Neural encoding of active multi-sensing enhances perceptual decision-making via a synergistic cross-modal interaction

Delis, I., Ince, R. A. A. , Sajda, P. and Wang, Q. (2022) Neural encoding of active multi-sensing enhances perceptual decision-making via a synergistic cross-modal interaction. Journal of Neuroscience, 42(11), pp. 2344-2355. (doi: 10.1523/JNEUROSCI.0861-21.2022) (PMID:35091504) (PMCID:PMC8936614)

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Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we employed an active sensing paradigm coupled with neuroimaging, multivariate analysis and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants’ control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (electroencephalography - EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.

Item Type:Articles
Additional Information:This work was supported by European Commission H2020-MSCA-IF-2018/845884 “NeuCoDe” to I.D.; Physiological Society 2018 Research Grant Scheme to I.D.; National Institutes of Health Grant R01-MH085092 to P.S.; U.S. Army Research Laboratory W911NF-10-2-0022 to P.S.; Wellcome Trust 214120/Z/18/Z to R.A.A.I.; United Kingdom Economic and Social Research Council ES/L012995/1 to P.S.; and National Alliance for Research on Schizophrenia and Depression Young Investigator Award to Q.W.
Glasgow Author(s) Enlighten ID:Sajda, Professor Paul and Delis, Dr Ioannis and Ince, Dr Robin
Authors: Delis, I., Ince, R. A. A., Sajda, P., and Wang, Q.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Journal of Neuroscience
Publisher:The Society for Neuroscience
ISSN (Online):1529-2401
Published Online:28 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of Neuroscience 42(11): 2344-2355
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)
170631Neural correlates of learning and confidence during decision making and their utility in developing "intelligent technologies".Marios PhiliastidesEconomic and Social Research Council (ESRC)ES/L012995/1Centre for Cognitive Neuroimaging