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Measuring decision weights in recognition experiments with multiple response alternatives: Comparing the correlation and multinomial-logistic-regression methods

机译:在具有多种响应选择的识别实验中测量决策权重:比较相关性和多项式逻辑回归方法

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摘要

Psychophysical “reverse-correlation” methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.
机译:心理物理“反相关”方法使研究人员可以洞察感知任务中单个对象的感知表示和决策权重策略。尽管这些方法获得了发展,但直到最近,它们的开发还仅限于仅涉及两个响应类别的实验。最近,提出了两种在m替代实验中估计决策权重的方法。一种方法将两类相关方法扩展到m> 2个备选方案。第二种使用多项式逻辑回归(MLR)。在本文中,讨论了这两种方法的相对优点,并使用蒙特卡洛模拟对这些方法的收敛性和统计效率问题进行了定量评估。结果表明,对于一定数量的试验数量值,与MLR方法相比,相关方法的估计加权模式更接近其渐近值。此外,对于MLR方法,不同刺激成分的权重估计值可能表现出很强的相关性,因此与相关方法相比,分析和解释测得的加权模式不那么直接。相关方法的这些和其他优点(包括计算简单和与其他公认的心理物理反向相关方法的紧密关系)使其成为揭示m替代实验中决策策略的有吸引力的工具。

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