August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Change detection and visual classification: two sides of the same coin
Author Affiliations
  • Bo Chen
    Computation and Neural Systems, California Institute of Technology
  • Ming Jiang
    Electrical and Computer Engineering, National University of Singapore
  • Mason McGill
    Computation and Neural Systems, California Institute of Technology
  • Qi Zhao
    Electrical and Computer Engineering, National University of Singapore
  • Pietro Perona
    Computation and Neural Systems, California Institute of Technology
Journal of Vision September 2016, Vol.16, 593. doi:10.1167/16.12.593
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Bo Chen, Ming Jiang, Mason McGill, Qi Zhao, Pietro Perona; Change detection and visual classification: two sides of the same coin. Journal of Vision 2016;16(12):593. doi: 10.1167/16.12.593.

      Download citation file:


      © 2017 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements
Abstract

Change is certain, but the what, the where and the when are unpredictable. E.g. approaching animals need to be detected promptly, and also classified as friend or foe. What is the best way to carry out detection and discrimination jointly? How do humans do it? We compared subjects' performance in three tasks: (1) detection with unpredictable stimulus onset time, (2) discrimination with known stimulus onset time, and (3) dual task: discrimination with unpredictable stimulus onset time. Four subjects participated in a novel random-dot motion discrimination task, where a trial begins with incoherent random-dot motion and coherent motion appears after a random delay. For different tasks, observers were required to detect the onset of the signal and/or discriminate the motion direction. In our stimuli motion directions are not necessarily opposite, so we could vary independently the difficulty of discrimination and detection by controlling the motion strength (% coherence) and the motion directions. Our data show that discrimination is more difficult when signal onset is uncertain, suggesting that the additional detection task imposes an extra cost on the system. We developed three models of joint detection and discrimination: an optimal model (CD: classify-then-detect), and two simplified versions of the optimal model (DCP: detection-classification-in-parallel, and DCS: detection-classification-in-series). We compare the predictions of the three models to human data, and find that CD does not fit the data well, as it has no flexibility to switch off the discrimination component, and will overestimate the difficulty of the detection-only task. In addition, DCS produces the most consistent parameter estimates among different tasks. DCS is sub-optimal because it discards signal for discrimination until detection. Thus our findings indicate that the visual system may not be optimal from a purely information-processing point of view.

Meeting abstract presented at VSS 2016

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×