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[一类针对奥佩尔 - 昆特错觉的信息整合模型]

[A class of information integration models for the Oppel-Kundt illusion].

作者信息

Erdfelder E, Faul F

机构信息

Psychologisches Institut, Universität Bonn.

出版信息

Z Psychol Z Angew Psychol. 1994;202(2):133-60.

PMID:7941680
Abstract

Oppel (1860/1861) first described the phenomenon that an equidistantly divided line usually looks longer than an undivided line of equal length (Oppel-Kundt illusion, OKI). The present paper begins with a discussion of the Hering-Kundt hypothesis (HKH) of the OKI. The HKH comprises two assumptions: (1) An assumption concerning the perceptual integration of the length of a single part of the divided line and the number of such parts; (2) an assumption about the psychophysical function of line length. There is no doubt that the HKH is empirically not tenable. However, nothing is known about the validity of the perceptual integration assumption when considered in isolation. It is shown that the HKH can be conceived as a special case of a more general information-integration model. According to this model, the subjective total length of a divided line is equal to the subjective length of one part of the line multiplied by the subjective number of parts. Two experiments with a total of 15 subjects are reported. The model is shown to be valid without any exception. On the background of these results published data on the OKI are re-analyzed, looking at whether they contain information about the psychophysical function for line length. It is shown that certain qualitative aspects of these data are inconsistent with a power function hypothesis while, at the same time, being compatible with a logarithmic (Fechner-) function.

摘要

奥佩尔(1860/1861)首次描述了这样一种现象:一条等距分割的线通常看起来比长度相等的未分割线更长(奥佩尔 - 昆特错觉,OKI)。本文首先讨论了关于OKI的黑林 - 昆特假说(HKH)。HKH包含两个假设:(1)关于分割线单个部分长度与这些部分数量的感知整合的假设;(2)关于线长心理物理学函数的假设。毫无疑问,HKH在经验上是站不住脚的。然而,当单独考虑感知整合假设的有效性时,却一无所知。研究表明,HKH可以被看作是一个更一般的信息整合模型的特殊情况。根据这个模型,分割线的主观总长度等于线的一个部分的主观长度乘以主观部分数量。报告了两项共有15名受试者参与的实验。结果表明该模型无一例外都是有效的。基于这些结果,对已发表的关于OKI的数据进行了重新分析,看它们是否包含有关线长心理物理学函数的信息。结果表明,这些数据的某些定性方面与幂函数假设不一致,同时却与对数(费希纳)函数相符。

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