A Bayesian model of distance perception from ocular convergence

Scarfe, P. ORCID: https://orcid.org/0000-0002-3587-6198 and Hibbard, P. B. (2025) A Bayesian model of distance perception from ocular convergence. PLoS Computational Biology, 21 (10). e1013506. ISSN 1553-734X doi: 10.1371/journal.pcbi.1013506

Abstract/Summary

Ocular convergence is one of the critical cues from which to estimate the absolutedistance to objects in the world, because unlike most other distance cues a one-to-one mapping exists between absolute distance and ocular convergence. However,even when accurately converging their eyes on an object, humans tend to underesti-mate its distance, particularly for more distant objects. This systematic bias in dis-tance perception has yet to be explained and questions the utility of vergence as anabsolute distance cue. Here we present a probabilistic geometric model that showshow distance underestimation can be explained by the visual system estimating themost likely distance in the world to have caused an accurate, but noisy, ocular con-vergence signal. Furthermore, we find that the noise in the vergence signal neededto account for human distance underestimation is comparable to that experimentallymeasured. Critically, our results depend on the formulation of a likelihood functionthat takes account of the generative function relating distance to ocular convergence.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/125136
Identification Number/DOI	10.1371/journal.pcbi.1013506
Refereed	Yes
Divisions	Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
Publisher	Public Library of Science
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