Abstract
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.
Highlights
We live in a rapidly changing world
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
Computational model of eye movements, incorporating neural constraints on stimulus encoding, that links these gaze metrics with change detection success
Summary
We live in a rapidly changing world. For adaptive survival, our brains must possess the ability to identify relevant, changing aspects of our environment and distinguish them from irrelevant, static ones. Our capacity for attention possesses key limitations One such limitation is revealed by the phenomenon of “change blindness”, in which observers fail to detect obvious changes in a sequence of visual images with intervening discontinuities [2,3]. Provides a useful framework for studying visual attention mechanisms and its lapses [4]. Such lapses have important real-world implications: observers’ success in change blindness tasks has been linked to their driving experience levels [5,6] and safe driving skills [7]
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