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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The Role of Self-Regulation in Forgiveness: A Regulatory Model of Forgiveness

1 Department of Social and Behavioral Sciences, City University of Hong Kong, Kowloon, Hong Kong

Daryl R. Van Tongeren

2 Psychology Department, Hope College, Holland, MI, United States

3 Department of Psychology, Wuhan University, Wuhan, China

Associated Data

The datasets analyzed in this manuscript are not publicly available for participants’ confidentiality reasons. Requests to access the datasets should be directed to [email protected] .

Forgiveness is an emotion regulation process that is important for both physical and mental health. Given its benefits, studying the facilitation of forgiveness is important. Researchers have already demonstrated the relationship between self-control and forgiveness. However, in this study, we aim to extend previous research by examining the regulating processes of forgiveness and the possible mediating role of emotion regulation in the relationship between self-regulatory strength and forgiveness. University students ( N = 317) in Hong Kong who were recruited to participate in this study completed an online survey. The results of this study indicated that both self-regulatory strength and emotion regulation were significant predictors of forgiveness. Interestingly, cognitive reappraisal significantly mediated the association between self-regulatory fatigue and forgiveness. This suggests a potential self-regulation mechanism that leads to a prorelationship response and provides evidence for a regulatory model of forgiveness.

Introduction

Forgiveness is a central feature of social life, helping to facilitate interactions between individuals and groups, as well as bolstering the functioning of committed, ongoing relationships (see Worthington and Wade, 2020 for a review). Empirical work has ranged from intergroup forgiveness ( Van Tongeren et al., 2014 ) to interpersonal relationships ( Fehr et al., 2010 ). One of the primary social benefits of forgiveness is its ability to preserve and restore valued relationships (e.g., Burnette et al., 2012 ). Though the conceptualizations of the forgiveness process have spanned the arenas of cognition, motivation, and behavior, the most consistent corpus of research has focused primarily on emotional processes. For example, researchers have examined the role of emotion in the forgiveness process in terms of emotion-focused coping strategies ( Worthington and Scherer, 2004 ), emotional intelligence ( Rey and Extremera, 2014 ), emotion regulation strategies in conflict resolution ( Halperin, 2014 ), and physiological responses ( Witvliet et al., 2001 ).

Previous research has demonstrated the relationship between emotion regulation and forgiveness (see Burnette et al., 2014 for a meta-analytic review), since forgiveness is often conceptualized as being rooted in emotions and requires a person to regulate their emotions toward the transgressor. Specifically, forgiveness is defined as the replacement of negative emotions (e.g., resentment, bitterness, anger, hatred, or hostility) toward a transgressor with positive emotions (i.e., empathy, sympathy, compassion, or love; Worthington et al., 2007 ). In other words, based on the emotional replacement hypothesis , forgiveness juxtaposes positive emotions against negative emotions; these positive emotions neutralize or replace all or part of the negative emotions ( Worthington and Wade, 1999 ). However, such emotional transformations do not occur naturally or easily; individuals must overcome their natural tendencies to respond to an offender with anger and vengeance and instead engage in some form of regulation to respond positively. Accordingly, self-regulatory strength or emotion regulation looms large for many instances of the forgiveness process.

Self-Regulatory Strength and Forgiveness

The ego-depletion model of self-regulation suggests that all forms of self-regulation draw on a common inner resource or limited pool of energy, called self-regulatory strength, which can be depleted. When individuals engage in an act of self-regulation that consumes considerable regulatory resources, subsequent self-control attempts can be impaired ( Baumeister et al., 1998 ). Self-regulatory strength refers to the overall amount of self-regulatory capacity available to an individual pursuing a given goal, such as an interpersonal goal ( Luchies et al., 2011 ). In short, when people engage in difficult tasks that require actively engaging the self and overriding a natural default behavior or sustained engagement in an arduous activity, the strain on one’s psychological resources can impair future endeavors that will require one to engage in similarly difficult processes.

However, the evidence regarding self-regulation, or, more specifically, ego-depletion, is mixed. For example, a preregistered study of the ego-depletion effect across multiple laboratories failed to replicate ( Tangney et al., 2004 ; Hagger et al., 2016 ). Whether this failure to replicate is indicative of the lack of a reliable effect or another problem of the experimental design (e.g., imprecise tuning of the manipulation to the participant sample; see Baumeister, 2019 ; Caspi, 2000 ) remains to be seen; but it must be noted that the self-regulatory model in social psychology—and, by extension, within forgiveness processes—is not entirely undisputed and requires closer scrutiny. Finally, given that the experimentally manipulated ego-depletion effect is contested, work that employs nonmanipulated (i.e., self-reported) indices may help provide insights into these processes (though these have their own limitations, including the inability to draw causal conclusions).

Forgiveness may be one such instance of a process requiring self-regulatory strength; and, indeed, there has been some evidence of the importance of self-regulatory capability for forgiveness. Finkel and Campbell (2001) found that dispositional self-regulatory strength was positively associated with individuals’ accommodative tendencies (e.g., forgiveness) in romantic relationships, and that temporary self-regulatory fatigue decreased individuals’ likelihood of engaging in accommodative responses (e.g., forgiveness) to their partner’s destructive behaviors. Several other studies found that self-control predicted interpersonal success, higher relationship quality, greater relationship satisfaction, and fewer relationship conflicts from childhood to adulthood ( Tangney et al., 2004 ; Luchies et al., 2011 ; Vohs et al., 2011 ). A meta-analytic review revealed that self-control had statistically robust association with small to moderate magnitude across 40 studies and 5,105 participants ( Burnette et al., 2014 ). Building on this prior work, because forgiveness is an emotion regulation process that falls within the broader self-regulatory domain, we hypothesized that low self-regulatory strength (or high self-regulatory fatigue) is associated with lower forgiveness levels.

Emotion Regulation and Forgiveness

Forgiveness is an emotion regulation strategy for coping with interpersonal conflict ( Worthington and Wade, 2020 ). In particular, people may use emotion-focused coping when the perceived best way of dealing with an interpersonal transgression is to attempt to ameliorate immediate negative responses such as anger and hostility. In this way, this emotion regulation strategy requires self-regulatory strength, as it is one instantiation of a self-regulatory process. Following an offense, individuals may also seek to regulate their emotional experiences through emotion-focused coping strategies, such as self-soothing or avoidance ( Worthington and Scherer, 2004 ).

Unforgiveness is theorized as a stress response to appraisals of interpersonal stressors, such as transgressions, betrayals, offenses, and wrongs ( Berry et al., 2001 ). According to Lazarus and Folkman (1984) stress and coping model, an interpersonal transgression is an interpersonal stressor, and the forgiveness process is one way of reacting to, or coping with it.

Cognitive reappraisal is an antecedent-focused emotion regulation strategy that plays a vital role in reinterpreting an interpersonal harm ( McCullough, 2001 ). Cognitive reappraisal involves transforming how an individual construes a situation in order to decrease its emotional impact ( Gross, 1998 ). Forgiveness can function as a cognitive reappraisal process that eradicates anger, hostility, rumination, and their adverse effects in spite of feeling emotional pain and the desire for revenge ( Worthington et al., 2007 ). Positive reappraisal of past negative events, such as reappraising the transgressor’s motivations in a benevolent manner ( McCullough, 2001 ), is a key step in the forgiveness process. Therefore, in this study, we hypothesized that emotion regulation, especially cognitive reappraisal, is associated with higher forgiveness levels.

The Mediating Role of Emotion Regulation

We see a gap in the existing forgiveness research regarding understanding how self-regulatory strength may operate through emotion regulation to impact the forgiveness process. Because forgiveness requires that individuals override their default, natural reactions to interpersonal offenses (i.e., unforgiveness) by instead regulating negative emotions and replacing them with neutral or positive emotions toward the offender, it seems that some degree of self-regulatory strength is necessary (see Burnette et al., 2014 for a review). To the degree that people have sufficient self-regulatory strength, they should be able to engage in emotion regulation strategies that facilitate forgiveness. Thus, these strategies are likely the mediating mechanisms by which self-regulatory capacity affects forgiveness.

According to the ego-depletion theory, an individual exercising self-control on one task who attempts to exert self-control on a second task simultaneously is more likely to fail due to overstrained resources ( Geisler and Schroder-Abe, 2015 ). Individuals with low self-regulatory strength may be unable to exert self-control in regulating their emotions at all. Studies have shown that trait self-control was associated with successful regulation of negative emotions. Given the associations of self-regulatory strength and emotion regulation with forgiveness, we hypothesized that self-regulatory strength (or low self-regulatory fatigue) would be associated with forgiveness because such people would be better able to engage in emotion regulation (cognitive reappraisal). Thus, we predicted an indirect effects (mediational) model.

In this study, we investigated interpersonal forgiveness via emotion regulation and in consideration of individual differences in self-regulatory strength. Building on the consideration derived from the strength model of self-regulation, we propose a new regulatory model of forgiveness, in which emotion regulation (cognitive reappraisal) mediates the effect of self-regulatory strength (self-regulatory fatigue) on forgiveness.

Participants

The participants were 317 students (92 men, 218 women) at a university in Hong Kong. They were between the ages of 18 and 36 ( M = 20.6 years, SD = 2.3). Most participants were single (65.8%), and a large number were in a relationship (25.4%). The majority of participants did not have any religious affiliation (60.2%). The rest identified as Christians (17.7%), Catholics (4.1), Buddhist (1.2%), or others (1.5%). Approximately half were majoring in humanities and social sciences (57.2%). The rest were studying commerce (12.4%), sciences and engineering (10.6%), creative media (4.9%), law (3.2%), veterinary medicine and life sciences (0.9%), and energy and environment (0.3%). Participants were recruited via various means, including recruitment posters displayed at university campuses, university-wide mailing list and in-class promotion. They participated in this study voluntarily. After they completed the study, they were automatically entered in a lottery for coffee coupons (HK$50, HK$100, HK$200) as an incentive.

Each participant completed an online questionnaire that assessed forgiveness, self-regulatory fatigue, and emotion regulation. Participants provided informed consent before participating in the study. This study was approved by the University Human Research Ethics Committee before it began.

Self-Regulatory Fatigue

The Self-Regulatory Fatigue Scale (SRF-S; Nes et al., 2013 ) was used to measure self-regulatory fatigue (the depletion of self-regulatory strength). The SRF-S consists of 16 items aimed at assessing participants’ self-regulatory fatigue. Sample items include, “I experience repeated unpleasant thoughts” and “I experience uncontrollable temper outbursts.” It employs a five-point Likert-type scale with responses ranging from 1 ( not at all true ) to five ( very true ); higher scores reflect chronic ego-depletion or a scarcity of self-regulatory resources. The SRF-S was shown to be a reliable and valid measurement in a Chinese sample ( Wang et al., 2015 ) and demonstrated acceptable reliability in this study (Cronbach’s alpha = 0.70).

Emotion Regulation

The Emotion Regulation Questionnaire (ERQ; Gross and John, 2003 ) was used to assess emotion regulation. The ERQ consists of 10 items that are assessed on a seven-point Likert-type scale to measures respondents’ tendency to regulate their emotions in two ways: (1) cognitive reappraisal and (2) expressive suppression. Example items include, “When I’m faced with a stressful situation, I make myself think about it in a way that helps me stay clam” (cognitive reappraisal) and “I control my emotions by not expressing them” (expressive suppression). Possible responses ranged from 1 ( strongly disagree ) to 7 ( strongly agree ). The Chinese version of the ERQ has been validated in a Chinese population ( Zhang et al., 2014 ). The cognitive reappraisal and expressive suppression subscales have demonstrated acceptable reliability in the present study (Cronbach’s alphas = 0.86, and 0.72, respectively).

Forgiveness

Forgiveness was assessed by the Trait Forgiveness Scale (TFS). The TFS consists of 10 items aimed at assessing participants’ self-appraisal of their proneness to forgive in interpersonal transgressions ( Berry et al., 2005 ). Sample items include “I can forgive a friend for almost anything” and “I am a forgiving person.” Respondents rate each item on a five-point Likert-type scale from 1 ( strongly disagree ) to five ( strongly agree ). This scale has been reported as being reliable and valid across various studies ( Berry et al., 2005 ). Two research assistants translated and back translated it into Chinese. The TFS demonstrated acceptable reliability in the present study (Cronbach’s alpha = 0.77).

A demographics questionnaire asking about the participants’ gender, age, marital status, religious beliefs, and academic major was also included in this study.

Descriptive statistics and zero-order correlations of study variables are shown in Table 1 . The results of the correlation analysis indicated that the tendency to forgive was negatively associated with self-regulatory fatigue and positively correlated with cognitive reappraisal. Furthermore, self-regulatory fatigue was negatively associated with cognitive reappraisal and positively associated with expressive suppression. As expressive suppression was not associated with forgiveness, it was removed from all subsequent analyses.

Descriptive statistics and correlations of study variables.

Correlation analyses were also conducted to examine the degree of associations between demographic variables (i.e., gender, age, marital status, religious beliefs, and academic major) and study variables. The results of Spearman’s rank correlation analyses showed that only religious beliefs were significantly associated with cognitive reappraisal and forgiveness. Therefore, the variable of religious beliefs (by using dummy coding) was statistically controlled for all subsequent analyses.

A hierarchical multiple regression analysis was performed to test the hypothesized mediation model. The religious beliefs variable was entered into the model in Step 1 to serve as a control. In Step 2, self-regulatory fatigue was added into the model as a predictor. In Step 3, cognitive reappraisal was entered into the model to test for a potential mediation effect. The results of the regression analysis indicated that religious beliefs (Christianity vs no religion) accounted for a significant amount of the variance in forgiveness (β = 0.13, p < 0.05). Self-regulatory fatigue accounted for a significant amount of the variance in forgiveness (β = −0.41, p < 0.001) and cognitive reappraisal (β = −0.33, p < 0.001). Cognitive reappraisal also accounted for a significant additional amount of variance in forgiveness (β = 0.17, p < 0.01) after controlling for self-regulatory fatigue. When cognitive reappraisal was included, the beta weight for self-regulatory fatigue decreased from −0.41 to −0.35 ( p < 0.001), which suggests a partial mediation model ( R 2 = 0.21, F (1,280) = 12.40, p < 0.001) (see Table 2 ).

Results of hierarchical multiple regression analysis on hypothesized model.

We further tested the significance of the indirect pathway from self-regulatory strength to forgiveness through the mediation of cognitive reappraisal using the PROCESS macro in SPSS. The bootstrapping procedure utilized 5,000 bootstrap samples and 95% bias-corrected confidence intervals (95% CIs). The exclusion of 0 from 95% CIs indicated a significant mediation effect ( Hayes, 2013 ). The results of the bootstrapping analyses indicated that self-regulatory fatigue had exerted a significant direct effect on forgiveness, c’ = −0.38, 95% CI [−0.50, −0.26] as well as a significant indirect effect on forgiveness through the mediator of cognitive reappraisal, ab = −0.07, 95% CI [−0.12, −0.02] (see Figure 1 ).

The regulatory model of forgiveness. The c and c ’ indicate the total and direct effects from self-regulatory fatigue to forgiveness.

Alternate models were tested to eliminate two other possibilities: (1) a forgiving tendency might reduce self-regulatory fatigue, which might make individuals more likely to engage in cognitive reappraisals and (2) people who are generally good at cognitive reappraisal might be more likely forgive, which might then provide more self-regulatory strength. However, the results of hierarchical multiple regression analyses indicated that the F values were decreased, F (1,280) = 9.77, p < 0.001 in alternate model 1, and the beta coefficients of the key predictor variable were diminished in alternate model 2, which suggests that the alternate models did not fit the data better than the proposed mediation model.

In this study, we sought to expand our understanding of the conditions of transforming the motives to forgive by examining whether self-regulatory strength (self-regulatory fatigue) and emotion regulation (cognitive reappraisal) would be associated with the tendency to forgive among Hong Kong Chinese college students. Moreover, this study sought to uncover the regulating processes of forgiveness by investigating the mediating role of emotion regulation (via cognitive appraisal) in the association between self-regulatory strength and forgiveness.

The results of this study revealed that self-regulatory strength depletion (self-regulatory fatigue) was negatively associated with the tendency to forgive. That is, participants who reported lower levels of self-regulatory fatigue demonstrated a higher tendency to forgive others. This is consistent with Luchies et al. (2011) finding that self-regulatory strength has a significant positive impacts on close relationships. This finding supports the strength model of self-regulation in understanding interpersonal outcomes, such as forgiveness. The results of this study therefore indicate that chronic self-regulatory fatigue inhibits individuals’ tendency to engage in the transformation of prosocial motivations (i.e., forgiveness).

We also found that emotion regulation was positively associated with forgiving tendencies. Specifically, participants who regulated their emotions via cognitive reappraisal tended to engage in forgiveness. Our findings align with previous studies that linked cognitive reappraisal to a variety of positive outcomes within the domain of interpersonal relationships, such as positive relationships with others, higher peer rated relationship closeness, and greater peer-rated likability ( Gross and John, 2003 ). Our findings may also point to the possibility that cognitive reappraisal may serve as an important self-regulatory process to transform the motivation of forgiveness.

Scientific research on the underlying processes between self-regulatory strength and forgiveness is lacking. In particular, whether emotion regulation plays a role in the relationship between self-regulatory fatigue and forgiveness remains unclear. The results of this study further elucidate the underpinnings of the transformation of prosocial motivation. Our findings indicate that cognitive reappraisal exerts additional effects beyond the effect of self-regulatory fatigue on forgiveness. More importantly, cognitive reappraisal mediate the negative relationship between self-regulatory fatigue and forgiveness. The findings of this study suggest the possibility that forgiveness may be an additive two-stage process through which individuals first inhibit destructive impulses by exercising their self-regulatory strength and then regulate their emotions via cognitive reappraisal. Both stages require separate exertions of self-regulation (self-regulatory fatigue and cognitive reappraisal). Self-regulatory depletion impairs the ability to engage in constructive cognitive processes, which in turn leads to lower forgiveness levels.

Interestingly, the results of this study showed that religious beliefs were positively associated with forgiveness. In particular, people who identified as Christian reported higher tendencies to forgive in comparison to those who had no religious affiliation. Forgiveness is one of the doctrines central to Christianity; therefore, it is possible that people who follow Christianity believed that they are supposed to forgive because they have been forgiven by God, and will thus be more prone to forgiving others. However, due to the unequal sample size of each religion, we need to be cautious in interpreting this result (see Davis et al., 2013 for a meta-analytic review on religion and forgiveness).

Implications of the Present Research

A possible implication of the fact that self-regulatory fatigue was negatively associated with individuals’ ability to forgive others is that impulse regulation may be something of a double-edged sword. On the one hand, impulse regulation allows for thriving relationship functioning in that inhibiting destructive impulses promotes optimal interpersonal interaction. On the other hand, overregulating impulses can be problematic because such regulation depletes individuals’ capacity to regulate future impulses. Thus, attempts at constant, perfect self-control are likely to result in self-regulatory depletion or self-regulatory fatigue.

However, indiscriminate impulse indulgence is unlikely to improve relationship outcomes. The present study, for example, suggests that individuals are better able to forgive depending on the extent to which they exert emotional impulse regulation through constructive cognitive processes. Such prorelationship transformation of motivation benefits healthier relationship functioning. Perhaps the best way to achieve a compromise between impulse indulgence and regulation is to learn to recognize cues that indicate depletion (e.g., physical and emotional exhaustion) and to build up self-regulatory strength through repeated exercises (e.g., self-control exercises). Prior theoretical analyses and empirical evidence suggested that self-regulatory strength can be enhanced through such exercises and that exerting self-regulation tends to strengthen and improve our self-regulatory strength, much like weightlifting tends to increase muscular strength ( Baumeister et al., 1994 ).

Emotion regulation can be used to downregulate negative emotions in the aftermath of an interpersonal offense. Emotion regulation involves complex cognitive processing. Cognitive emotion regulation refers to cognitive responses to emotional events that involve the attempt to alter individual emotional experiences, events and/or emotional types ( Liu et al., 2019 ). Specifically, cognitive reappraisal is an effective emotion regulation strategy in which individuals change their understanding of emotional events by giving such events new meaning ( McRae et al., 2012 ). Reframing potentially emotion-eliciting events (e.g., interpersonal transgressions) influences an individual’s willingness to forgive in an interpersonal context.

Limitations and Strengths of the Present Research

We noted several limitations in the present research. First, we examined the tendency to forgive only in college students in Hong Kong. Therefore, the findings of this study may not be applicable to other populations (e.g., married couples) and other cultures (e.g., other Asian cultures). To test the generalizability of our findings, future research should examine the effects of self-regulation processes on forgiveness in other populations and in other cultures.

The use of the self-report method constituted another limitation. The results of this study might be influenced by socially desirable response tendencies, acquiescence bias, and the retrospective reconstruction of prior events. It is also difficult to interpret the results based on the criticism of self-report methodology. Although we used reliable and valid measure to assess people’s general propensity to forgive, forgiveness is not a one-size-fits all process—it takes all shapes and forms, and the magnitude of the offense is different from situation to situation. Thus, individual differences in forgiveness are to be expected. Consequently, future research should be conducted to incorporate behavioral or physiological measures of forgiveness and self-regulation in the context of ongoing interpersonal relationships.

This study also adopted a cross-sectional design, which may prevent us from ruling out reverse causalities and testing the multiple-stage self-regulatory processes of forgiveness. It is plausible that individuals high in trait forgiveness tend to preserve self-regulatory strength by disengaging from costly rumination and facilitating constructive cognitive reappraisal. Alternatively, it is also likely that individuals who are generally good at cognitive reappraisal tend to forgive due to less ego-depletion and more self-regulatory strength. Future studies should test the mediation model proposed in this study with a longitudinal design or a laboratory experimental design. Finally, we focused on trait-level variables, which may be less sensitive to temporal shifts than state-level variables. Thus, our findings speak to one’s general tendencies or dispositional patterns.

Despite the above limitations, an important strength of the present research is our adoption of a new approach to understand the self-regulation processes of forgiveness. This study demonstrated that self-regulatory fatigue is negatively associated with forgiveness via its association with cognitive reappraisal. This regulatory model of forgiveness indicates that high self-regulatory fatigue hampers cognitive self-regulation processing (cognitive reappraisal), which leads to less accommodative tendencies toward others.

This study contributes to the existing literature by proposing a new regulatory model of forgiveness. This model facilitates the understanding of the regulatory processes of forgiveness in two ways: (1) direct pathways from both self-regulatory fatigue and cognitive reappraisal to forgiveness and (2) an indirect pathway from self-regulatory fatigue to forgiveness via cognitive reappraisal. This study furthers our understanding of how self-regulation processes influence the likelihood of forgiveness in an interpersonal context. Self-regulatory strength (i.e., self-regulatory fatigue) and emotion regulation (i.e., cognitive reappraisal) promote the transformation of prosocial motivations (i.e., forgiveness). The nuanced findings regarding the underlying mechanism of forgiveness from self-regulatory fatigue through cognitive reappraisal are more interesting. Consistent with our expectations, high self-regulatory fatigue weakens individuals’ ability to resist self-interested, instinctive reactions in favor of more personally costly, prorelationship responses (e.g., forgiveness) and this connection can be partly explained by their failure to engage in constructive cognitive processes.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by City University of Hong Kong. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

MH has done most of the conceptual thinking, theoretical model building, the data collection and the data analysis. DV has offered conceptual framework on self-regulation process. JY has advised on model testing and statistical analysis.

Conflict of Interest

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.

Funding. This work was supported by start up grant by City University of Hong Kong (7200532).

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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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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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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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• The Strength of Internet Ties
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Table of Contents

• Acknowledgments
• What Is the Internet Doing to Relationships?
• Social Networks in America
• Keeping in Contact with Core and Significant Ties
• Email and Network Size
• Information Is a Conduit to Help

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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The Great Replacement Theory: Linkage to The Possessive Investment in Whiteness

This piece originally published in The Community Psychologist (TCP) Summer 2022 Volume 55 Number 3. All TCP columns are available online, at  https://www.scra27.org/publications/tcp/

Written by Geraldine (Geri) Palmer, Edited by Sindhia Colburn

“To be a Negro in this country and to be relatively conscious is to be in a rage almost all the time.” – James Baldwin

Theories help us identify what is important in addition to allowing us to describe, predict and better understand phenomena. Theories are helpful tools, and in community psychology they play important roles in explaining behavior in the context of one’s environment, understanding the structure and function of community, guiding community prevention and intervention efforts, and more (Jimenez, Hoffman, & Grant, 2019). On the other side, theories have been and are used as powerful and dangerous weapons to motivate and justify murder and destruction. For example, one of the earliest conspiracy theories known in America points to a situation that occurred in 1826 in the tiny town of Batavia, New York. Citizens woke one day to find William Morgan, a member of the Batavia community kidnapped and murdered. Records offer that Morgan had threatened to expose the secret of the Freemasons by publishing their rituals. Shortly after this public display, Morgan was gone, never to be seen again. To most people, it was a no-brainer. Morgan’s disappearance and his public disclosure were linked. This connection, believed widely, led to protests against the Freemasons, ending with the Anti-Masonic Party. Yet, evidence of the linkage was never found (Staykov, 2022, para. 1).

Conspiracy theories have been used to frame the government, such as the “anti-vax” theory, while others have been used by politicians for their own purposes, such as the “birther theory” and the “voter fraud theory” which dates as far back as Andrew Jackson. A conspiracy theory I recently heard discussed in the news, relative to the Buffalo, New York murders which occurred on May 14, is the “replacement theory” or the “great replacement theory” (para. 6). According to the National Immigration Forum (2021), the replacement theory posits that when states welcome non-white immigrants through immigration policies, they are participating in a plot designed to undermine or “replace” the political power and culture of White people living in Western countries. This theory and iterations of it have been and continue to be pushed forward by anti-immigrant groups, those that uphold white supremacy, and others. Iterations include thoughts of migrant invasion that must be halted before White America is taken down, voter replacement, and other xenophobic conspiracies.

The consequences of the replacement theory have been violent and fraught with what I see as evil, and hate filled. Recent history shows the replacement theory was practiced in Charlottesville, Virginia in 2007 when white supremacist groups including the Ku Klux Klan took part in the Unite the Right rally and consistently shouted throughout the rally, “You will not replace us!” and “Jews will not replace us!” In 2018, 11 Pittsburgh congregants worshipping in a synagogue were killed, a result of the shooter believing that HIAS, a Jewish American nonprofit organization, was working to bring invaders to kill [White] people. Further, the replacement theory’s tenets can be found in the 2019 New Zealand massacre as 51 people were killed, where the title of the shooter’s manifesto was “The Great Replacement.” In that same year, 23 people were killed in a mass attack at a Walmart in El Paso, Texas where the shooter targeted Latino/a/x shoppers and his manifesto referenced “the great replacement” theory speaking to a Hispanic invasion of Texas.

Just over the weekend in May of 2022, the replacement theory was once again put into praxis as a lone shooter killed 10 Black/African Americans and wounded three, in what has been declared a racist mass shooting. It has been reported that Gendron, the shooter, posted on social media platforms and in his found manifesto, his hatred of the Black community, and his confession of being a fascist, White supremacist and an anti-Semite. Further, reports showed the shooter wrote about his beliefs that the dwindling size of the White population in America is a result of White people being replaced by non-Whites in a type of “White genocide.” This most recent example of the replacement theory in praxis has moved the theory from the fringes of someone’s wicked mind and into a grocery store in Buffalo, New York.

The Possessive Investment in Whiteness How do we begin to make sense out of these continuous attacks on Black and Brown bodies in America? George Lipsitz (1998; 2006) has one answer: disinvest in whiteness. Writing in his seminal book The Possessive Investment in Whiteness: How White People Profit from Identity Politics, Lipsitz argues that public policy and private prejudice work together to create a “possessive investment in whiteness” which is responsible for racialized hierarchies of our society (p. vii). Lipsitz uses the term possessive investment in whiteness both literally and figuratively and explains,

“Whiteness has a cash value…and White Americans are encouraged to invest in whiteness, to remain true to an identity that provides them with resources, power, and opportunity. However this whiteness has no biological or anthropological foundation.

Yet, it is a social fact, an identity created and continued with all-too-real consequences for the distribution of wealth, prestige, and opportunity (p. vii).”

Lipsitz (2006) further contended that this possessive investment in whiteness is not a simple matter of black and white—all racialized groups have experienced this in diverse ways and levels. Its origination has its roots in the racialized history of the U.S., but today’s whiteness currency is not simply the residue of colonialism and conquest, of enslavement and Jim Crow laws, of immigrant exclusion and “Indian” extermination, but is created, recreated and reproduced by policies that keep these societal scourges active and perpetual. Lipsitz believes racism changes over time, taking on different forms and serving different social purposes in each time period. Contemporary racism showed up at the grocery store resulting in the murder of 10 Black/African Americans and 3 wounded—all for the social purpose of stopping the so-called replacement of White culture and political power. The possessive investment in whiteness is real, and supremacy currency is being poured into it religiously.

Interestingly, Gendron, the shooter in Buffalo pled “not guilty” of murdering the 10 Black/African Americans in the store. I wonder, then, does he believe he is guilty of anything? Or does he believe he is simply conducting the social order of things as they should be and should not be imprisoned for his crime? Afterall, the possessive investment in whiteness is a deeply embedded ideology that has all types of rewards. Yet, failure to acknowledge conspiracy theories for what they are and raise our voices against this evil increasingly put BIPOC in harm’s way in our churches, synagogues, mosques, grocery stores, recreational facilities, homes, communities, and more.

What Can You Do? I typically try not to write anything where I am not adding proposed solutions to the problem. For those of you who do not identify as BIPOC, when you step up to help remember to:

(1) Decenter whiteness and move your equity and inclusion lens to the forefront. What does it mean to decenter whiteness? Whiteness averts, denies, adopts a “there’s only one way to do things” position, blames, defends its values, restricts and places negative sanctions on people who stand up and raise their voices. Changing lenses looks like placing whiteness at the margins. Understand that opposing white supremacy culture or the ideology of whiteness is different from opposing people who identify as White.

(2) Reclaim reality in public discourse and other spaces whenever possible. Help others to understand where conspiracy theories come from. Share articles, resources, write about, and speak out.

(3) Consider supporting BIPOC immigrants specifically, especially given the recent supreme court case decision (Patel v. Garland) that essentially aligns with the sentiments behind the replacement theory, that immigrants are allowed mercy as an exception and not a rule. It’s not just that BIPOC folx are being murdered at great rates such as in Buffalo due to this ideology, but that the legal system also is endorsing these notions that immigration should be halted or even reversed (the Patels were deported after losing their case). Supporting the replacement theory not only incites violence but helps gain legal traction within the system. This can be scary for BIPOC immigrants in particular, who can’t seek safety in communities for fear of violence or within institutions for fear of deportation.

The work of decentering whiteness and dismantling all sorts of conspiracy theories and ideologies that seek to destroy and harm is no easy task. Yet, we must continue to resist and push back against these practices and praxis. I have noticed in situations that once conspiracy theories are popularized, and often result in harm and destruction those that push these platforms and positions are often silent. Yet don’t let this silence confuse you. The possessive investment in whiteness is still profitable (Lipsitz, 1998; 2006).

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• Published: 22 November 2021

Psychologists update their beliefs about effect sizes after replication studies

• Alex D. McDiarmid   ORCID: orcid.org/0000-0002-7817-580X 1 ,
• Alexa M. Tullett   ORCID: orcid.org/0000-0001-8662-5885 1 ,
• Cassie M. Whitt 1 ,
• Simine Vazire   ORCID: orcid.org/0000-0002-3933-9752 2 ,
• Paul E. Smaldino   ORCID: orcid.org/0000-0002-7133-5620 3 &
• Jeremy E. Stephens 1  

Nature Human Behaviour volume  5 ,  pages 1663–1673 ( 2021 ) Cite this article

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Self-correction—a key feature distinguishing science from pseudoscience—requires that scientists update their beliefs in light of new evidence. However, people are often reluctant to change their beliefs. We examined belief updating in action by tracking research psychologists’ beliefs in psychological effects before and after the completion of four large-scale replication projects. We found that psychologists did update their beliefs; they updated as much as they predicted they would, but not as much as our Bayesian model suggests they should if they trust the results. We found no evidence that psychologists became more critical of replications when it would have preserved their pre-existing beliefs. We also found no evidence that personal investment or lack of expertise discouraged belief updating, but people higher on intellectual humility updated their beliefs slightly more. Overall, our results suggest that replication studies can contribute to self-correction within psychology, but psychologists may underweight their evidentiary value.

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Acknowledgements

We received funding from grant 1728332 from the National Science Foundation (A.M.T. and S.V.) The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to K. Finnigan and J. Sun for their assistance in recruiting study participants, J. Miranda for helping upload and organize data files on the Open Science Framework, C. Ebersole, R. Klein and D. Simons for providing updates on the timelines for publication of Many Labs 2 and Many Labs 5 and W. Hart and D. McDiarmid for feedback on statistical analyses.

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A.D.M., A.M.T. and S.V. designed the study. A.D.M., A.M.T. and C.M.W. developed stimuli and collected data. A.D.M. created the analytic plan and analysed data with contributions from A.M.T., P.E.S. and E.E.S. A.D.M. and A.M.T. wrote the manuscript, and all authors edited the manuscript and gave conceptual advice.

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McDiarmid, A.D., Tullett, A.M., Whitt, C.M. et al. Psychologists update their beliefs about effect sizes after replication studies. Nat Hum Behav 5 , 1663–1673 (2021). https://doi.org/10.1038/s41562-021-01220-7

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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

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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.

Replacement—A theory of stereotypy: A review

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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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Strongman, K.T. Replacement—A theory of stereotypy: A review. Current Psychology 3 , 72–83 (1984). https://doi.org/10.1007/BF02686525

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Accepted : 19 December 1983

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DOI : https://doi.org/10.1007/BF02686525

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Did Humans First Evolve in Africa?

Véronique PAGNIER/Wikimedia Commons/CC BY 3.0, 2.5, 2.0, 1,0

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The Out of Africa (OOA), or African replacement, hypothesis is a well-supported theory. It argues that every living human being is descended from a small group of Homo sapiens (abbreviated Hss) individuals in Africa, who then dispersed into the wider world, meeting and displacing earlier forms such as Neanderthals and Denisovans. Early major proponents of this theory were led by British paleontologist Chris Stringer in direct opposition to scholars supporting the multiregional hypothesis , who argued that Hss evolved several times from Homo erectus in several regions.

The Out of Africa theory was bolstered in the early 1990s by research on mitochondrial DNA studies by Allan Wilson and Rebecca Cann, which suggested that all humans ultimately descended from one female: Mitochondrial Eve. Today, the vast majority of scholars have accepted that human beings evolved in Africa and migrated outward, likely in multiple dispersals. However, recent evidence has shown that some sexual interaction between Hss and Denisovans and Neanderthals occurred, although at present their contribution to Homo sapiens DNA is considered fairly minor.

Early Human Archaeological Sites

Probably the most influential site for paleontologists' most recent change in understanding evolutionary processes was the 430,000-year-old Homo heidelbergensis site of Sima de los Huesos in Spain. At this site, a large community of hominins was found to encompass a wider range of skeletal morphology than was previously considered within one species. That has led to a reassessment of species in general. In essence, Sima de los Huesos allowed paleontologists to be able to identify Hss with less stringent expectations.

A few of the archaeological sites associated with early Hss remains in Africa include:

• Jebel Irhoud (Morocco). The oldest known Hss site in the world to date is Jebel Irhoud, in Morocco, where the skeletal remains of five archaic Homo sapiens have been found alongside Middle Stone Age tools. At 350,000-280,000 years old, the five hominids represent the best-dated evidence of an early "pre-modern" phase in Homo sapiens evolution. The human fossils at Irhoud include a partial skull and lower jaw. Although they retain some archaic features, such as an elongated and low braincase, they are thought to be more similar to Hss skulls found at Laetoli in Tanzania and Qafzeh in Israel. Stone tools at the site are from the Middle Stone Age, and the assemblage includes Levallois flakes, scrapers, and unifacial points. The animal bone at the site shows evidence of human modification, and charcoal indicating the likely controlled use of fire .
• Omo Kibish (Ethiopia) contained the partial skeleton of an Hss who died around 195,000 years ago, alongside Levallois flakes, blades, core-trimming elements, and pseudo-Levallois points.
• Bouri (Ethiopia) is located within the Middle Awash study area of East Africa and includes four archaeological and paleontological-bearing members dated between 2.5 million and 160,000 years ago. The Upper Herto Member (160,000 years BP) contained three hominin crania identified as Hss, associated with Middle Stone Age Acheulean transition tools, including hand axes , cleavers, scrapers, Levallois flake tools, cores, and blades. Although not considered Hss because of its age, Bouri's Herto Lower Member (260,000 years ago) contains later Acheulean artifacts, including finely-made bifaces and Levallois flakes. No hominid remains were found within the Lower Member, but it will likely be reevaluated given the results at Jebel Irhoud.

Leaving Africa

Scholars largely agree that our modern species ( Homo sapiens ) originated in East Africa by 195-160,000 years ago, although those dates are clearly undergoing revision today. The earliest known pathway out of Africa probably occurred during Marine Isotope Stage 5e , or between 130,000-115,000 years ago, following along the Nile Corridor and into the Levant, evidenced by Middle Paleolithic sites at Qazfeh and Skhul. That migration (sometimes confusingly called "Out of Africa 2" because it was more recently proposed than the original OOA theory but refers to an older migration) is generally regarded as a "failed dispersal" because only a handful of Homo sapiens sites have been identified as being this old outside of Africa. One still controversial site reported in early 2018 is Misliya Cave in Israel, said to contain an Hss maxilla associated with full-fledged Levallois technology and dated between 177,000-194,000 BP. Fossil evidence of any kind this old is rare and it may be too early to completely rule that out.

A later pulse from northern Africa, which was recognized at least 30 years ago, occurred from about 65,000-40,000 years ago [MIS 4 or early 3], through Arabia. That group, scholars believe, eventually led to the human colonization of Europe and Asia, and the eventual replacement of Neanderthals in Europe .

The fact that these two pulses occurred is largely undebated today. A third and increasingly convincing human migration is the southern dispersal hypothesis , which argues that an additional wave of colonization occurred between those two better-known pulses. Growing archaeological and genetic evidence supports this migration from southern Africa following the coasts eastward and into South Asia.

Denisovans, Neanderthals and Us

Over the past decade or so, evidence has been piling up that although pretty much all paleontologists agree that humans did evolve in Africa and move out from there. We did meet other human species — specifically Denisovans and Neanderthals — as we moved out into the world. It is possible that the later Hss interacted with the descendants of the earlier pulse as well. All living humans are still one species. However, it is now undeniable that we share different levels of the mixture of species which developed and died out in Eurasia. Those species are no longer with us except as tiny pieces of DNA.

The paleontological community is still somewhat divided on what that means to this ancient debate: John Hawks argues that "we are all multiregionalists now," but Chris Stringer recently disagreed by saying "we are all out-of-Africanists who accept some multi-regional contributions."

Three Theories

The three main theories concerning human dispersal were, up until recently:

• Multiregional Theory 
• Out of Africa Theory
• Southern Dispersal Route

But with all the evidence pouring in from around the world, paleoanthropologist Christopher Bae and colleagues suggest there are now four variations of the OOA hypothesis, ultimately incorporating elements of all three of the original ones:

• A single dispersal during MIS 5 (130,000–74,000 BP)
• Multiple dispersals beginning MIS 5
• A single dispersal during MIS 3 (60,000–24,000 BP)
• Multiple dispersals beginning MIS 3

Akhilesh, Kumar. "Early Middle Palaeolithic culture in India around 385–172 ka reframes Out of Africa models." Shanti Pappu, Haresh M. Rajapara, et al., Nature, 554, pages 97–101, February 1, 2018.

Árnason, Úlfur. "The Out of Africa hypothesis and the ancestry of recent humans: Cherchez la femme (et l'homme)" Gene, 585(1):9-12. doi: 10.1016/j.gene.2016.03.018, U.S. National Library of Medicine National Institutes of Health, July 1, 2016.

Bae, Christopher J. "On the Origin of Modern Humans: Asian Perspectives." Katerina Douka, Michael D. Petraglia, Vol. 358, Issue 6368, eaai9067, Science, December 8, 2017.

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Hershkovitz, Israel. "The earliest modern humans outside Africa." Gerhard W. Weber, Rolf Quam, et al., Vol. 359, Issue 6374, pp. 456-459, Science, January 26, 2018.

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Lamb, Henry F. "150,000-year palaeoclimate record from northern Ethiopia supports early, multiple dispersals of modern humans from Africa." C. Richard Bates, Charlotte L. Bryant, et al., Scientific Reports volume 8, Article number: 1077, Nature, 2018.

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Nicoll, Kathleen. "A revised chronology for Pleistocene paleolakes and Middle Stone Age – Middle Paleolithic cultural activity at Bîr Tirfawi – Bîr Sahara in the Egyptian Sahara." Quaternary International, Volume 463, Part A, ScienceDirect, January 2, 2018.

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