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

Jagatap, A and Purokayastha, S and Jain, H and Sridharan, D (2021) Neurally-constrained modeling of human gaze strategies in a change blindness task. In: PLoS Computational Biology, 17 (8).

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Official URL: https://doi.org/10.1371/journal.pcbi.1009322


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 Jagatap et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Item Type: Journal Article
Publication: PLoS Computational Biology
Publisher: Public Library of Science
Additional Information: The copyright for this article belongs to Authors
Keywords: adult; Article; Bayesian network; benchmarking; deep neural network; female; gaze; human; human experiment; male; model; normal human; prediction; probability; simulation; visual system examination
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Dec 2021 13:02
Last Modified: 02 Dec 2021 13:02
URI: http://eprints.iisc.ac.in/id/eprint/70088

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