The Psychophysics of Human Three-Dimensional Active Visuospatial Problem-Solving
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Abstract (arXiv)
Our understanding of how visual systems detect, analyze and interpret visual stimuli has advanced greatly. However, the visual systems of all animals do much more; they enable visual behaviours. How well the visual system performs while interacting with the visual environment and how vision is used in the real world have not been well studied, especially in humans. It has been suggested that comparison is the most primitive of psychophysical tasks. Thus, as a probe into these active visual behaviours, we use a same-different task: are two physical 3D objects visually the same? This task seems to be a fundamental cognitive ability. We pose this question to human subjects who are free to move about and examine two real objects in an actual 3D space. Past work has dealt solely with a 2D static version of this problem. We have collected detailed, first-of-its-kind data of humans performing a visuospatial task in hundreds of trials. Strikingly, humans are remarkably good at this task without any training, with a mean accuracy of 93.82%. No learning effect was observed on accuracy after many trials, but some effect was seen for response time, number of fixations and extent of head movement. Subjects demonstrated a variety of complex strategies involving a range of movement and eye fixation changes, suggesting that solutions were developed dynamically and tailored to the specific task.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 36 references that are in the map. First posted on arXiv 19 Jun 2023.
Builds on
5 of 36 references.
References · 5
- 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,457
- 2017T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objectscited by 210
- 2021Active Learning in Robotics: A Review of Control Principlescited by 26
- 2020PESAO: Psychophysical Experimental Setup for Active Observerscited by 4
- 2021Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networkscited by 4
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Citing papers per year: 2025: 2.
2 papers.