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 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).

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.

Self-Control, Agency, and the Placebo Brain Stimulation

2020 ◽  
pp. 189-202
Author(s):  
Davide Rigoni ◽  
Naomi Vanlessen ◽  
Rossella Guerini ◽  
Mario De Caro ◽  
Marcel Brass
Keyword(s):  
Brain Stimulation ◽  
Prediction Error ◽  
Metacognitive Knowledge ◽  
Self Control ◽  
Control Beliefs ◽  
Control Performance ◽  
The Core ◽  
Actual Outcome ◽  
Core Idea ◽  
The Relationship

This chapter focuses on the relationship between control beliefs and self-control. After providing an overview of the research showing how control beliefs affect self-control performance, the authors present a novel experimental procedure based on a placebo brain stimulation that aims at altering people’s belief about their own self-control. They then describe a heuristic framework that accounts for belief-related changes in self-control performance. The core idea is that beliefs should be conceptualized as metacognitive knowledge about the self and that such metacognitive knowledge is used to predict the success of self-control behavior. When people form the expectation that they can exert self-control but experience failure, they perceive a discrepancy between their expectation and the actual outcome. Under specific circumstances, the perception of such discrepancy or prediction error will motivate people to exert more effort to match their expectation, which will lead to increased self-control.

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.

Experienced Wholeness

2018 ◽  
Author(s):  
Wanja Wiese
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Second Step ◽  
Error Minimization ◽  
Gestalt Theory ◽  
Bodily Experiences ◽  
Prediction Error Minimization ◽  
Hierarchical Bayesian Inference ◽  
The Brain ◽  
Phenomenal Unity

The unity of the experienced world and the experienced self have puzzled humanity for centuries. How can we understand this and related types of phenomenal (i.e., experienced) unity? This book develops an interdisciplinary account of phenomenal unity. It focuses on examples of experienced wholes such as perceived objects (chairs and tables, but also groups of objects), bodily experiences, successions of events, and the attentional structure of consciousness. As a first step, the book investigates how the unity of consciousness can be characterized phenomenologically: what is it like to experience wholes, what is the experiential contribution of phenomenal unity? This raises conceptual and empirical questions. In addressing these questions, connections are drawn to phenomenological accounts and research on Gestalt theory. As a second step, the book suggests how phenomenal unity can be analyzed computationally, by drawing on concepts and ideas of the framework of predictive processing. The result is a conceptual framework, as well as an interdisciplinary account of phenomenal unity: the regularity account of phenomenal unity. According to this account, experienced wholes correspond to a hierarchy of connecting regularities. The brain tracks these regularities by hierarchical prediction error minimization, which approximates hierarchical Bayesian inference.

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.

Musicality as a predictive process

2021 ◽  
Vol 44 ◽  
Author(s):  
Nils Kraus ◽  
Guido Hesselmann
Keyword(s):  
Cognitive Functioning ◽  
Prediction Error ◽  
Social Bonding ◽  
Predictive Processing ◽  
Error Minimization ◽  
Predictive Process ◽  
Prediction Error Minimization

Abstract Savage et al. argue for musicality as having evolved for the overarching purpose of social bonding. By way of contrast, we highlight contemporary predictive processing models of human cognitive functioning in which the production and enjoyment of music follows directly from the principle of prediction error minimization.

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.

scholarly journals Toward an Embodied, Embedded Predictive Processing Account

2021 ◽  
Vol 12 ◽  
Author(s):  
Elmarie Venter
Keyword(s):  
Free Energy ◽  
Prediction Error ◽  
The Body ◽  
The Other ◽  
Predictive Processing ◽  
Error Minimization ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Prediction Error Minimization

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism.

Sustainable Development is a Dead-End: The Logic of Modernity and Ecological Crisis

2020 ◽  
Author(s):  
Simon Lumsden
Keyword(s):  
Sustainable Development ◽  
Ecological Crisis ◽  
Self Determination ◽  
Ecological Sustainability ◽  
Planetary Boundaries ◽  
Western Modernity ◽  
Conceptual Frame ◽  
The Core ◽  
Dead End ◽  
Core Idea

This paper examines the theory of sustainable development presented by Jeffrey Sachs in The Age of Sustainable Development. While Sustainable Development ostensibly seeks to harmonise the conflict between ecological sustainability and human development, the paper argues this is impossible because of the conceptual frame it employs. Rather than allowing for a re-conceptualisation of the human–nature relation, Sustainable Development is simply the latest and possibly last attempt to advance the core idea of western modernity — the notion of self-determination. Drawing upon Hegel’s account of historical development it is argued that Sustainable Development and the notion of planetary boundaries cannot break out of a dualism of nature and self-determining agents.

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