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Unimodal statistical learning produces multimodal object-like representations.

Published version
Peer-reviewed

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Authors

Lengyel, Gábor 
Žalalytė, Goda 
Pantelides, Alexandros 
Ingram, James Neilson 
Fiser, József 

Abstract

The concept of objects is fundamental to cognition and is defined by a consistent set of sensory properties and physical affordances. Although it is unknown how the abstract concept of an object emerges, most accounts assume that visual or haptic boundaries are crucial in this process. Here, we tested an alternative hypothesis that boundaries are not essential but simply reflect a more fundamental principle: consistent visual or haptic statistical properties. Using a novel visuo-haptic statistical learning paradigm, we familiarised participants with objects defined solely by across-scene statistics provided either visually or through physical interactions. We then tested them on both a visual familiarity and a haptic pulling task, thus measuring both within-modality learning and across-modality generalisation. Participants showed strong within-modality learning and 'zero-shot' across-modality generalisation which were highly correlated. Our results demonstrate that humans can segment scenes into objects, without any explicit boundary cues, using purely statistical information.

Description

Keywords

human, neuroscience

Journal Title

eLife

Conference Name

Journal ISSN

2050-084X
2050-084X

Volume Title

8

Publisher

eLife Sciences Publications Ltd
Sponsorship
Wellcome Trust (095621/Z/11/Z)
Wellcome Trust (097803/Z/11/Z)
European Research Council (Consolidator Grant ERC-2016-COG/726090) Máté Lengyel Royal Society (Noreen Murray Professorship in Neurobiology RP120142) Daniel M Wolpert Seventh Framework Programme (Marie Curie CIG 618918) József Fiser Wellcome Trust (New Investigator Award 095621/Z/11/Z) Máté Lengyel National Institutes of Health (NIH R21 HD088731) József Fiser Wellcome Trust (Senior Investigator Award 097803/Z/11/Z) Daniel M Wolpert