Hélie Sébastien, Turner Benjamin O, Cousineau Denis
Purdue University, United States of America.
Nanyang Technological University, Singapore.
Acta Psychol (Amst). 2018 Nov;191:52-62. doi: 10.1016/j.actpsy.2018.08.016. Epub 2018 Sep 13.
Smith, Redford, Gent, and Washburn (2005) have proposed a new categorization paradigm called the visual-search categorization task to study how display size affects categorization performance. Their results show that, in a wide range of conditions, category knowledge collapses as soon as multiple stimuli are simultaneously displayed in a scene. This result is surprising and important considering that humans parse and categorize objects from complex scenes on a daily basis. However, Smith et al. only studied one kind of category structure. This article presents the results of three experiments exploring the effect of display size on perceptual categorization as a function of category structure. We show that rule-based and information-integration categories are differently affected by display size in the visual search categorization task. For rule-based structures, target-present and target-absent trials are not much affected by display size. However, the effect of display size is bigger for information-integration category structures, and much more pronounced for target-absent trials than for target-present trials. A follow-up experiment shows that target redundancy (i.e., having more than one target in the display) does not improve performance with information-integration category structures. These results suggest that categories may be learned differently depending on their underlying structure, and that the resulting category representation may influence performance in the visual search categorization task.
史密斯、雷德福、根特和沃什伯恩(2005年)提出了一种名为视觉搜索分类任务的新分类范式,以研究显示大小如何影响分类性能。他们的结果表明,在广泛的条件下,一旦场景中同时显示多个刺激,类别知识就会瓦解。考虑到人类每天都要从复杂场景中解析和分类物体,这一结果既令人惊讶又很重要。然而,史密斯等人只研究了一种类别结构。本文呈现了三个实验的结果,这些实验探讨了作为类别结构函数的显示大小对感知分类的影响。我们表明,在视觉搜索分类任务中,基于规则的类别和信息整合类别受显示大小的影响不同。对于基于规则的结构,有目标和无目标试验受显示大小的影响不大。然而,显示大小对信息整合类别结构的影响更大,并且在无目标试验中比在有目标试验中更为明显。一项后续实验表明,目标冗余(即显示中有多个目标)并不能提高信息整合类别结构的性能。这些结果表明,类别可能因其底层结构的不同而有不同的学习方式,并且由此产生的类别表征可能会影响视觉搜索分类任务中的性能。