December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Haptic object recognition abilities correlate across feature types and with visual object recognition ability
Author Affiliations & Notes
  • Jason Chow
    Vanderbilt University
  • Thomas Palmeri
    Vanderbilt University
  • Isabel Gauthier
    Vanderbilt University
  • Footnotes
    Acknowledgements  This work was supported by the David K. Wilson Chair Research Fund (Vanderbilt University).
Journal of Vision December 2022, Vol.22, 3260. doi:
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      Jason Chow, Thomas Palmeri, Isabel Gauthier; Haptic object recognition abilities correlate across feature types and with visual object recognition ability. Journal of Vision 2022;22(14):3260.

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

  • Supplements

The selection, use, and memory for the information that support visual object recognition is a general ability that applies across varied tasks and categories. In convergence with cross-modal experimental studies, individual differences research suggests that this ability also predicts haptic object recognition. An important component in the measurement of a general construct for haptic object recognition is the use of multiple tasks to form a latent construct that can explain behavior across tasks with different constraints. While the ultimate goal is to use latent variable modeling, as an intermediate step, we developed two new haptic tests and use the principle of aggregation to create representative estimates for abilities of interest. One test emphasizes the use of texture features with Braille stimuli and another test emphasizes shape features using palm-sized 3D-printed Greebles. We measured object recognition ability using existing visual and haptic object recognition tests alongside our new tests (n=87). Our new tests have acceptable reliability (omegas > .72). The new Braille test correlates with an existing haptic test also using texture features with flat buttons (r=.40, BF10=1490). Similarly, the new Greebles test correlates with an existing shape-reliant test with 3D-printed spaceships, though weaker (r=.19, BF10=2.33). Combining these pairs of tests into two aggregate measures of haptic object recognition ability dependent on feature type reveals a correlation (r=.41, BF10=2999). Furthermore, combining all four haptic test scores into a single haptic object recognition measure reveals a correlation with general visual object recognition ability measured by two tests (r=.34, BF10=132.41). Our results suggest a general factor for haptic object recognition regardless of feature types, one that overlaps with visual object recognition ability. This is an important step in building a latent construct for general haptic object recognition ability to test its relationship with the analogous general visual ability.


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