The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Optimal integration of texture and motion cues to depth
Open accessAbstract
We report the results of a depth-matching experiment in which subjects were asked to adjust the height of an ellipse until it matched the depth of a simulated cylinder defined by texture and motion cues. In one-third of the trials the shape of the cylinder was primarily given by motion information, in another one-third of the trials it was given by texture information, and on the remaining trials it was given by both sources of information. Two optimal cue combination models are described where optimality is defined in terms of Bayesian statistics. The parameter values of the models are set based on subjects' responses on trials when either the motion cue or the texture cue was informative. These models provide predictions of subjects' responses on trials when both cues were informative. The results indicate that one of the optimal models provides a good fit to the subjects' data, and the second model provides an exceptional fit. Because the predictions of the optimal models closely match the experimental data, we conclude that observers' cue-combination strategies are indeed optimal, at least under the conditions studied here.
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Cites
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Cited by
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
The Bayesian brain: The role of uncertainty in neural coding and computation
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Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
Merging the senses into a robust percept
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The Ventriloquist Effect Results from Near-Optimal Bimodal Integration
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Metadata
- Authors
- Jacobs, R. A.
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 1999-10
- Volume
- 39
- Issue
- 21
- Pages
- 3621-3629
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W1999018741
- Cited by
- 324
- References
- 19
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
