Analyzing recognition performance with sparse data

Ching Fan Sheu, Yuh Shiow Lee, Pei Ying Shih

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Issue number3
Publication statusPublished - 2008 Aug

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)


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