September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Unraveling the Intricacies of Human Visuospatial Problem-Solving
Author Affiliations & Notes
  • Markus D. Solbach
    York University
  • John K. Tsotsos
    York University
  • Footnotes
    Acknowledgements  This research was supported by grants to the senior author (John K. Tsotsos) from the following sources: Air Force Office of Scientific Research USA, The Canada Research Chairs Program, and the NSERC Canadian Robotics Network.
Journal of Vision September 2024, Vol.24, 360. doi:https://doi.org/10.1167/jov.24.10.360
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      Markus D. Solbach, John K. Tsotsos; Unraveling the Intricacies of Human Visuospatial Problem-Solving. Journal of Vision 2024;24(10):360. https://doi.org/10.1167/jov.24.10.360.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Computational learning of visual systems has seen remarkable success, especially during the last decade. A large part of it can be attributed to the availability of large data sets tailored to specific domains. Most training is performed over unordered and assumed independent data samples and more data correlates with better performance. This work considers what we observe from humans as our sample. In hundreds of trials with human subjects, we found that samples are not independent, and ordered sequences are our observation of internal visual functions. We investigate human visuospatial capabilities through a real-world experimental paradigm. Previous literature posits that comparison represents the most rudimentary form of psychophysical tasks. As an exploration into dynamic visual behaviours, we employ the same-different task in 3D: are two physical 3D objects visually identically? Human subjects are presented with the task while afforded freedom of movement to inspect two real objects within a physical 3D space. The experimental protocol is structured to ensure that all eye and head movements are oriented toward the visual task. We show that no training was needed to achieve good accuracy, and we demonstrate that efficiency improves with practice on various levels, contrasting with modern computational learning. Extensive use is made of eye and head movements to acquire visual information from appropriate viewpoints in a purposive manner. Furthermore, we exhibit that fixations and corresponding head movements are well-orchestrated, encompassing visual functions, which are composed dynamically and tailored to task instances. We present a set of triggers that we observed to activate those functions. Furthering the understanding of this intricate interplay plays an essential role in developing human-like computational learning systems. The "why" behind all the functionalities - unravelling their purpose - poses an exciting challenge. While human vision may appear effortless, the intricacy of visuospatial functions is staggering.

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