scholarly journals The Effect of Uncertainty on Prediction Error in the Action-Perception Loop

2020 ◽  
Author(s):  
Kelsey Perrykkad ◽  
Rebecca P. Lawson ◽  
Sharna Jamadar ◽  
Jakob Hohwy
Keyword(s):  
Active Control ◽  
Prediction Error ◽  
Predictive Processing ◽  
Action Perception ◽  
Autism Traits ◽  
Policy Selection ◽  
External Causes ◽  
Human Participants ◽  
Processing Framework

AbstractAmong 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 variability is low, but also when volatility is high. Further, when human participants report agency, they show steeper prediction error minimisation. 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.

The Embodiment of Concepts

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.

Useful misrepresentation: perception as embodied proactive inference

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.

scholarly journals Maximizing entropy of the Predictive Processing framework

2018 ◽  
Author(s):  
Maria Otworowska ◽  
Iris van Rooij ◽  
Johan Kwisthout
Keyword(s):  
Computer Simulations ◽  
Environmental Conditions ◽  
Prediction Error ◽  
Living Systems ◽  
Predictive Processing ◽  
The Core ◽  
Cognitive Success ◽  
Entropy Minimization ◽  
Processing Framework

The Predictive Processing (PP) framework offers a unifying view on the existence and working of all living systems. The core premise of PP states that as long as agents minimize prediction error, and consequently entropy, they are successful. Current developments and advances in PP indicate that the interaction between agents and their environments is an important component of entropy minimization. In this paper, we explore by means of computer simulations, the interaction between PP-agents and their environments under different conditions. We argue the need to redefine the notion of success in PP in terms of entropy, behavioral and cognitive success, as we show that the environmental conditions that lead to entropy success, are different from conditions that lead to behavioral or cognitive success. Furthermore, we show that being equipped in and applying the mechanisms to minimize prediction error, do not in practice guarantee that the agents will be successful in any sense (entropy, cognitive or behavioral).

scholarly journals Predictive Processing in Cognitive Robotics: A Review

2021 ◽  
Vol 33 (5) ◽  
pp. 1402-1432
Author(s):  
Alejandra Ciria ◽  
Guido Schillaci ◽  
Giovanni Pezzulo ◽  
Verena V. Hafner ◽  
Bruno Lara
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Cognitive Robotics ◽  
Predictive Processing ◽  
Limited Attention ◽  
Active Inference ◽  
Perceptual Inference ◽  
Energy Principle ◽  
Clear Cut ◽  
Processing Framework

Abstract Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.

scholarly journals A Predictive Processing Account of Bottom-Up Visual Saliency Using Cross-Predicting Autoencoders

2018 ◽  
Author(s):  
Beren Millidge ◽  
Richard Shillcock
Keyword(s):  
Prediction Error ◽  
Visual Saliency ◽  
Generative Models ◽  
Predictive Processing ◽  
Bottom Up ◽  
Low Level ◽  
Sense Data ◽  
Saliency Maps ◽  
Human Participants ◽  
The Brain

We propose a novel predictive processing account of bottom-up visual saliency in which salience is simply the low-level prediction error between the sense-data and the predictions produced by the generative models in the brain. We test this with modelling in which we use cross-predicting deep autoencoders to create salience maps in an entirely unsupervised way. The resulting maps closely mimic experimentally derived human saliency maps and also spontaneously learn a centre bias, a robust viewing behaviour seen in human participants.

scholarly journals Why use predictive processing to explain psychopathology? The case of anorexia nervosa

2019 ◽  
Author(s):  
Stephen Gadsby ◽  
Jakob Hohwy
Keyword(s):  
Anorexia Nervosa ◽  
Mental Disorder ◽  
Statistical Learning ◽  
Prediction Error ◽  
Predictive Processing ◽  
The Core ◽  
Prediction Error Minimization ◽  
Core Idea ◽  
Perceptual Reasoning ◽  
Processing Framework

Predictive processing accounts are increasingly called upon to explain mental disorder. They seem to provide an attractive explanatory framework because the core idea of prediction error minimization can be applied to simultaneously account for several perceptual, attentional and reasoning deficits often implicated in mental disorder. However, it can be unclear how much is gained by such accounts: the proffered explanations can appear to have several weaknesses such as being too liberal, too shallow, or too wedded to formal notions of statistical learning. Here, we taxonomise the relatively unrecognised variety of explanatory tools under the framework and discuss how they can be employed to provide substantial explanations. We then apply the framework to anorexia nervosa, an eating disorder that is characterised by a complex set of perceptual, reasoning and decision-making problems. We conclude that the predictive processing framework is a valuable type of explanation for psychopathology.

scholarly journals The effect of uncertainty on prediction error in the action perception loop

Cognition ◽  
2021 ◽  
Vol 210 ◽  
pp. 104598
Author(s):  
Kelsey Perrykkad ◽  
Rebecca P. Lawson ◽  
Sharna Jamadar ◽  
Jakob Hohwy
Keyword(s):  
Prediction Error ◽  
Action Perception

Priors and Prejudice: How prior knowledge shapes intergroup perception

2021 ◽  
Author(s):  
Hugh McGovern ◽  
Marte Otten
Keyword(s):  
Social Psychology ◽  
Prior Knowledge ◽  
Cognitive Processes ◽  
Social Neuroscience ◽  
Intergroup Bias ◽  
Predictive Processing ◽  
Future Research ◽  
Intergroup Perception ◽  
The Brain ◽  
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.

scholarly journals Toward the unity of pathological and exertional fatigue: A predictive processing model

2021 ◽  
Author(s):  
A. Greenhouse-Tucknott ◽  
J. B. Butterworth ◽  
J. G. Wrightson ◽  
N. J. Smeeton ◽  
H. D. Critchley ◽  
...  
Keyword(s):  
Prediction Error ◽  
Conscious Experience ◽  
Physical Exertion ◽  
Generative Models ◽  
The Body ◽  
Predictive Processing ◽  
Research Fields ◽  
Common Foundation ◽  
Health And Disease ◽  
Sensory Evidence

AbstractFatigue is a common experience in both health and disease. Yet, pathological (i.e., prolonged or chronic) and transient (i.e., exertional) fatigue symptoms are traditionally considered distinct, compounding a separation between interested research fields within the study of fatigue. Within the clinical neurosciences, nascent frameworks position pathological fatigue as a product of inference derived through hierarchical predictive processing. The metacognitive theory of dyshomeostasis (Stephan et al., 2016) states that pathological fatigue emerges from the metacognitive mechanism in which the detection of persistent mismatches between prior interoceptive predictions and ascending sensory evidence (i.e., prediction error) signals low evidence for internal generative models, which undermine an agent’s feeling of mastery over the body and is thus experienced phenomenologically as fatigue. Although acute, transient subjective symptoms of exertional fatigue have also been associated with increasing interoceptive prediction error, the dynamic computations that underlie its development have not been clearly defined. Here, drawing on the metacognitive theory of dyshomeostasis, we extend this account to offer an explicit description of the development of fatigue during extended periods of (physical) exertion. Accordingly, it is proposed that a loss of certainty or confidence in control predictions in response to persistent detection of prediction error features as a common foundation for the conscious experience of both pathological and nonpathological fatigue.

scholarly journals Content-based Dissociation of Hippocampal Involvement in Prediction

2020 ◽  
Vol 32 (3) ◽  
pp. 527-545 ◽  
Author(s):  
Peter Kok ◽  
Lindsay I. Rait ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Brain Activity ◽  
Predictive Processing ◽  
Auditory Cues ◽  
Domain Specific ◽  
Visual Objects ◽  
Complex Shapes ◽  
Different Types ◽  
Human Participants ◽  
Abstract Shapes

Recent work suggests that a key function of the hippocampus is to predict the future. This is thought to depend on its ability to bind inputs over time and space and to retrieve upcoming or missing inputs based on partial cues. In line with this, previous research has revealed prediction-related signals in the hippocampus for complex visual objects, such as fractals and abstract shapes. Implicit in such accounts is that these computations in the hippocampus reflect domain-general processes that apply across different types and modalities of stimuli. An alternative is that the hippocampus plays a more domain-specific role in predictive processing, with the type of stimuli being predicted determining its involvement. To investigate this, we compared hippocampal responses to auditory cues predicting abstract shapes (Experiment 1) versus oriented gratings (Experiment 2). We measured brain activity in male and female human participants using high-resolution fMRI, in combination with inverted encoding models to reconstruct shape and orientation information. Our results revealed that expectations about shape and orientation evoked distinct representations in the hippocampus. For complex shapes, the hippocampus represented which shape was expected, potentially serving as a source of top–down predictions. In contrast, for simple gratings, the hippocampus represented only unexpected orientations, more reminiscent of a prediction error. We discuss several potential explanations for this content-based dissociation in hippocampal function, concluding that the computational role of the hippocampus in predictive processing may depend on the nature and complexity of stimuli.

Sign in / Sign up

Export Citation Format

Share Document