Forest Tess Allegra, Siegelman Noam, Finn Amy S
Department of Psychology, University of Toronto.
Haskins Laboratories, New Haven, Connecticut.
Psychol Sci. 2022 Dec;33(12):2059-2072. doi: 10.1177/09567976221114055. Epub 2022 Oct 11.
Our environments are saturated with learnable information. What determines which of this information is prioritized for limited attentional resources? Although previous studies suggest that learners prefer medium-complexity information, here we argue that what counts as medium should change as someone learns an input's structure. Specifically, we examined the hypothesis that attention is directed toward more complicated structures as learners gain more experience with the environment. College students watched four simultaneous streams of information that varied in complexity. RTs to intermittent search trials (Experiment 1, = 75) and eye tracking (Experiment 2, = 45) indexed where participants attended during the experiment. Using two participant- and trial-specific measures of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also predicted attention allocation, with better learners attending to complex structures earlier in learning, suggesting that the ability to prioritize different information over time is related to learning success.
我们的环境中充斥着可学习的信息。是什么决定了在有限的注意力资源下哪些信息会被优先处理呢?尽管先前的研究表明学习者更喜欢中等复杂度的信息,但我们在此认为,随着人们学习输入信息的结构,中等复杂度的定义也应有所变化。具体而言,我们检验了这样一个假设:随着学习者对环境有更多的体验,注意力会指向更复杂的结构。大学生观看了四个同时播放的、复杂度各异的信息流。对间歇性搜索试验的反应时(实验1,n = 75)和眼动追踪(实验2,n = 45)记录了参与者在实验过程中的注意力所在。通过使用两种针对参与者和试验的复杂度测量方法,我们证明了随着时间推移,参与者会关注越来越复杂的信息流。结构学习中的个体差异也预测了注意力分配,学习能力较强的人在学习早期就会关注复杂结构,这表明随着时间推移对不同信息进行优先排序的能力与学习成功有关。