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Crossmodal correspondences: A tutorial review
Open accessAbstract
In many everyday situations, our senses are bombarded by many different unisensory signals at any given time. To gain the most veridical, and least variable, estimate of environmental stimuli/properties, we need to combine the individual noisy unisensory perceptual estimates that refer to the same object, while keeping those estimates belonging to different objects or events separate. How, though, does the brain "know" which stimuli to combine? Traditionally, researchers interested in the crossmodal binding problem have focused on the roles that spatial and temporal factors play in modulating multisensory integration. However, crossmodal correspondences between various unisensory features (such as between auditory pitch and visual size) may provide yet another important means of constraining the crossmodal binding problem. A large body of research now shows that people exhibit consistent crossmodal correspondences between many stimulus features in different sensory modalities. For example, people consistently match high-pitched sounds with small, bright objects that are located high up in space. The literature reviewed here supports the view that crossmodal correspondences need to be considered alongside semantic and spatiotemporal congruency, among the key constraints that help our brains solve the crossmodal binding problem.
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“According to Bayesian decision theory, the brain uses prior knowledge about what sensory signals go together-whether inborn or explicitly learned-to integrate appropriate sensory stimuli with the goal of maximising the reliability of perceived information [139-141] and, presumably, to reduce cognitive load by combining disparate sensory cues into a single object.”
From The Role of Intrinsic and Extrinsic Sensory Factors in Sweetness Perception of Food and Beverages: A Review (2019) · cites this paper as [141]
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Dawes, C., Beganovic, S., Schwalk, M., Mai, M., Pawlizak, S., Zhang, J., Nelles, G., Krasteva, N., & Obrist, M. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1268849 · Cited by 16
Metadata
- Authors
- Spence, C.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Spence, Charles
- ORCID
- Spence, Charles
- Venue
- Attention, Perception, & Psychophysics
- Published
- 2011-01-19
- Volume
- 73
- Issue
- 4
- Pages
- 971-995
- ISSN
- 1943-3921 (ISSN-L)
- 1943-393X
- OpenAlex
- W2010927954
- Cited by
- 1,618
- References
- 254
- Citations by year
- 2026: 144 · 2025: 121 · 2024: 128 · 2023: 139 · 2022: 109 · 2021: 124 · 2020: 125 · 2019: 160 · 2018: 123 · 2017: 98 · 2016: 79 · 2015: 95 · 2014: 65 · 2013: 58 · 2012: 33
- FWCI
- 7.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Attention Perception & Psychophysics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- cross-modal correspondence, multisensory integration, auditory-visual correspondence, semantic congruence, perceptual binding, feature binding
- MeSH terms
- Association, Attention, Auditory Perception, Bayes Theorem, Discrimination, Psychological, Humans, Psychophysics, Research, Symbolism, Time Perception, Visual Perception
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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