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      Action perception as hypothesis testing

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          Abstract

          We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions – and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing.

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          Most cited references56

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          Predictive coding: an account of the mirror neuron system.

          Is it possible to understand the intentions of other people by simply observing their actions? Many believe that this ability is made possible by the brain's mirror neuron system through its direct link between action and observation. However, precisely how intentions can be inferred through action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.
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            Active Inference: A Process Theory.

            This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.
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              Eye movements in natural behavior.

              The classic experiments of Yarbus over 50 years ago revealed that saccadic eye movements reflect cognitive processes. But it is only recently that three separate advances have greatly expanded our understanding of the intricate role of eye movements in cognitive function. The first is the demonstration of the pervasive role of the task in guiding where and when to fixate. The second has been the recognition of the role of internal reward in guiding eye and body movements, revealed especially in neurophysiological studies. The third important advance has been the theoretical developments in the fields of reinforcement learning and graphic simulation. All of these advances are proving crucial for understanding how behavioral programs control the selection of visual information.
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                Author and article information

                Contributors
                Journal
                Cortex
                Cortex
                Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
                Masson
                0010-9452
                1973-8102
                1 April 2017
                April 2017
                : 89
                : 45-60
                Affiliations
                [a ]Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
                [b ]Centre for Brain Science, Department of Psychology, University of Essex, Colchester, UK
                [c ]Laboratory of Neuropsychology and Cognitive Neuroscience, Department of Neuroscience and Imaging, University G. d'Annunzio, Chieti, Italy
                [d ]Institute for Advanced Biomedical Technologies – ITAB, Foundation University G. d'Annunzio, Chieti, Italy
                [e ]Department of Neuroscience, University of Padua, Padua, Italy
                [f ]The Wellcome Trust Centre for Neuroimaging, UCL, London, UK
                Author notes
                [] Corresponding author. Institute of Cognitive Sciences and Technologies, National Research Council, Via S. Martino della Battaglia, 44, 00185 Rome, Italy.Institute of Cognitive Sciences and TechnologiesNational Research CouncilVia S. Martino della Battaglia44Rome00185Italy giovanni.pezzulo@ 123456istc.cnr.it
                Article
                S0010-9452(17)30030-8
                10.1016/j.cortex.2017.01.016
                5383736
                28226255
                d062b3c6-145e-456a-b04f-cd896ec26a75
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 May 2016
                : 21 November 2016
                : 18 January 2017
                Categories
                Research Report

                Neurology
                active inference,action observation,hypothesis testing,active perception,motor prediction

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