ArticleScientific reports2022
Image statistics determine the integration of visual cues to motion-in-depth.
Article in Scientific reports, 2022. 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, 2 citations in OpenAlex.
- The farther, the earlier: perceived depth biases temporal order judgments.Psychological research · 2026Article
- Exploring Augmented Reality for Dental Implant Surgery: Feasibility of Using Smartphones as Navigation Tools.Clinical and experimental dental research · 2025Article
- Luminance contrast provides metric depth information.Royal Society open science · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motion-in-depth perception is critical in enabling animals to avoid hazards and respond to potential threats. For humans, important visual cues for motion-in-depth include changing disparity (CD) and changing image size (CS). The interpretation and integration of these cues depends upon multiple scene parameters, such as distance moved, object size and viewing distance, posing a significant computational challenge. We show that motion-in-depth cue integration depends upon sensitivity to the joint probabilities of the scene parameters determining these signals, and on the probability of CD and CS signals co-occurring. Models that took these factors into account predicted human performance in speed-in-depth and cue conflict discrimination tasks, where standard linear integration models could not. These results suggest that cue integration is affected by both the uncertainty of sensory signals and the mapping of those signals to real-world properties. Evidence of a role for such mappings demonstrates the importance of scene and image statistics to the processes underpinning cue integration and the perception of motion-in-depth.
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