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感知集均值和范围:自动性与精确性。

Perceiving set mean and range: Automaticity and precision.

作者信息

Khayat Noam, Hochstein Shaul

机构信息

Life Sciences Institute and Edmond and Lily Safra Center (ELSC) for Brain Research, Hebrew University, Jerusalem, Israel.

出版信息

J Vis. 2018 Sep 4;18(9):23. doi: 10.1167/18.9.23.

Abstract

To compensate for the limited visual information that can be perceived and remembered at any given moment, many aspects of the visual world are represented as summary statistics. We acquire ensemble representations of element groups as a whole, spreading attention over objects, for which we encode no detailed information. Previous studies found that different features of items (from size/orientation to facial expression/biological motion) are summarized to their mean, over space or time. Summarizing is economical, saving time and energy when the environment is too rich and complex to encode each stimulus separately. We investigated set perception using rapid serial visual presentation sequences. Following each sequence, participants viewed two stimuli, member and nonmember, indicating the member. Sometimes, unbeknownst to participants, one stimulus was the set mean, and or the nonmember was outside the set range. Participants preferentially chose stimuli at/near the mean, a "mean effect," and more easily rejected out-of-range stimuli, a "range effect." Performance improved with member proximity to the mean and nonmember distance from set mean and edge, though they were instructed only to remember presented stimuli. We conclude that participants automatically encode both mean and range boundaries of stimulus sets, avoiding capacity limits and speeding perceptual decisions.

摘要

为了弥补在任何给定时刻所能感知和记忆的视觉信息有限的问题,视觉世界的许多方面都被表示为汇总统计信息。我们将元素组的整体表示作为一个整体来获取,将注意力分散到对象上,对于这些对象我们不编码详细信息。先前的研究发现,项目的不同特征(从大小/方向到面部表情/生物运动)在空间或时间上被汇总为它们的平均值。当环境过于丰富和复杂而无法分别对每个刺激进行编码时,汇总信息是经济的,可节省时间和精力。我们使用快速序列视觉呈现序列研究了集合感知。在每个序列之后,参与者查看两个刺激,成员和非成员,并指出成员。有时,参与者不知道的是,一个刺激是集合平均值,或者非成员超出了集合范围。参与者优先选择平均值处/附近的刺激,即“平均效应”,并且更容易拒绝超出范围的刺激,即“范围效应”。尽管只要求参与者记住呈现的刺激,但随着成员与平均值的接近程度以及非成员与集合平均值和边缘的距离增加,表现会有所提高。我们得出结论,参与者会自动编码刺激集的平均值和范围边界,避免容量限制并加快感知决策。

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