scholarly journals A predictive processing model of episodic memory and time perception

2020 ◽  
Author(s):  
Zafeirios Fountas ◽  
Anastasia Sylaidi ◽  
Kyriacos Nikiforou ◽  
Anil K. Seth ◽  
Murray Shanahan ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Cognitive Load ◽  
Time Perception ◽  
Large Scale ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Natural Scenes ◽  
Attention And Memory ◽  
Time Experience ◽  
Experience Of Time

ABSTRACTHuman 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 you’re 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.

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 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.

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.

scholarly journals Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

2016 ◽  
Vol 2 (11) ◽  
pp. e1601335 ◽  
Author(s):  
Jorge F. Mejias ◽  
John D. Murray ◽  
Henry Kennedy ◽  
Xiao-Jing Wang
Keyword(s):  
Large Scale ◽  
Complex Dynamics ◽  
Predictive Coding ◽  
Causality Analysis ◽  
Frequency Dependent ◽  
Brain Functions ◽  
Wide Range ◽  
Cortical Laminar ◽  
Feedback Frequency ◽  
Visual Cortical

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

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 Large-scale functional integration, rather than functional dissociation along dorsal and ventral streams, underlies visual perception and action

2019 ◽  
Author(s):  
Dipanjan Ray ◽  
Nilambari Hajare ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Visual Perception ◽  
Large Scale ◽  
Premotor Cortex ◽  
Functional Integration ◽  
Predictive Coding ◽  
Dorsal Stream ◽  
Brain Regions ◽  
Neural Pathways ◽  
Healthy Human ◽  
Mri Environment

AbstractVisual dual stream theory posits that two distinct neural pathways of specific functional significance originate from primary visual areas and reach the inferior temporal (ventral) and posterior parietal areas (dorsal). However, there are several unresolved questions concerning the fundamental aspects of this theory. For example, is the functional dissociation between ventral and dorsal stream driven by features in input stimuli or is it driven by categorical differences between visuo-perceptual and visuo-motor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between two streams? We addressed these questions using fMRI recordings on healthy human volunteers and employing stimuli and tasks that can tease out the divergence between visuo-perceptual and visuo-motor models of dual stream theory. fMRI scans were repeated after seven practice sessions that were conducted in a non-MRI environment to investigate the effects of neuroplasticity. Brain activation analysis supports an input-based functional dissociation and existence of context-dependent neuroplasticity in dual stream areas. Intriguingly, premotor cortex activation was observed in the position perception task and distributed deactivated regions were observed in all perception tasks thus, warranting a network level analysis. Dynamic causal modelling (DCM) analysis incorporating activated and deactivated brain areas during perception tasks indicates that the brain dynamics during visual perception and actions could be interpreted within the framework of predictive coding. Effectively, the network level findings point towards the existence of more intricate context-driven functional networks selective of “what” and “where” information rather than segregated streams of processing along ventral and dorsal brain regions.

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