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使用加权和无放回抽样对顺序呈现的类别进行判断建模。

Modeling judgment of sequentially presented categories using weighting and sampling without replacement.

机构信息

Kingston University London, Kingston upon Thames, UK.

出版信息

Behav Res Methods. 2012 Dec;44(4):1129-34. doi: 10.3758/s13428-012-0218-9.

Abstract

In a series of experiments, Kusev et al. (Journal of Experimental Psychology: Human Perception and Performance 37:1874-1886, 2011) studied relative-frequency judgments of items drawn from two distinct categories. The experiments showed that the judged frequencies of categories of sequentially encountered stimuli are affected by the properties of the experienced sequences. Specifically, a first-run effect was observed, whereby people overestimated the frequency of a given category when that category was the first repeated category to occur in the sequence. Here, we (1) interpret these findings as reflecting the operation of a judgment heuristic sensitive to sequential patterns, (2) present mathematical definitions of the sequences used in Kusev et al. (Journal of Experimental Psychology: Human Perception and Performance 37:1874-1886, 2011), and (3) present a mathematical formalization of the first-run effect-the judgments-relative-to-patterns model-to account for the judged frequencies of sequentially encountered stimuli. The model parameter w accounts for the effect of the length of the first run on frequency estimates, given the total sequence length. We fitted data from Kusev et al. (Journal of Experimental Psychology: Human Perception and Performance 37:1874-1886, 2011) to the model parameters, so that with increasing values of w, subsequent items in the first run have less influence on judgments. We see the role of the model as essential for advancing knowledge in the psychology of judgments, as well as in other disciplines, such as computer science, cognitive neuroscience, artificial intelligence, and human-computer interaction.

摘要

在一系列实验中,Kusev 等人(《实验心理学杂志:人类感知与表现》37:1874-1886, 2011)研究了从两个不同类别中抽取的项目的相对频率判断。实验表明,连续遇到的刺激类别的判断频率受到所经历序列特性的影响。具体来说,观察到了首次运行效应,即当给定类别是序列中首次重复出现的类别时,人们会高估该类别的频率。在这里,我们 (1) 将这些发现解释为反映了对序列模式敏感的判断启发式的运作,(2) 提出了 Kusev 等人(《实验心理学杂志:人类感知与表现》37:1874-1886, 2011)中使用的序列的数学定义,(3) 提出了首次运行效应的数学形式化——相对于模式的判断模型,以解释连续遇到的刺激的判断频率。模型参数 w 用于解释首次运行长度对频率估计的影响,给定总序列长度。我们将 Kusev 等人(《实验心理学杂志:人类感知与表现》37:1874-1886, 2011)的数据拟合到模型参数中,因此,随着 w 的值增加,首次运行中的后续项目对判断的影响会减小。我们认为该模型的作用对于推进判断心理学以及计算机科学、认知神经科学、人工智能和人机交互等其他学科的知识至关重要。

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