A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni

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.

2013 ◽  
Vol 36 (3) ◽  
pp. 227-228 ◽  
Author(s):  
Anil K. Seth ◽  
Hugo D. Critchley

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.


2021 ◽  
Author(s):  
Cosimo Urgesi ◽  
Niccolò Butti ◽  
Alessandra Finisguerra ◽  
Emilia Biffi ◽  
Enza Maria Valente ◽  
...  

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.


2019 ◽  
Author(s):  
Beren Millidge

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.


2021 ◽  
Vol 15 ◽  
Author(s):  
Fabian Kiepe ◽  
Nils Kraus ◽  
Guido Hesselmann

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.


Author(s):  
Sahil Luthra ◽  
Monica Y. C. Li ◽  
Heejo You ◽  
Christian Brodbeck ◽  
James S. Magnuson

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.


2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  

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.


2019 ◽  
pp. 254-265
Author(s):  
Barbara Webb

Insect systems can provide useful “edge cases” against which to test the generality of Clark’s views on the nature of perception, cognition, and action. Insect brains emerged from an independent evolutionary pathway to the mammalian brain but show a comparable capacity in some key areas, such as prediction (internal emulation?), cue integration for spatial memory (Bayesian?), and exploiting structures in the world to extend their behavioural repertoire (extended minds?). Have they converged on the same solutions? Or are there different principles for cognition that they exploit? Reviewing some of the relevant current evidence about behavioural and brain function in insects suggests there is some remaining tension between Clark’s endorsement of the predictive processing principle and his account of the embodied mind.


2021 ◽  
Author(s):  
Marie Estelle Bellet ◽  
Marion Gay ◽  
Joachim Bellet ◽  
Bechir Jarraya ◽  
Stanislas Dehaene ◽  
...  

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.


2019 ◽  
Vol 28 (4) ◽  
pp. 225-239 ◽  
Author(s):  
Maxwell JD Ramstead ◽  
Michael D Kirchhoff ◽  
Karl J Friston

The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists.


2013 ◽  
Vol 36 (3) ◽  
pp. 210-211 ◽  
Author(s):  
Tobias Egner ◽  
Christopher Summerfield

AbstractClark makes a convincing case for the merits of conceptualizing brains as hierarchical prediction machines. This perspective has the potential to provide an elegant and powerful general theory of brain function, but it will ultimately stand or fall with evidence from basic neuroscience research. Here, we characterize the status quo of that evidence and highlight important avenues for future investigations.


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