ArticlePLoS computational biology2025
A Bayesian model of distance perception from ocular convergence.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Static Estimation of Vista-Space Egocentric Distance with Iterative Feedback: A Cognitive-Perceptual Task.Life (Basel, Switzerland) · 2026Article
- Human-like monocular depth biases in deep neural networks.PLoS computational biology · 2025Article
- Does vision extract absolute distance from vergence?Attention, perception & psychophysics · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
Ocular convergence is one of the critical cues from which to estimate the absolute distance 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 underestimate its distance, particularly for more distant objects. This systematic bias in distance perception has yet to be explained and questions the utility of vergence as an absolute distance cue. Here we present a probabilistic geometric model that shows how distance underestimation can be explained by the visual system estimating the most likely distance in the world to have caused an accurate, but noisy, ocular convergence signal. Furthermore, we find that the noise in the vergence signal needed to account for human distance underestimation is comparable to that experimentally measured. Critically, our results depend on the formulation of a likelihood function that takes account of the generative function relating distance to ocular convergence.
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Registered trials
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