Understanding the development of face and emotion processing under a predictive processing framework.

Bayesian processing has become a popular framework by which to understand cognitive processes. However, relatively little has been done to understand how Bayesian processing in the brain can be applied to understanding intergroup cognition. We assess how categorization and evaluation processes unfold based on priors about the ethnic outgroup being perceived. We then consider how the precision of prior knowledge about groups differentially influence perception depending on how the information about that group was learned affects the way in which it is recalled. Finally, we evaluate the mechanisms of how humans learn information about other ethnic groups and assess how the method of learning influences future intergroup perception. We suggest that a predictive processing framework for assessing prejudice could help accounting for seemingly disparate findings on intergroup bias from social neuroscience, social psychology, and evolutionary psychology. Such an integration has important implications for future research on prejudice at the interpersonal, intergroup, and societal levels.

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The Embodiment of Concepts

The Oxford Handbook of 4E Cognition ◽

10.1093/oxfordhb/9780198735410.013.34 ◽

2018 ◽

pp. 640-660

Author(s):

Michiel Van Elk ◽

Harold Bekkering

Keyword(s):

Prediction Error ◽

Predictive Processing ◽

Prediction Errors ◽

Conceptual Representation ◽

Recurrent Processing ◽

Different Dimensions ◽

Prior Models ◽

Processing Framework

We characterize theories of conceptual representation as embodied, disembodied, or hybrid according to their stance on a number of different dimensions: the nature of concepts, the relation between language and concepts, the function of concepts, the acquisition of concepts, the representation of concepts, and the role of context. We propose to extend an embodied view of concepts, by taking into account the importance of multimodal associations and predictive processing. We argue that concepts are dynamically acquired and updated, based on recurrent processing of prediction error signals in a hierarchically structured network. Concepts are thus used as prior models to generate multimodal expectations, thereby reducing surprise and enabling greater precision in the perception of exemplars. This view places embodied theories of concepts in a novel predictive processing framework, by highlighting the importance of concepts for prediction, learning and shaping categories on the basis of prediction errors.

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Making Sense of the World: Infant Learning From a Predictive Processing Perspective

Perspectives on Psychological Science ◽

10.1177/1745691619895071 ◽

2020 ◽

Vol 15 (3) ◽

pp. 562-571 ◽

Cited By ~ 6

Author(s):

Moritz Köster ◽

Ezgi Kayhan ◽

Miriam Langeloh ◽

Stefanie Hoehl

Keyword(s):

Social Environment ◽

Infant Development ◽

Predictive Processing ◽

Human Infants ◽

Making Sense ◽

Working Principle ◽

Learning Principles ◽

Theory Research ◽

Infant Learning ◽

Processing Framework

For human infants, the first years after birth are a period of intense exploration—getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one’s own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants’ motor and proprioceptive learning, and infants’ basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants’ early learning processes in theory, research, and application.

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Explanation Within Arm’s Reach: A Predictive Processing Framework for Single Arm Use in Octopuses

Erkenntnis ◽

10.1007/s10670-021-00424-7 ◽

2021 ◽

Author(s):

Sidney Carls-Diamante

Keyword(s):

Nervous System ◽

Motor Control ◽

Spatial Representation ◽

Motor Task ◽

The Body ◽

Predictive Processing ◽

Motor Tasks ◽

Functional Characteristics ◽

Somatotopic Map ◽

Processing Framework

AbstractOctopuses are highly intelligent animals with vertebrate-like cognitive and behavioural repertoires. Despite these similarities, vertebrate-based models of cognition and behaviour cannot always be successfully applied to octopuses, due to the structural and functional characteristics that have evolved in their nervous system in response to the unique challenges posed by octopus morphology. For instance, the octopus brain does not support a somatotopic or point-for-point spatial map of the body—an important feature of vertebrate nervous systems. Thus, while octopuses are capable of motor tasks whose vertebrate counterparts require detailed interoceptive monitoring, these movements may not be explainable using motor control frameworks premised on internal spatial representation. One such motor task is the extension of a single arm. The ability of octopuses to select and use a single arm without the guidance of a somatotopic map has been regarded as a motor control puzzle. In an attempt at a solution, this paper develops a predictive processing account of single-arm extension in octopuses.

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Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

10.1101/365361 ◽

2018 ◽

Author(s):

E. Kayhan ◽

L. Heil ◽

J. Kwisthout ◽

I. van Rooij ◽

S. Hunnius ◽

...

Keyword(s):

Young Children ◽

Bayesian Network ◽

Computational Model ◽

Predictive Models ◽

Predictive Processing ◽

Prediction Errors ◽

Model Based ◽

Pupillary Responses ◽

Causal Bayesian Network ◽

Processing Framework

AbstractFrom early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent’s preferences from systematic violations of random sampling. We investigated how young children build and update models of an agent’s sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others’ actions. We measured pupillary responses as a behavioral marker of ‘prediction errors’ (i.e., the perceived mismatch between what one’s model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.These findings shed light on the mechanisms behind toddlers’ inferences about agent-caused events. To our knowledge, this is the first study in which young children’s pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons’ actions.HighlightsWe present three formalized hypotheses on how young children generate predictive models of others’ sampling actions.We measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.Results showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others’ actions.A computational model based on the causal Bayesian network formalization of predictive processing can explain this process.

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Predictive processing as a systematic basis for identifying the neural correlates of consciousness

Philosophy and the Mind Sciences ◽

10.33735/phimisci.2020.ii.64 ◽

2020 ◽

Vol 1 (II) ◽

Author(s):

Jakob Hohwy ◽

Anil Seth

Keyword(s):

Brain Function ◽

Neural Substrates ◽

Neural Correlates ◽

Predictive Processing ◽

Promising Candidate ◽

Neural Correlates Of Consciousness ◽

Significant Potential ◽

Strong Potential ◽

Processing Framework

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

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Useful misrepresentation: perception as embodied proactive inference

10.31234/osf.io/rz4w7 ◽

2022 ◽

Author(s):

Joshua Martin

Keyword(s):

Prediction Error ◽

Predictive Processing ◽

Predictive Information ◽

Objective Truth ◽

Embodied Action ◽

Processing Framework

According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate ‘useful’ inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain’s attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology.

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The Regularity Account of Phenomenal Unity

Experienced Wholeness ◽

10.7551/mitpress/9780262036993.003.0009 ◽

2018 ◽

Author(s):

Wanja Wiese

Keyword(s):

Conscious Experience ◽

Predictive Processing ◽

Formal Description ◽

Rigorous Formulation ◽

Formal Concepts ◽

The Brain ◽

Processing Framework ◽

Phenomenal Unity

This chapter presents the regularity account of phenomenal unity (RPU). The basic idea of RPU is that when the brain tracks a regularity that is predictive of different features (or of different objects or events), there will be an experienced connection between those features (or the respective objects or events). We can then say that the regularity connects those features (or objects or events). According to RPU, unity comes in degrees, and in ordinary conscious experience we find a hierarchy of experienced wholes. This chapter provides a preliminary taxonomy of experienced wholes, with many examples. Drawing on formal concepts of the predictive processing framework, a formal description of possible computational underpinnings of experienced wholeness is given. Finally, a rigorous formulation of the mélange model (first proposed in chapter 4) is provided.

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Introduction

Andy Clark and His Critics ◽

10.1093/oso/9780190662813.003.0001 ◽

2019 ◽

pp. 1-6

Author(s):

Matteo Colombo ◽

Liz Irvine ◽

Mog Stapleton

Keyword(s):

Distributed Processing ◽

Extended Mind ◽

Predictive Processing ◽

Andy Clark ◽

Cognitive Scientist ◽

Mind And Brain ◽

Opening Up ◽

Intense Debate ◽

Processing Framework

Andy Clark is a leading philosopher and cognitive scientist. His work has been wide-ranging and inspiring. The extended mind hypothesis, the power of parallel distributed processing, the role of language in opening up novel paths for thinking, the flexible interface between biological minds and artificial technologies, the significance of representation in explanations of intelligent behaviour, the promise of the predictive processing framework to unify the cognitive sciences: these are just some of the ideas illuminated by Clark’s work that have sparked intense debate across the sciences of mind and brain. This introduction puts into focus some of the major motifs running through Clark’s work and outlines the content and structure of the volume.

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