视觉空间概率分布的启发式表征:k-means聚类方式
其他题名Humans represent visuo-spatial probability distribution as k-means clusters
Sun Jingwei; Li Jian; Zhang Hang
2016
会议名称2016年第一届北京视觉科学会议
会议日期2016-07
会议地点北京
会议主办者中国科学院心理研究所
其他摘要

PURPOSE: Many behavioral and neuroimaging studies have shown that human decisions are sensitive to the statistical moments (mean, variance, etc.) of reward distributions. However, little is known about how reward distributions—or, probability distributions in general—are represented in the human brain. When the possible values of a probability distribution is numerous (infinite for a continuous distribution), it would be unrealistic or at least cognitively costly to maintain the probability for each possible value. Here we explored potential heuristic representations of probability distributions and tested them on human subjects. In particular, we tested a recently developed hypothesis that human representations of probability distributions are mixtures of a small number of non-overlapping basis distributions.
METHODS: In two experiments, we constructed a variety of multimodal distributions of spatial positions. On each trial, 70 vertical lines—the horizontal coordinates of which were samples independently drawn from the distributions—were briefly presented, one at a time on the computer screen. Human subjects were asked to locate (on the axis where stimuli were presented) the mean and the mode of the samples. A total of 19 naive subjects participated and completed 144–162 trials each.
RESULTS: All subjects’ mean and mode responses were highly correlated with the true mean and mode of the samples. Interestingly, all subjects’ mean and mode responses had systematic deviations from the true means and modes. The deviation patterns could be well predicted by computational models that assume a division of samples into a small number of clusters following the k-means clustering algorithm. Only the centroid and the relative weight of each cluster were necessary for the further calculation of mean and mode responses.
CONCLUSIONS: Humans represent probability distribution as k-means clusters, and use the centroid and relative weight of each cluster to calculate concerned statistics of the distribution.

关键词distribution representation k-means clustering probabilistic calculation decision under risk
学科领域感知觉心理学
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语种英语
文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/20825
专题心理所主办学术会议_2016年第一届北京视觉科学会议_会议摘要
作者单位1.School of Psychological and Cognitive Sciences, Peking University, 52 Haidian Road, Haidian District, Beijing, China, 100082
2.PKU-IDG/McGovern Institute for Brain Research, Peking University, 52 Haidian Road, Haidian District, Beijing, China, 100082
3.Peking-Tsinghua Center for Life Science, Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, 100871
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GB/T 7714
Sun Jingwei,Li Jian,Zhang Hang. 视觉空间概率分布的启发式表征:k-means聚类方式[C]. 见:2016年第一届北京视觉科学会议. 北京. 2016-07.http://vision.csp.escience.cn/dct/page/1.
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