biostudies-literatureJagatap APratiksha Trusttata trustsdepartment of biotechnology-indian institute of science partnership program grantScience and Engineering Research Board Early Career Awardindia-trento programme for advanced research (itpar) grantDBT/Wellcome Trust India Alliancesonata software foundation grante1009322https://www.ebi.ac.uk/biostudies/studies/S-EPMC8478260biostudies-literatureUnknown17(8)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.PLoS computational biologyNeurally-constrained modeling of human gaze strategies in a change blindness task.PMC8478260FG/SMCH-19-2047IA/I/15/2/502089INT/ITAL Y/ITPAR-IV/COG/2018/GECR/2016/000403Jagatap ASridharan DJain HPurokayastha SfalseNeurally-constrained modeling of human gaze strategies in a change blindness task.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.2021-01-01T00:00:00Z2021 Aug2024-11-12T19:03:24.753Z2022-02-11T11:24:41.189ZS-EPMC84782603442820110.1371/journal.pcbi.1009322