Abstract / Description of output
Spatial neglect has been a phenomenon of interest for perceptual and neuropsychological researchers for decades. However, the underlying cognitive processes remain unclear. We provide a Bayesian framework for the classic line bisection task in spatial neglect, regarding it as rational inferences in the face of uncertain information. A Bayesian observer perceives the left and right endpoints of a line with uncertainty, and leverages prior expectations about line lengths to compensate for this uncertainty. This Bayesian model provides a basis for characterizing different patterns of behavior. Our model also captures the paradoxical cross-over effect observed in earlier studies as a natural outcome when uncertainty is high and the observer falls back on priors. It provides measures that correlate well with measures from other neglect tests, and can accurately distinguish stroke patients from healthy controls. It has the potential to facilitate spatial neglect studies and inform clinical decisions.
Original language | English |
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Title of host publication | Proceedings of the 45th Annual Meeting of the Cognitive Science Society |
Editors | Micah Goldwater, Florencia Anggoro, Brett Hayes, Desmond Ong |
Publisher | The Cognitive Science Society |
Pages | 896-902 |
Number of pages | 7 |
Volume | 45 |
Publication status | Published - 29 Jul 2023 |
Event | 45th Annual Conference of the Cognitive Science Society: Cognition in Context - ICC Sydney, Sydney, Australia Duration: 26 Jul 2023 → 29 Jul 2023 https://cognitivesciencesociety.org/cogsci-2023/ |
Publication series
Name | Proceedings of the Cognitive Science Society |
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Publisher | Cognitive Science Society |
ISSN (Electronic) | 1047-1316 |
Conference
Conference | 45th Annual Conference of the Cognitive Science Society |
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Abbreviated title | CogSci 2023 |
Country/Territory | Australia |
City | Sydney |
Period | 26/07/23 → 29/07/23 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- spatial neglect
- visual neglect
- line bisection
- attention
- perception
- Bayes