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Scene Construction, Visual Foraging, and Active Inference

Mirza, MB; Adams, RA; Mathys, CD; Friston, KJ; (2016) Scene Construction, Visual Foraging, and Active Inference. Frontiers in Computational Neuroscience , 10 (ARTN 56) 10.3389/fncom.2016.00056. Green open access

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Abstract

This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia).

Type: Article
Title: Scene Construction, Visual Foraging, and Active Inference
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fncom.2016.00056
Publisher version: http://dx.doi.org/10.3389/fncom.2016.00056
Language: English
Additional information: © 2016 Mirza, Adams, Mathys and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, Neurosciences, Neurosciences & Neurology, active inference, visual search, Bayesian inference, scene construction, free energy, information gain, epistemic value, salience, SUPERIOR COLLICULUS, PARIETAL CORTEX, EYE-MOVEMENTS, MODELS, REPRESENTATION, SCHIZOPHRENIA, ORGANIZATION, HIPPOCAMPUS, COMPLEXITY, PREDICTION
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1504305
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