Journal
CORTEX
Volume 141, Issue -, Pages 1-15Publisher
ELSEVIER MASSON, CORP OFF
DOI: 10.1016/j.cortex.2021.03.021
Keywords
Concepts; Semantic memory; Tool network; Object recognition; Context
Funding
- NIH [R01 NS099061]
- Moss Rehabilitation Research Institute/University of Pennsylvania postdoctoral training fellowship [NIH 5T32HD07184405]
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The study suggests that certain regions in the tool network reflect category information about tools, and this category information has a stronger impact on the representational geometry in use contexts. Furthermore, scene context helps shape the representational geometry of the tool network.
Tools are ubiquitous in human environments and to think about them we use concepts. Increasingly, conceptual representation is thought to be dynamic and sensitive to the goals of the observer. Indeed, observer goals can reshape representational geometry within cortical networks supporting concepts. In the present study, we investigated the novel hypothesis that task-irrelevant scene context may implicitly alter the representational geometry of regions within the tool network. Participants performed conceptual judgments on images of tools embedded in scenes that either suggested their use (i.e., a kitchen timer sitting on a kitchen counter with vegetables in a frying pan) or that they would simply be moved (i.e., a kitchen timer sitting in an open drawer with other miscellaneous kitchen items around). We investigated whether representations in the tool network reflect category, grip, and shape information using a representational similarity analysis (RSA). We show that a) a number of regions of the tool network reflect category information about tools and b) category information predicts patterns in supramarginal gyrus more strongly in use contexts than in move contexts. Together, these results show that information about tool category is distributed across different regions of the tool network and that scene context helps shape the representational geometry of the tool network. Crown Copyright (c) 2021 Published by Elsevier Ltd. All rights reserved.
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