Neurally-constrained modeling of human gaze strategies in a change blindness task

PLoS Comput Biol. 2021 Aug 24;17(8):e1009322. doi: 10.1371/journal.pcbi.1009322. eCollection 2021 Aug.


Despite possessing the capacity for selective attention, we often fail to notice the obvious. We investigated participants' (n = 39) failures to detect salient changes in a change blindness experiment. Surprisingly, change detection success varied by over two-fold across participants. These variations could not be readily explained by differences in scan paths or fixated visual features. Yet, two simple gaze metrics-mean duration of fixations and the variance of saccade amplitudes-systematically predicted change detection success. We explored the mechanistic underpinnings of these results with a neurally-constrained model based on the Bayesian framework of sequential probability ratio testing, with a posterior odds-ratio rule for shifting gaze. The model's gaze strategies and success rates closely mimicked human data. Moreover, the model outperformed a state-of-the-art deep neural network (DeepGaze II) with predicting human gaze patterns in this change blindness task. Our mechanistic model reveals putative rational observer search strategies for change detection during change blindness, with critical real-world implications.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blindness / physiopathology*
  • Humans
  • Models, Neurological*
  • Neural Networks, Computer
  • Probability
  • Saccades

Associated data

  • figshare/10.6084/m9.figshare.8247860

Grants and funding

All the awards are received by Dr. Devarajan Sridharan (DS). The sponsors/funders and the corresponding grant numbers are listed below: Wellcome Trust-Department of Biotechnology India Alliance Intermediate fellowship -- IA/I/15/2/502089 Science and Engineering Research Board Early Career award -- ECR/2016/000403 Pratiksha Trust award -- FG/SMCH-19-2047 India-Trento Programme for Advanced Research (ITPAR) grant -- INT/ITAL Y/ITPAR-IV/COG/2018/G Department of Biotechnology-Indian Institute of Science Partnership Program grant Sonata Software foundation grant Tata Trusts grant The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.