The psychophysics of human three-dimensional active visuospatial problem-solving

• Published in: Scientific reports Vol. 13; no. 1; pp. 19967 - 10
• Main Authors: Solbach, Markus D., Tsotsos, John K.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477/2811; 639/705/117; Accuracy; Cognitive ability; Experimental design; Humanities and Social Sciences; multidisciplinary; Observational learning; Problem solving; Psychophysics; Science; Science (multidisciplinary); Visual stimuli; Visual system
• DOI: 10.1038/s41598-023-47188-4
• ISSN: 2045-2322

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 is far from fully understood, 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 is a fundamental cognitive ability. We pose this question to human subjects who are free to move about and examine two real objects in a physical 3D space. The experimental design is such that all behaviours are directed to viewpoint change. Without any training, our participants achieved 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. Our probe task, even though easily executed at high-performance levels, uncovered a surprising variety of complex strategies for viewpoint control, suggesting that solutions were developed dynamically and deployed in a seemingly directed hypothesize-and-test manner tailored to the specific task. Subjects need not acquire task-specific knowledge; instead, they formulate effective solutions right from the outset, and as they engage in a series of attempts, those solutions progressively refine, becoming more efficient without compromising accuracy.