Probing the architecture of visual number sense with parietal tRNS.


Journal

Cortex; a journal devoted to the study of the nervous system and behavior
ISSN: 1973-8102
Titre abrégé: Cortex
Pays: Italy
ID NLM: 0100725

Informations de publication

Date de publication:
05 2019
Historique:
received: 25 11 2017
revised: 12 05 2018
accepted: 30 08 2018
pubmed: 15 10 2018
medline: 4 8 2020
entrez: 15 10 2018
Statut: ppublish

Résumé

Theoretical accounts of the visual number sense (VNS), i.e., an ability to discriminate approximate numerosities, remain controversial. A proposal that the VNS represents a process of numerosity extraction, leading to an abstract number representation in the brain, has been challenged by the view that the VNS is non-numerical in its essence and amounts to a weighted integration of continuous magnitude features that typically change with numerosity. In the present study, using two-alternative forced-choice paradigm, we aimed to distinguish between these proposals by probing brain areas implicated in the VNS with transcranial random noise stimulation (tRNS). We generated predictions for the stimulation-related changes in behavioural performance which would be compatible with alternative mechanisms proposed for the VNS. First, we investigated whether the superior parietal (SP) area hosts a numerosity code or whether its function is to modulate weighting of continuous stimulus features. We predicted that stimulation may affect the VNS precision if the SP role is representational, and that it may affect decision threshold if its role is modulatory. Second, we investigated whether the intraparietal (IP) area hosts a numerosity code independently of codes for continuous stimulus features, or whether their representations overlap. If the numerosity code is independent, we predicted that IP stimulation may improve the VNS but not continuous magnitude judgements. Our results were consistent with the hypotheses of a modulatory role of the SP and of the independence of the numerosity code in the IP, whereby suggesting that VNS is an emergent abstract property based on continuous magnitude statistics.

Identifiants

pubmed: 30316449
pii: S0010-9452(18)30282-X
doi: 10.1016/j.cortex.2018.08.030
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

54-66

Informations de copyright

Copyright © 2018 Elsevier Ltd. All rights reserved.

Auteurs

Vyacheslav R Karolis (VR)

Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, UK; Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Salpêtrière, France. Electronic address: slava.karolis@kcl.ac.uk.

Mikhail Grinyaev (M)

International Centre for Research in Human Development, Tomsk State University, Russia.

Andreea Epure (A)

Department of Experimental Psychology, University of Oxford, UK.

Vyacheslav Tsoy (V)

International Centre for Research in Human Development, Tomsk State University, Russia.

Ebba Du Rietz (E)

MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

Michael J Banissy (MJ)

Department of Psychology, Goldsmiths, University of London, UK.

Marinella Cappelletti (M)

Department of Psychology, Goldsmiths, University of London, UK; Institute of Cognitive Neuroscience, University College London, UK.

Yulia Kovas (Y)

International Centre for Research in Human Development, Tomsk State University, Russia; Department of Psychology, Goldsmiths, University of London, UK.

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Classifications MeSH