scholarly journals Cerebral hierarchies: predictive processing, precision and the pulvinar

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140169 ◽  
Author(s):  
Ryota Kanai ◽  
Yutaka Komura ◽  
Stewart Shipp ◽  
Karl Friston
Keyword(s):  
Prediction Error ◽  
Message Passing ◽  
Predictive Coding ◽  
Gain Control ◽  
Predictive Processing ◽  
Special Focus ◽  
First Order ◽  
Neuronal Populations ◽  
Neurobiological Substrates ◽  
The Brain

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

scholarly journals Gender fluidity as affordance negotiation

2020 ◽  
Author(s):  
Mahault Albarracin ◽  
Pierre Poirier
Keyword(s):  
Prediction Error ◽  
Probabilistic Models ◽  
Predictive Processing ◽  
Gender Fluidity ◽  
Social Scripts ◽  
Sensorimotor Interaction ◽  
Binary State ◽  
Gender Scripts ◽  
Cultural Niche ◽  
The Brain

Gender is often viewed as static binary state for people to embody, based on the sex they were assigned at birth. However, cultural studies increasingly understand gender as neither binary nor static, a view supported both in psychology and sociology. On this view, gender is negotiated between individuals, and highly dependent on context. Specifically, individuals are thought to be offered culturally gendered social scripts that allow them and their interlocutors the ability to predict future actions, and to understand the scene being set, establishing roles and expectations. We propose to understand scripts in the framework of enactive-ecological predictivism, which integrates aspects of ecological enactivism, notably the importance of dynamical sensorimotor interaction with an environment conceived as a field of affordances, and predictive processing, which views the brain as a predictive engine that builds its probabilistic models in an effort to reduce prediction error. Under this view, script-based negotiation can be linked to the enactive neuroscience concept of a cultural niche, as a landscape of cultural affordances. Affordances are possibilities for action that constrain what actions are pre-reflectively felt possible based on biological, experiential and cultural multisensorial cues. With the shifting landscapes of cultural affordances brought about by a number of recent social, technological and epistemic developments, the gender scripts offered to individuals are less culturally rigid, which translates in an increase in the variety of affordance fields each individual can negotiate. This entails that any individual has an increased possibility for gender fluidity, as shown by the increasing number of people currently identifying outside the binary.

scholarly journals Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

2020 ◽  
Author(s):  
Alejandro Lerer ◽  
Hans Supèr ◽  
Matthias S.Keil
Keyword(s):  
Visual System ◽  
Computational Model ◽  
Predictive Coding ◽  
Gain Control ◽  
Physical World ◽  
Input Image ◽  
Visual Illusions ◽  
Contrast Effects ◽  
Dynamic Filtering ◽  
The Brain

AbstractThe visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast.Author summaryWe hardly notice that what we see is often different from the physical world “outside” of the brain. This means that the visual experience that the brain actively constructs may be different from the actual physical properties of objects in the world. In this work, we propose a hypothesis about how the visual system of the brain may construct a representation for achromatic images. Since this process is not unambiguous, sometimes we notice “errors” in our perception, which cause visual illusions. The challenge for theorists, therefore, is to propose computational principles that recreate a large number of visual illusions and to explain why they occur. Notably, our proposed mechanism explains a broader set of visual illusions than any previously published proposal. We achieved this by trying to suppress predictable information. For example, if an image contained repetitive structures, then these structures are predictable and would be suppressed. In this way, non-predictable structures stand out. Predictive coding mechanisms act as early as in the retina (which enhances luminance changes but suppresses uniform regions of luminance), and our computational model holds that this principle also acts at the next stage in the visual system, where representations of perceived luminance (brightness) are created.

scholarly journals A tale of two densities: active inference is enactive inference

2019 ◽  
Vol 28 (4) ◽  
pp. 225-239 ◽  
Author(s):  
Maxwell JD Ramstead ◽  
Michael D Kirchhoff ◽  
Karl J Friston
Keyword(s):  
Predictive Coding ◽  
Generative Models ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
The One ◽  
And Control ◽  
The Brain ◽  
Selection Of

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.

scholarly journals Amplitude modulations of sensory responses, and deviations from Weber’s Law in pulsatile evidence accumulation

2020 ◽  
Author(s):  
Sue Ann Koay ◽  
Stephan Y. Thiberge ◽  
Carlos D. Brody ◽  
David W. Tank
Keyword(s):  
Feedback Loop ◽  
Gain Control ◽  
Ongoing Activity ◽  
Posterior Cortex ◽  
Neural Data ◽  
Evidence Accumulation ◽  
Neuronal Populations ◽  
Visual Evidence ◽  
Sensory Responses ◽  
The Brain

AbstractHow do animals make behavioral decisions based on noisy sensory signals, which are moreover a tiny fraction of ongoing activity in the brain? Some theories suggest that sensory responses should be accumulated through time to reduce noise. Others suggest that feedback-based gain control of sensory responses allow small signals to be selectively amplified to drive behavior. We recorded from neuronal populations across posterior cortex as mice performed a decision-making task based on accumulating randomly timed pulses of visual evidence. Here we focus on a subset of neurons, with putative sensory responses that were time-locked to each pulse. These neurons exhibited a variety of amplitude (gain-like) modulations, notably by choice and accumulated evidence. These neural data inspired a hypothetical accumulation circuit with a multiplicative feedback-loop architecture, which parsimoniously explains deviations in perceptual discrimination from Weber-Fechner Law. Our neural observations thus led to a model that synthesizes both accumulation and feedback hypotheses.

scholarly journals Attenuated sensory precision and contextual prediction error in persistent depression

2021 ◽  
Author(s):  
Anna Bevan ◽  
Caitlin Hitchcock ◽  
Daniel Mitchell ◽  
Tim Dalgleish
Keyword(s):  
Prediction Error ◽  
Covert Attention ◽  
Predictive Processing ◽  
Attention Task ◽  
Sensory Data ◽  
New Information ◽  
Sensory Modalities ◽  
Persistent Depression ◽  
The Brain ◽  
High Uncertainty

Chronic and recurrent forms of clinical depression can persist for a lifetime and often respond poorly to intervention. Psychological formulations implicate rigid, negative expectations of self and world which are resistant to updating with new information, a phenomenology consistent with a Bayesian account of brain function. Bayesian predictive processing models suggest that sensory data which is represented with low precision (high uncertainty) in the brain cannot exert much influence on existing beliefs, giving rise to the hypothesis that persistent forms of depression may be characterised by disturbances in sensory precision optimization. We optimized a computational model with data from a cross-modal (visual, auditory, somatic) covert attention task to estimate sensory precision in persistently depressed participants relative to healthy controls. Results suggested that both sensory precision and the salience of attentional targets were attenuated in depressed participants across sensory modalities, contributing to a suppression of contextual prediction error in this group. These outcomes provide support for a novel theoretical account of depression chronicity and suggest avenues for enhancing the effectiveness of psychological interventions for this population.

Resetting the brain

2021 ◽  
pp. 110-123
Author(s):  
Chris Letheby
Keyword(s):  
Neural Networks ◽  
Cognitive Functions ◽  
Prediction Error ◽  
Cognitive Process ◽  
Large Scale ◽  
Predictive Processing ◽  
Therapeutic Benefits ◽  
High Level ◽  
The Brain

‘Resetting the brain’ examines the hypothesis that (i) large-scale neural networks become stuck in dysfunctional configurations in pathology, and (ii) psychedelics cause therapeutic benefits by disrupting these configurations, providing an opportunity to ‘reset’ the relevant networks into a healthier state. This chapter argues that this view is correct but limited; per Chapter 5, it needs to be supplemented with an account of these networks’ cognitive functions. To this end, the chapter introduces the predictive processing (PP) theory of cognition, which views the brain as an organ for prediction error minimisation. One PP-based theory of psychedelic action claims that (i) the networks targeted by psychedelics encode high-level beliefs, and (ii) psychedelic disruption of these beliefs provides an opportunity to revise them. This is the cognitive process that corresponds to the ‘resetting’ of neural networks. The chapter concludes by proposing that the beliefs most often revised in successful psychedelic therapy are self-related beliefs.

scholarly journals Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

AbstractAccording to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Multistability in auditory stream segregation: a predictive coding view

2012 ◽  
Vol 367 (1591) ◽  
pp. 1001-1012 ◽  
Author(s):  
István Winkler ◽  
Susan Denham ◽  
Robert Mill ◽  
Tamás M. Bőhm ◽  
Alexandra Bendixen
Keyword(s):  
Predictive Coding ◽  
Stream Segregation ◽  
Predictive Processing ◽  
Conscious Perception ◽  
Auditory Streaming ◽  
Sound Sources ◽  
Auditory Stream ◽  
Auditory Stream Segregation ◽  
Perceptual Stability ◽  
The Brain

Auditory stream segregation involves linking temporally separate acoustic events into one or more coherent sequences. For any non-trivial sequence of sounds, many alternative descriptions can be formed, only one or very few of which emerge in awareness at any time. Evidence from studies showing bi-/multistability in auditory streaming suggest that some, perhaps many of the alternative descriptions are represented in the brain in parallel and that they continuously vie for conscious perception. Here, based on a predictive coding view, we consider the nature of these sound representations and how they compete with each other. Predictive processing helps to maintain perceptual stability by signalling the continuation of previously established patterns as well as the emergence of new sound sources. It also provides a measure of how well each of the competing representations describes the current acoustic scene. This account of auditory stream segregation has been tested on perceptual data obtained in the auditory streaming paradigm.

scholarly journals Predictive coding is a consequence of energy efficiency in recurrent neural networks

2021 ◽  
Author(s):  
Abdullahi Ali ◽  
Nasir Ahmad ◽  
Elgar de Groot ◽  
Marcel A. J. van Gerven ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Recurrent Neural Networks ◽  
Brain Function ◽  
Message Passing ◽  
Sensory Input ◽  
Predictive Coding ◽  
Computational Modelling ◽  
Top Down ◽  
The Brain

AbstractPredictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

scholarly journals Disentangling Predictive Processing in the Brain: A Meta-analytic Study in Favour of a Predictive Network

2021 ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signal. Despite extensive research has investigated the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a task-based meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

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