Adaptation to mean and variance: Interrelationships between mean and variance representations in orientation perception
Adaptation to mean and variance: Interrelationships between mean and variance representations in orientation perception
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对均值和方差的适应：方向感知中均值和方差表示之间的相互关系
DOI：
10.1167/19.10.192c
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发表时间：
2019
期刊：
影响因子：
1.8
通讯作者：
S. C. Chong
中科院分区：
文献类型：
--
作者：
Jinhyeok Jeong;S. C. Chong
当存在许多视觉项目时，视觉系统可以表示它们的汇总统计(例如，均值、方差)以有效地处理它们。虽然以前的许多研究都研究了均值或方差表征本身，但这两种集合表征之间的关系还没有得到太多的研究。在这项研究中，我们使用视觉适应方法测试了均值和方差表征之间的潜在交互作用。我们推断，如果均值和方差表征相互作用，对均值或方差的适应后效将影响对另一种表征的感知。参与者在适应期间观看了一系列包含特定统计属性的取向数组。为了产生特定于方差或均值的适应后效，适配器阵列的一个属性(方差或均值)具有固定值，而另一个属性是随机变化的。在适应之后，参与者被要求辨别在适应过程中随机变化的测试阵列的属性。研究发现，定向方差的适应后效影响了平均定向辨别的敏感性(实验1)，而平均定向的适应后效影响了定向方差辨别的偏差(实验2)。这些结果表明，均值和方差表示法确实存在密切的相互作用。考虑到均值和方差分别反映了多个项目的代表性价值和离散度，均值和方差表征之间的相互作用可以反映它们的互补作用，从而有效地概括复杂的视觉信息。
When there are many visual items, the visual system could represent their summary statistics (e.g., mean, variance) to process them efficiently. Although many previous studies have investigated the mean or variance representation itself, a relationship between these two ensemble representations has not been investigated much. In this study, we tested the potential interaction between mean and variance representations by using a visual adaptation method. We reasoned that if mean and variance representations interact with each other, an adaptation aftereffect to either mean or variance would influence the perception of the other. Participants watched a sequence of orientation arrays containing a specific statistical property during the adaptation period. To produce an adaptation aftereffect specific to variance or mean, one property of the adaptor arrays (variance or mean) had a fixed value while the other property was randomly varied. After the adaptation, participants were asked to discriminate the property of the test array that was randomly varied during the adaptation. We found that the adaptation aftereffect of orientation variance influenced the sensitivity of mean orientation discrimination (Experiment 1), and that the adaptation aftereffect of mean orientation influenced the bias of orientation variance discrimination (Experiment 2). These results suggest that mean and variance representations do closely interact with each other. Considering that mean and variance reflect the representative value and dispersion of multiple items respectively, the interactions between mean and variance representations may reflect their complementary roles to summarize complex visual information effectively.
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