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Right time, right place: implicit learning of target onsets in a visual search task

计算机科学 任务(项目管理) 时间知觉 感知 视觉搜索 象限(腹部) 人工智能 认知心理学 心理学 神经科学 医学 病理 经济 管理
作者
Nir Shalev,Sage Boettcher,Anna C. Nobre
出处
期刊:Journal of Vision [Association for Research in Vision and Ophthalmology (ARVO)]
卷期号:19 (10): 255b-255b 被引量:2
标识
DOI:10.1167/19.10.255b
摘要

The brain constitutes a predictive system that is constantly learning about regularities in the environment and utilizing these regularities to anticipate events. Studies have shown that expectations improve performance by altering perception and allowing motor preparation. However, behaviour also benefits from temporal predictions by guiding spatial attention to specific locations at specific times. For example, we can effectively divert our spatial attention from the road while waiting for a traffic light to turn green; predictions in time allow us to estimate when is a good time to re-engage. We investigated the guidance of spatial attention based on the online formation of temporal predictions. To this end, we designed a novel variation of a visual search task. On each trial (lasting approximately 10 seconds) individuals searched for eight targets among distractors in a dynamic display in which the items faded in and out at different locations and times. The screen was split into four distinct quadrants. On each trial, four of the eight targets were spatially and temporally predictable in that they always appeared in the same quadrant at the same time (e.g. predictive target 1 always appeared in quadrant 3 after 2 seconds; Figure#1a). Crucially, the other four targets were distributed randomly in time and space, making it impossible to use sequential information to find targets. We showed that subjects found predictable targets more often and faster than random targets (Figure#1b). Additionally, we showed a large drop off in the hit rate of random targets which immediately follow predictable targets, suggesting that observers inhibit predicted spatial locations in the time following the prediction (Figure#1c). We support our behavioural data with eye-tracking, to identify how temporal predictions modulate search trajectories. These results provide important insight into the interactions between spatial and temporal predictions in a natural task such as visual search.

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