REPRESENTATIONS: RICH OR PARTIAL?

METOD ◽  
2020 ◽  
pp. 157-173
Author(s):  
Mikhail Sushcin
Keyword(s):  
Research Program ◽  
Mental Representations ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Crucial Element ◽  
Vision Science ◽  
Human Practices ◽  
Modern Vision ◽  
Classical Cognitivism ◽  
Motor Representations

This article focuses on how the idea of mental representations is understood in an influential modern research program in cognitive science - the program of predictive processing or predictive coding. It is pointed out that, as with earlier programs of classical cognitivism and connectionism, the idea of mental representations is also a crucial element of the program of predictive processing. According to the key assumption of this program, cognition is based on a rich internal generative model of reality that produces predictive perceptual and motor representations of what the organism can interact with at the next moment. The article considers possible challenges to this understanding of mental representations, coming from research in modern vision science and externalist approaches to memory, both in perception and in human practices, mediated by special intellectual artifacts. It is argued that there are no fundamental contradictions between these frameworks, which opens up opportunities for their interaction and integration.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Extending predictive processing to the body: Emotion as interoceptive inference

2013 ◽  
Vol 36 (3) ◽  
pp. 227-228 ◽  
Author(s):  
Anil K. Seth ◽  
Hugo D. Critchley
Keyword(s):  
Psychiatric Disorders ◽  
Physiological Condition ◽  
Predictive Coding ◽  
The Body ◽  
Predictive Processing ◽  
Bayesian Brain ◽  
New View

AbstractThe 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.

scholarly journals Neuropsychological assessment and virtual reality training of social prediction in patients with cerebellar malformation

2021 ◽  
Author(s):  
Cosimo Urgesi ◽  
Niccolò Butti ◽  
Alessandra Finisguerra ◽  
Emilia Biffi ◽  
Enza Maria Valente ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Social Cognition ◽  
Contextual Information ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Cerebellar Malformation ◽  
Use Context ◽  
Predictive Role ◽  
Social Prediction

AbstractIt has been proposed that impairments of the predictive function exerted by the cerebellum may account for social cognition deficits. Here, we integrated cerebellar functions in a predictive coding framework to elucidate how cerebellar alterations could affect the predictive processing of others’ behavior. Experiment 1 demonstrated that cerebellar patients were impaired in relying on contextual information during action prediction, and this impairment was significantly associated with social cognition abilities. Experiment 2 indicated that patients with cerebellar malformation showed a domain-general deficit in using contextual information to predict both social and physical events. Experiment 3 provided first evidence that a social-prediction training in virtual reality could boost the ability to use context-based predictions to understand others’ intentions. These findings shed new light on the predictive role of the cerebellum and its contribution to social cognition, paving the way for new approaches to the rehabilitation of the Cerebellar Cognitive Affective Syndrome.

scholarly journals Implementing Predictive Processing and Active Inference: Preliminary Steps and Results

2019 ◽  
Author(s):  
Beren Millidge
Keyword(s):  
Predictive Coding ◽  
Predictive Processing ◽  
Active Inference ◽  
Inference Models ◽  
Perceptual Model ◽  
Challenging Tasks ◽  
Generalised Coordinates

Initial and preliminary implementations of predictive processing and active inference models are presented. These include the baseline hierarchical predictive coding models of (Friston 2003, 2005), and dynamical predictive coding models using generalised coordinates (Friston 2008, 2010, Buckley 2017). Additionally, we re-implement and experiment with the active inference thermostat presented in (Buckley 2017) and also implement an active inference agent with a hierarchical predictive coding perceptual model on the more challenging cart-pole task from OpanAI gym. We discuss the initial performance, capabilities, and limitations of these models in their preliminary stages and consider how they might be further scaled up to tackle more challenging tasks.

scholarly journals Sensory Attenuation in the Auditory Modality as a Window Into Predictive Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Fabian Kiepe ◽  
Nils Kraus ◽  
Guido Hesselmann
Keyword(s):  
Predictive Coding ◽  
Research Area ◽  
Predictive Processing ◽  
Auditory Modality ◽  
Auditory Input ◽  
Current State ◽  
Recent Developments ◽  
Sensory Attenuation ◽  
The Impact ◽  
Predictive Mechanisms

Self-generated auditory input is perceived less loudly than the same sounds generated externally. The existence of this phenomenon, called Sensory Attenuation (SA), has been studied for decades and is often explained by motor-based forward models. Recent developments in the research of SA, however, challenge these models. We review the current state of knowledge regarding theoretical implications about the significance of Sensory Attenuation and its role in human behavior and functioning. Focusing on behavioral and electrophysiological results in the auditory domain, we provide an overview of the characteristics and limitations of existing SA paradigms and highlight the problem of isolating SA from other predictive mechanisms. Finally, we explore different hypotheses attempting to explain heterogeneous empirical findings, and the impact of the Predictive Coding Framework in this research area.

scholarly journals Does signal reduction imply predictive coding in models of spoken word recognition?

2021 ◽  
Author(s):  
Sahil Luthra ◽  
Monica Y. C. Li ◽  
Heejo You ◽  
Christian Brodbeck ◽  
James S. Magnuson
Keyword(s):  
Language Processing ◽  
Cognitive Neuroscience ◽  
Spoken Word Recognition ◽  
Predictive Coding ◽  
Spoken Word ◽  
Predictive Processing ◽  
Neural Signal ◽  
Lexical Activation ◽  
Trace Model ◽  
Interactive Activation

AbstractPervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction, the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding.

scholarly journals Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

Perceiving

2017 ◽  
Author(s):  
Daniel D. Hutto ◽  
Erik Myin
Keyword(s):  
Mental Representations ◽  
A Priori ◽  
Visual Illusions ◽  
Predictive Processing ◽  
Optical Effects

Chapter 7 begins to puts REC positive story into action. It opens by questioning the value of appealing to a priori intuitions in trying to understand the character of perceiving. Focusing on explanatory concerns, it revisits Predictive Processing or PPC proposals about perceiving and defuses arguments that the explanatory punch of PPC requires characterizing perceptual processes and products in representational terms. Instead the chapter shows how REC can successfully appropriate the main apparatus of PPC to explain perception. It demonstrates that mental representations are not needed to explain how intramodal and intermodal forms of perceiving integrate. The chapter concludes by showing how contentless forms of perceiving can connect with contentful attitudes, enabling us to make sense of a range of perceptual phenomena – including our capacity to attune to optical effects and the ways in which we respond to visual illusions.

Prefrontal neural ensembles encode an internal model of visual sequences and their violations

2021 ◽  
Author(s):  
Marie Estelle Bellet ◽  
Marion Gay ◽  
Joachim Bellet ◽  
Bechir Jarraya ◽  
Stanislas Dehaene ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Internal Model ◽  
Visual Experience ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Sensory Inputs ◽  
Neuronal Ensembles ◽  
Neural Ensembles ◽  
Visual Events ◽  
Image Identity

Theories of predictive coding hypothesize that cortical networks learn internal models of environmental regularities to generate expectations that are constantly compared with sensory inputs. The prefrontal cortex (PFC) is thought to be critical for predictive coding. Here, we show how prefrontal neuronal ensembles encode a detailed internal model of sequences of visual events and their violations. We recorded PFC ensembles in a visual local-global sequence paradigm probing low and higher-order predictions and mismatches. PFC ensembles formed distributed, overlapping representations for all aspects of the dynamically unfolding sequences, including information about image identity as well as abstract information about ordinal position, anticipated sequence pattern, mismatches to local and global structure, and model updates. Model and mismatch signals were mixed in the same ensembles, suggesting a revision of predictive processing models that consider segregated processing. We conclude that overlapping prefrontal ensembles may collectively encode all aspects of an ongoing visual experience, including anticipation, perception, and surprise.

Sign in / Sign up

Export Citation Format

Share Document