September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Temporal Dynamics Gap between Position Tracking and Attribute Tracking
Author Affiliations & Notes
  • Yen-Ju Chen
    Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Japan
  • Zitang Sun
    Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Japan
  • Shin’ya Nishida
    Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Japan
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
  • Footnotes
    Acknowledgements  Supported by JSPS Kakenhi 20H00603, 20H05957.
Journal of Vision September 2024, Vol.24, 465. doi:https://doi.org/10.1167/jov.24.10.465
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      Yen-Ju Chen, Zitang Sun, Shin’ya Nishida; Temporal Dynamics Gap between Position Tracking and Attribute Tracking. Journal of Vision 2024;24(10):465. https://doi.org/10.1167/jov.24.10.465.

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

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Abstract

Continuous tracking is a promising psychophysical paradigm to quickly estimate the temporal dynamics of the visuomotor system. It asks observers to mouse-track a randomly changing target spatial position (position tracking: PT) or attribute value (attribute tracking: AT). The impulse response can be computed from the cross-correlation in velocity between stimulus and response. To understand the visuomotor mechanisms underlying continuous tracking, we examined the effects of task (PT or AT) and stimulus. We used eleven stimuli: a Gaussian blob changing either in position (for PT) or in luminance (for AT); a color-modulated concentric Gabor (red-green or blue-yellow) changing in position or chromatic contrast; a luminance-modulated concentric Gabor (one of eight spatial frequencies) changing in position or luminance contrast. The results show that stimulus parameters affected the estimated impulse response. PT and AT responses were weakly correlated with regard to the effects of stimulus in either peak latency or impulse response width, which suggests a partial commonality of the underlying mechanism. However, the AT response was approximately 2.6 times slower in latency and 5.0 times broader in width than the PT response. Where does this difference come from? It cannot be ascribed to rapid automatic vision-hand mapping in PT since even when compared with an anti-PT condition where observers had to move the mouse in the opposite direction of the visual stimulus movement, the AT response was 1.7 times slower and 2.3 times broader. Another task difference is that PT does not need to evaluate the attribute value, but AT does. To test this factor, we measured the simple reaction time to the detection of a single stimulus change and found it agreed well with PT’s peak latency. The results suggest that slower responses for AT than for PT can be ascribed to the extra process to access the attribute content.

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