Cited by 422Visual perception and processing mechanismsAdvanced Vision and ImagingMultisensory perception and integration

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Slant from texture and disparity cues: Optimal cue combination

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Hillis, James M., Watt, Simon J., Landy, Michael S., Banks, Martin S.·2004·Journal of Vision

DOI 10.1167/4.12.1

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Abstract

How does the visual system combine information from different depth cues to estimate three-dimensional scene parameters? We tested a maximum-likelihood estimation (MLE) model of cue combination for perspective (texture) and binocular disparity cues to surface slant. By factoring the reliability of each cue into the combination process, MLE provides more reliable estimates of slant than would be available from either cue alone. We measured the reliability of each cue in isolation across a range of slants and distances using a slant-discrimination task. The reliability of the texture cue increases as |slant| increases and does not change with distance. The reliability of the disparity cue decreases as distance increases and varies with slant in a way that also depends on viewing distance. The trends in the single-cue data can be understood in terms of the information available in the retinal images and issues related to solving the binocular correspondence problem. To test the MLE model, we measured perceived slant of two-cue stimuli when disparity and texture were in conflict and the reliability of slant estimation when both cues were available. Results from the two-cue study indicate, consistent with the MLE model, that observers weight each cue according to its relative reliability: Disparity weight decreased as distance and |slant| increased. We also observed the expected improvement in slant estimation when both cues were available. With few discrepancies, our data indicate that observers combine cues in a statistically optimal fashion and thereby reduce the variance of slant estimates below that which could be achieved from either cue alone. These results are consistent with other studies that quantitatively examined the MLE model of cue combination. Thus, there is a growing empirical consensus that MLE provides a good quantitative account of cue combination and that sensory information is used in a manner that maximizes the precision of perceptual estimates.

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Authors: Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S.
Affiliations: University of Pennsylvania (US)
Bangor University (GB)
New York University (US)
University of California, Berkeley (US)
ORCID: Hillis, James M.
Watt, Simon J.
Landy, Michael S.
Banks, Martin S.
Venue: Journal of Vision
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Published: 2004-12-01
Volume: 4
Issue: 12
Pages: 1
ISSN: 1534-7362 (ISSN-L)
DOI: 10.1167/4.12.1
OpenAlex: W2158527951
Cited by: 421
References: 48
Citations by year: 2026: 11 · 2025: 9 · 2024: 13 · 2023: 12 · 2022: 18 · 2021: 13 · 2020: 13 · 2019: 21 · 2018: 25 · 2017: 20 · 2016: 16 · 2015: 28 · 2014: 16 · 2013: 27 · 2012: 28
FWCI: 7.05
Type: journal article
Language: en
Open access: gold
doi.org
Locations: Journal of Vision (publishedVersion)
PubMed (publishedVersion)
Funders: National Eye Institute (R01EY012851, EY 08266, EY12851, R01EY008266)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
Keywords: surface slant perception, cue combination, binocular disparity, maximum likelihood estimation, cue reliability, perceptual precision, depth perception
MeSH terms: Cues, Depth Perception, Form Perception, Humans, Pattern Recognition, Visual, Vision, Binocular, Vision Disparity, Likelihood Functions

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Slant from texture and disparity cues: Optimal cue combination

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