This chapter focuses on what’s novel in the perspective that the prediction error minimization (PEM) framework affords on the cognitive-scientific project of explaining intelligence by appeal to internal representations. It shows how truth-conditional and resemblance-based approaches to representation in generative models may be integrated. The PEM framework in cognitive science is an approach to cognition and perception centered on a simple idea: organisms represent the world by constantly predicting their own internal states. PEM theories often stress the hierarchical (...) structure of the generative models they posit. The novel explanatory power of the PEM account derives largely from the way in which pairs of generative and recognition models interact. “Predictive coding” refers to an encoding strategy in which predicted portions of an input signal are subtracted from the actual signal received, so that only the difference between the two is passed as output to the next stage of information processing. (shrink)

This chapter seeks to recover an approach to consciousness from a general theory of brain function, namely the prediction error minimization theory. The way this theory applies to mental and developmental disorder demonstrates its relevance to consciousness. The resulting view is discussed in relation to a contemporary theory of consciousness, namely the idea that conscious perception depends on Bayesian metacognition; this theory is also supported by considerations of psychopathology. This Bayesian theory is first disconnected from the higher-order thought (...) theory, and then, via a prediction error conception of action, connected instead to the global neuronal workspace theory. Considerations of mental and developmental disorder therefore show that a very general theory of brain function is relevant to explaining the structure of conscious perception; furthermore, this theory can subsume and unify two contemporary approaches to consciousness, in a move that seeks to elucidate the fundamental mechanism for selection of representational content into consciousness. (shrink)

1. Introduction 2. Reward-Guided Decision Making 3. Content in the Model 4. How to Deflate a Metarepresentational Reading Proust and Carruthers on metacognitive feelings 5. A Deflationary Treatment of RPEs? 5.1 Dispensing with prediction errors 5.2 What is use of the RPE focused on? 5.3 Alternative explanations—worldly correlates 5.4 Contrast cases 6. Conclusion Appendix: Temporal Difference Learning Algorithms.

The two-factor theory (Davies, Coltheart, Langdon & Breen 2001; Coltheart 2007; Coltheart, Menzies & Sutton 2010) is an influential account of delusion formation. According to the theory, there are two distinct factors that are causally responsible for delusion formation. The first factor is supposed to explain the content of the delusion, while the second factor is supposed to explain why the delusion is adopted and maintained. Recently, another remarkable account of delusion formation has been proposed, in which the notion of (...) “prediction error” plays the central role (Fletcher & Frith 2009; Corlett, Krystal, Taylor & Fletcher 2009; Corlett, Taylor, Wang, Fletcher & Krystal 2010). According to this account, the prediction-error theory, delusions are formed in response to aberrant prediction-error signals, those signals that indicate a mismatch between expectation and actual experience. -/- In this chapter, we examine the relationship between the two-factor theory and the prediction-error theory in some detail. Our view is that the prediction-error theory does not have to be understood as a rival to the two-factor theory. We do not deny that there are some important differences between them. However, those differences are not as significant as they have been presented in the literature. Moreover, the core ideas of the prediction-error theory may be incorporated into the two- factor framework. For instance, the aberrant prediction-error signal that is posited by prediction-error theorists can be (or underlie) the first factor contributing to the formation of some delusions, and help explain the content of those delusions. Alternatively, the aberrant prediction-error signal can be (or underlie) the second factor, and help explain why the delusion is adopted and maintained. (shrink)

Predictive processing is an increasingly popular approach to cognition, perception and action. It says that the brain is essentially a hierarchical prediction machine. It is typically construed in a representationalist and inferentialist fashion so that the brain makes contentful inferences on the basis of representational models. In this paper, I argue that the predictive processing framework is inconsistent with this epistemic position. In particular, I argue that the combination of hierarchical modeling, contentful inferentialism and representationalism entail an internal inconsistency. (...) Specifically, for a particular set of states, there will be both a representation requirement and not. Yet a system cannot both be required to represent a certain set of states and not be required to represent those states. Due to this contradiction, I propose to reject the standard view. I suggest that predictive processing is best interpreted in terms of reliable covariation instead, entailing an instrumentalist approach to the statistical machinery. (shrink)

The main aim of this article is to give an assessment of prediction error minimization as a unifying theoretical framework for the study of social cognition. We show how this framework can be used to synthesize and systematically relate existing data from social cognition research, and explain how it introduces new constraints for further research. We discuss PEM in relation to other theoretical frameworks of social cognition, and identify the main challenges that this approach to social cognition will (...) need to address. (shrink)

In his paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization, which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of (...) embodied cognition. Focusing on this aspect of Hohwy’s argument, we first outline the key components of the PEM framework such as the “evidentiary boundary,” before looking at why this leads Hohwy to reject certain theses of embodied cognition. We will argue that although Hohwy may be correct to reject specific theses of embodied cognition, others are in fact implied by the PEM framework and may contribute to its development. We present the metaphor of the “body as a laboratory” in order to highlight wha... (shrink)

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. (...) More generally, we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. (shrink)

In a recent paper, Jakob Hohwy argues that the emerging predictive processing perspective on cognition requires us to explain cognitive functioning in purely internalistic and neurocentric terms. The purpose of the present paper is to challenge the view that PP entails a wholesale rejection of positions that are interested in the embodied, embedded, extended, or enactive dimensions of cognitive processes. I will argue that Hohwy’s argument from analogy, which forces an evidentiary boundary into the picture, lacks the argumentative resources to (...) make a convincing case for the conceptual necessity to interpret PP in solely internalistic terms. For this reason, I will reconsider the postulation and explanatory role of the evidentiary boundary. I will arrive at an account of prediction error minimization and its foundation on the free energy principle that is fully consistent with approaches to cognition that emphasize the embodied and interactive properties of cognitive processes. This gives rise to the suggestion that explanatory pluralism about the application of PP is to be preferred over Hohwy’s explanatory monism that follows from his internalistic and neurocentric view of predictive cognitive systems. (shrink)

I will put forward a short, simple argument for a pair of realist claims: metaphysical realism and what I will refer to as epistemological realism. The argument will rely upon nothing more than our apparent memories. Having presented the argument, I will go on to consider possible objections to it, of which there will be a number but none of which will do more than complicate the matter. The argument I present borrows from Peirce’s view that the world’s capacity to (...) surprise us plays a vital role in guiding us toward truth, and from the argument to the best explanation. However, it combines the two views in a way I have not seen done previously. (shrink)

In the past two decades, reinforcement learning has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating (...) the salience or novelty of a stimulus, and many other interpretations. Anterior cingulate cortex has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex. (shrink)

According to the reward-prediction error hypothesis (RPEH) of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the (...) formulation and subsequent success of the RPEH. Second, in light of this historical account, it explains in which sense the RPEH is explanatory and under which conditions it can be justifiably deemed deeper than the incentive salience hypothesis of dopamine, which is arguably the most prominent contemporary alternative to the RPEH. (shrink)

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences (...) we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. (shrink)

The proposal that probabilistic inference and unconscious hypothesis testing are central to information processing in the brain has been steadily gaining ground in cognitive neuroscience and associated fields. One popular version of this proposal is the new theoretical framework of predictive processing or prediction error minimization, which couples unconscious hypothesis testing with the idea of ‘active inference’ and claims to offer a unified account of perception and action. Here we will consider one outstanding issue that still looms large (...) at the core of the PEM framework: the lack of a clear criterion for distinguishing conscious states from unconscious ones. In order to fulfill the promise of becoming a unifying framework for describing and modeling cognition, PEM needs to be able to differentiate between conscious and unconscious mental states or processes. We will argue that one currently popular view, that the contents of conscious experience are determined by the ‘winning hypothesis’, falls short of fully accounting for conscious experience. It ignores the possibility that some states of a system can control that system’s behavior even though they are apparently not conscious. What follows from this is that the ‘winning hypothesis’ view does not provide a complete account of the difference between conscious and unconscious states in the probabilistic brain. We show how this problem for the received view can be resolved by augmenting PEM with Daniel Dennett’s multiple drafts model of consciousness. This move is warranted by the similar roles that attention and internal competition play in both the PEM framework and the multiple drafts model. (shrink)

According to the reward-prediction error hypothesis of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the formulation (...) and subsequent success of the RPEH. Second, in light of this historical account, it explains in which sense the RPEH is explanatory and under which conditions it can be justifiably deemed deeper than the incentive salience hypothesis of dopamine, which is arguably the most prominent contemporary alternative to the RPEH. (shrink)

Among all their sensations, agents need to distinguish between those caused by themselves and those caused by external causes. The ability to infer agency is particularly challenging under conditions of uncertainty. Within the predictive processing framework, this should happen through active control of prediction error that closes the action-perception loop. Here we use a novel, temporally-sensitive, behavioural proxy for prediction error to show that it is minimised most quickly when volatility is high and when participants report (...) agency, regardless of the accuracy of the judgement. We demonstrate broad effects of uncertainty on accuracy of agency judgements, movement, policy selection, and hypothesis switching. Measuring autism traits, we find differences in policy selection, sensitivity to uncertainty and hypothesis switching despite no difference in overall accuracy. (shrink)

For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index. Empirical analysis suggests that the prediction of returns from (...) patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE, for the period 2010–2020. (shrink)

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist and rational paradigms in (...) causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment. (shrink)

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of (...) cortical processing to adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

Adaptive behavior critically depends on performance monitoring, the ability to monitor action outcomes and the need to adapt behavior. PM-related brain activity has been linked to guiding decisions about whether action adaptation is warranted. The present study examined whether PM-related brain activity in a flanker task, as measured by electroencephalography, was associated with adaptive behavior in daily life. Specifically, we were interested in the employment of self-control, operationalized as self-control failures, and measured using ecological momentary assessment. Analyses were conducted using (...) an adaptive elastic net regression to predict SCFs from EEG in a sample of 131 participants. The model was fit using within-subject averaged response-locked EEG activity at each electrode and time point within an epoch surrounding the response. We found that higher amplitudes of the error-related negativity were related to fewer SCFs. This suggests that lower error-related activity may relate to lower recruitment of interventive self-control in daily life. Altered cognitive control processes, like PM, have been proposed as underlying mechanisms for various mental disorders. Understanding how alterations in PM relate to regulatory control might therefore aid in delineating how these alterations contribute to different psychopathologies. (shrink)

The prediction error minimization framework denotes a family of views that aim at providing a unified theory of perception, cognition, and action. In this paper, I discuss some of the theoretical limitations of PEM. It appears that PEM cannot provide a satisfactory explanation of motivated reasoning, as instantiated in phenomena such as self-deception, because its cognitive ontology does not have a separate category for motivational states such as desires. However, it might be thought that this objection confuses levels (...) of explanation. Self-deception is a personal level phenomenon, while PEM offers subpersonal explanations of psychological abilities. Thus, the paper examines how subpersonal explanations couched in the PEM framework can be thought of as related to personal level explanations underlying self-deception. In this regard, three views on the relation between personal and subpersonal explanations are investigated: the autonomist, the functionalist, and the co-evolutionary perspective. I argue that, depending on which view of the relation between the personal and subpersonal is adopted, the PEM paradigm faces a dilemma: either its explanatory ambitions should be reduced to the subpersonal domain, or it cannot provide a satisfactory account of motivated reasoning as instantiated in self-deception. (shrink)

A postcompletion error is a type of procedural error that occurs after the main goal of a task has been accomplished. There is a strong theoretical foundation accounting for postcompletion errors (Altmann & Trafton, 2002; Byrne & Bovair, 1997). This theoretical foundation has been leveraged to develop a logistic regression model of postcompletion errors based on reaction time and eye movement measures (Ratwani, McCurry, & Trafton, 2008). This study further develops and extends this predictive model by (a) validating (...) the model and the general set of predictors on a new task to test the robustness of the model, and (b) determining which specific theoretical components are most important to postcompletion error prediction. (shrink)

Prediction-based accounts of language acquisition have the potential to explain several different effects in child language acquisition and adult language processing. However, evidence regarding the developmental predictions of such accounts is mixed. Here, we consider several predictions of these accounts in two large-scale developmental studies of syntactic priming of the English dative alternation. Study 1 was a cross-sectional study (N = 140) of children aged 3−9 years, in which we found strong evidence of abstract priming and the lexical boost, (...) but little evidence that either effect was moderated by age. We found weak evidence for a prime surprisal effect; however, exploratory analyses revealed a protracted developmental trajectory for verb-structure biases, providing an explanation as for why prime surprisal effects are more elusive in developmental populations. In a longitudinal study (N = 102) of children in tightly controlled age bands at 42, 48, and 54 months, we found priming effects emerged on trials with verb overlap early but did not observe clear evidence of priming on trials without verb overlap until 54 months. There was no evidence of a prime surprisal effect at any time point and none of the effects were moderated by age. The results relating to the emergence of the abstract priming and lexical boost effects are consistent with prediction-based models, while the absence of age-related effects appears to reflect the structure-specific challenges the dative presents to English-acquiring children. Overall, our complex pattern of findings demonstrates the value of developmental data sets in testing psycholinguistic theory. (shrink)