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  A data-driven investigation of human action representations

Dima, D. C., Hebart, M. N., & Isik, L. (2022). A data-driven investigation of human action representations. bioRxiv. doi:10.1101/2022.09.22.509054.

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Dima, Diana C.1, 2, Autor
Hebart, Martin N.3, Autor                 
Isik, Leyla2, Autor
Affiliations:
1Dept. of Cognitive Science, Johns Hopkins University, Baltimore, USA, ou_persistent22              
2Dept. of Computer Science, Western University, London, Canada, ou_persistent22              
3Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378              

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 Zusammenfassung: Understanding actions performed by others requires us to integrate different types of information about people, scenes, objects, and their interactions. What organizing dimensions does the mind use to make sense of this complex action space? To address this question, we collected intuitive similarity judgments across two large-scale sets of naturalistic videos depicting everyday actions. We used cross-validated sparse non-negative matrix factorization (NMF) to identify the structure underlying action similarity judgments. A low-dimensional representation, consisting of nine to ten dimensions, was sufficient to accurately reconstruct human similarity judgments. The dimensions were robust to stimulus set perturbations and reproducible in a separate odd-one-out experiment. Human labels mapped these dimensions onto semantic axes relating to food, work, and home life; social axes relating to people and emotions; and one visual axis related to scene setting. While highly interpretable, these dimensions did not share a clear one-to-one correspondence with prior hypotheses of action-relevant dimensions. Together, our results reveal a low-dimensional set of robust and interpretable dimensions that organize intuitive action similarity judgments and highlight the importance of data-driven investigations of behavioral representations.

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 Datum: 2022-09-23
 Publikationsstatus: Online veröffentlicht
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 Identifikatoren: DOI: 10.1101/2022.09.22.509054
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Titel: bioRxiv
Genre der Quelle: Webseite
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