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  Human visual motion perception shows hallmarks of Bayesian structural inference

Yang, S., Bill, J., Drugowitsch, J., & Gershman, S. (2021). Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports, 11(1): 3714, pp. 1-14. doi:10.1038/s41598-021-82175-7.

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Yang, S1, 2, Author           
Bill, J, Author
Drugowitsch, J, Author
Gershman, SJ, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              

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 Abstract: Motion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene's motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task's Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence-especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.

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 Dates: 2021-02
 Publication Status: Published online
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 Identifiers: DOI: 10.1038/s41598-021-82175-7
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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 11 (1) Sequence Number: 3714 Start / End Page: 1 - 14 Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322