Jones, SA ORCID: https://orcid.org/0000-0002-1767-9414 and Noppeney, U, 2024. Multisensory integration and causal inference in typical and atypical populations. In: Gu, Y and Zaidel, A, eds., Advances of multisensory integration in the brain. Singapore: Springer, pp. 59-76. ISBN 9789819976102
Text
1862586_Jones.pdf - Post-print Full-text access embargoed until 25 January 2025. Download (493kB) |
Abstract
Multisensory perception is critical for effective interaction with the environment, but human responses to multisensory stimuli vary across the lifespan and appear changed in some atypical populations. In this review chapter, we consider multisensory integration within a normative Bayesian framework. We begin by outlining the complex computational challenges of multisensory causal inference and reliability-weighted cue integration, and discuss whether healthy young adults behave in accordance with normative Bayesian models. We then compare their behaviour with various other human populations (children, older adults, and those with neurological or neuropsychiatric disorders). In particular, we consider whether the differences seen in these groups are due only to changes in their computational parameters (such as sensory noise or perceptual priors), or whether the fundamental computational principles (such as reliability weighting) underlying multisensory perception may also be altered. We conclude by arguing that future research should aim explicitly to differentiate between these possibilities.
Item Type: | Chapter in book |
---|---|
Creators: | Jones, S.A. and Noppeney, U. |
Publisher: | Springer |
Place of Publication: | Singapore |
Date: | 26 January 2024 |
ISBN: | 9789819976102 |
Identifiers: | Number Type 10.1007/978-981-99-7611-9_4 DOI 1862586 Other |
Divisions: | Schools > School of Social Sciences |
Record created by: | Laura Ward |
Date Added: | 08 Mar 2024 14:19 |
Last Modified: | 08 Mar 2024 14:19 |
URI: | https://irep.ntu.ac.uk/id/eprint/51028 |
Actions (login required)
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year