Temporal regularities guide attention when combined with spatial or featural predictions within dynamic environments

crossref(2022)

引用 0|浏览1
暂无评分
摘要
This study tested whether observers can benefit from temporal regularities occurring within dynamic and distraction-filled contexts to identify target stimuli. Across two online experiments, participants searched for multiple targets amongst distractors in dynamic visual- search tasks in which coloured stimuli faded in and out of the display at different times and locations. In Experiment 1, half of the targets across trials were spatiotemporally predictable, occurring at consistent time points and spatial quadrants. The other targets and the many competing distracting stimuli were completely unpredictable. Colour was unpredictable for all stimuli. In Experiment 2, half of the targets across trials were feature-temporally predictable. They appeared at consistent time points and with consistent colours. The other targets and competing distractors had unpredictable timings and colours, and all stimuli had unpredictable spatial locations. In both experiments, temporal predictability of targets was separate from just temporal order. The pattern of results was consistent across experiments, showing that participants benefitted significantly from spatiotemporal (Experiment 1) and feature-temporal (Experiment 2) predictions for identifying targets. The ability to utilise temporal regularities that occur across unpredictable intervening stimuli, and the ability to utilise such temporal regularities independently of spatial predictions, carry important theoretical implications for understanding the nature of temporal expectations. An exploratory questionnaire after each experiment indicated that participants learned spatiotemporal and feature-temporal predictions incidentally and used them implicitly and without awareness. Our study highlights the importance of the temporal dimension for guiding behaviour within dynamic and competitive contexts mirroring ecological situations and opens new questions for investigation.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要