TY - JOUR
T1 - Measuring decision weights in recognition experiments with multiple response alternatives
T2 - Comparing the correlation and multinomial-logistic- regression methods
AU - Dai, Huanping
AU - Micheyl, Christophe
N1 - Funding Information:
We thank Dr. Qiduan Liu for her advice on statistics, and three reviewers for their comments and suggestions on earlier versions of the paper. This research was supported by University of Arizona and NIH Grant Nos. R01 DC05216 and R01 DC07657. 1
PY - 2012/11
Y1 - 2012/11
N2 - 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.
AB - 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.
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U2 - 10.1121/1.4754523
DO - 10.1121/1.4754523
M3 - Article
C2 - 23145622
AN - SCOPUS:84869137945
SN - 0001-4966
VL - 132
SP - 3418
EP - 3427
JO - Journal of the Acoustical Society of America
JF - Journal of the Acoustical Society of America
IS - 5
ER -