首页 | 本学科首页   官方微博 | 高级检索  
   检索      


Neurally-constrained modeling of human gaze strategies in a change blindness task
Authors:Akshay Jagatap  Simran Purokayastha  Hritik Jain  Devarajan Sridharan
Institution:1. Centre for Neuroscience, Indian Institute of Science, Bangalore, India ; 2. Computer Science and Automation, Indian Institute of Science, Bangalore, India ; Dartmouth College, UNITED STATES
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.
Keywords:
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号