The Experts below are selected from a list of 38529 Experts worldwide ranked by ideXlab platform
Paul C. Fletcher - One of the best experts on this subject based on the ideXlab platform.
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Inequalities in mental health: Predictive Processing and social life.
Current opinion in psychiatry, 2021Co-Authors: Michael Kelly, Ann Louise Kinmonth, Carol Brayne, Natasha Kriznik, John Ford, Paul C. FletcherAbstract:Purpose of review The paper applies recent conceptualisations of Predictive Processing to the understanding of inequalities in mental health. Recent findings Social neuroscience has developed important ideas about the way the brain models the external world, and how the interface between cognitive and cultural processes interacts. These resonate with earlier concepts from cybernetics and sociology. These approaches could be applied to understanding some of the dynamics leading to the patterning of mental health problems in populations. Summary The implications for practice are the way such thinking might help illuminate how we think and act, and how these are anchored in the social world.
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the brain self and society a social neuroscience model of Predictive Processing
Social Neuroscience, 2019Co-Authors: Michael Kelly, Natasha M. Kriznik, Ann Louise Kinmonth, Paul C. FletcherAbstract:This paper presents a hypothesis about how social interactions shape and influence Predictive Processing in the brain. The paper integrates concepts from neuroscience and sociology where a gulf presently exists between the ways that each describe the same phenomenon - how the social world is engaged with by thinking humans. We combine the concepts of Predictive Processing models (also called Predictive coding models in the neuroscience literature) with ideal types, typifications and social practice - concepts from the sociological literature. This generates a unified hypothetical framework integrating the social world and hypothesised brain processes. The hypothesis combines aspects of neuroscience and psychology with social theory to show how social behaviors may be "mapped" onto brain processes. It outlines a conceptual framework that connects the two disciplines and that may enable creative dialogue and potential future research.
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The brain, self and society: a social-neuroscience model of Predictive Processing.
Social neuroscience, 2018Co-Authors: Michael Kelly, Natasha M. Kriznik, Ann Louise Kinmonth, Paul C. FletcherAbstract:This paper presents a hypothesis about how social interactions shape and influence Predictive Processing in the brain. The paper integrates concepts from neuroscience and sociology where a gulf pre...
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Predictive Processing, Source Monitoring, and Psychosis
Annual Review of Clinical Psychology, 2017Co-Authors: Juliet Griffin, Paul C. FletcherAbstract:A comprehensive understanding of psychosis requires models that link multiple levels of explanation: the neurobiological, the cognitive, the subjective, and the social. Until we can bridge several explanatory gaps, it is difficult to explain how neurobiological perturbations can manifest in bizarre beliefs or hallucinations, or how trauma or social adversity can perturb lower-level brain processes. We propose that the Predictive Processing framework has much to offer in this respect. We show how this framework may underpin and complement source monitoring theories of delusions and hallucinations and how, when considered in terms of a dynamic and hierarchical system, it may provide a compelling model of several key clinical features of psychosis. We see little conflict between source monitoring theories and Predictive coding. The former act as a higher-level description of a set of capacities, and the latter aims to provide a deeper account of how these and other capacities may emerge.
Sam Wilkinson - One of the best experts on this subject based on the ideXlab platform.
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Distinguishing volumetric content from perceptual presence within a Predictive Processing framework.
Phenomenology and the cognitive sciences, 2019Co-Authors: Sam WilkinsonAbstract:I argue for an overlooked distinction between perceptual presence and volumetric content, and flesh it out in terms of Predictive Processing. Within the Predictive Processing framework we can distinguish between agent-active and object-active expectations. The former expectations account for perceptual presence, while the latter account for volumetric content. I then support this position with reference to how experiences of presence are created by virtual reality technologies, and end by reflecting on what this means for the relationship between sensorimotor enactivism and Predictive Processing.
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Getting warmer: Predictive Processing and the nature of emotion
The Value of Emotions for Knowledge, 2019Co-Authors: Sam Wilkinson, George Deane, Kathryn Nave, Andy ClarkAbstract:Predictive Processing accounts of neural function view the brain as a kind of prediction machine that forms models of its environment in order to anticipate the upcoming stream of sensory stimulation. These models are then continuously updated in light of incoming error signals. Predictive Processing has offered a powerful new perspective on cognition, action, and perception. In this chapter we apply the insights from Predictive Processing to the study of emotions. The upshot is a picture of emotion as inseparable from perception and cognition, and a key feature of the embodied mind.
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Predictive Processing and the Varieties of Psychological Trauma.
Frontiers in psychology, 2017Co-Authors: Sam Wilkinson, Guy Dodgson, Kevin MearesAbstract:A recently popular framework in the cognitive sciences takes the human nervous system to be a hierarchically arranged Bayesian prediction machine. In this paper, we examine psychological trauma through the lens of this framework. We suggest that this can help us to understand the nature of trauma, and the different effects that different kinds of trauma can have. We end by exploring synergies between our approach and current theories of PTSD, and gesture toward future directions.
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Accounting for the phenomenology and varieties of auditory verbal hallucination within a Predictive Processing framework.
Consciousness and cognition, 2014Co-Authors: Sam WilkinsonAbstract:Two challenges that face popular self-monitoring theories (SMTs) of auditory verbal hallucination (AVH) are that they cannot account for the auditory phenomenology of AVHs and that they cannot account for their variety. In this paper I show that both challenges can be met by adopting a Predictive Processing framework (PPF), and by viewing AVHs as arising from abnormalities in Predictive Processing. I show how, within the PPF, both the auditory phenomenology of AVHs, and three subtypes of AVH, can be accounted for.
Tobias Kube - One of the best experts on this subject based on the ideXlab platform.
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Rethinking post-traumatic stress disorder - A Predictive Processing perspective.
Neuroscience and biobehavioral reviews, 2020Co-Authors: Tobias Kube, Max Berg, Birgit Kleim, Philipp HerzogAbstract:Abstract Predictive Processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the Predictive Processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a Predictive Processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.
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Rethinking post-traumatic stress disorder – A Predictive Processing perspective
2020Co-Authors: Tobias Kube, Max Berg, Birgit Kleim, Philipp HerzogAbstract:Predictive Processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the Predictive Processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a Predictive Processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.
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distorted cognitive processes in major depression a Predictive Processing perspective
Biological Psychiatry, 2020Co-Authors: Tobias Kube, Rainer K W Schwarting, Liron Rozenkrantz, Julia Anna Glombiewski, Winfried RiefAbstract:Abstract The cognitive model of depression has significantly influenced the understanding of distorted cognitive processes in major depression; however, this model’s conception of cognition has recently been criticized as possibly too broad and unspecific. In this review, we connect insights from cognitive neuroscience and psychiatry to suggest that the traditional cognitive model may benefit from a reformulation that takes current Bayesian models of the brain into account. Appealing to a Predictive Processing account, we explain that healthy human learning is normally based on making predictions and experiencing discrepancies between predicted and actual events or experiences. We present evidence suggesting that this learning mechanism is distorted in depression: current research indicates that people with depression tend to negatively reappraise or disregard positive information that disconfirms negative expectations, thus resulting in sustained negative predictions and biased learning. We also review the neurophysiological correlates of such deficits in Processing prediction errors in people with depression. Synthesizing these findings, we propose a novel mechanistic model of depression suggesting that people with depression have the tendency to predominantly expect negative events or experiences, which they subjectively feel confirmed due to reappraisal of disconfirming evidence, thus creating a self-reinforcing negative feedback loop. Computationally, we consider too much precision afforded to negative prior beliefs as the main candidate of pathology, accompanied by an attenuation of positive prediction errors. We conclude by outlining some directions for future research into the understanding of the behavioral and neurophysiological underpinnings of this model and point to clinical implications of it.
Anil K. Seth - One of the best experts on this subject based on the ideXlab platform.
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Predictive Processing as a systematic basis for identifying the neural correlates of consciousness
Philosophy and the Mind Sciences, 2020Co-Authors: Jakob Hohwy, Anil K. SethAbstract: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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a Predictive Processing model of episodic memory and time perception
bioRxiv, 2020Co-Authors: Zafeirios Fountas, Anil K. Seth, Anastasia Sylaidi, Kyriacos Nikiforou, Murray Shanahan, Warrick RoseboomAbstract:Human perception and experience of time is strongly affected by environmental context. When paying close attention to time, time experience seems to expand; when distracted from time, experience of time seems to contract. Contrasts in experiences like these are common enough to be exemplified in sayings like "time flies when you9re having fun". Similarly, experience of time depends on the content of perceptual experience - more rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among stimulation, attention, and memory that characterise time experience is likely the reason that a single overarching theory of time perception has been difficult to achieve. In the present study we propose a framework that reconciles these interactions within a single model, built using the principles of the Predictive Processing approach to perception. We designed a neural hierarchical Bayesian system, functionally similar to human perceptual Processing, making use of hierarchical Predictive coding, short-term plasticity, spatio-temporal attention, and episodic memory formation and recall. A large-scale experiment with ~13,000 human participants investigated the effects of memory, cognitive load, and stimulus content on duration reports of natural scenes up to ~1 minute long. Model-based estimates matched human reports, replicating key qualitative biases including differences by cognitive load, scene type, and judgement (prospective or retrospective). Our approach provides an end-to-end model of duration perception from natural stimulus Processing to estimation and from current experience to recalling the past, providing a new understanding of this central aspect of human experience.
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the felt presence of other minds Predictive Processing counterfactual predictions and mentalising in autism
Consciousness and Cognition, 2015Co-Authors: Colin J Palmer, Anil K. Seth, Jakob HohwyAbstract:The mental states of other people are components of the external world that modulate the activity of our sensory epithelia. Recent probabilistic frameworks that cast perception as unconscious inference on the external causes of sensory input can thus be expanded to enfold the brain's representation of others' mental states. This paper examines this subject in the context of the debate concerning the extent to which we have perceptual awareness of other minds. In particular, we suggest that the notion of perceptual presence helps to refine this debate: are others' mental states experienced as veridical qualities of the perceptual world around us? This experiential aspect of social cognition may be central to conditions such as autism spectrum disorder, where representations of others' mental states seem to be selectively compromised. Importantly, recent work ties perceptual presence to the counterfactual predictions of hierarchical generative models that are suggested to perform unconscious inference in the brain. This enables a characterisation of mental state representations in terms of their associated counterfactual predictions, allowing a distinction between spontaneous and explicit forms of mentalising within the framework of Predictive Processing. This leads to a hypothesis that social cognition in autism spectrum disorder is characterised by a diminished set of counterfactual predictions and the reduced perceptual presence of others' mental states.
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a Predictive Processing theory of sensorimotor contingencies explaining the puzzle of perceptual presence and its absence in synesthesia
Cognitive Neuroscience, 2014Co-Authors: Anil K. SethAbstract:Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “Predictive Processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of Predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception.
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extending Predictive Processing to the body emotion as interoceptive inference
Behavioral and Brain Sciences, 2013Co-Authors: Anil K. Seth, Hugo D CritchleyAbstract:The Bayesian brain hypothesis provides an attractive unifying framework for perception, cognition, and action. We argue that the framework can also usefully integrate interoception, the sense of the internal physiological condition of the body. Our model of “interoceptive Predictive coding” entails a new view of emotion as interoceptive inference and may account for a range of psychiatric disorders of selfhood.
Jakob Hohwy - One of the best experts on this subject based on the ideXlab platform.
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Predictive Processing as a systematic basis for identifying the neural correlates of consciousness
Philosophy and the Mind Sciences, 2020Co-Authors: Jakob Hohwy, Anil K. SethAbstract: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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Predictive Processing as a systematic basis for identifying the neural correlates of consciousness
2020Co-Authors: Jakob Hohwy, Anil SethAbstract: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 finding 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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Events, Event Prediction, and Predictive Processing
Topics in cognitive science, 2020Co-Authors: Jakob Hohwy, Augustus Hebblewhite, Tom DrummondAbstract:Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the Predictive Processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events.
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Fidgeting as self-evidencing: A Predictive Processing account of non-goal-directed action
New Ideas in Psychology, 2020Co-Authors: Kelsey Perrykkad, Jakob HohwyAbstract:Abstract Non-goal-directed actions have been relatively neglected in cognitive science, but are ubiquitous and related to important cognitive functions. Fidgeting is seemingly one subtype of non-goal-directed action which is ripe for a functional account. What's the point of fidgeting? The Predictive Processing framework is a parsimonious account of brain function which says the brain aims to minimise the difference between expected and actual states of the world and itself, that is, minimise prediction error. This framework situates action selection in terms of active inference for expected states. However, seemingly aimless, idle actions, such as fidgeting, are a challenge to such theories. When our actions are not obviously goal-achieving, how can a Predictive Processing framework explain why we regularly do them anyway? Here, we argue that in a Predictive Processing framework, evidence for the agent's own existence is consolidated by self-stimulation or fidgeting. Endogenous, repetitive actions reduce uncertainty about the system's own states, and thus help continuously maintain expected rates of prediction error minimisation. We extend this explanation to clinically distinctive self-stimulation, such as in Autism Spectrum Conditions, in which effective strategies for self-evidencing may be different to the neurotypical case.
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Andy Clark and His Critics - Quick’n’Lean or Slow and Rich?: Andy Clark on Predictive Processing and Embodied Cognition
Andy Clark and His Critics, 2019Co-Authors: Jakob HohwyAbstract:Andy Clark’s exciting work on Predictive Processing provides the umbrella under which his hugely influential previous work on embodied and extended cognition seeks a unified home. This chapter argues that in fact Predictive Processing harbours internalist, inferentialist and epistemic tenets that cannot leave embodied and extended cognition unchanged. Predictive Processing cannot do the work Clark requires of it without relying on rich, preconstructive internal representations of the world, nor without engaging in paradigmatically rational integration of prior knowledge and new sensory input. Hence, next to Clark’s image of fluid “uncertainty surfing” is an equally valid image of more emaciated and plodding world-modelling. Rather than underpinning orthodox embodied and extended approches, Predictive Processing therefore presents an opportunity for a potentially fruitful new synthesis of cognitivist and embodied approaches to cognition.