Big and small numbers: Empirical support for a single, flexible mechanism for numerosity perception

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• 作者：Rakesh Sengupta ; S. Bapiraju ; David Melcher
• 关键词：Theoretical and computational models ; Reaction time methods ; Spatial vision
• 刊名：Attention, Perception, & Psychophysics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：79
• 期：1
• 页码：253-266
• 全文大小：
• 刊物主题：Cognitive Psychology;
• 出版者：Springer US
• ISSN：1943-393X
• 卷排序：79

文摘

The existence of perceptually distinct numerosity ranges has been proposed for small (i.e., subitizing range) and larger numbers based on differences in precision, Weber fractions, and reaction times. This raises the question of whether such dissociations reflect distinct mechanisms operating across the two numerosity ranges. In the present work, we explore the predictions of a single-layer recurrent on-center, off-surround network model of attentional priority that has been applied to object individuation and enumeration. Activity from the network can be used to model various phenomena in the domain of visual number perception based on a single parameter: the strength of inhibition between nodes. Specifically, higher inhibition allows for precise representation of small numerosities, while low inhibition is preferred for high numerosities. The model makes novel predictions, including that enumeration of small numerosities following large numerosities should result in longer reaction times than when a small numerosity trial following small numerosities. Moreover, the model predicts underestimation of number when a display containing a large number of items follows a trial with small numerosities. We behaviorally confirmed these predictions in a series of experiments. This pattern of results is consistent with a single, flexible object individuation system, which can be modeled successfully by dynamic on-center, off-surround network model of the attentional priority (saliency) map.