replacement hypothesis psychology

HYPOTHESIS AND THEORY article

Embodied cognition is not what you think it is.

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• School of Social, Psychological and Communication Sciences, Leeds Metropolitan University, Leeds, UK

The most exciting hypothesis in cognitive science right now is the theory that cognition is embodied. Like all good ideas in cognitive science, however, embodiment immediately came to mean six different things. The most common definitions involve the straight-forward claim that “states of the body modify states of the mind.” However, the implications of embodiment are actually much more radical than this. If cognition can span the brain, body, and the environment, then the “states of mind” of disembodied cognitive science won’t exist to be modified. Cognition will instead be an extended system assembled from a broad array of resources. Taking embodiment seriously therefore requires both new methods and theory. Here we outline four key steps that research programs should follow in order to fully engage with the implications of embodiment. The first step is to conduct a task analysis, which characterizes from a first person perspective the specific task that a perceiving-acting cognitive agent is faced with. The second step is to identify the task-relevant resources the agent has access to in order to solve the task. These resources can span brain, body, and environment. The third step is to identify how the agent can assemble these resources into a system capable of solving the problem at hand. The last step is to test the agent’s performance to confirm that agent is actually using the solution identified in step 3. We explore these steps in more detail with reference to two useful examples (the outfielder problem and the A-not-B error), and introduce how to apply this analysis to the thorny question of language use. Embodied cognition is more than we think it is, and we have the tools we need to realize its full potential.

Introduction

The most exciting idea in cognitive science right now is the theory that cognition is embodied . It is, in fact one of the things interested lay people know about cognitive science, thanks to many recent high profile experiments. These experiments claim to show (1) how cognition can be influenced and biased by states of the body (e.g., Eerland et al., 2011 ) or the environment ( Adam and Galinsky, 2012 ) or (2) that abstract cognitive states are grounded in states of the body and using the former affects the latter (e.g., Lakoff and Johnson, 1980 , 1999 ; Miles et al., 2010 ).

The problem, however, is that this is not really what embodied cognition is about . Embodiment is the surprisingly radical hypothesis that the brain is not the sole cognitive resource we have available to us to solve problems. Our bodies and their perceptually guided motions through the world do much of the work required to achieve our goals, replacing the need for complex internal mental representations. This simple fact utterly changes our idea of what “cognition” involves, and thus embodiment is not simply another factor acting on an otherwise disembodied cognitive processes.

Many cognitive scientists, see this claim occupying the extreme end of an embodiment continuum, and are happy with the notion that there can be many co-existing notions of embodiment – maybe three ( Shapiro, 2011 ) or even six ( Wilson, 2002 ). Why rule out other research programs that seem to be showing results? Why not have one strand of embodied cognition research that focuses on how cognition can be biased by states of the body, and another strand that focuses on brain-body-environment cognitive systems? The issue is that the former type of research does not follow through on the necessary consequences of allowing cognition to involve more than the brain. These consequences, we will argue, lead inevitably to a radical shift in our understanding of what cognitive behavior is made from. This shift will take cognitive science away from tweaking underlying competences and toward understanding how our behavior emerges from the real time interplay of task-specific resources distributed across the brain, body, and environment, coupled together via our perceptual systems.

This paper will proceed as follows. After laying out the standard cognitive psychological approach to explaining behavior, we’ll briefly point to some interesting lines of empirical research from robotics and animal cognition that support the stronger replacement hypothesis of embodied cognition ( Shapiro, 2011 ). We’ll then lay out a recommended research strategy based on this work. Specifically, we will detail how to use a task analysis to identify the cognitive requirements of a task and the resources (in brain, body, and environment) available to fill these requirements. According to this analysis, it is the job of an empirical research program to find out which of the available resources the organism is actually using, and how they have been assembled, coordinated, and controlled into a smart , task-specific device for solving the problem at hand ( Runeson, 1977 ; Bingham, 1988 ). We’ll focus on two classic examples in detail: the outfielder problem (e.g., McBeath et al., 1995 ) and the A-not-B task (e.g., Thelen et al., 2001 ). We’ll then contrast this task-specific approach with some embodied cognition research in the standard cognitive psychology mold, and see how this latter research fails to successfully motivate any role for the body or environment, let alone the one identified in the research. Finally, we’ll conclude with some thoughts on how to begin to apply this approach to one of the harder problems in cognitive science, specifically language use. Language is the traditional bête noir of this more radical flavor of embodiment, and our goal in this final section will be to demonstrate that, with a little work, a truly embodied analysis of language can, in fact, get off the ground.

Standard Cognitive Explanations for Behavior

The insight of early cognitive psychologists was that our behavior appears to be mediated by something internal to the organism. The classic example is Chomsky’s (1959) critique of “Verbal Behavior” ( Skinner, 1957 ) in which he argues that language learning and use cannot be explained without invoking mental structures (in this case, innate linguistic capabilities). In general, the theoretical entities cognitive psychologists invoke to do this internal mediation are mental representations .

At the time these ideas were taking off, research on perception suggested that our perceptual access to the world wasn’t very good (see Marr, 1982 ; Rock, 1985 for reviews). This creates the following central problem for representations to solve. The brain is locked away inside our heads with only impoverished, probabilistic perceptual access to the world, but it has the responsibility of coordinating rapid, functional, and successful behavior in a dynamic physical and social environment. Because perception is assumed to be flawed, it is not considered a central resource for solving tasks. Because we only have access to the environment via perception, the environment also is not considered a central resource. This places the burden entirely on the brain to act as a storehouse for skills and information that can be rapidly accessed, parameterized, and implemented on the basis of the brain’s best guess as to what is required, a guess that is made using some optimized combination of sensory input and internally represented knowledge. This job description makes the content of internal cognitive representations the most important determinant of the structure of our behavior. Cognitive science is, therefore, in the business of identifying this content and how it is accessed and used (see Dietrich and Markman, 2003 for a discussion of this).

Advances in perception-action research, particularly Gibson’s work on direct perception ( Gibson, 1966 , 1979 ), changes the nature of the problem facing the organism. Perception is not critically flawed. In fact, we have extremely high quality, direct perceptual access to the world. This means that perception (and by extension, the environment) can be a useful resource, rather than a problem to be overcome by cognitive enrichment. Embodied cognition (in any form) is about acknowledging the role perception, action, and the environment can now play.

A radical conclusion emerges from taking all this seriously: if perception-action couplings and resources distributed over brain, body, and environment are substantial participants in cognition, then the need for the specific objects and processes of standard cognitive psychology (concepts, internally represented competence, and knowledge) goes away, to be replaced by very different objects and processes (most commonly perception-action couplings forming non-linear dynamical systems, e.g., van Gelder, 1995 ). This, in a nutshell, is the version of embodiment that Shapiro (2011) refers to as the replacement hypothesis and our argument here is that this hypothesis is inevitable once you allow the body and environment into the cognitive mix . If such replacement is viable, then any research that keeps the standard assumptions of cognitive psychology and simply allows a state of the body to tweak cognition misses the point. To earn the name, embodied cognition research must, we argue, look very different from this standard approach.

Embodied Cognition: Four Key Questions

The core question in psychology is why does a given behavior have the form that it does? The standard cognitive psychology explanation for the form of behavior is that it reflects the contents and operation of an internal algorithm (implemented as a mental representation) designed to produce that behavior on demand (e.g., Fodor, 1975 , 2008 ). The work discussed below replaces complex internal control structures with carefully built bodies perceptually coupled to specific environments. (Of course, embodied cognition solutions will also sometimes require internal control structures. Critically, though, these internal control structures are taking part in the activity of distributed perceptually coupled systems from which behavior emerges online, in real time, in a context. Thus, explicit representations of behavior or knowledge have no place in embodied solutions.)

To get a rigorous handle on this claim, we suggest that there are four key questions any embodied cognition research program must address:

1. What is the task to be solved? Embodied cognition solutions solve specific tasks, not general problems, so identifying how an organism produces a given behavior means accurately identifying the task it is trying to solve at the time. Taking things one task at a time opens up the possibility of smart solutions ( Runeson, 1977 ). Organisms using smart solutions solve particular problems using heuristics made possible by stable features of the task at hand, rather than general purpose rote devices which apply algorithms to solve the task. For common tasks, smart solutions are typically more efficient, more stable, and more economical than rote solutions (e.g., Zhu and Bingham, 2008 , 2010 ).

2. What are the resources that the organism has access to in order to solve the task? Embodied cognition implies that there are resources, plural, available to the organism. These resources include the brain but also the body, the environment, and the relations between these things (e.g., the motion of our bodies through the environment). A task analysis should include an exhaustive list of resources available that might contribute, beginning with those available via perception and action and only hypothesizing more complex cognitive resources once the capabilities of these other resources have been exhausted. An exhaustive list is possible if you are able to characterize your task formally; tasks are differentiated from each other in terms of their underlying dynamics (e.g., Bingham, 1995 ) and thus it is becoming common practice to formalize the task description using the tools of dynamical systems (e.g., Fajen and Warren, 2003 ; Bingham, 2004a , b ; Schöner and Thelen, 2006 ).

3. How can these resources be assembled so as to solve the task? Solving a specific task means creating a smart, task-specific device that can do the job ( Bingham, 1988 ). To be more specific, it means assembling the required resources into a dynamical system that solves the task at hand as its behavior unfolds over time. Remember, these resources can be distributed over brain, body, and environment. Since we only have access to information about our bodies and the environment via perception, an embodied analysis must include a detailed account of the perceptual information used to connect the various resources ( Golonka and Wilson, 2012 ).

4. Does the organism, in fact, assemble, and use these resources? It is always an empirical question whether the dynamical system hypothesized in step 3 is, in fact, an accurate description of the system the organism has assembled to solve the task. The basic experimental tool for establishing the identity of a dynamical system is the perturbation experiment; systems respond to perturbations of resources in a manner that is specific to the role that resource plays in the system, and this allows you to map the composition and organization of the system at hand (e.g., Kay et al., 1987 , 1991 ; Wilson and Bingham, 2008 ).

The next sections will review what this new research looks like in practice; we will begin with some simpler cases that tackle and clarify some of our key questions, and end up with two cases of human behavior that demonstrate how to tie these four questions into a coherent research program.

Embodiment in Action

Embodiment in action i: robots.

One of the most productive areas to demonstrate the strength of the replacement hypothesis is robotics . Robots built on the principles of embodiment are capable of interestingly complex behavior, demonstrating how far you can get without representational enrichment. When you build something yourself from scratch, you know exactly what is (and is not) included in the control systems. This means that your pool of potential explanations for a given behavior is constrained and enumerated, and you can answer questions 2 and 3 in great detail.

“Swiss” robots

An early example of embodied cognition robotics comes from Maris and te Boekhorst (1996) , who built small Didabots with infra-red detectors placed around their body and a very simple internal control structure: a single rule, “turn away from a detected obstacle.” In this paper, the detector at the front of the robot was deactivated – the robot could no longer “see” anything directly ahead, but it could “see” off to the sides and behind. If it hit an obstacle (a white block) head on, it simply kept moving and pushed the block along until it turned to avoid the next obstacle (either another block or a wall). The first block was then left behind, and the net result (if there was more than one robot at work) was that the randomly scattered blocks were “tidied up” into heaps. This tidying behavior is not specified in the control structure of the robots; it emerges, in real time, from the relationship between the rule, the environment (the size and number of obstacles, the presence, or absence of other robots), and the bodies of the robots (the working front sensors have to be far enough apart to allow a block to fit, or else the robot simply successfully avoids the blocks). Importantly, then, the robots are not actually tidying – they are only trying to avoid obstacles, and their errors, in a specific extended, embodied context, leads to a certain stable outcome that looks like tidying (see also Pfeifer and Scheier, 1999 ; Pfeifer and Bongard, 2007 for extended reviews of this style of robotics). Understanding the resources the robots had available and how they were organized was what enabled the researchers to identify that the robots were not, in fact, trying to tidy anything up.

Locomotion and passive dynamics

Why does walking have the form that it does? One explanation is that we have internal algorithms which control the timing and magnitude of our strides. Another explanation is that the form of walking depends on how we are built and the relationship between that design and the environments we move through.

Considering the resources available to solve this task highlights the centrality of an organism’s design. Humans don’t walk like lions because our bodies aren’t designed like lions’ bodies. The properties of our design are referred to as passive dynamics ( McGeer, 1990 ). How are the segments arranged? How are they connected to each other? How springy are the connections? Robotics work on walking show that you can get very far in explaining why walking has a particular form just by considering the passive dynamics. For example, robots with no motors or onboard control algorithms can reproduce human gait patterns and levels of efficiency simply by being assembled correctly (e.g., Collins et al., 2005 ) 1 . Work at MIT has added simple control algorithms to this kind of system, which allows the robots to maintain posture and control propulsion more independently. The same algorithm can produce a wide variety of locomotion behaviors, depending on which robotic body they control (e.g., Raibert, 1986 ) 2 . None of these systems include a representation of the final form of their locomotion; this form emerges in real time from the interaction of the passive dynamics with the environment during the act of moving. These robots demonstrate how organisms might use distributed task resources to replace complex internal control structures.

Robot crickets

A fascinating example of embodiment in nature has been replicated in the lab in the form of a robot (see Barrett, 2011 for the more detailed analysis of this case that we draw from here). Female crickets need to find male crickets to breed with. Females prefer to breed with males who produce the loudest songs. This means that the task facing female crickets is to find the males who sing the loudest. What resources do they use to solve this task? Female crickets have a pair of eardrums, one on each front leg, which are connected to each other via a tube. Sounds entering from the side activate that side’s eardrum directly, and also travel through the tube from the other eardrum as well. These signals are out of phase if the sound is off to one side, and this increases the amplitude of that side’s eardrum’s response; this arrangement is therefore directional. This explains how the female can tell what direction a sound is coming from, but it doesn’t explain how she uses this information to move toward this sound or how she manages to tune in to crickets of her own species. It so happens that the eardrums connect to a small number of interneurons that control turning; female crickets always turn in the direction specified by the more active interneuron. Within a species of cricket, these interneurons have a typical activation decay rate. This means that their pattern of activation is maximized by sounds with a particular frequency. Male cricket songs are tuned to this frequency, and the net result is that, with no explicit computation or comparison required, the female cricket can orient toward the male of her own species producing the loudest song. The analysis of task resources indicates that the cricket solves the problem by having a particular body (eardrum configuration and interneuron connections) and by living in a particular environment (where male crickets have songs of particular frequencies).

Webb (1995 , 1996 ) have built robots that only have these basic capacities, and these robots successfully reproduce the form of the female cricket’s exploratory behavior. The robots have no stored information about the male cricket’s songs, and simply perceive and act using a particularly arranged body. It is clear that the robot doesn’t explicitly implement “choosing the male with the strongest song”; finding him is simply the result of this embodied strategy operating in the context of multiple male crickets singing and is driven (this robotics work predicts) by the onset of chirps within the song. The success of this work results from carefully analyzing the task at hand, identifying available resources, and specifying how these resources are assembled by the agent (questions 1–3 outlined above).

This robotics work and more like it (e.g., Brooks, 1999 ; Pfeifer and Scheier, 1999 ; Beer, 2003 ; Pfeifer and Bongard, 2007 ) reveal a great deal of complex behavior (from tidying, to locomotion, to mate selection) can emerge from placing the right type of body into a specific environmental context, without any explicit representation of the form of that behavior anywhere in the system. This work is a proof of concept that embodiment and embedding can therefore replace internal algorithms and lead to stable, functional behavior.

Embodiment in Action II: Animals

The robot work is fascinating is one part of a strong argument in favor of the replacement hypothesis. Of course, the next critical step is to establish whether biological organisms actually take advantage of these embodied solutions (question 4) or whether they follow a different, more computational path.

Crickets again

Webb’s robot crickets implement a simple embodied perception-action strategy to perform mate selection. A hypothesis that follows from this work is that females use the onset of a male’s song to drive exploration, rather than attending to the entire song and “choosing” the best one. Observation of real crickets shows that female crickets do indeed move before they could possibly have processed an entire song, supporting this embodied “chirp onset” hypothesis ( Hedwig and Webb, 2005 ; see also Barrett, 2011 for an overview).

Swarming, herding, hunting

Many animals produce carefully coordinated activities with large numbers of conspecifics. Forming large groups (swarms, or herds, or flocks) is a valuable defense against predators, and maintaining these groups requires ongoing coordination across many individuals. This coordination is not centrally controlled, however, and is not the result of an explicit attempt to maintain a swarm. Instead, the coordination emerges from and is maintained by the operation of straight-forward perception-action coupling rules in a suitable context. Bird flocking is elegantly explained as a coupling between individuals constrained by three principles ( Reynolds, 1987 ): separation (avoid crowding neighbors), alignment (steer toward average heading of neighbors), and cohesion (steer toward average position of neighbors). Interestingly, cohesion exhibits asymmetries that relate to the perceptual capabilities of birds; the average position is a center of mass of only the nearest 5–10 birds, weighted in favor of birds off to the side (reflecting the field of view for bird vision; Ballerini et al., 2007 ). Sheep herding is similarly straight-forward. Sheep head for the geometric center of the flock when a predator approaches, implementing a “selfish herd” strategy without any individual in the herd being “selfish” per se ( Hamilton, 1971 ; King et al., 2012 ).

A more complex example of coordinated social activity is the pack hunting of wolves. The pattern of their activity, however, is readily explained by two simple rules: (1) move toward the prey until a minimum safe distance is reached, and then (2) move away from any other wolves that are also close to the prey ( Muro et al., 2011 ). No leader is required, no instructions need be given; the form of the group’s hunting activity emerges from a simple perception-action coupling strategy implemented by each individual, operating in a specific context.

Continuing the hunting theme, Barrett (2011) has an extended discussion on what she refers to as “the implausible nature of Portia ,” the jumping spider. Portia is capable of some remarkable feats: deceptive mimicry, creating diversions to distract prey, and taking extended detours in order to sneak up on dinner. This last is especially impressive – detours mean Portia must operate for extended periods without direct perceptual contact with its prey animal. This would seem to require some form of route planning ( Heil, 1936 ; Barrett, 2011 ). As Barrett notes, this hypothesis seemed initially plausible because of the way in which Portia scans its environment – prior to taking the detour, it will sit and sway from side to side, seemingly evaluating potential routes and making a selection. However, this scanning behavior, coupled with the anatomy of the spider’s eyes, is actually an embodied strategy that enables Portia to generate successful detours using currently available perceptual information (e.g., Tarsitano and Jackson, 1997 ; Tarsitano and Andrew, 1999 ); Portia is perceiving, not planning.

The advantage of examples from the animal literature is that researchers are less likely to want to attribute performance to complex internal representations (only less likely, of course; the temptation is always there – Kennedy, 1992 ; Barrett, 2011 ). However, once we identify that embodied, situated perception-action couplings can produce complex adaptive behavior in other animals, it becomes more difficult to deny the existence of such solutions in our own repertoire unless one wishes to deny the evolutionary continuity between ourselves and the rest of the animal kingdom.

Embodiment in Action III: People

We will now review in some detail two excellent examples of successful replacement style embodied cognition in psychology. These examples are the outfielder problem and the A-not-B error (see Clark, 1999 ; Smith and Gasser, 2005 for other uses of these examples). They are useful because (a) they address all four key questions of good embodiment research and (b) both examples have standard cognitive psychology explanations that have been successfully replaced after numerous studies implementing the kind of embodied approach we are advocating for here. These sections will begin by describing the standard cognitive psychology explanations for the outfielder problem and the A-not-B error. We will then take a step back and analyze each task from an embodied cognition perspective, asking our four key questions:

1. What is the task to be solved?

2. What are the resources that the organism has access to in order to solve the task?

3. How can these resources be assembled so as to solve the task?

4. Does the organism, in fact, assemble, and use these resources?

Embodiment in Action III.I: The Outfielder Problem

How does a baseball outfielder catch a fly ball? There are many factors that make this task difficult; the fielder is far away from the batter, the ball is optically very small and remains so until it is very close to the fielder, the fielder has to move from their starting location to the location where the ball will land at some point in the future, and they have to arrive at this location in time to intercept the ball.

The standard explanation

The initial hypothesis is that we catch fly balls by predicting their future location based on the physics of the ball’s motion. A fly ball is an instance of projectile motion, and the physics of this kind of ballistic flight are relatively straight-forward. For an object of a given size and mass, the primary variables that determine the flight are initial direction, velocity, and angle (plus some local constants such as drag, air density, and gravity). Saxberg (1987a , b ) suggested that outfielders perceive these initial parameters and then use them as input to an internal simulation (representation) of projectile motion. This representation allows outfielders to predict the future location of the ball (Trajectory Prediction). Once the future location of the ball has been predicted, the fielder can simply run to that location and wait.

The embodied solution

Saxberg’s (1987a , b ) solution assumes that the act of catching a fly ball is a lot like solving a physics problem, relying on some limited resources (the ball’s initial conditions) and some internal simulation. In contrast, the embodied solution first asks if that’s true by asking “What are the resources that are available in this task, and how might they help a person trying to catch a ball?”

What is the task to be solved?

A fielder stands in the outfield of a baseball diamond, around 250 ft from home plate. The batter pops a fly ball (projectile motion along a parabolic trajectory) into the air and the fielder must locomote from where they are, to where the ball will be when it hits the ground (hopefully in time to catch it before it hits the ground). So, the fielder’s task is to move themselves so that they arrive at the right place at the right time to intercept a fly ball . Sometimes fielders are in a direct line with the flight of the ball, but the general problem to be solved involves the fielder being off to one side .

What are the resources available?

The first thing to note is that, at the distances involved, the optical projection of the baseball is tiny. Any attempt to figure out how far away the ball is and where it’s going using changes in optical projection size will be riddled with errors (if it’s possible at all; Cutting and Vishton, 1995 ). These errors would propagate through any simulation, which makes solutions based on computing simulations of projectile motion unstable. This means that the simulation solution is not a likely resource (and in fact the evidence suggests it is not an option; Shaffer and McBeath, 2005 ). What else is available?

To identify the full range of available resources, we need to understand the physical properties of the fly ball event. Events unfold over time, and are distinguished from one another by their underlying dynamics (which describe both how the system changes over time and the forces which produced the change; Bingham, 1995 ). In the present example, the relevant dynamics are that of projectile motion. As a given example of the projectile motion dynamic plays out, it creates kinematic information which can be detected and used by an observer. Kinematic descriptions include only how the system changes over time, without reference to the underlying forces. Perceptual systems can only detect kinematic patterns, but observers actually want to know about the underlying dynamic event; this is the perceptual bottleneck ( Bingham, 1988 ). Kinematics can specify the underlying dynamics, however ( Runeson and Frykholm, 1983 ) and detecting a specifying kinematic pattern is equivalent to perceiving the underlying dynamic (solving the bottleneck problem and allowing direct perception as suggested by Gibson, 1966 , 1979 ). The information that an outfielder might use to continuously guide their actions to the future position of the ball must therefore be kinematic and specific to this future position.

The batter provides the initial conditions of the ball’s trajectory (direction, velocity, and angle) and, after that, the flight unfolds according to the dynamics of projectile motion. This dynamic produces motion along a parabolic trajectory. The form of this motion is that the ball initially rises and decelerates until it reaches a peak height when its velocity reaches zero; it then accelerates as it falls down the other side of the parabola. This motion is the kinematic information that is available to the observer.

The fielder also brings resources with them: these include the ability to detect kinematic information and (most usefully) to locomote over a range of speeds along any trajectory across the field.

How might these resources be assembled to solve the task?

How can the perceptual information specifying the dynamics of the fly ball be used in conjunction with the fielder’s ability to perceive kinematics and locomote? The parabolic flight of the ball creates the possibility of two basic solutions. Each strategy requires the outfielder to move in a particular way so as to offset some aspect of the parabolic flight, either the acceleration or the curve of the path. If the fielder is able to successfully offset either the acceleration or the curve of the path, then they will end up in the right place in the right time to intercept the ball. When reading about these solutions in more detail below notice that neither one requires the fielder to predict anything about the ball’s future location, only to move in a particular way with respect to the ball’s current motion; this is prospective control (e.g., Montagne et al., 1999 ).

The first solution is called optical acceleration cancelation (OAC; e.g., Chapman, 1968 ; Fink et al., 2009 ) and requires the fielder to align themselves with the path of the ball and run so as to make the ball appear to move with constant velocity. The second strategy is called linear optical trajectory (LOT; e.g., McBeath et al., 1995 ) and requires the fielder to move laterally so as to make the ball appear to trace a straight line. Which strategy is adopted depends on where the fielder is relative to the ball (OAC works best if the ball is coming straight for you, LOT allows you to intercept a ball that is heading off to one side).

Does the organism, in fact, assemble, and use these resources?

The computational strategy suggests that the outfielder will run in a straight line to the predicted landing site. This is because the fielder computes the future landing site based on input variables that the fielder detects before setting off. Since the shortest path to a known landing site in open terrain is a straight line, the fielder should run directly to the place where they intercept the ball. Outfielders do not typically run in straight lines, ruling the computational strategy out. LOT and OAC predict either a curving path or one with a velocity profile that offsets the acceleration of the ball. The evidence generally favors LOT (e.g., McBeath et al., 1995 ) but there is evidence that OAC is a viable and utilized strategy under certain conditions (e.g., Fink et al., 2009 ).

These solutions have numerous advantages over the computational solution. First, instead of relying on an initial estimate of the ball’s motion, which could be in error, they allow the fielder to continuously couple themselves to the ball. This coupling provides fielders with numerous opportunities for error detection and correction. Second, the strategies provide a continuous stream of information about how well the fielder is doing. If the ball still seems to be accelerating, or if its trajectory is still curved, this tells the fielder both that there is an error and what to do to fix the error. If the fielder is running flat out and is still unable to correct the errors, this specifies an uncatchable ball, and the fielder should switch to intercepting the ball on the bounce instead. The affordance property “catchableness” is therefore continuously and directly specified by the visual information, with no internal simulation or prediction required.

In both LOT and OAC, various task resources (the motion of the ball, the fielder, and the relation between them specified by the kinematics of the ball viewed by the moving observer) have been assembled into a task-specific device ( Bingham, 1988 ) to solve the task at hand (intercepting the projectile). This assembly is smart , in the sense described by Runeson (1977) ; it takes advantage of certain local facts of the matter to create a robust but task-specific solution (neither LOT nor OAC are a general solution to the problem of interception, for example). The most important lesson here is that the relation between perceptual information (about the motion of the ball) and an organism (the outfielder) replaces the need for internal simulation of the physics of projectile motion.

Embodiment in Action III.II: The A-Not-B Error

What do children know about objects and their properties, and when do they come to this knowledge? Piaget (1954) investigated this question by asking children of various ages to search for objects that were hidden behind some obstacle in view of the children. Prior to about 7 months, children simply don’t go looking for the object, as if it has ceased to exist. From around 12 months, children will happily go and retrieve the hidden object, seemingly now understanding that even though they can’t see the toy they want, it’s still there to be found. In the transition, however, children make a rather unusual “error” – after successfully reaching several times for a hidden object at a first location A, they will then fail to reach for the object hidden at location B, even though the hiding happened in full view of the object. They will instead reach to A again (hence “A-not-B error”).

There are a variety of standard cognitive explanations for this error, but all in essence assume that (a) the child has developed the necessary object concept that includes the knowledge that objects persist even when out of view but (b) there is something about reaching that cannot tap that knowledge reliably. The child’s underlying competence can be demonstrated using looking behavior as a measure, for example; children look longer at displays showing the error trial, suggesting they know something is not right (e.g., Baillargeon and Graber, 1988 ). The problem, therefore, is in the reaching performance : reaching cannot yet access the knowledge necessary. This performance-competence distinction is a common theme in the cognitive developmental literature. It assumes that the goal of the science is to understand the core competence, and that to do so you must devise clever methods to bypass the potential limitations of performance.

Thelen et al. (2001) challenged every single aspect of this account with their embodied dynamical systems model of the reaching task. This model was the end result of numerous experiments motivated by a rejection of the performance-competence distinction and a renewed focus on the details of the task at hand. As Thelen et al. put it, “The A-not-B error is not about what infants have and don’t have as enduring concepts, traits, or deficits, but what they are doing and have done ” (p. 4). The end result was an account of the A-not-B error that replaces object knowledge and performance deficits with the dynamics of perceiving and acting over time in the context of the reaching task.

This is actually quite a complicated question. The canonical version of the task requires the infant to watch as an attractive toy is hidden at location A. The child is then allowed to search for and retrieve the object several times, after which the object is hidden at location B in full sight of the baby.

One of the inspirations for pursuing a dynamical system, embodied approach here was that almost every parameter of this task is known to affect infants’ performance. These parameters include the distance to the targets, the distinctiveness of the covers, the delay between hiding and search, what the infant is searching for (food or a toy), whether the infant is moved and how much crawling experience they have (see Thelen et al., 2001 for a detailed overview). If the A-not-B error reflects object knowledge, why do these factors matter so much?

To get a handle on this question, the first thing that Thelen et al. (2001) did was to enumerate the details of the canonical task (Section 2.2) so that they had a clear understanding of the available resources that might impact infants’ performance. First, the infant gets continuous visual input (Section 2.2.1) from two wells in a box placed a certain distance away from the child and apart from one another. The experimenter draws the infant’s attention to the object, and then hides the object in well A. This specific visual input (Section 2.2.2) indicates which well the reaching target is in. After a short delay (Section 2.2.3) during which infants typically look at the cued location, they perform a visually guided reach (Section 2.2.4) to retrieve the object. This reach requires them to remember (Section 2.2.5) the location of the hidden object for the duration of the delay. This is repeated several times until the switch to the B location, at which point the infants make the error around 70–80% of the time (depending on their developmental status ; Section 2.2.6).

In this version of the task, the resources that might impact performance include the details of the continuous and specific visual input, the length of the delay, and the delay’s relationship to the temporal dynamics of the memory of the previous reaches. The infant also brings resources to the task. For instance, their performance depends on their ability to maintain visual attention and the way in which they perform visually guided reaches. Thelen et al. (2001) do not include an object concept as a resource. The purpose of this seeming omission is to see how well they can model the behavior without invoking any core competence separate from observed performance.

The reason why this work by Thelen et al. (2001) is such a powerful example of replacement style embodied cognition is that their model is an excellent example of using dynamical systems to explain how perceptual and embodied resources might be assembled to produce an error that, on the face of it, seems to require a representational explanation (in the form of an infant’s object concept). The model specifies two locations in a metric field representing the infant’s reach space and takes specific perceptual input about where to reach. This input raises activation at the appropriate location in the motor planning field and generates a reach in the right direction once a threshold is crossed. Reach direction planning unfolds continuously over time using population coding (c.f. Georgopoulos, 1995 ). Activation in this field has a temporal dynamic that prevents it from fading immediately; the movement planning field has memory about its recent behavior. Activations at different locations in the field interact, allowing for competition and cooperation between them. The model is initialized and presented with specific input; the behavior of the model emerges as the various competing dynamics (specific input, task input, memory, reach planning, etc.) unfold and change the shape of the field controlling reaching. By the time the specific input is switched to location B, the field has taken on a shape which reflects this competition, and the perceptual input from B is effectively being detected by a very different system than the one which first detected input from location A. Its behavior is correspondingly different; specifically, if the parameters match the canonical version of the task, the model will make the A-not-B error. Note there is no mention of an “object concept” in the model specification. Yet, the model is able to re-create the A-not-B error simply by implementing a reach system with its own dynamical properties.

The model is extremely successful at capturing the key phenomena of the A-not-B task. It also captures how performance is affected by changes to task details (e.g., variation in reach delay, changes in object properties). Object concept based explanations have been proposed for these effects (e.g., see Diamond’s, 2001 response to Thelen et al.’s, 2001 target article). However, there are other aspects of task performance that object concept explanations struggle to cope with. Most interestingly, the model predicts and then explains the novel experimental finding that the A-not-B error occurs in the absence of hidden objects ( Smith et al., 1999 ). If there is no object to remember, then object concept based explanations are at a loss to explain why the error persists; after all, there is no object to conceptualize. In contrast, the embodied model predicts that the “error” comes from the immature dynamics of reaching, and not an incomplete object concept. This then suggests that you should be able to generate the error in older children by increasing the complexity of the reaching requirements. Consistent with this, Smith et al. (1999) and Spencer et al. (2001) generated the error in 2 year olds and similar reach biases have been observed in children up to 11 ( Hund and Spencer, 2003 ) and even adults ( Spencer and Hund, 2002 ). There is no clear reason to expect these biases on the basis of an object concept explanation. The best explanation for this pattern of results is that the observed reaching behavior does indeed emerge from the kind of embodied task dynamic described by the model.

The A-not-B task has a long history of explanations based in standard, representational cognitive psychology. These explanations assume that the reach is an error caused by an incomplete object concept, to which the immature motor system has limited access until around the age of 12 months. Thelen et al.’s (2001) embodied approach replaces the object concept with the dynamics of reaching to grasp and successfully accounts for the wide variety of context effects, as well as explaining novel versions of the error generated without any hidden objects and in older children.

The Conceptualization Hypothesis for Embodiment: Concepts and Grounding

We have identified embodied cognition as a cluster of research tied together by the same basic research strategy; (1) identify the task at hand, (2) identify the resources available within that task space that might help an organism solve the task, (3) generate hypotheses about how these resources are assembled and coordinated (perhaps formalizing this hypothesis in a model; see Bingham, 2001 , 2004a , b for another example, and Golonka and Wilson, 2012 for a detailed analysis of that model), and finally (4) empirically test whether people, indeed, use these resources assembled in this way. This is not, however, the only style of research going under the banner of embodiment, and it’s fair to ask on what basis we are ruling this other research out from our classification.

Many examples fall under what Shapiro (2011) calls the conceptualization hypothesis . This is the hypothesis that how we conceive of our world is grounded in and constrained by the nature of the perception-action systems that we are (our bodies). For example, Lakoff and Johnson (1980 , 1999 ) describe how common metaphors are typically grounded in the nature of our bodies and experiences in the world (the future is forward , power is up , relationships are a journey ). This style of research doesn’t seek to replace the concept with a different process. Instead, it looks to find examples where use of the concept can be primed or altered by manipulations of the grounding state of the body.

There are many recent examples of this type of research in the literature; we will briefly focus on two representative studies. The first claims to demonstrate how a state of the body affects our access to a mental representation for magnitude estimation ( Eerland et al., 2011 ) while the second claims to show an effect in the other direction, with a mental state biasing the body state the mental state is supposedly grounded in Miles et al. (2010) .

Leaning to the left makes the Eiffel Tower seem smaller

People can generate sensible estimates of the magnitude of things, such as the height of the Eiffel Tower, even when they don’t know the exact answer. These magnitudes are hypothesized to be generated by a mental representation of magnitudes organized like a number line, with small numbers at the left end and larger numbers to the right ( Restle, 1970 ). Eerland et al. (2011) had people stand balanced slightly to the left or to the right of center to test the hypothesis that this postural bias would make either the left or right end of the number line more accessible. If it did, then people should be primed to generate lower estimates of magnitude when leaning left and greater ones when leaning right.

The results were mixed. When people leaned left they did, on average, make slightly smaller estimates than when leaning right and the authors concluded that these data support the hypothesis; access to the mental number line, arranged left to right, is, at least, partly grounded in the left to right sway of the body. It should be noted, however, that the effect size was very small, the effect was not observed for all the questions, and there was no effect of leaning to the right.

Thinking about the future makes you sway forward

The second example of conceptualization style research is Miles et al. (2010) , who had people engage in “mental time travel” by thinking about events in either the past of the future. They measured postural sway at the knee, and found that as people thought about the future this sway was biased toward the front (the future is in front ). When people thought about events in the past, their sway was biased backward (the past is behind ). Again the effect was small (peaking at a bias of approximately 2 mm in each direction) but the authors concluded that their data demonstrate a connection between the state of the body and the contents of the cognitive representation of time.

Where is the embodiment?

Neither of these studies begins with a task analysis and neither considers what perceptual and embodied resources are available to solve the task. This eliminates the opportunity to discover what substantive role these resources can play in cognition. Instead, the assumption made in both these studies is that the task is solved internally, representationally, by a cognitive process that can tweak or be tweaked by a state of the body. There isn’t any compulsory, critical, constitutive role for the body and environment in the proposed mechanism for solving the task at hand, as there is in all the other work reviewed. You cannot catch a fly ball without moving. The fielder’s movement inevitably creates the information for either LOT or OAC, which can then structure the observed behavior. You cannot do the A-not-B task without reaching. Reaching inevitably invokes the dynamics of visually guided reaching, which can then structure the observed behavior. You can, however, lean left and not have it affect your estimates of magnitude, and you can think about the future without leaning forward. Conceptualization style embodiment research does not identify the body as a task-critical resource, nor does it generate any formal account of how the body forms part of a task-specific solution to the task at hand. At best, it demonstrates that sometimes thoughts and actions go together.

Taking the Next Step – An Embodied Analysis of Language

This paper has laid out what we propose is a necessary research strategy for a genuine embodied cognitive science. We’ve looked at a progression of existing research that follows this strategy, beginning with simple robotic systems up through non-human animal behavior, and on to two cases of human behavior – one straight-forward perception-action system (catching a fly ball) and one more traditional cognitive task (the A-not-B task). The point was to show that this approach is productive across a wide variety of tasks and behaviors, and that it demonstrates the kind of continuity evolutionary theory tells us exists across biology.

We would like to round this article out with an initial foray into an embodied analysis of that classic cognitive task, language . Our goal here is simply to take what we think is the first step: identifying the nature of a critical resource present in a language event, specifically the form and content of linguistic information . This can then guide and constrain the non-representational empirical investigations that we hope will follow.

Language: It’s Special, but It’s Not Magical

Most psychologists generally assume that catching a fly ball and talking about catching a fly ball are two different kinds of task, in the sense that you can’t use the tools appropriate to studying how to catch a fly ball to understand how we communicate through language. Language is a very interesting kind of behavior, and it has some properties that make it very special. But it is not magical; it is a product of evolution, the same as the rest of our behavior, so it makes perfect sense to expect it to be amenable to the analyses that have been so successful in other domains. In other words, our first move is simply to treat perception-action problems and language problems as the same kind of thing.

As we will discuss shortly, there is one important difference to worry about, specifically in how perceptual and linguistic information come to have their meaning. This difference, however, can only be seen by the third person, scientific analysis of the situation. An embodied approach should never forget that it’s trying to explain the first person experience of the organism (a point made forcefully by Barrett, 2011 ) and from this perspective there is no difference at all between the two types of information. In its day-to-day life the organism never gets to “peer behind the curtain” – kinematic patterns in energy arrays are all we ever have access to. The job of the learning organism is to detect these patterns, and come to learn what they mean by using that information to do something. If you can use some information to intercept a fly ball, then you have demonstrated that you know that that’s what the information means. Similarly, if you can use linguistic information to reply correctly to an interlocutor, you have again demonstrated that you know that that’s what the information means. The basic process is the same; learn to detect the relevant structure and learn to use it appropriately.

How Information Gets Its Meaning

Events in the world are identified by their underlying dynamics; these dynamics create kinematic patterns in energy arrays and these patterns can serve as perceptual information about the dynamics that created them ( Bingham, 1995 ). For perception, structure in an energy array is about the dynamic event in the world that created the structure in the moment (for example, the optical information created by the motion of a fly ball is about the motion of the fly ball). This relationship is underwritten by ecological laws ( Turvey et al., 1981 ) and detecting the information allows the organism to perceive the dynamical event.

Every language event (speech, writing, gesture) also creates structure in energy arrays (speech creates acoustic structure; writing and gesture creates optical structure). To an organism capable of language use, this structure can serve as linguistic information , and because we are treating them as the same kind of thing, we can analyze linguistic information the same way we analyze perceptual information. The only difference between perceptual information and linguistic information is in the relationship between the structure in the energy array and the meaning of the information. For language, the structure in the energy array is not about the dynamics of, say, articulation; it’s about whatever the words mean. The structure comes to have this meaning because of the social conventions of the language environment and what we learn is, therefore, a conventional meaning of the pattern. This conventional underpinning gives stability to linguistic information, but the difference between a law and a convention is very important. Conventions can change and so can the meaning of words; language is much less stable than perception. This decreased stability is, of course, a fact of language to be explained, so perhaps it is not a disaster for the analogy we are developing here.

Do We Need Representation?

This is the point where standard cognitive science usually jumps in and claims that conventional meaning requires representational support. Linguistic information is created by the unfolding of a complex dynamic in the present time, but the meaning of this information is the conventional one that may be about something not present at that time; we can talk about things in their absence in a way that has no analogy in perception. So in what sense can linguistic information have meaning if not in the form of internal models of the people, objects, places, etc., to which the words refer?

This sticking point is, to some extent, a product of the form of the question. To ask what a word means implies something static and internal – words have meanings. So, our approach here is to ask the same question in a different way. As we said earlier, if someone is able to respond appropriately to linguistic information, then it is fair to say that this person knows what the information means. Instead of asking how we learn the meaning of words, we can ask, instead, how do we learn to use and respond to linguistic information? Can we respond appropriately to linguistic information without possessing mental representations? As discussed in the previous sections on robotics, quite interesting, and complex behavior can emerge without explicit internal models of it. Still, none of these robots used language.

In perception, the argument goes, representations are not necessary because the specification relationship between perceptual information and the world makes perceiving the information identical to perceiving the world ( Gibson, 1966 , 1979 ; Turvey et al., 1981 ). What this means is that organisms can respond appropriately to perceptual information without the need to cognitively enrich the perceptual input. The critical issue for language is whether the conventional relationship between linguistic information and what that information is about is sufficient to support something like direct perception.

Chemero (2009) has an extensive argument about how convention can indeed be sufficient, as part of his suggestion that even perceptual information can be grounded in convention. Specifically, he uses conventions as defined by the situation semantics of Barwise and Perry (1983) and we suggest that this analysis will be the place to begin to address this question in the future. To summarize the key points, Barwise and Perry proposed that information is created for organisms by situations ; a given situation will be an instance (token) of a type of situation, and situations can be connected by constraints. If two types of situation, S1 and S2, are connected via a constraint, then a token S2 is informative about a token S1 by virtue of that constraint. An organism has access to that information if and only if they have access to one of the tokens and the constraint. This is precisely the case in the example of language. If S1 is “the situation being discussed” and S2 is “the language event of the discussion,” these are connected by the constraints of the local language environment. By this account, a token of S2 (e.g., the utterance “the rain in Spain stays mainly in the plain”) is informative about a token of S1 (the typical pattern of rain fall in Spain) but only to a skilled user of the English language. If the utterance was instead “La lluvia en España se mantiene principalmente en la llanura,” our English language user would not be informed about S1 because they don’t have access to the relevant constraints of Spanish. Situation semantics provides a formal language for talking about how linguistic information can be informative about the world even despite its basis in convention. There is much work still to do here, but as Chemero (2009) notes, this framework has the benefit of treating specifying and conventional information as the same kind of thing and it therefore seems like a good place to start a non-representational account of language meaning.

It is worth saying outright that arguing against the need for representations to support language is not the same thing as claiming that the brain has no role in language. The brain is clearly involved (as it is involved in perception/action) and an embodied approach to language will need to engage with this fact, so long as hypotheses about what the brain is doing are consistent with the embodied analysis we are applying here. For example, there is a literature on the coupling between articulation and neural dynamics as a mechanism for language comprehension. This work focuses on the production of syllables and models that in terms of oscillator dynamics which can then be coupled to the oscillator dynamics of the cortex ( Luo and Poeppel, 2007 ; Giraud and Poeppel, 2012 ; Peelle and Davis, 2012 ). There is some dispute about whether the syllable is the correct phonetic level of analysis ( Cummins, 2012 ), but regardless, the form of this argument matches parts of the analysis we propose here. In particular, this framework suggests a way to link linguistic information to cortical dynamics. Thus, in principle, there is no need to invoke representations to explain how linguistic information can precipitate actions. The non-representational alternative is a non-linear dynamical system where structure in energy arrays (in the form of perceptual and linguistic information) cause changes in cortical dynamics, which are coupled to limbs, mouths, etc., capable of taking action. Taking action (moving, speaking) changes the landscape of perceptual and/or linguistic information, which impacts the cortical dynamics, and so on.

Language, though Special, is Amenable to an Embodied Analysis

We create linguistic information (e.g., speech or written text) to achieve goals (e.g., directing and regulating the behavior of ourselves and others). The dynamical system creating linguistic information entails the coupled dynamics of the articulators and the brain, both of which are nested in a socially defined language environment with its own dynamical properties. Language dynamics are therefore complex and defined across multiple coupled dynamical systems, but linguistic information is still being created by a dynamical event the same way perceptual information is; they are not different in kind.

This information is a critical task resource, in exactly the same way as perceptual information is a critical task resource. In fact, we argue that the similarities between the two are strong enough to import the analyses used with perception directly over to an analysis of language. The most important similarity is that from the first person perspective of a perceiving, acting language user, learning the meaning of linguistic information, and learning the meaning of perceptual information is the same process . The differences in the behavior supported by these two types of information (which are, indeed, important) arise from the differences in the way these two types of information come about and connect to their meaning. But the similarities mean the same basic approach to studying how we use information to perceive meaning can apply to language as much as to perception and action; a step forward in and of itself.

Although language is clearly a tremendous step up in terms of the complexity of the dynamics involved, the essential form of the analysis can remain the same. Linguistic information is a task resource in exactly the same way as perceptual information is a task resource, and we should treat it as such when we try to figure out how it fits into the task-specific device an organism is forming to solve a given problem. We suggest that it is vital to exhaust this strategy first , before leaping to the conclusion that it simply can’t be done without the representations that many other cognitive systems just don’t seem to require.

Other Embodied Approaches to Language: Another Note on Grounding

This is not the first attempt to embody language, but the previous efforts are more in line with the conceptualization hypothesis we reviewed above and suffer from the problems we highlighted there (as well as others; see Willems and Francken, 2012 ). They hypothesize that meaning is grounded in a simulation of previous experiences, a simulation which would include embodied elements of those previous experiences. Tasks measuring comprehension should reflect the presence of this kind of simulation ( Barsalou, 1999 ). Two high profile attempts to measure these embodied simulation effects are the action-sentence compatibility effect (e.g., Glenberg and Kaschak, 2002 ) and the sentence-picture verification task (e.g., Stanfield and Zwaan, 2001 ).

Action-sentence compatibility

Glenberg and Kaschak (2002) had participants rate whether sentences were sensible. Some of the sentences implied a directional movement (e.g., “close the drawer” implies a movement away from the person). Participants responded by moving to press a button, and the movement was either compatible or not with the implied direction in the sentence. Participants were faster when the response direction and the implied direction were compatible, and slower when they were not. The authors suggest that this demonstrates people are mentally simulating the action in the sentence in order to comprehend the sentence; “language understanding is grounded in bodily action” ( Glenberg and Kaschak, 2002 , p. 562).

Sentence-verification task

Stanfield and Zwaan (2001) tested the simulation hypothesis by providing people with sentences that implied an orientation for an object, e.g., “the pencil is in the cup” implies a vertical orientation while “the pencil is in the drawer” implies a horizontal orientation. They then showed people a picture of the object in a compatible or incompatible orientation and asked people to verify if the pictured object matched the sentence; participants were faster to respond in the compatible condition and vice versa.

The major problem with this research is that it again assumes all the hard work is done in the head, with perception and action merely tweaking the result. Before this type of research can tell us anything meaningful about language comprehension, more work must be done to answer some basic questions. There is no account of the resources that exist in the task presented to participants, and this is a critical part of identifying what the task is from the participants’ perspective. For example, what is the information content of a picture of an object, what are the dynamics of button pressing behavior (or any response type being used), and what is the relationship between these two things – what happens if you try to control the latter using the former? These are not easy questions; for example, Gibson himself highlighted how difficult it is to establish exactly what the information content of a picture of something actually is ( Gibson, 1979 ). But without this, you cannot begin to explain how hearing different sentences influences a button press response to those pictures. There may indeed be a story there; after all, the results have been demonstrated multiple times. But it is a story remaining to be told, and as in the rest of the work surveyed here, we think that the answer to these questions will likely lead to mental simulations being replaced the relevant dynamics identified by a task analysis.

At the beginning of the twentieth century, a German teacher named Wilhelm von Osten owned a horse called Hans. Hans, he claimed, could count and do simple maths and he demonstrated this ability for several years in free shows. It wasn’t until psychologist Oskar Pfungst tested this claim rigorously that the truth was revealed: Hans did not know maths, but he did know to stop tapping his hoof when his owner indicated that he had reached the correct answer (by visibly but subconsciously relaxing; von Osten was not a fraud). Abstract knowledge such as how to add is typically seen as requiring some form of internal representational state, but here, the cognitive explanation (that Hans had the internal ability to count) was replaced by a straight-forward perceptual coupling to his environment.

The story of Clever Hans has stood as a cautionary tale in psychology ever since; identifying an organism’s actual solution to a problem requires the ability to identify all the potential solutions to a task followed by careful experimental testing to identify which of all the possible options are actually being used. This remains as true now as it did in 1907 when Pfungst ran his tests.

Standard cognitive science proceeds under two related assumptions that interfere with its ability to identify the actual solutions. These are poverty of stimulus, and the consequent need for internal, representational enrichment of perception. The objects and processes of standard cognitive psychology have a specific job to do that reflects the hypothesized need to enrich perceptual information. But these assumptions mean that cognitive research never even tests the genuinely embodied alternative solutions we now know are viable options.

Replacement style embodied cognition removes these assumptions and instead looks at all the resources in the environment that might support complex behavior and, critically, the information that might serve to tie them together. One of the most important discoveries of the last 40 years has been that there is, in fact, rich and varied information in the environment ( Gibson, 1966 , 1979 ) 3 that we are able to use to produce all manner of complex behaviors. The availability of this high quality perceptual information removes the need to invoke any additional cognitive constructs to explain interesting behaviors. Our behavior emerges from a pool of potential task resources that include the body, the environment and, yes, the brain. Careful analysis is required to discover exactly which of these resources and the relations between them form the actual solution used to solve a given task.

It is true that replacement style embodied cognition cannot currently explain everything that we do ( Shapiro, 2011 ). Even some of the most enthusiastic researchers in embodied cognition think that there are “representation hungry” problems, which simply cannot be solved without something like an object or process from standard cognitive psychology ( Clark and Toribio, 1994 ); language is the major case here. We are more optimistic. All that we can really conclude at this time is that replacement style embodied cognition cannot explain these problems yet . We believe that there is no principled reason why these behaviors cannot be explained with replacement style embodied solutions, given that human beings are, we think, best described as the kind of perceiving, acting, embodied, non-linear dynamical systems doing the replacing. This optimism reflects the successes we’ve described here, and especially the fact that when embodied cognition researchers have turned their attention to “representation hungry” problems, they have actually had great success. The embodied analysis of the A-not-B error remains the best example of this; it literally replaces “thinking about things in their absence” with embodied action. Another example is the work with Portia spiders (see above and Barrett, 2011 for a review). We have suggested a further step forward here, with an initial analysis of language that replaces what words mean with what language lets us do ; of course, it remains to be seen if this is as successful (but, see also Port and Leary, 2005 ; Port, 2007 ; for more on tackling language).

Replacement style embodied cognition research has produced methods, formal tools (primarily in the form of dynamical systems models) and a great number of empirical successes. The explanations it produces place embodiment at the center of the organism’s solution to a given task, rather than on the periphery, and this is the research we feel deserves the name embodied cognition.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ http://ruina.tam.cornell.edu/research/ for videos and more details of these robots.
• ^ http://www.ai.mit.edu/projects/leglab/ for videos and more details.
• ^ Three recent reviews of how Gibson’s work in visual perception underpins much of the embodied cognition literature include Barrett (2011) , Chemero (2009) , and Shapiro (2011) .

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Keywords: embodied cognition, dynamical systems, replacement hypothesis, robotics, outfielder problem, A-not-B error, language

Citation: Wilson AD and Golonka S (2013) Embodied cognition is not what you think it is. Front. Psychology 4 :58. doi: 10.3389/fpsyg.2013.00058

Received: 31 August 2012; Accepted: 26 January 2013; Published online: 12 February 2013.

Reviewed by:

Copyright: © 2013 Wilson and Golonka. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Andrew D. Wilson, School of Social, Psychological and Communication Sciences, Leeds Metropolitan University, Civic Quarter, Calverley Street, Leeds LS1 3HE, UK. e-mail: a.d.wilson@leedsmet.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Testing the replacement hypothesis, does email substitute for or augment in-person and phone contact.

Since the internet became popular, analysts have wondered about the relationship between email and other means of social contact. Some studies test the replacement hypothesis by examining whether the frequency of email messages sent or received corresponds to decreases (or even increases) in the frequency of in-person or telephone contact. 16 Other studies test this hypothesis by measuring time spent online and time spent on other social activities. 17 Results from both types of studies have been consistent: The internet does not reduce in-person or telephone contact, or any other form of social activity; it replaces only sleeping or TV watching.

This study examines this issue in a different way. Instead of looking for associations between the frequency or duration of email contact and in-person (or phone) contact, this study looks for associations between the percentages of social ties contacted through various media. If the replacement hypothesis holds true, we would expect that high percentages of social ties contacted by email would be associated with low percentages of social ties contacted through other communication media. This survey has the further advantage of being able to test the replacement hypothesis for core ties and significant ties. For example, Caroline Haythornthwaite and Barry Wellman (1998) found that core ties rely on multiple modes of contact, while significant ties rely on only one or two. By separating core ties from significant ties, we can take into account the strength of the tie when assessing the kinds of communication media used.

Email does not replace other forms of contact for core ties.

Generally, the higher the percentage of core ties that are contacted by email, the higher the percentage of core ties that are contacted by phone and IM. We find the opposite of the replacement hypothesis: There is no evidence that email replaces other forms of contact (Figure 8). To the contrary, those who have weekly email contact with a high percentage of their core and significant ties usually have weekly contact with a high percentage of their ties by phone (landline and cell) and by IM. For example, people who send weekly emails to the great majority (80%–100%) of their core ties are also in weekly landline phone contact with 50% of their core ties. By contrast, those who do not use email are in weekly phone contact with 40% of their core ties. This is an increase of 25% (or 10 percentage points) in phone contact from those who do not email any core ties to those who email almost every core tie at least weekly.

Email contact with core ties does not reduce in-person contact.

The replacement hypothesis also is not supported for in-person contact with core ties: People see about the same number of core ties regardless of whether they email a few or many core ties (Figure 8). The percent of weekly in-person contact does not decrease as the percent of weekly email contact increases. For example, the percentage of core ties seen in-person at least weekly is the same, 41%, for both those who do not use email and for those who email 80%-100% of their core ties at least weekly. These findings are consistent with a 2002 study by Anabel Quan-Haase and Barry Wellman that uses a larger, but less representative, sample.

Email does not replace other forms of contact for significant ties. The higher the percentage of significant ties contacted by email, the higher the percentage of significant ties contacted by other media.

The replacement hypothesis is even more strongly contradicted for significant ties. The greater the percentage of significant ties contacted weekly by email, the greater the percentage of significant ties in that network that are contacted weekly by all other means of communication we surveyed — cell phone, landline phone, IM, and in person. The steep lines in Figure 9 for significant ties show that the positive relationships between emailing and other forms of contact are stronger for significant ties than for core ties.

Heavy email users have more than twice as much landline phone contact and three times as much cell phone contact than email non-users. People who email weekly with almost all of their significant ties (80%-100%) have weekly contact with 48% of their significant ties by landline phone and 47% of their significant ties by cell phone. By contrast, non-users of email have weekly landline phone contact with 23% of their significant ties and cell phone contact with only 14%.

The same pattern holds for in-person contact although the differences are not as marked. Those people who use email for weekly contact with 80%-100% of their significant ties have weekly in-person contact with 48% of their significant ties. By contrast, email non-users have weekly in-person contact with 32% of their significant ties. There is an increase in in-person weekly contact of 50% (or 16 percentage points) between non-email users and heavy users.

There is “media multiplexity”: the more contact by one communication medium, the more by others.

The findings suggest media multiplexity: people who communicate frequently use multiple media to do so. The more contact by one medium, the more contact by others. At this time, we can only speculate as to why.

It could be that one thing leads to another, so that email leads to in-person contact (“let’s get together”) or phone contact (“this is too complicated for email; phone me!”). Similarly, phone conversations can lead to more email (“I’ll send you that joke or web address as soon as I get online”) as can in-person encounters (“It was fun meeting you; let’s keep in touch by email.”). It could be that some social networks are more gregarious than others, so that there is a greater norm and practice of sociability. Or, it could be that some people are more gregariously active in maintaining their networks through frequent communication.

The current generation of email users is communicating more often than recent generations and possibly more often than any previous generation.

Whatever the cause, it is clear that email is adding on to other communication media. This means that the current generation of email users is communicating much more often than recent generations and possibly more often than any previous generation since people huddled in caves with only conversation to pass the nights away. Couple this high rate of communication with the sizable networks we have found, and we have suggestive evidence that while Americans may be bowling alone — as Robert Putnam warned — they are networking together.

• See, for example, the studies in Wellman and Haythornthwaite (2002) and in Kraut, Brynin, and Kiesler (2005). ↩
• For example, see Franzen (2003), Nie and Hillygus (2002), and Pronovost (2002). ↩

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Exploring the belief replacement hypothesis: What secular beliefs do non-believers have, and what psychological functions do they serve?

Pi: dr valerie van mulukom, psychology, coventry university, uk.

Valerie van Mulukom

Project Team

Dr Valerie van Mulukom, Psychology, Coventry University, UK (Principal Investigator)

Dates: 1 October 2017- 31 March 2019

Award: £ 14,918.15

There is a global increase in so-called ‘unbelievers’, or people who do not hold any religious beliefs. However, while unbelievers may not hold religious beliefs, they still hold beliefs about reality, such as ontological, epistemological, and ethical beliefs. The Belief Replacement Hypothesis suggests that such secular beliefs have psychological functions highly similar to those of religious beliefs. However, currently there is no overview of what psychological functions secular beliefs have, nor which secular beliefs there are. This project aims to discover which secular beliefs individuals have, in the UK but also countries with different cultural backgrounds, and whether secular beliefs serve similar psychological functions to religious beliefs. In Study 1, a systematic review of evidence in the current literature of the psychological functions of secular beliefs will be written, focusing on the range of beliefs and the similarity of functions to those of religious beliefs. In Study 2, questionnaires will be run in three culturally diverse countries to investigate what secular beliefs people hold. In a pilot study conducted by our lab, we found that 62.3% of the unbelievers in our sample had at least one kind of secular belief. These beliefs included belief in science, belief in humanity, belief in self, and belief in nature. In the surveys we will conduct in the UK, Denmark, and Turkey, we will ask participants about their secular beliefs, using our pilot findings. Together, these studies will elucidate our understanding of secular beliefs and their psychological functions.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Replacement—A theory of stereotypy: A review

• Published: 01 September 1984
• Volume 3 , pages 72–83, ( 1984 )

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Three forms of stereotypy are described and an outline given of previous theories of simple stereotypy in the retarded and autistic. An alternative theory is put forward in which stereotypy is characterized as a replacement of unpalleatable stimuli and/or responses that are overloading the system in a way with which the individual cannot cope. Although the theory derives from cognitive psychology, it can also be expressed behaviorally or experientially without loss of impact. The manner in which replacement theory applies to the three types of stereotypy is described. Testable hypotheses are derived, predictions made, and implications considered. Finally, the more general ramifications of the theory are explored.

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Alexander F. Danvers , Jing I. Hu , Makenzie J. O’Neil; Emotional Congruence and Judgments of Honesty and Bias. Collabra: Psychology 1 January 2018; 4 (1): 40. doi: https://doi.org/10.1525/collabra.178

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Psychological and philosophical discussions typically understand honesty as reporting truth with propositional statements. In this model, emotions are often seen as irrelevant or a hindrance to honesty, because they can bias our reports. In relational contexts, however, emotions can provide information about deep-seated convictions. We report the results of a study (N = 827) finding that individuals whose emotional responses are congruent with their explicitly stated egalitarian positions are judged as significantly more honest and less prejudiced than those with incongruent emotional responses. This is seen in judgments of white male targets who have negative emotional responses to a black man, a gay man, and a female supervisor. These results suggest that emotional reactions provide information used when judging the honesty and bias of an individual.

Honesty is typically defined as not intentionally distorting the truth ( Miller, 2017 ). In the philosophical conception, honesty involves a deliberate decision to report or act on a propositional statement (e.g. “I did see the man in question on the night of the 13 th ”). In social situations, however, truth can involve emotional content not captured by explicit propositional statements. Illustrating this, Furtak ( 2017 ) reinterprets a philosophical scenario from Roberts ( 2003 ) in which an individual says he believes black and white people are equal, yet he feels strong negative emotions in reaction to seeing his sister dating a black man ( Roberts, 2003 ). Roberts argues that this demonstrates how emotion and reason are separate. Furtak argues that the lack of congruence between the individual’s explicit statement and emotional responses reveals that at a deep level the individual does not really know that black and white people are equal. In the philosophical literature, therefore, there are two conflicting theories of the role of emotion. In Roberts’ theory, emotion is separate from propositional knowledge, and so emotional responses cannot undercut beliefs. In Furtak’s theory, the truth is constituted by both the explicit proposition and the learned, emotional association, so that both must be considered when judging whether an individual is making an honest statement; incongruence can indicate that the person does not fully understand himself.

These conflicting claims about the role of associative, emotional processes in truth relate to a theoretical point about bias. Expressing bias against others on the basis of race, gender, and sexual orientation influences judgments of character so strongly that stereotyping and prejudice researchers have suggested people are no longer willing to respond honestly to questions assessing explicit bias ( Dowden & Robinson, 1993 ; Dunton & Fazio, 1997 ). Measures of implicit bias were developed in part to bypass this socially desirable responding ( Fazio, Jackson, Dunton, & Williams, 1995 ; Greenwald, McGhee, & Schwartz, 1998 ). Implicit bias toward a group is thought to be an automatic, associative process that is facilitated by experiencing certain negative emotions, including anger, contempt, and disgust ( Dasgupta, DeSteno, Williams, & Hunsinger, 2009 ; Matsumoto, Hwang, & Frank, 2017 ). Empirical results indicate that it is only weakly correlated with explicit bias and discriminatory behavior ( Oswald, Mitchell, Blanton, Jaccard, & Tetlock, 2013 ). In modern society, people may truly believe they are non-biased and hold explicit egalitarian beliefs, but experience negative emotions in response to minority groups ( Gaertner & Dovidio, 2005 ). As in the philosophical debate described above, this raises a question about whether someone with an explicit egalitarian belief but an implicit, emotional bias against members of a minority group is “really” unbiased or not.

Little is known about how people view and judge those who harbor biased emotional responses while maintaining strong explicit egalitarian beliefs and behavior. Are their explicit statements held out as the true barometer of bias, or do perceivers judge that these targets cannot fully grasp the truth that is embedded in their emotional responses? This incongruence between biased emotional reactions and egalitarian beliefs and behaviors is a serious issue, because it might influence an individual’s ability to appear honest and unbiased in others’ eyes.

Whether emotional reactions are associated with an individual’s core beliefs—such as whether that person is racist—is also closely related to questions that have been addressed by attribution theory in psychology ( Jones & Davis, 1965 ; Heider, 1958 ). Central to attribution theory is the question of whether behavior can be attributed to internal or external causes (often recapitulated as person versus situation), and factors that lead to one categorization or the other. For example, Kelley’s covariation model proposes that consensus, distinctiveness, and consistency of behavior can be combined to make internal or external attributions about the cause of a given behavior ( Kelley, 1973 ). Traditional attribution theory would appear to apply to the current investigation based on the social desirability of racist behaviors: in the case of the reformed racist, the negative emotional reaction might be considered an undesirable behavior and so should be especially informative about the man’s underlying dispositions. However, a further distinction made in the attribution literature—that of personal and impersonal causation—suggests a complication to the case of inferring honesty from an emotional reaction ( Heider, 1958 ; Malle, 2011 ).

Personal causality involves a desire to achieve an outcome—such as the desire to act in an egalitarian way— that can be dispositional or momentary, and is associated with intention. Impersonal causation involves elements that arise spontaneously and uncontrollably—such as having a sudden emotional reaction to seeing a member of a stigmatized group—and is associated with unintentional action. Indeed, Malle ( 2011 ) uses an emotional reaction—feeling sad—as an example of an impersonal cause. In modern theories of attribution, emotions are unintentional causes of behavior, considered separately from belief and desire ( Malle & Knobe, 1997 ; Malle, 1999 ).

The role of emotion in judgments of honesty, therefore, leads to theoretical tension in attribution theory: the commonsense conception of honesty suggests that it is about intentional action only (e.g. someone cannot be accidentally dishonest), but attribution theory suggests that emotions should be categorized as unintentional actions. Assuming that honesty should be about intentional actions, current research in attribution theory would therefore suggest that emotion cannot play a role in determining honesty. If emotion does play a role in judgments of honesty, then, it suggests that current understanding of attribution needs to be updated. One theoretical implication would be that emotions have a special status in causal explanations, because they are unintentional in the moment, but are associated with a person’s history of behaviors and mental associations. Emotions may therefore need to be considered differently at different time scales in attribution theory.

Another implication would be that judgments of honesty are determined by the coherence of different sources of information about the individual. While attribution theory does consider personal history, it typically considers beliefs and emotions (as unintentional actions) to be separable causes without emphasizing the ways they may be integrated in an overall judgment of a person’s disposition. The degree to which emotional reactions change judgments of honesty will give a sense of the importance for coherence across belief, behavior, and emotion in making dispositional attributions. Examining people’s judgments of the philosophical scenario described by Furtak and Roberts therefore has the potential to give new insight into two areas of attribution theory: the status of emotions as momentary, unintentional causes, and the role of integration across information sources in making dispositional attributions.

It is further worth noting that the thought experiment—and scenario presented to participants in the experiment below—deals with the case of an individual who earnestly believes he is egalitarian, but still has incongruent emotional reactions based on his earlier life history. The target being judged is not presented as intentionally misleading others about his beliefs or emotions. He simply has not attended to information embedded in his own emotional responses. Our theoretical prediction is based on a conceptualization of emotions as containing rich information about a person’s perceptions of and judgments about the world. Having access to information embedded in one’s own emotions is important, but not always easy. In this conceptualization, perceivers are judging whether a person who doesn’t account for his own emotional reactions is truly honest.

This study examines the way that implicitly biased emotional responses influence judgments of honesty in the context of prejudice. The research addresses two core issues: (1) the role of emotions in judging honesty from a philosophical perspective, and (2) the role of emotional processes in judging whether someone is racist, sexist, or homophobic. Our preregistered hypothesis was that a mismatch between a person’s stated, pro-egalitarian beliefs and his emotional reaction would lead him to be seen as less honest and more biased.

Participants . 900 online workers from Mturk were recruited to participate in the approximately 10-minute study for $1 each using the service TurkPrime ( Litman, Robinson, & Abberbock, 2017 ). Power calculations indicated that 500 participants were needed to detect a small effect ( f 2 = 0.025). 1 Participants who spent less than one minute completing the survey—typically those who did not complete any items—were not included (N = 73), yielding a final sample of 827.

Procedures . Each participant read three narratives about individuals who were prejudiced and then changed their views. There were two versions of each narrative: one in which the individual in the narrative had emotional responses that were congruent with his changed, anti-prejudiced view, and one in which he had non-congruent emotional responses. The narratives examined prejudice against black people, against women, and against gay people. In all scenarios the individual being described was a white male. Participants were randomly assigned to see one version of each narrative; how often an individual saw a non-congruent version (0 to 3 times) was not controlled within participants, but was evenly distributed across participants. The narrative demonstrating prejudice against black people is reproduced below; the full text of all materials used in the study is provided in the Supplementary Materials.

Robert is a white man who grew up in a region of the United States where black people were regarded as sub-human. He believed that their being elevated to a status of equality was unjustifiable and threatened the very fabric of white civilization. His emotions towards black people were a mixture of fear, resentment, and contempt. Years later, Robert has become convinced that his earlier beliefs were false. He no longer believes that black people are sub-human, and he believes they should be elevated to a status of equality. His sister has started to date a black man, and when he sees them together he feels [Congruent: warmth and happiness]/[Non-congruent: revulsion and anxiety] . He always makes sure to speak to his sister’s boyfriend respectfully, asking about his family and trying to get to know him better.

After reading each narrative, participants rated their agreement, with a series of nine statements about the individual being described, using slider scales running from 1 to 100. Four of the statements pertained to the individual’s honesty: (1) asks whether the individual “truly believes” that people are equal (e.g. that black people and white people are equal); (2) whether a pro-equality statement attributed to him (e.g. “Robert says that he truly believes that black people and white people are equal”) is honest; (3) whether the individual is honest with himself; and (4) whether the individual is honest with others. Another statement (5) asks directly whether the individual is biased (e.g. “Robert is racist). In tables and figures, the statements are referred to using the following labels: (1) Belief in Egalitarian Statement, (2) Honesty of Statement, (3) Honesty to Self, (4) Honesty to Others, (5) Not Prejudiced.

Three statements to be used on an exploratory basis for planning future research assess dimensions of personality evaluation seen in ratings of strangers: warmth, competence, and morality. The statements describe the individual in the story as trustworthy (morality), intelligent (competence), and warm (warmth). One statement assessed whether the individual is able to “manage his emotions well.”

Data were analyzed using multi-level modeling, with responses to narratives nested in individuals. Responses to questions regarding honesty were heavily skewed, with many respondents rating the target individual’s honesty as 100 out of 100. To deal with this non-normality, responses were converted to a 0 to 1 scale and a regression using the beta distribution was conducted. 2 Beta regression is appropriate when data are bounded between 0 and 1, and accounts for clustering at one end of the scale ( Ferrari & Cribari-Neto, 2004 ). We implemented the model in a Bayesian framework using the R package BRMS ( Bürkner, 2016 ). The model was estimated using a log link, and so we report both the model-estimated regression coefficient in log units and the coefficient converted to raw units. Default priors were chosen for the model.

In all analyses, the effects of the particular narrative and of the order in which the narratives were seen were included as covariates, as well as all of the interactions between experimental condition, narrative, and order. Results indicate that the 95% credible interval for all of these covariates included zero, and so these effects are not considered further here. Descriptive data and correlations among outcome variables, not accounting for nesting within participants, are provided in Table 1 . Means by condition for all outcome variables are provided in Table 2 .

Descriptive Statistics and Correlations for Outcome Variables. Means, standard deviations, and correlations with confidence intervals .

Note . M and SD are used to represent mean and standard deviation, respectively. Possible values range from 0 to 100. Values in square brackets indicate the 95% confidence interval for each correlation. * indicates p < .05. ** indicates p < .01.

Cell Means and Standard Deviations of Outcomes by Group.

Note . Possible values range from 0 to 100.

The main test of our hypothesis was whether or not the emotion congruent condition led to a significant difference in judgments about honesty. Emotional congruence led to significantly higher judgments of honesty of the statement that a person is not prejudiced ( b = 1.34, [0.94, 1.75], raw units: 29), honesty of the person to himself ( b = 1.14, [0.71, 1.59], raw units: 13), and honesty of the person to others ( b = 1.27, [0.84, 1.71], raw units: 24). These results are illustrated in Figure 1A .

A) Honesty Ratings by Emotion Congruence. B) Bias Ratings by Emotion Congruence. Notes: Ridge plots indicate the density distribution of responses in a given condition. The black line indicates the median of the distribution. These ridge plots were formed using the raw data, not the output of the model adjusting for order and narrative. The plots demonstrate that, although the mean differences were significantly different in these conditions, there was not a clear central tendency in the non-congruent condition.

We also tested whether the emotion congruence condition led to a significant difference in judgments about prejudice. Emotional congruence led to significantly higher ratings on the question asking if the person was not prejudiced ( b = 1.26, [0.82, 1.70], raw units: 23), and the question asking if the person truly believed that people are equal ( b = 1.34, [0.92, 1.75], raw units: 29). These results are illustrated in Figure 1B . Data, analysis scripts, and full output are included in the Supplementary Materials, hosted at https://osf.io/tfmjp/ .

Participants judged that individuals who explicitly endorse egalitarian views—saying, for example, that black people and white people are equal—are less honest if they do not have emotional responses congruent with these views. This suggests that people do not judge honesty based solely on explicit propositional knowledge, but that the intuitive conception of honesty accounts for non-propositional, emotional information. This emotional information is used when making judgments about prejudice.

Our pre-registered hypothesis stated that “how honest an individual’s explicit statement is” would serve as our primary dependent variable, but that we were collecting data on related outcomes—including how honest the individual is to himself and to others. Our results found a large effect on our primary outcome of interest, but the pattern of results observed in these secondary variables can also provide insight into the underlying psychological processes. If the target was not judged as honest himself, but the individual was judged as being honest with others, this would suggest that participants saw the target as deluded—responding to the best of his ability, but not being aware of his own biases. If the target was not judged as honest with others, but was judged as honest with himself, this would suggest that participants saw the target as manipulative—knowing that he was biased but misleadingly claiming he was not. Instead, we found that participants judged the target as dishonest in his statement, to himself, and to others. Observed judgments were therefore not consistent with perceptions of the target as particularly deluded or manipulative, but as consistently dishonest.

Our results are in line with Furtak’s philosophical account of emotion as integral to knowledge ( Furtak, 2017 ). An individual who says he believes black and white people are equal while having negative emotional responses to black people is not honest, in this account, because he ignores or has lost contact with his own emotional reactions. Our broader theoretical conceptualization of honesty as a psychological construct involves an important role for emotions. We suggest that honesty, as it is understood by perceivers, does not just involve the accurate reporting of reasoned statements; it must also involve a felt commitment to those statements ( Furtak, 2017 ). True beliefs involve having the right kinds of emotional reactions in relevant situations, and perceivers use information about the consistency of these emotional reactions with propositional statements when judging honesty and character.

In reference to modern attribution theory, our results suggest that honesty is not just a willful act. Emotional responses, which are taken to be unintentional causes in the attribution literature, play into the judgment of honesty. An immediate, uncontrollable emotional response can therefore theoretically render an individual dishonest, despite the fact that the individual is trying to act honestly. This suggests that honesty might require the cultivation not just of a certain set of beliefs, but of habitual emotional responses. Futher, emotions might have different implications for attribution when considered at different time scales; at a larger time scale an individual may be able to become honest through the training of emotional associations. Coherence between emotion, behavior, and belief also have reasonably large effects on dispositional attributions of biased beliefs—23 or 29 points on a 100 point scale, depending on the outcome—suggesting that further research into the integration of information sources would be beneficial in attribution theory.

Emotional reactions are not changed simply through deliberate analysis; they must be cultivated through learned associations and patterns of thinking. There are different practices and discussion in ethics and moral education to enhance one’s self-awareness and to attain the congruence. For example, two of the focuses in Confucian ethical tradition are the practice of rituals (not necessarily religious) and of self-cultivation and reflection (considered jointly). Through ritual practice, including following a certain course of movements in music with other members of a community, the individual has an opportunity to wholeheartedly engage with her or his emotions. The practice also gives the individual a chance to adjust her or his beliefs with the emotions that arise, thereby encouraging frequent adjustment of beliefs in response to emotions. The practice of self-cultivation and reflection on the other hand, stresses the importance of understanding , meaning increasing self-knowledge and thereby changing oneself, through frequently accessing and managing one’s deeper emotions. An individual who is transparent to oneself, as well as to others, is seen as more straightforward and reliable; Confucius himself is an example. This is in line with our result that not being aware of and not being able to manage one’s emotions and the information embedded in them undercut perceptions of the individual’s honesty.

The initial Western philosophical analysis of this situation presented by Roberts ( 2003 ) suggests that belief and emotion are two separate spheres. There is no contradiction in the individual in the story saying that he is not racist, based on his reasoned beliefs, but still having emotional reactions consistent with racism. Participants in this study could have provided responses consistent with this understanding of honesty. This would have led to judgments of honesty in the consistent vs. inconsistent conditions to have no systematic difference, and would suggest that participants generally view emotions as unreliable signals of belief. Instead, results showed clear differentiation between the consistent and inconsistent conditions, suggesting that participants view emotions as inherently connected to propositional statements. The statement must be considered honest in light of the emotional responses the individual had.

Academic theorizing suggests that racism is defined primarily by a system of beliefs ( Garner, 2010 ). Beliefs may be assessed by examining the explicit statements made by individuals about their own views. At issue in this study, then, is whether lay observers judge the honesty of a belief statement as separable from an emotional reaction, or as influenced by emotional reactions. Our results suggest that people assume racism and similar oppressive systems of beliefs inherently have an emotional component. Under this interpretation it is not surprising that manipulating emotional reactions leads to changes in judgments of racism; these emotional reactions partially constitute what it is to have that belief. Inconsistent with these results is the idea that, for U.S. participants, beliefs and emotional reactions are separable constructs. Our results support the position that coherence with emotional reactions is at least part of how people judge the honesty of beliefs.

This study provides insight into specific judgments, but many questions remain. This study does not address emotional incongruence with negative or hateful statements (for example, an online troll who posts negative explicit statements but has positive emotional reactions to a diverse friend group). This study also holds constant the identity of the prejudiced person, choosing not to explore biases of people in less powerful groups (e.g., biases a woman might hold against men). The study also does not make normative claims about how people should be perceived.

These conditions provide constraints on the generality of our findings. Our results do not provide a test of emotional congruence when hateful statements are undercut by positive emotion, nor do they provide a test of emotion-to-statement congruence when the speaker is a member of a minority group, nor do they provide evidence for what is right or ought to be done morally. Further, while we find qualitatively similar results when examining judgments of racism, sexism, and homophobia, other forms of bias—such as those against overweight or obese individuals—are not addressed by these results.

It would be theoretically consistent with our results to find that emotional congruence of negative statements with positive emotions are judged as less honest; that judgements of minority speakers would also be influenced by the congruence of their emotional reactions with their statements; and that congruence between emotion and statements would be important when judging people’s character in other situations, such as regards prejudice against overweight others. However, these related questions have not been directly addressed by this research. A failure to find an effect of emotion/statement congruence on judgements of honesty in these contexts would establish a constraint on the generality of our theory, but would not constitute a failure to replicate the core effect we have reported.

We here demonstrate that emotional associations, which are thought to underlie implicit associations, are combined with information about explicit bias when judging an individual’s overall moral character. More broadly, these results suggest an important role for emotion in judging an individual’s honesty.

Materials, data, analysis scripts, and outputs in R Markdown are available at https://osf.io/tfmjp/ .

The preregistration specifies a sample size of 500, but due to an error more responses were collected. The first author was attempting to collect data on two studies for separate research projects (both dealing with judgments of honesty) on the platform TurkPrime simultaneously. Instead of providing two unique links for each of the two studies, the author put the same study link into the online form used to set up the two studies. Participants who signed up for either study were therefore all routed to the study reported on here. The first author could not identify the source of the additional participants being added to the study immediately, and therefore did not stop data collection until 900 participants (before data trimming) were collected. Analyses using only the first 500 individuals yield identical results, and are presented in Supplementary Materials. All participants were included in the analyses presented here to yield the best parameter estimates for the literature.

The distribution of the data made a regression model based on the normal distribution inappropriate. When conducted using analyses assuming an underlying normal distribution, the results were identical in significance. We chose to report only the results using the beta distribution, despite the fact that they represent a deviation from our preregistered analysis plan, because this model yields more accurate parameter estimates given the observed response distribution.

AFD and JH acknowledge support from a John Templeton Foundation grant for their postdoctoral fellowships at the Institute for the Study of Human Flourishing at the University of Oklahoma.

The authors have no competing interests to declare.

A. Danvers and J. Hu developed the study concept. A. Danvers collected and analyzed data. J. Hu, A. Danvers, and M. O’Neil wrote the manuscript. All authors approve the final version of the manuscript for submission.

The author(s) of this paper chose the Open Review option, and the peer review comments are available at: http://doi.org/10.1525/collabra.178.pr

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Replicability and Reproducibility in Comparative Psychology

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Psychology faces a replication crisis. The Reproducibility Project: Psychology sought to replicate the effects of 100 psychology studies. Though 97% of the original studies produced statistically significant results, only 36% of the replication studies did so (Open Science Collaboration, 2015 ). This inability to replicate previously published results, however, is not limited to psychology (Ioannidis, 2005 ). Replication projects in medicine (Prinz et al., 2011 ) and behavioral economics (Camerer et al., 2016 ) resulted in replication rates of 25 and 61%, respectively, and analyses in genetics (Munafò, 2009 ) and neuroscience (Button et al., 2013 ) question the validity of studies in those fields. Science, in general, is reckoning with challenges in one of its basic tenets: replication.

Comparative psychology also faces the grand challenge of producing replicable research. Though social psychology has born the brunt of most of the critique regarding failed replications, comparative psychology suffers from some of the same problems faced by social psychology (e.g., small sample sizes). Yet, comparative psychology follows the methods of cognitive psychology by often using within-subjects designs, which may buffer it from replicability problems (Open Science Collaboration, 2015 ). In this Grand Challenge article, I explore the shared and unique challenges of and potential solutions for replication and reproducibility in comparative psychology.

1. Replicability and reproducibility: definitions and challenges

Researchers often use the terms replicability and reproducability interchangeably, but it is useful to distinguish between them. Replicability is “re-performing the experiment and collecting new data,” whereas reproducibility is “re-performing the same analysis with the same code using a different analyst” (Patil et al., 2016 ). Therefore, one can replicate a study or an effect (outcome of a study) but reproduce results (data analyses). Each of these three efforts face their own challenges.

1.1. Replicating studies

Though science depends on replication, replication studies are rather rare due to an emphasis on novelty: journal editors and reviewers value replication studies less than original research (Neuliep and Crandall, 1990 , 1993 ). This culture is changing with funding agencies (Collins and Tabak, 2014 ) and publishers (Association for Psychological Science, 2013 ; McNutt, 2014 ) adopting policies that encourage replications and reproducible research. The recent wave of replications, however, has resulted in a backlash, with replicators labeled as bullies, ill-intentioned, and unoriginal (Bartlett, 2014 ; Bohannon, 2014 ). Much of this has played out in opinion pieces, blogs, social media, and comment sections, leading some to allege a culture of “shaming” and “methodological intimidation” (Fiske, 2016 ). Nevertheless, replication studies are becoming more common, with some journals specifically soliciting them (e.g., Animal Behavior and Cognition, Perspectives on Psychological Science ).

1.2. Replicating effects

When studies are replicated, the outcomes do not always match the original studies' outcomes. This may result from differences in design and methods between the original and replication studies or from a false negative in the replication (Open Science Collaboration, 2015 ). However, it also may occur because the original study was a false positive; that is, the original result was spurious. Unfortunately, biases in how researchers decide on experimental design, data analysis, and publication can produce results that fail to replicate. At many steps in the scientific process, researchers can fall prey to confirmation bias (Wason, 1960 ; Nickerson, 1998 ) by focusing on positive confirmations of hypotheses. At the experimental design stage, researchers may develop tests that attempt to confirm rather than disconfirm hypotheses (Sohn, 1993 ). This typically relies on null hypothesis significance testing, which is frequently misunderstood and misapplied by researchers (Nickerson, 2000 ; Wagenmakers, 2007 ) and focuses on a null hypothesis rather than alternative hypotheses. At the data collection phase, researchers may perceive behavior in a way that aligns with their expectations rather than the actual outcomes (Marsh and Hanlon, 2007 ). When analyzing data, researchers may report results that confirm their hypotheses while ignoring disconfirming results. This “p-hacking” (Simmons et al., 2011 ; Simonsohn et al., 2014 ) generates an over-reporting of results with p -values just under 0.05 (Masicampo and Lalande, 2012 ) and is a particular problem for psychology (Head et al., 2015 ). Finally, after data are analyzed, studies with negative or disconfirming results may not get published, causing the “file drawer problem” (Rosenthal, 1979 ). This effect could also result in under-reporting of replication studies when they fail to find the same effects as the original studies.

1.3. Reproducing results

“An article […] in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures” (Buckheit and Donoho, 1995 , p. 59, emphasis in the original). There are many steps between collecting data and generating statistics and figures reported in a publication. For research to be truly reproducible, researchers must open that entire process to scrutiny. Currently, this is not possible for most publications because the relevant information is not readily accessible to other scientists. For example, in a survey of 441 biomedical articles from 2000 to 2014, only one was fully reproducible (Iqbal et al., 2016 ). When data and the code generating analyses are unavailable, this prevents truly reproducible research.

2. Unique challenges for comparative psychology

In addition to the general factors contributing to the replication crisis across science, working with non-human animals poses unique challenges for comparative psychology.

• Small sample sizes —With over 7 billion humans on the planet, many areas of psychology have a large population to draw from for research participants. Indeed, given that undergraduate students comprise most of the psychology study participants (Arnett, 2008 ) and that most colleges and universities have hundreds to tens of thousands of students, recruiting psychology study subjects is relatively easy. Yet, for comparative psychologists, large sample sizes can prove more challenging to acquire due to low numbers of individual animals in captivity, regulations limiting research, and the expense of maintaining colonies of animals. These small sample sizes can prove problematic, potentially resulting in spurious results (Agrillo and Petrazzini, 2012 ).
• Repeated testing —For researchers studying human psychology, colleges and universities refresh the subject pool with a cohort of new students every year. The effort, expense, and logistics of acquiring new animals for a comparative psychology lab, however, can be prohibitive. Therefore, for many labs working with long-lived species, such as parrots, corvids, and primates, researchers test the same individuals repeatedly. Repeated testing can result in previous experimental histories influencing behavioral performance, which can impact the ability of other researchers to replicate results based on these individuals.
• Exploratory data analysis —Having few individuals may also drive researchers to extract as much data as possible from subjects. Collecting large amounts of data and conducting extensive analysis on those data is not problematic itself. However, if these analyses are conducted without sufficient forethought and a priori predictions (Anderson et al., 2001 ; Wagenmakers et al., 2012 ), exploratory analyses can result in “data-fishing expeditions” (Bem, 2000 ; Wagenmakers et al., 2011 ) that produce false positives.

Changes in species studied in Journal of Comparative Psychology from 1983 to 1987 and from 2010 to 2015. (A) shows the frequency of taxonomic groups included in empirical articles for both time periods. Because the time periods differed in the number of articles published, the percent of species included in all articles is presented. (B) shows a subset of the data that includes the top 10 most frequently studied species for each time period. * Top 10 most frequent from 1983 to 1987. ∧ Top 10 most frequent from 2010 to 2015.

The increase in species studied is clearly advantageous to the field because it expands the scope of our understanding across a wide range of taxa. But it also has the disadvantage of reducing the depth of coverage for each species, and depth is required for replication. In Journal of Comparative Psychology from 1983 to 1987, researchers published 2.3 articles per species. By 2010-2015, that number dropped to 1.8 articles per species. From 1983 to 1987, 62 species were studied in a single article during that 5-year span, whereas from 2010 to 2015, 103 species were studied only in a single article. Some of the species tested are quite rare, which can limit access to them. For example, Gartner et al. ( 2014 ) explored personality in clouded leopards, which have fewer than 10,000 individuals in the wild (Grassman et al., 2016 ) and fewer than 200 in captivity (Wildlife Institute of India, 2014 ). Expanding comparative psychology to a wide range of species spreads out resources, making replication less likely.

• Substituting species —When attempting to replicate or challenge another study's findings, comparative psychologists sometimes turn to the most convenient species to test rather than testing the species used in the original study. This is problematic because substituting a different species is not a direct replication (Schmidt, 2009 ; Makel et al., 2012 ), and it is not clear what a failure to replicate or an alternative outcome means across species. Even within a species, strains of mice and rats, for instance, vary greatly in their behavior. Thus, for comparative psychology, a direct replication requires testing the same species and/or strain to match the original study as closely as possible.

3. Resolving the crisis

The replicability crisis in psychology has spawned a number of solutions to the problems (Wagenmakers, 2007 ; Frank and Saxe, 2012 ; Koole and Lakens, 2012 ; Nosek et al., 2012 ; Wagenmakers et al., 2012 ; Asendorpf et al., 2013 ). In addition to encouraging more direct replications, these solutions address the problems of null hypothesis significance testing, p-hacking, and reproducing analyses.

3.1. Null hypothesis significance testing

• Effect sizes —Despite decades of warnings about the perils of null hypothesis significance testing (Rozeboom, 1960 ; Gigerenzer, 1998 ; Marewski and Olsson, 2009 ), psychology has been slow to move away from this tradition. However, a number of publishers in psychology have begun requiring or strongly urging authors to include effect sizes in their statistical analyses. This diverts focus from the binary notion of “significant” or “not significant” to a description of the strength of effects.
• Bayesian inference —Another recent trend is to abandon significance testing altogether and switch to Bayesian statistics. While significance testing yields the probability of the data given a hypothesis (Cohen, 1990 ), a Bayesian approach provides the probability of a hypothesis given the data, which is what researchers typically seek (Wagenmakers, 2007 ). A key advantage of this approach is that it offers the strength of evidence favoring one hypothesis over another.
• Multiple hypotheses —Rather than testing a single hypothesis against a null, researchers can make stronger inferences by developing and testing multiple hypotheses (Chamberlin, 1890 ; Platt, 1964 ). Information-theoretic approaches (Burnham and Anderson, 2010 ) and Bayesian inference (Wagenmakers, 2007 ) allow researchers to test the strength of evidence among these hypotheses.

3.2. P-hacking

• Labeling confirmatory and exploratory analyses —Confirmatory data analysis tests a priori hypotheses. Analyzing data after observing the data, however, is exploratory analysis. Though exploratory analysis is not inherently ‘bad’, it is disingenuous and statistically invalid to treat exploratory analyses as confirmatory analyses (Wagenmakers et al., 2011 , 2012 ). To clarify between these types of analyses, researchers should clearly label confirmatory and exploratory analyses. Also, researchers can convert exploratory analyses to confirmatory analyses by collecting follow-up data to replicate the exploratory effects.
• Pre-registration —A more rigorous way for researchers to avoid p-hacking and data-fishing expeditions is to commit to specific data analyses before collecting data. Researchers can pre-register their studies at pre-registration websites (e.g., https://aspredicted.org/ ) by specifying in advance the research questions, variables, experimental methods, and planned analyses (Wagenmakers et al., 2012 ). Registered reports take this a step forward by subjecting the pre-registration to peer review. Journals that allow registered reports agree that “manuscripts that survive pre-study peer review receive an in-principle acceptance that will not be revoked based on the outcomes”, though they may be rejected for other reasons (Center for Open Science, 2016 ).

3.3. Reproducing analyses

• Archiving data and analyses —A first step toward reproducing data analysis is to archive the data (and a description of it) publicly, which allows other researchers to access the data for their own analyses. An important second step is to archive a record of the analysis itself. Many software packages allow researchers to output the scripts that generate the analyses. The statistical software package R (R Core Team, 2017 ) is free, publicly available software that allows researchers to save scripts of the statistical analysis. Archiving the data and R scripts makes the complete data analysis reproducible by anyone without requiring costly software licenses. Data repositories, such as Dryad ( http://datadryad.org/ ) and Open Science Framework ( https://osf.io/ ) archive these files.
• Publishing workflows —The process from developing a research question to submitting a manuscript for publication takes many steps and long periods of time, usually on the order of years. A perfectly reproducible scientific workflow would track each step of this process and make them available for others to access (Nosek et al., 2012 ). Websites, such as the Open Science Framework ( https://osf.io/ ) can manage scientific workflows for projects by providing researchers a place to store literature, IRB materials, experimental materials (stimuli, software scripts), data, analysis scripts, presentations, and manuscripts. This workflow management system allows researchers to collaborate remotely and make the materials publicly available for other researchers to access.

3.4. Replicability in comparative psychology

Comparative psychologists can improve the rigor and replicability by following these general recommendations. However, a number of practices specific to the field will improve our scientific rigor.

• Multi-species studies —Though many comparative psychology studies have smaller samples sizes than is ideal, testing multiple species in a study can boost sample size. Journal of Comparative Psychology showed an increase in the number of species tested per article from 1.2 in 1983–1987 to 1.5 in 2010–2015. Many of these studies explore species differences in learning and cognition, but they can also act as replications across species.
• Multi-lab collaborations —To investigate the replication problem in human psychology, researchers replicate studies across different labs (Klein et al., 2014 ; Open Science Collaboration, 2015 ). Multi-lab collaborations are more challenging for comparative psychologists because there is a limited number of other facilities with access to the species under investigation. Nevertheless, comparative psychologists do engage in collaborations testing the same species in different facilities (e.g., Addessi et al., 2013 ). A more recent research strategy is to conduct the same experimental method across a broad range of species in different facilities (e.g., MacLean et al., 2014 ). Again, though these studies are often investigating species differences, showing similarities across species acts as a replication.
• Accessible species —Captive animal research facilities are increasingly under pressure due to increased costs and changing regulations and funding priorities. In the last decade, many animal research facilities have closed, and researchers have turned to more accessible species, especially dogs. Because researchers do not have to house people's pets, the costs of conducting research on dogs is much lower than maintaining an animal facility. A key advantage of dog research is that they are abundant, with about 500 million individuals worldwide (Coren, 2012 ). This allows ample opportunities for large sample sizes. Moreover, researchers are opening dog cognition labs all over the world, which provides the possibility of multi-lab collaborations and replications.
• Accessible facilities —Another alternative to maintaining animal research facilities is to leverage existing animal colonies. Zoos provide a wide variety of species to study and are available in many metropolitan areas. Though the sample sizes per species may be low, collaboration across zoos is possible. Animal sanctuaries provide another avenue for studying more exotic species, potentially with large sample sizes.

In summary, like all of psychology and science in general, comparative psychology can improve its scientific rigor by rewarding and facilitating replication, strengthening statistical methods, avoiding p-hacking, and ensuring that our methods, data, and analyses are reproducible. In addition, comparative psychologists can use field-specific strategies, such as testing multiple species, collaborating across labs, and using accessible species and facilities to improve replicability.

Frontiers in Comparative Psychology will continue to publish high-quality research exploring the psychological mechanisms underlying animal behavior (Stevens, 2010 ). To help meet the grand challenge of replicability and reproducability in comparative psychology, I highly encourage authors to (1) conduct and submit replications of their own or other researchers' studies, (2) participate in cross-lab collaborations, (3) pre-register methods and data analysis, (4) use robust statistical methods, (5) clearly delineate confirmatory and exploratory analyses/results, and (6) publish data and statistical scripts with their research articles and on data repositories. Combining these solutions can ensure the validity of our science and the importance of animal research for the future.

Author contributions

The author confirms being the sole contributor of this work and approves it for publication.

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author thanks Michael Beran for comments on this manuscript.

1 I downloaded from Web of Science citation information from all articles published in Journal of Comparative Psychology ( JCP ) from 1983 (when Journal of Comparative and Physiological Psychology separated into JCP and Behavioral Neuroscience ) to 1987 ( N = 235) and from 2010 to 2015 ( N = 254). Based on the title and abstract, I coded the species tested in all empirical papers. Data and the R script used to analyze the data are available as supplementary materials.

Supplementary material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00862/full#supplementary-material

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Psychologily

Displacement Psychology: Why We Sometimes Take Our Emotions Out on Others

Displacement psychology is a fascinating topic that explores how people redirect their emotions from one source to another. This defense mechanism can positively and negatively affect a person’s mental health and well-being. At its core, displacement is a way for people to cope with difficult emotions by shifting them onto a less threatening target.

One typical example of displacement is when a person becomes angry with their boss but instead takes out that anger on their spouse or children. This can be harmful to relationships and can cause further stress and tension. However, displacement can also be a valuable tool for managing emotions in certain situations, such as when a person needs to express their anger in a safe and controlled manner.

In this article, we will explore the concept of displacement psychology in more detail, including its definition, causes, and effects. We will also provide real-world examples of displacement and discuss how it can be recognized and addressed in therapy and everyday life. We can better manage our emotions and improve our mental health by understanding the displacement mechanisms.

Displacement Psychology: Understanding the Concept

Displacement is a psychological defense mechanism that involves shifting an emotional response or impulse from one object to another, often a less threatening, one. This is a fundamental concept in psychology, particularly in psychoanalytic theory. It is a way of coping with complicated feelings to express or manage.

For example, if a person is angry with their boss but feels unable to express it, they may displace it onto a family member or friend. The person may become irritable and short-tempered with their loved one, who becomes the target of their displaced anger.

Displacement can also occur in situations where a person feels powerless or overwhelmed. For instance, students struggling with schoolwork may displace their frustration onto their peers, blaming them for their struggles.

It is important to note that displacement is often an unconscious process . People may not realize they are displacing their emotions onto another person or object. However, becoming aware of this defense mechanism can help individuals better understand their emotional responses and improve their relationships with others.

Displacement Theory in Freudian Psychology

Freud’s view on displacement.

According to Sigmund Freud , displacement is a defense mechanism when an individual redirects their negative emotions or impulses from their source onto a less threatening target. Freud believed this mechanism was an unconscious process allowing individuals to cope with anxiety and stress.

For example, if an individual is angry with their boss but cannot express it, they may displace their feelings onto a family member or friend. This displacement allows the individual to release their emotions without fearing consequences from their boss.

Role of the Ego in Displacement

Freud believed that the ego played a crucial role in displacement. The ego mediates between the id (our primitive desires) and the superego (our moral and ethical standards). When the id’s impulses conflict with the superego’s restrictions, the ego may use defense mechanisms such as displacement to manage the conflict.

The ego may use displacement to protect the individual from the anxiety arising from the id and superego conflict. By redirecting negative emotions onto a less threatening target, the individual can avoid the anxiety from expressing their emotions to the source.

Displacement Psychology: Examples in Everyday Life

As discussed earlier, displacement is a defense mechanism where we redirect negative emotions from their source to a less threatening recipient. Here are some common examples of displacement in everyday life.

Displacement in Relationships

Displacement often occurs in our relationships when we cannot express our negative emotions to the person who caused them. Instead, we may vent our frustration on someone not involved in the situation. For instance, when we are angry with our partner but cannot express it, we may snap at our children or pets.

Another example of relationship displacement is when we transfer our feelings of rejection or disappointment from one person to another. For instance, if a love interest rejects us, we may take out our frustration on our friends or family members.

Displacement at Work

Displacement can also occur in the workplace, where we may be unable to express our negative emotions towards our boss or colleagues. For example, if we are frustrated with our boss but cannot express it, we may anger our coworkers or subordinates.

Another example of displacement at work is when we transfer our feelings of inadequacy or failure from one project to another. For instance, if we fail to complete a task successfully, we may transfer our feelings of failure to another project, even if it has nothing to do with the first one.

Displacement vs. Projection

When it comes to defense mechanisms in psychology, two terms that are often used interchangeably are Displacement and Projection . However, they are not the same thing.

Displacement is a defense mechanism in which a person redirects their emotions from the source to a less threatening recipient. For example, if you are angry with your boss but can’t express it because of fear of losing your job, you might go home and take your frustration out on your spouse or children. In this case, your anger has been displaced from your boss to your family.

On the other hand, Projection is a defense mechanism in which a person attributes their unwanted thoughts, feelings, or motives to another person. For instance, if someone feels jealous of their friend’s success, they might accuse their friend of being jealous of them instead. In this case, the person is projecting their jealousy onto their friend.

The critical difference between Displacement and Projection is that Displacement is about redirecting emotions, while Projection is about attributing emotions to someone else.

It is important to note that Displacement and Projection are unconscious defense mechanisms, and people may not even realize they are using them. However, becoming aware of these defense mechanisms can help individuals better understand their behavior and improve their relationships with others.

Displacement and Other Defense Mechanisms

When we experience negative emotions, it can be challenging to cope with them healthily. Defense mechanisms are psychological tools we use to protect ourselves from the discomfort of these emotions. Displacement is just one of many defense mechanisms that we can employ.

Displacement and Repression

Displacement is a defense mechanism that redirects negative emotions from their source to a less threatening recipient. For example, if someone is angry with their boss, they may take that anger on a family member or friend instead. On the other hand, repression involves pushing negative emotions down into our unconscious mind so that we don’t have to deal with them. Both displacement and repression can be helpful in certain situations, but they can also be harmful if used too frequently or inappropriately.

Displacement and Sublimation

Another defense mechanism that is related to displacement is sublimation . Sublimation involves channeling negative emotions into a more socially acceptable outlet, such as art, music, or sports. For example, an angry or frustrated person may channel those emotions into a painting or a piece of music. Sublimation can be a healthy way to deal with negative emotions, but it can also be challenging to achieve and may not always be possible.

Displacement Psychology: Coping with Displacement

When we use displacement as a defense mechanism, we must recognize it and find ways to cope. Here are some strategies we can use to manage displacement:

• Awareness : The first step in managing displacement is to be aware of it as a defense mechanism. We can start by paying attention to our thoughts and emotions and noticing when we displace our feelings onto others.
• Identify the source of the emotions : When we find ourselves displacing our emotions onto others, it can be helpful to identify the source of those emotions. Are we feeling stressed at work? Anxious about a relationship? Once we identify the source of our emotions, we can work on addressing those underlying issues.
• Find healthy outlets : Instead of displacing our emotions onto others, we can find healthy outlets to express those emotions. This can include talking to a therapist or trusted friend, engaging in physical activity, or practicing mindfulness and meditation.
• Practice empathy : When we find ourselves on the receiving end of someone else’s displacement, it can be helpful to practice empathy. Instead of reacting defensively, we can understand where the other person is coming from and offer support and compassion.

By being aware of displacement as a defense mechanism and finding healthy ways to cope with our emotions, we can avoid the negative consequences of relying too heavily on displacement.

Displacement in Children and Adolescents

Displacement is a psychological defense mechanism that is common in children and adolescents. It occurs when people redirect their negative emotions from their source to a less threatening recipient. Children and adolescents may use displacement to cope with difficult situations, such as conflict or displacement from their homes due to natural disasters or war.

Displacement can have a significant impact on children and adolescents. It can lead to feelings of anger, frustration, and anxiety. For example, a child bullied at school may come home and take their frustration out on a sibling or parent. In this case, the sibling or parent receives the displaced aggression.

Children and adolescents displaced from their homes due to natural disasters or war may also experience displacement. They may feel angry and frustrated about their situation and take their emotions out on others. Displacement can also lead to feelings of depression and anxiety, which can have long-term effects on a child’s mental health.

Parents, caregivers, and teachers must recognize the signs of displacement in children and adolescents. Some common symptoms include aggressive behavior, withdrawal from social activities, and difficulty sleeping. If you suspect a child or adolescent is experiencing displacement, it is essential to seek professional help.

Displacement in Clinical Psychology

Displacement is a defense mechanism commonly used by individuals to cope with negative emotions. In clinical psychology, this mechanism is often observed in patients struggling with anxiety or depression. When individuals cannot express their feelings towards the source of their distress, they tend to redirect their emotions towards another person or object.

This mechanism can be observed in patients who are struggling with substance abuse. For example, an individual struggling with addiction may displace their negative emotions towards their family members or friends, blaming them for their addiction. This displacement allows the individual to avoid confronting the source of their addiction and the emotions that come with it.

In addition to substance abuse, displacement can be observed in patients struggling with eating disorders. For example, an individual struggling with anorexia may displace negative emotions towards their body, blaming it for their lack of control. This displacement allows the individual to avoid confronting the actual source of their negative emotions and the emotions that come with it.

Clinicians need to be aware of the use of displacement in their patients. By identifying this mechanism, clinicians can help their patients confront the source of their negative emotions and work towards resolving them.

Future Directions in Displacement Research

As we continue to explore the complex nature of displacement psychology, it is essential to identify areas where future research can make valuable contributions to our understanding of this phenomenon. Here are some potential avenues for future research:

1 . Displacement in the Digital Age

With the rise of social media and other digital communication platforms, there is a growing need to understand how displacement manifests in these contexts. For example, does the anonymity and distance provided by online communication make individuals more likely to displace negative emotions onto others? Are there unique challenges associated with identifying and addressing displacement in digital contexts?

2. Displacement and Cultural Differences

While displacement is a universal psychological phenomenon, cultural differences may exist in how it is experienced and expressed. Future research could explore how collectivism vs. individualism, power distance, and communication styles influence the prevalence and manifestation of displacement across cultures.

3. Displacement and Physical Health

Evidence suggests that displacement can have adverse effects on physical health, including increased risk of cardiovascular disease and other health issues. Further research is needed to understand better the mechanisms through which displacement impacts physical health and identify potential interventions to mitigate these effects.

4. Displacement in Group Settings

While much of the existing research on displacement focuses on individual experiences, it is also essential to consider how displacement manifests in group settings. For example, how do group power dynamics influence the likelihood of displacement? Are there certain types of groups (e.g., sports teams, work teams) where displacement is more common?

Frequently Asked Questions

How is displacement different from projection in psychology.

Displacement and projection are defense mechanisms in psychology, but they differ in their mechanisms. Displacement involves redirecting an emotion from its source to a less threatening recipient, while projection involves attributing one’s unacceptable thoughts or feelings to someone else. In other words, displacement is about shifting the target of emotion, while projection is about denying one’s feelings and projecting them onto others.

What is the definition of displacement in psychology?

Displacement is a psychological defense mechanism in which a person redirects a negative emotion from its source to a less threatening recipient. This can happen when the source of the emotion is too frightening or when the person cannot express the emotion directly. Displacement can be adaptive in some situations, but it can also be maladaptive if it leads to inappropriate or harmful behavior.

What is the displacement theory in psychology?

The psychological displacement theory refers to the idea that people tend to displace their aggression onto others when they cannot express it directly. According to this theory, aggression is a natural and necessary part of human behavior, but it needs to be expressed in a socially acceptable way. When people cannot express their aggression directly, they may displace it onto others who are less threatening or have nothing to do with the source of the emotion.

What are some examples of displacement as a defense mechanism?

There are many examples of displacement as a defense mechanism in psychology. For instance, a person angry with their boss may come home and yell at their spouse or kick the dog. A student frustrated with their teacher may take it out on their classmates or parents. A person afraid of their anger may become obsessed with cleaning or organizing their home instead of addressing the source of their anxiety.

Can you provide an example of displaced aggression in psychology?

Displaced aggression is a typical example of displacement in psychology. For instance, a person angry with their boss may replace their aggression with a coworker who had nothing to do with the source of the emotion. Or a person angry with their spouse may displace their aggression onto a stranger on the street. Displaced aggression can be harmful if it leads to physical or emotional harm to others.

How does displacement manifest in daily life?

Displacement can manifest in many ways in daily life. For instance, a person afraid of confrontation may displace their anger onto a friend or family member who had nothing to do with the source of the emotion. A person who is anxious about their job may displace their anxiety onto their partner or children. Displacement can avoid or minimize a threatening emotion, but it can also lead to inappropriate or harmful behavior if it is not managed effectively.

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30 The Psychology of Atheism

Miguel Farias is the founding director of the Brain, Belief, and Behaviour Lab and an Associate Professor and at the Centre for Trust, Peace, and Social Relations, in Coventry University. Previously he was a lecturer in Experimental Psychology at Oxford University. His book with C. Wikholm, The Buddha Pill: Can Meditation Change You? (2015), has been translated into various languages.

• Published: 01 October 2013
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This essay suggests that atheists endorse a range of naturalistic beliefs, such as belief in progress and in science. Social-psychological evidence for this belief replacement hypothesis, where naturalistic beliefs take the place of supernatural ones, is reviewed. Atheists seem to implicitly use their naturalistic beliefs to alleviate feelings of uncertainty, anxiety and stress, a psychological function which, until recently, had only been reported for religious beliefs. The second part of the essay focuses on motivational implications of being an atheist. Here, it is argued that atheists are particularly driven by a desire for self-mastery and, secondarily, by a sensation seeking need to engage in intense and pleasurable activities. A number of sociological, social-psychological, narrative, and sexual-behavioural studies are reviewed to support this idea. The essay concludes by highlighting the human need to believe and the importance of studying the process, rather than the content, of beliefs.

Introduction

I was a born in a time when the majority of young people had lost their belief in God, for the same reason their elders had had it: without knowing why. And then, since the human spirit naturally tends towards judgements based on feelings instead of reason, most of these young people chose Humanity to replace God. Fernando Pessoa, The Book of Disquiet ([1998] 2001 : 11)

There have been two major misconceptions in recent writings on the psychology of atheism. The trend was started by Beit-Hallahmi (2007) when he suggested that the psychology of religion was also the study of irreligion—one simply had to conceive of an atheist as scoring zero on a continuous religiosity scale (0–100). An earlier psychological analysis of atheism, denounces this position as conceptually untenable—for how can one elaborate a science of a negative phenomenon ( Vergote 1996 )? 1 Methodologically, his suggestion is no less suspicious: it confounds a zero score on a religiosity scale with denial of a supernatural dimension, and it completely obliterates the varieties of atheism. The second misconception is a general shortcoming of psychological studies, which have been criticized for an almost exclusive use of North American educated participants ( Henrich et al. 2010 ). In what concerns atheism, the case is particularly severe because there is a paucity of research and most of the existing studies are from the USA. If one adds to this sampling bias the particularly negative image of atheists in that country and the prejudice or distrust they have to face ( Gervais and Norenzayan 2012 ), the scientific value of the US studies on atheism count for little more than an anthropological vignette on the beliefs of an exotic group. 2 Other than culture, social learning factors, like parental beliefs, are crucial to understand the development of an individual’s atheism. After all, religious people generally come from religious families, and atheists from nonreligious families. 3

The psychology of atheism cannot be a mirror image of the psychology of religion for another reason: Atheists have beliefs that deserve to be studied in their own right. By this I don’t simply mean that they hold strong moral values or different attitudes about their atheism (see Zuckerman 2009 ), but that they have distinct ontological, epistemological and ethical beliefs about reality. This is true for both positive and negative atheists, as they are defined in the Oxford Handbook of Atheism . An individual who denies or lacks beliefs in gods will hold other, meaningful , types of beliefs, that can provide a basis to distinguish what is right and wrong, as well as offer emotional reassurance, very much like supernatural beliefs do for a religious individual.

In this essay, I will explore atheists’ beliefs by looking at recent psychological experiments on belief in science and in progress. This topic is of special relevance for a psychology of atheism, for two reasons: first, beliefs work not only at the cognitive, but also at the motivational and emotional levels. This means they can, like Freud and Marx highlighted, have a comforting role in our lives by alleviating uncertainty and anxiety (for recent evidence, see Kay et al. 2009 ; Norenzayan and Hansen 2006 ). Second, despite rational attempts to articulate why one is a Christian, an atheist or an agnostic, the causes of our beliefs (or lack of) are, as Pessoa wrote in the early 1920s, largely unknown to us. In other words, the process of believing is largely an implicit and automatic one, not only in the way beliefs are acquired, but also how they function in everyday life.

This claim is not a novel one. Whether looking at the roots of violence or developing neuro-cognitive models of decision-making, one of psychology’s major accomplishments has been the discovery of how so many of our feelings, thoughts, and behaviours are driven by mental processes taking place beneath our conscious awareness. Along these lines, it has been suggested that religious belief is the outcome of a generally intuitive and non-reflective process ( Barrett 2004 ). Can the same be said of atheism and the types of non-supernatural beliefs held by atheists? The ‘deconversion’ into atheism data are contradictory, with some research showing that this is preceded by rational doubt rather than emotional crisis ( Hunsberger and Altemeyer 2006;   Caldwell-Harris et al. 2011 ), and other studies reporting an underlying emotional process of losing one’s religion ( Exline and Rose 2005 ). Other data on deconversion favour the intuitive hypothesis; a greater proportion of individuals leave religion behind for motivational rather than rational reasons, and the majority of deconversions happen at adolescence and young adulthood, i.e., at a time when one is emotionally particularly volatile ( Streib and Klein 2013 ).

Recent evidence from social-cognitive psychology has strengthened the case for the intuitiveness of religious beliefs. Religious people tend to make more errors in probability-reasoning tasks and to increase their belief in God when experimentally stimulated to value past intuitions ( Shenhav et al. 2011 ). This conclusion has been confirmed by another study that found an association between lower performance on analytical tasks and religious beliefs. Unconventional views of God, agnosticism and atheism were, on the other hand, positively associated with analytical thinking ( Pennycook et al. 2012 ). Yet another study, which used a variety of experimental techniques, found that stimulating analytical thinking decreased religious belief ( Gervais and Norenzayan 2012 ). While these studies show atheists’ higher reliance on analytical thinking, they do not imply that atheists are more conscious or reflective of their own beliefs, or that atheism is the outcome of a conscious refutation of previously held religious beliefs. 4 They may simply be showing that analytical thinking inhibits the expression of one’s intuitive beliefs—and while the focus of these studies was on religion, it is likely that they can be generalized to other kinds of beliefs, including naturalistic ones. The evidence I present in this essay, on the implicit compensatory role of belief in science and in progress, indirectly supports this idea.

Other than the propensity towards analytical thinking, there are other potentially distinctive psychological implications of being an atheist, particularly at the motivational level. In the second part of this essay, I propose that contemporary atheists are specifically driven by a Gnostic motivation, which seeks self-mastery through knowledge; secondarily, they also present a higher sensation seeking need to engage in intense and pleasurable activities.

The Belief Replacement Hypothesis: Faith in Progress and in Science

In the second part of the opening quotation by Pessoa, he suggests that people are naturally predisposed to believe; and that those who reject religion, intuitively choose something else to replace it with. This can be briefly enunciated as the belief replacement hypothesis . Whether explicitly or implicitly, atheists will espouse various types of naturalistic beliefs that are meaningful, help them to explain the world and, ultimately, can play a compensatory role in dealing with adverse circumstances. Existentialism, New Atheism, Humanism and Marxism are examples of beliefs systems associated with atheism. 5 But even less structured beliefs, like conspiracy theories, can also appeal to atheists, echoing Popper’s suggestion that when the gods are abandoned powerful men or groups take their place ([1945] 2003). Indeed, a study on Dan Brown’s Da Vinci Code conspiracy novel suggests that atheists may be more inclined to believe in conspiracies ( Newheiser et al. 2011 ). The novel claims that Jesus had been married to Mary Magdalene, that their descendants were protected throughout the centuries by a secret group called The Priory of Sion , and that the Catholic Church is aware of this and has tried to hide the truth from the public. The study found that the more anxious a person was about death the more they believed in the Da Vinci Code conspiracy, except if they were strongly religious. Furthermore, the atheist part of the sample (35 per cent) showed a higher belief in the conspiracy than religious participants. 6

If the belief replacement hypothesis is true, that is, if atheists espouse naturalistic beliefs, whether explicitly or implicitly, that take the place of supernatural ones, then we’d expect these beliefs to be particularly relevant, not only at the cognitive, but motivational and emotional levels. In other words, if they are as meaningful as supernatural beliefs are for religious people, they should fulfil similar psychological functions to those observed for religious beliefs, such as emotional reassurance in the face of adversity.

In 2009 and 2010, Bastiaan Rutjens and his Dutch colleagues published two articles about the psychological role of belief in progress that provides support for the belief replacement hypothesis. They reported a series of studies where, using various procedures which included laboratory and field experiments, they showed how the humanist belief in moral progress helped secular individuals to cope better with existential anxiety and uncertainty ( Rutjens et al. 2009 ; 2010 ). What Rutjens did was to put John Gray’s (2004) suggestion to the test—that the idea of progress is, for atheists, what providence was for theists: that having faith in a progressive course of history (i.e. that we are progressing not just technologically and scientifically but also morally) provides people with emotional reassurance. Thus, secular people may use this sense of faith in humanity’s moral progress to find comfort or security, in the same way religious people use their belief in God.

They tested this using two main paradigms that either stimulated lack of control or existential anxiety. To elicit existential anxiety the participants were asked to write about their feelings and thoughts concerning their own deaths. This popular social psychology task is based on the premise that human beings are terrified of death; and that one way of alleviating the anxiety provoked by our awareness of death is to affirm our beliefs or world-views ( Greenberg et al. 1997 ). Typically, when stimulating anxiety in this way, people react by strengthening their beliefs.

In the first experiment of the 2009 paper, half of the participants filled in the mortality salience task while the rest wrote about an experience of dental pain. Then, participants read a short essay which argued that progress was an illusion, and had to rate how much they agreed with the author’s views. This is an excerpt of the essay:

There’s plenty of evidence that we haven’t witnessed real progress since the Middle Ages: we fail to find answers to environmental problems, political systems do not function better than say 100 years ago, there is still poverty in the world and so on. We don’t seem to learn from history and keep making the same mistakes over and over again…People are people, and morally, politically, and socially, we simply do not make any progress. All in all, I think we have to face reality: progress is an illusion!

People stimulated with death thoughts agreed less with the author of the essay than those in the control condition. In a second experiment, they found that showing this anti-progress essay made people more aware of death, supposedly because their belief in progress was being undermined. In a third—and crucial — experiment, they tested whether increased belief in progress alleviated death anxiety. To increase belief in progress, they asked half of the participants to read a text describing progress in society through human efforts; after this they had to think about ways in which they felt there had been progress in the last decade. As expected, participants whose belief in progress had been enhanced experienced fewer death related thoughts than those in a control condition. It is then likely, the authors conclude, that the belief in human progress can offer a secular version of faith that alleviates our fear of death.

In the 2010 paper, Rutjens and colleagues again looked at belief in progress, but now used a paradigm where they tested how a felt lack of control may lead to increased belief in progress. This paradigm is rooted in the concept of secondary control—when you can’t directly control your environment, you will resort to your beliefs to ascertain a sense of subjective control and predictability over events ( Kay et al. 2009 ; Rothbaum et al. 1982 ). They found that stimulating lack of control led people to more vigorously defend the concept of progress, as well as valuing more progressive scientific and environmental research (such as stem cell research and development of electric cars). Amongst the studies, there was a field experiment where they looked at people’s faith in progress when they were aboard a plane—a situation where most of us would feel to have less control than in everyday life. When comparing this group of airplane participants with those of a grounded group, they again found increased belief in progress. 7

One could say that belief in progress entails a kind of hope in a utopian society. In that sense, it is understandable that it brings about emotional comfort. Allegiance to science, on the other hand, doesn’t necessarily involve any positive hope in the evolution of society and is in direct conflict with supernatural explanations. Science is, for many atheists, more than a method of acquiring knowledge about the world, the only legitimate way of ascertaining truth. As a belief system, science promotes not only a physical reductionist view of nature, but extends itself into the area of morality. For example, New Atheist philosopher Sam Harris claims that science can tell us what’s objectively right and wrong, while traditional morality can’t ( Harris 2010 ). Ideas about science have permeated the whole fabric of modern culture and many atheists, explicit or implicitly, mention science as a belief system that replaces religion. In a recent sociological paper, which includes a profusion of statements from Scandinavian and US atheists about their views on God, some interviewees justify not believing in God because they are scientists, they have been ‘convinced by science’, or because they believe that ‘everything is created by science’ ( Zuckerman 2012 ).

Recent social-cognitive evidence suggests that belief in science can, for atheists, replace religion as a provider of meaning and emotional reassurance. This hypothesis was recently tested by means of a field and a laboratory experiment ( Farias et al. 2013 ). In the field study, rowers in two different stress conditions were assessed. They were either minutes away from competing (high stress condition) or simply about to start their usual training (low stress condition). Individuals in both conditions filled in a short questionnaire that included measures of stress and belief in science. 8 We would expect people in the high stress condition to intuitively heighten their beliefs in order to alleviate the stress of the imminent competition. That is exactly what happened: competing rowers showed greater levels of stress and increased belief in science than the rowers that were having a usual training session. In a separate laboratory experiment, the death anxiety paradigm described above was used to increase anxiety in half of the participants. Again, it was found that people who had to write about their own deaths had a stronger belief in science. Further, amongst the participants who had been primed with thoughts about their deaths, those with higher beliefs in science also endorsed a particularly deterministic view of how natural laws shape us ( Paulhus and Carey 2011 ). So, perhaps belief in science is emotionally reassuring when an atheist faces adverse situations, because it provides a tightly ordered understanding of the world that eschews randomness—similar to what religion achieves through the idea of a governing deity ( Kay et al. 2010 ).

These findings are supported by other experimental evidence. Another set of experiments, which explored preference for various types of scientific theories, suggests that when we feel threatened we prefer theories that describe an orderly sequence of stages, such as Freud or Piaget’s theories of development, over discontinuous development ( Rutjens et al. 2013 ). One of the experiments discloses that, when faced with unpredictability, we rather choose a theory of Alzheimer’s disease which describes a straightforward decline, than one which stresses individual variability. We know that we are creatures of habit, and detest uncertainty. Can this also account for the survival and moderate success of Intelligent Design over Evolution theory? Apparently, yes, even amongst secular university students. Although generally preferring Darwin’s theory, when students had their sense of control undermined, they were more likely to prefer Intelligent Design over Darwin’s theory of evolution ( Rutjen et al. 2010 ). This also is true when students were stimulated to think about their own deaths ( Tracy et al. 2011 ).

The very notion of ‘believing in science’ is naturally open to criticism. Scientists and philosophers who claim the superiority, or even exclusivity, of scientific methods and results have been accused of dogmatism, or scientism ( Stenmark 2001 ). After all, it is one thing to use science as a method, and another to grant it metaphysical status. The accusation of dogmatism, which is traditionally held against religious people, is probably less related to the content of belief than to its strength and resilience. That is, the more central beliefs are for one’s structuring of the world, including meaning-making, the more likely they are to be inflexible to counter-evidence. Related to this, although atheists have been portrayed as more rational and open minded, in the very same study they also showed to be dogmatic about their beliefs and prejudiced against religious people ( Hunsberger and Altemeyer 2006 ).

The social psychological studies reviewed above provide supporting evidence for the claim that atheists do have beliefs, albeit of a non-supernatural type, and that these beliefs are not dissimilar to religious ones in their psychological function. This clearly lends support to the belief replacement hypothesis. Further, this finding has a relevance which extends far beyond the study of atheism and religion. It tells us something about the nature and role of beliefs. It seems that it is not so much the content of the belief, but its meaningfulness and strength that truly matters. Despite the difficulty in deliberately choosing what we believe in, beliefs offer structure to our lives, and we cling on to them when facing trying and uncertain situations.

In this section, I have focused on psychological commonalities between atheist and religious people. I will now turn my attention to what differentiates them.

Motivations in Atheists: Self-Mastery and Sensation Seeking

If I were to use a single term to characterize what psychologically distinguishes modern atheists from other people, I would say: a Gnostic drive. By Gnostic, I refer both to the etymology of the word—knowledge—and the theological system that proposes a strong dualism between humankind and the world; the world being a place of ignorance created by a minor demiurge, which we can only be liberated from by knowledge. Rational knowledge is, of course, very different from the knowledge Gnostics were seeking, and I am also not implying that atheists believe in a wicked demiurge. However, at a plain psychological level, both modern atheists and Gnostics are deeply driven by a desire for self-mastery—and knowledge acquisition is a privileged way of attaining mastery. But it represents something else: knowledge, as the Gnostics rightly argued, also allows for transcendence of the world, a transcendence which, for modern atheists, can be simple existentialist self-reflection, but can also be an attempt to break with or transform our biological nature—through the application of knowledge. In this context, modern atheists’ reliance on science carries deeper layers of meaning; science can indeed work as a psychological crutch because it has an added metaphysical value. It is ‘a candle in the dark’, in Carl Sagan’s statement, the knowledge that liberates us from ignorance and the threateningly void universe, once it has been emptied of deities.

In Hans Jonas’ analysis of the parallels between Gnosticism and modern existentialism, he stresses how both movements are characterized by a dualistic mood: humans are inexorably separated from the universe (1952). The idea of a divine will at work behind nature has been replaced by rules of power and necessity. This inexorable rift between humankind and the universe makes self-mastery an absolute necessity. The Existentialist movement expressed this in a crystal-clear way: our autonomy from God means that we are free from all constraints, but at the same time utterly alone (see Camus [1942] 2000 ; Sartre 1946 ). We are left with no choice but to master ourselves. The somewhat introverted, or intellectual, expression of self-mastery in existentialism has become an extraverted one in twenty-first century atheism: self-reflective autonomy has given way to competitive individualism, the desire to master and portray oneself as distinct from the rest of humanity. 9

There is a plethora of research on individualism in the social sciences, but little that focuses on atheism. Nevertheless, the available evidence is unambiguous: self-reported atheists and agnostics are more individualistic than religious individuals, and value more motivations of self-direction, hedonism, and stimulation ( Houtman and Mascini 2002 ; Farias and Lalljee 2008 ). A psychological correlate of individualism and self-mastery, in particular, is the need to feel in control of your own life. Although perceived control has been heavily researched in social and health psychology ( Levenson 1981 ), there is almost no investigation of this construct in atheists. In a recent analysis of individual differences in atheism, Caldwell-Harris references two articles which suggest that atheists have a higher internal locus of control, but neither of them actually present the data to support this view (2012). In my own doctoral thesis, I compared internal and external perceptions of control in atheists, Catholics, and New Agers ( Farias 2005 ). As expected, atheists thought of themselves as more in control of their lives than the other groups, though Catholics did not score higher than atheists on external control (whether it was chance/fate or powerful others). 10

There is another psychological literature of particular significance to the discussion of individualistic motivations, which finds its roots in psychoanalytical thought. Bakan proposed that individualism is a motivation towards separation and autonomy; the opposite pole being communion, which is characterized as a motivation towards union, cooperation and contact with other human beings (1966). Following Weber, he traces the modern Western emphasis on agency and individualism to the Protestant revolution, not only because of Protestantism’s notion of a private and unmediated contact with God, but also due to its theological interest in the world, which gave rise to the expansion of science as a way of studying the manifested glory of God. 11

Bakan’s dual model of motivations has been refined and extended by Dan McAdams, one of the leading authors in the narrative study of personality ( McAdams et al. 1996 ). This model has been used to analyse autobiographical narratives of atheists, Catholics and New Age individuals ( Farias and Lalljee 2006 ). When asked to write about a high point in their lives, people express agency or communion motivations. Self-mastery is the prototypical theme of agency as separation, as the individual seeks isolation to perfect and control the self. The following excerpt shows a narrative of self-mastery:

Whilst reading a book my perspective on life and my place in it shifted…I became more philosophical! I read the book in my room, on a bus and on a train journey, digesting each of the chapters and their wisdom over those days. I was the only person involved, I don’t think that I spoke to anyone about the book for a few weeks afterwards. I arrived at a mindset, albeit a shaky one, where I realized that I dictated what I felt and how I reacted to certain situations and fundamentally I was able to, in small and large ways, control my feelings.

An atheist wrote this. By contrast, Catholics wrote significantly fewer narratives centred on self-mastery. 12 However, New Age participants had an even higher proportion of self-mastery stories, though not significantly different from atheists. Consider the following story by a New Age individual:

Last spring inspired by a book I drastically changed my diet which after about 15 years of existing rather than living left me with heaps of energy, my headaches stopped and my digestive problems literally disappeared. Because I also had emotional problems I started listening to self-help tapes, read more books, started to meditate and use affirmations/positive thinking. The change in me was so profound that it’s difficult to put into words. I couldn’t recognize myself and neither could others. I became more confident, happy, efficient in every way and stopped isolating myself from others. Unlike a year ago now I really do accept and believe that we create our experiences and things don’t just happen to us.

The similarity between the two stories is not a coincidence. Concerning motivations, atheists are practically indistinguishable from New Age individuals. The characterization of atheists as more individualistic, non-conformist, liberal and open to new experiences ( Caldwell-Harris 2012 ) applies equally to individuals engaged in modern spirituality. 13 Even more explicitly than atheists, they cultivate self-mastery and endeavour to acquire knowledge, either intellectual or experiential, in order to transcend the abyss between the self and the world (see Heelas 1996 ).

On the other pole of self-mastery through knowledge lies a different kind of motivation. As mentioned earlier, atheists were also found to adhere more strongly to hedonistic and stimulation motivations. The concept of stimulation simply means a search for novelty, excitement and challenge in life ( Schwartz 1992 ). In addition, atheists score higher on openness to experience ( Gallen 2009 ), a personality trait which is defined by a drive towards curiosity, engagement with various activities, and trying out new ideas and experiences. There is an intellectual—or rather imaginative—aspect in these various concepts, but they are predominantly sensorial, which is why I will refer to this as a sensation seeking motivation (adapted from Zuckerman 1990 ). This motivation entails a desire to express one’s physical nature and can be characterized as a search for new, intense and pleasurable sensations and feelings. That atheism may lead to greater personal freedom and even hedonism is not a new idea. If the gods are out of the picture, if we are truly alone and free, why shouldn’t we do as we please? Most atheists do not follow this to its nihilistic consequences; nevertheless, we would expect less behavioural constraints, especially if they are not associated with social sanctions.

In particular, if atheists are motivated by a desire for new and intense experiences this ought to be reflected in their sexual behaviour. 14 This wouldn’t translate necessarily in the frequency of sexual practice, but in its variety—including variety of partners. The available evidence, from US sources alone, confirms this hypothesis. An exhaustive report on a national US sample shows that people without religion are more likely to engage in uncommon heterosexual practices, like anal sex (35 per cent), and to have had a greater number of sexual partners than religious people ( Laumann et al. 1994 ). Nonreligious men, but not women, also have twice as high a preference for sexual voyeurism (10 per cent) than religious men. Another US national survey reports that nonreligious people are the most likely to engage in extramarital sex (44 per cent) and to have had an earlier sexual initiation (82 per cent of men and 69 per cent of women before the age of 18) ( Janus and Janus 1993 ).

Despite the limited nature of these data, it gives us a glimpse of how atheism may be linked with different sexual behaviours. It is quite possible that other factors may help account for these differences. A lower sense of guilt about sexual experimentation, for example, may influence atheists’ behaviour. Future research can address this by assessing both feelings of guilt and sensation seeking motivations. Another possibility is to experimentally stimulate religious disbelief and assess to what an extent this has an impact on motivation.

Christ and progress are, for me, similar myths. I don’t believe in the Virgin Mary or electricity. Fernando Pessoa, The Education of a Stoic (1999 : 26)

It’s very likely that no functioning human can live without beliefs. Old and new religions show a deep awareness of how difficult it is to break one’s established beliefs, whether in the Old Testament the wrath of Jehovah upon the adoration of the golden calf, Jesus’ admonition of the importance of believing without seeing, Zen Buddhism’s use of physical and cognitive strategies to challenge one’s beliefs of reality, or even Scientology’s gadgets to assess and deprogram beliefs.

The benefits of beliefs, both cognitive and emotive, are not driven by their distinctive supernatural content but instead stem from the process of believing, which structures reality in a causal and meaningful form ( Preston and Epley 2005 ). I have suggested that atheists, in shedding off the skin of supernatural beliefs, end up internalizing other types of beliefs. It is unclear how this process occurs, since it does not consist of a conscious replacement. One possibility is that the congruence, or proximity, between one’s atheism and non-supernatural beliefs an individual is exposed to leads to an implicit endorsement of these beliefs. This process occurs regardless of one being a positive, God denying, atheist or a negative one, who simply lacks belief in gods.

This is a new field, poorly researched, and in dire need of cross-cultural research. Above, I suggested that atheists have a Gnostic drive. Some have indicated that this movement has provided a wealth of ‘holy’ iconoclastic inspiration throughout the centuries ( Silva 1997 ). Perhaps modern atheists are, in some subtle and veiled manner, heirs of this tradition and are thus helping to illuminate biases in religious people’s perceptions of God.

Acknowledgements

I am indebted to the philosopher David Leech for having introduced me to the history and varieties of atheism. He is the author of the University of Cambridge-based website Investigating Atheism (< http://www.investigatingatheism.info/ >).

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There is, of course, a negative theology in Hinduism and Christianity where God is defined by what he is not . I don’t think Beit-Hallahmi (2007) had in mind suggesting a link between mysticism and atheism; however, this link exists, and with considerable force, in the Jewish followers of the prophetic figure of Sabbatai Zevi (1626–76). Within the space of a few generations, mystical beliefs were transformed, first into disillusionment, and then into a nihilistic movement (see Scholem [1944] 1995 : 287–324).

To give an example of the difficulties of generalizing from the US studies, let us consider the positive association between atheism and level of education, which is often quoted in review articles of atheism ( Beit-Hallahmi 2007 ; Zuckerman 2009 ; Streib and Klein 2013 ). A cursory look at the references shows that these data come almost entirely from US studies, going as far back as the early 20th century ( Leuba, 1934 ). Now, if we look at the UK, which at the cultural level is the closest to the USA in Europe, such correlation becomes ambiguous: the older group (>35) shows a U pattern between religiosity and education; that is, both the highly secular and highly religious have had more years of education. But for younger people (<35), the data from the same national survey indicates that the highly religious are the best educated ( Voas and McAndrew 2012 ). An earlier study using a British large sample (>16,000) of young people (aged 13–15) reported that 38% of atheists came from working class homes, as opposed to 32% of theists. Atheist youngsters also felt considerably more alienated from their school than theists ( Kay and Francis 1995 ).

The 2001 Census in England and Wales shows that 88% of infants whose parents are Christian are also reported being Christian. In contrast, when both parents have no religion, only 5% of their offspring are identified as Christian ( Voas and McAndrew 2012 ).

The same applies to religious people who can be highly analytical, and still hold largely non-rational supernatural beliefs. There is, of course, a rationalization of belief through theology and philosophy; however, generally, this merely strengthens a previously held belief system rather than lead to its initial endorsement.

That psychologists have neglected the variety of atheistic belief systems is of no great surprise, since they have also generally turned a blind way to differences between religious faiths (for exceptions at the motivational, social-cognitive and perceptual levels, see Schwartz and Huismans 1995 ; Li et al. 2012 ; Colzato et al. 2010 ).

This was not reported in the original paper. Atheists ( M =49.38, SD =15.51) had significantly stronger beliefs in the Da Vinci Code conspiracy than religious participants ( M =40.01, SD =18.83), t (130)=2.99, p =.003.

This was assessed via a short questionnaire that included items like ‘In two decades, we will live in a better world than that of today’.

Some of the items for belief in science were: ‘Science tells us everything there is to know about what reality consists of’, ‘All the tasks human beings face are soluble by science’, and ‘The scientific method is the only reliable path to knowledge’.

Individualism is not the only possible outcome of atheism, as collectivist Marxist regimes have shown.

Atheists ( M = 36.22, SD = 4.98) were significantly higher on internal control than Catholics ( M = 31.25, SD = 5.22) and New Agers ( M = 30.80, SD = 6.31), F (2, 138) = 10.62, p < .001).

Since Bakan, other authors have analysed the relationship between the Protestant Reformation and science ( Brooke 1991 ), and with modern individualism ( Lukes 1973 ).

Atheists ( M =.20, SD =.41) had significantly more narratives of self-mastery than Catholics ( M = .02, SD = .14), F (1, 138) = 5.87, p = .017). The original article compares the results for the three groups only ( Farias and Lalljee 2006 ).

Openness to experience is also positively associated with modern spirituality, and negatively with traditional religiosity ( Saucier and Skrzypinska 2006 ).

One interesting historical anecdote connecting atheism and sex concerns the early French atheist pamphlets. These used to be distributed together with pornographic pamphlets ( Haug et al. 2011 ). See also the recent issue of Philosophical Forum on early modern libertinism and atheism ( Lackey and Nematollahy 2011 ).

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