence all published answers have been declared as winners!!

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Already have an account? Sign in here.

Forum Statistics

Total Topics 3.3k
Total Posts 16.7k

Member Statistics

Total Members 55,088
Most Online 990 January 13, 2023

Existing user? Sign In
Think Pieces
Guest Posts

Discussions on Topics

Hypothesis Testing
Business Analytics
Customer Satisfaction
Metrics and Measurement Systems
DFSS - Design for Six Sigma
Lean Management
Root Cause Analysis
Problem Solving Methodologies
Efficiency and Productivity
Online Green Belt
Online Black Belt
Create New...

IMAGES

Inverted U Theory Explained
A representation of the inverted-U hypothesis by Yerkes-Dodson
The Inverted-U Hypothesis -depicting the Relationship between
The inverted-U hypothesis
Inverted U Theory
inverted u theory strengths and weaknesses

VIDEO

Fact vs Theory vs Hypothesis vs Law
Preference similarity Hypothesis (Linder Hypothesis)
What is Hypothesis #hypothesis
WHAT TO DO WHEN THE NULL HYPOTHESIS CONTRADICTS THE FINDINGS OF THE STUDY
Word Of The Day
Hypothesis Representation Stanford University Coursera

COMMENTS

The Inverted-U Theory
The Inverted-U Theory helps you to observe and manage these four factors, aiming for a balance that supports engagement, well-being, and peak performance. You can use the model by managing these four influencers, and by being aware of how they can positively or negatively influence your people's performance.
Inverted U Theory Explained
The inverted u theory may also be referred to as the Yerkes-Dodson law due to its creation by two researchers - Yerkes and Dodson. In 1908, these researchers were trying to understand the relationship between the strength of a stimulus and forming habits in mice. They found that there was a negative relationship between the two i.e. the ...
Inverted-U Theory of Stress (Yerkes & Dodson)
If even more pressure is added, performance is negatively influenced and efficiency decreases. The worker's efficiency and performance can reach an optimal point if the pressure or arousal have reached an optimal point. Inverted-U Theory was developed by psychologists Robert Yerkes and John Dodson in 1908. Despite the fact that the model was ...
Inverted U hypothesis
The optima vary between people doing the same task and one person doing different tasks. A basic assumption in the hypothesis is that arousal is unidimensional and that there is, consequently, a very close correlation between indicators of arousal; this is not the case. See also catastrophe theory.inverted-U hypothesis
Inverted U Theory in Sport
The Inverted U theory in sports aims to explain the relationship between arousal levels and performance. The theory also suggests how different levels of arousal can lead to either an increase or decrease in performance. In 1908, researchers Yerkes and Dodson published a study that forms the foundation of the Inverted U theory.
What the Yerkes-Dodson Law Says About Stress and Performance
The inverted-U curve looks a little different for each person and probably even changes at different points in your life. ... The Yerkes-Dodson law is the theory that there's an optimal level of ...
The Theory of Inverted U: A Comprehensive Exploration
The Theory of Inverted U, also known as the Yerkes-Dodson Law, is a critical psychological concept that explores the complex relationship between arousal, stress, and performance. Introduced by psychologists Robert Yerkes and John Dodson in 1908, the law suggests that a certain level of stress can enhance performance, but there's a threshold ...
Arousal Control in Sport
There are two early major approaches to explaining the arousal-performance relationship that have been applied to sport: drive theory and the inverted U-hypothesis. Drive Theory Drive theory was outlined by Hull ( 1943 ) and then later modified by Spence ( 1956 ); it is sometimes referred to as the Hull-Spence theory of behavior (Spence, 1956 ).
Arousal, anxiety, and performance: a reexamination of the Inverted-U
Abstract. Until recently, the traditional Inverted-U hypothesis had been the primary model used by sport psychologists to describe the arousal-performance relationship. However, many sport psychology researchers have challenged this relationship, and the current trend is a shift toward a more "multidimensional" view of arousal-anxiety and its ...
Motivation and emotion/Book/2019/Zone of optimal functioning hypothesis
In 1908 scientists Yerkes and Dodson created the theory known as the "Inverted U Hypothesis" (Yerkes & Dodson, 1908). ... The ZOFH is a theory that was developed to improve the awareness of the emotion-performance relationship in a sports-specific environment. Hanin's work has developed since the 1980s and continues to challenge how ...
Arousal, Anxiety, and Performance: A Reexamination of the Inverted-U
The inverted-U hypothesis proposes that there is an optimal range of arousal level for most athletes (Arent & Landers, 2003). Presumably, an NBA game without fans shouting and waiving distracting ...
Sport-related anxiety: current insights
Theoretical conceptualizations. How anxiety impacts performance has received much attention in the sport psychology literature. Some of the early theories include the inverted-U hypothesis,6 drive theory,7 and reversal theory.8 The inverted-U hypothesis suggests that performance and anxiety should be viewed on an inverted U-shaped continuum. According to Yerkes and Dodson,6 low arousal/anxiety ...
Exploring the inverted-U relationship between ...
Hypothesis 1 predicted that an inverted-U relationship exists between level of achievement and health management performance. According to the empirical results in model 2 of Table 4 , the coefficient of the nonlinear term of achievement is negative and statistically significant (β 7 = −1.138, p < 0.01), and the coefficient of the linear ...
Inverted U Theory: Balancing Stress for Optimal Performance
The Inverted U Theory was developed by psychologists Robert Yerkes and John Dodson more than a century ago in 1908. This theory shed light on the relationship between stress, or arousal, and performance. Yerkes and Dodson found that performance increases with physiological or mental arousal, but only up to a certain point.
The Quest for the Inverted U
The quest for the inverted U, although not without historical precedents, received its major modern impetus with the publication of Daniel E. Berlyne's Conflict, Arousal and Curiosity (1960). In this and in later volumes Berlyne developed a conception of motivation that was in sharp contrast to the prevailing formulations of psychiatry, psychology, and behavior theory.
Inverted-U Theory in Sport
The Inverted-U hypothesis proposes that performance is best at a moderate level of arousal. Both low and high levels of arousal are associated with similar decrements in performance. The original work done on the Inverted-U hypothesis related to the strength of stimulus and habit-formation (learning) in mice (Yerkes and Dodson, 1908).
Arousal and the inverted-U hypothesis: A critique of Neiss's
After reviewing the literature linking threat, incentive, and relaxation to motor performance, R. Neiss (see record 1989-14248-001) concluded that both the construct of arousal and the hypothesis of an inverted-U relationship between performance and arousal should be abandoned. These arguments were, however, based on research that does not permit clear evaluation of either the construct of ...
Quantifying the inverted U: A meta-analysis of prefrontal dopamine, D1
These findings lead to the hypothesis that working memory follows an inverted U-shaped function, in which optimal working memory performance is achieved with optimal levels of prefrontal dopamine and D1DR activation. ... We developed models based on the relationship between working memory effect sizes and prefrontal dopamine and D1DR effect ...
Mental preparation
The 'inverted U' theory proposes that sporting performance improves as arousal levels increase but that there is a threshold point. Any increase in arousal beyond the threshold point will worsen ...
Learning under stress: The inverted-U-shape function revisited
During spatial training over three consecutive days, an inverted-U shape was found, with animals trained at 19°C making fewer errors than animals trained at either higher (16°C) or lower (25°C) stress conditions. Interestingly, this function was already observed by the last trial of day 1 and maintained on the first day trial of day 2.
Spatial inequality and development
This paper studies the hypothesis of an inverted-U-shaped relationship between spatial inequality and economic development. The theory of Kuznets (1955) and Williamson (1965) suggests that (spatial) inequality first increases \in the process of development, peaks, and then decreases. With the exception of the initial study of Williamson (1965 ...
Kuznets Hypothesis
Ram, Rati. 1991. Kuznets ' s Inverted-U Hypothesis: Evidence from a Highly Developed Country. Southern Economic Journal 57 (4): 1112 - 1123. Saith, Ashwani. 1983. Development and Distribution: A Critique of the Cross-Country U-Hypothesis. Journal of Development Economics 13 (3): 367 - 382. Tribble, Romie, Jr. 1999. A Restatement of the S ...
Inverted U Theory
Inverted U Theory (or Yerkes-Dodson Law) is a stress management tool that depicts the relationship between performance and pressure.. An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Aritra Das Gupta, Jayanth Sura, Santosh Sharma, Subodh Tripathi and Sanjay Singh.

The Inverted-U Theory

What Is the Inverted-U Theory?

Understanding the Inverted-U Curve

What's the Difference Between Pressure and Stress?

The Four Influencers of the Inverted-U Theory

1. Skill Level

2. Personality

3. Trait Anxiety

4. Task Complexity

Using the Inverted-U Theory

Discover more content

Don't Just Do Something, Stand There! Ten Principles for Leading Meetings that Matter

Try Mind Tools for free

Discover something new today

What Is Frederick Taylor's Scientific Management Theory?

How Emotionally Intelligent Are You?

What's Your Leadership Style?

Recommended for you

Inverted U Theory Explained

History of the Inverted u Theory

Understanding the Inverted-U Curve

What is arousal?

Factors Influencing the Curve

Task Difficulty

Skill Level

Examples of the Inverted U Theory in Sport

Why is the Inverted U Theory Important in Sport?

How Can This Theory Help Athletes?

How can this theory help coaches?

What is the Difference Between Pressure and Stress?

Responding to stress and pressure

Coping Strategies

Final Thoughts

Further Reading

Nicole Wells

Inverted-U Theory of Stress (Yerkes & Dodson)

What is Inverted-U Theory?

Interpreting the Model

Four Influencing Factors of the Inverted-U Theory

Personality

Task Difficulty

Complexity and Motivation

Inverted-U Theory: difference between pressure and stress

Inverted-U Theory: responding to stress and pressure

Now It’s Your Turn

More information

Related ARTICLES

Stress and Happiness at Work

4 Types of Stress by Karl Albrecht

Social Defeat: a Stress Theory

Holmes and Rahe Stress Scale: Theory and Test

Cognitive Restructuring: Worksheet and Theory

PAEI Model by Ichak Adizes

Occupational Safety and Health (OSH) explained

Job Characteristics Model (Hackman and Oldham)

HR Analytics: the basics explained

BOOST YOUR SKILLS

POPULAR TOPICS

ABOUT TOOLSHERO

Recently viewed (0)

Related Content

More Like This

inverted U hypothesis

inverted-U hypothesis

Related content in Oxford Reference

Inverted U Theory in Sport – What is it and why is it important?

The Inverted U Theory in Sport

Why is the Inverted U Theory in Sports Important?

What is involved in the Inverted U theory?

What is the Inverted U Theory in Sport?

Sporting Examples of the Inverted U Theory in Sport

How Can This Theory Help Athletes and Coaches?

References:

Share this:

Table of Contents

Understanding the Inverted U Theory: An Introduction

Unpacking the Inverted U Model

A Day in the Life: The Inverted U Model in Action

Practical Applications of the Inverted U Theory

Workplace Productivity