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Visual statistical learning is modulated by arbitrary and natural categories
Psychonomic Bulletin & Review ( IF 3.2 ) Pub Date : 2021-03-31 , DOI: 10.3758/s13423-021-01917-w
Leeland L Rogers 1 , Su Hyoun Park 1 , Timothy J Vickery 1
Affiliation  

Visual statistical learning (VSL) describes the unintentional extraction of statistical regularities from visual environments across time or space, and is typically studied using novel stimuli (e.g., symbols unfamiliar to participants) and using familiarization procedures that are passive or require only basic vigilance. The natural visual world, however, is rich with a variety of complex visual stimuli, and we experience that world in the presence of goal-driven behavior including overt learning of other kinds. To examine how VSL responds to such contexts, we exposed subjects to statistical contingencies as they learned arbitrary categorical mappings of unfamiliar stimuli (fractals, Experiment 1) or familiar stimuli with preexisting categorical boundaries (faces and scenes, Experiment 2). In a familiarization stage, subjects learned by trial and error the arbitrary mappings between stimuli and one of two responses. Unbeknownst to participants, items were paired such that they always appeared together in the stream. Pairs were equally likely to be of the same or different category. In a pair recognition stage to assess VSL, subjects chose between a target pair and a foil pair. In both experiments, subjects’ VSL was shaped by arbitrary categories: same-category pairs were learned better than different-category pairs. Natural categories (Experiment 2) also played a role, with subjects learning same-natural-category pairs at higher rates than different-category pairs, an effect that did not interact with arbitrary mappings. We conclude that learning goals of the observer and preexisting knowledge about the structure of the world play powerful roles in the incidental learning of novel statistical information.



中文翻译:

视觉统计学习由任意和自然的类别调制

视觉统计学习 (VSL) 描述了从跨时间或空间的视觉环境中无意提取统计规律,通常使用新的刺激(例如,参与者不熟悉的符号)和使用被动或仅需要基本警惕的熟悉程序进行研究。然而,自然视觉世界充满了各种复杂的视觉刺激,我们在存在目标驱动行为的情况下体验这个世界,包括其他类型的公开学习。为了检查 VSL 如何对此类上下文做出反应,我们让受试者在学习不熟悉的刺激(分形,实验 1)的任意分类映射或具有预先存在的分类边界(面部和场景,实验 2)的熟悉刺激时,将其暴露于统计偶然性。在熟悉阶段,受试者通过反复试验学习刺激和两种反应之一之间的任意映射。参与者不知道的是,项目是配对的,因此它们总是一起出现在信息流中。对属于相同或不同类别的可能性相同。在评估 VSL 的配对识别阶段,受试者在目标配对和箔配对之间进行选择。在这两个实验中,受试者的 VSL 是由任意类别塑造的:同类别对比不同类别对学习得更好。自然类别(实验 2)也发挥了作用,受试者学习相同自然类别对的速率高于不同类别对,这种效果与任意映射没有相互作用。

更新日期:2021-04-01
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