Analyzing recognition performance with sparse data

Ching-Fan Sheu, Yuh Shiow Lee, Pei Ying Shih

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Experiments in which recognition performance is measured sometimes involve only a small number of observations per subject, rendering d' analysis unreliable (Schooler & Shiffrin, 2005). Here, we introduce, in signal detection models, subject-specific random variables to account for heterogeneous hit and false alarm rates among individuals. Population d' effects for comparing groups are estimated, in this approach, by pooling information from a sample of subjects across experimental conditions. The method is validated by a simulation study and is illustrated with an analysis of the effect of neutral and emotional words on recognition performance, employing the emotional Stroop task (Lee & Shih, 2007).

Original languageEnglish
Pages (from-to)722-727
Number of pages6
JournalBehavior Research Methods
Volume40
Issue number3
DOIs
Publication statusPublished - 2008 Aug 1

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Population
Emotion
Experiment
Rendering
Simulation
Signal Detection
Stroop

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Psychology (miscellaneous)
  • Experimental and Cognitive Psychology

Cite this

Sheu, Ching-Fan ; Lee, Yuh Shiow ; Shih, Pei Ying. / Analyzing recognition performance with sparse data. In: Behavior Research Methods. 2008 ; Vol. 40, No. 3. pp. 722-727.
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Analyzing recognition performance with sparse data. / Sheu, Ching-Fan; Lee, Yuh Shiow; Shih, Pei Ying.

In: Behavior Research Methods, Vol. 40, No. 3, 01.08.2008, p. 722-727.

Research output: Contribution to journalArticle

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