title: Analyzing recognition performance with sparse data.
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).
authors.affiliation: Department of Psychology, National Chung Cheng University, Chia-Yi, Taiwan. psycfs@ccu.edu.tw
authors.forename: Ching-Fan
authors.initials: CF
authors.lastname: Sheu
authors.forename: Yuh-Shiow
authors.initials: YS
authors.lastname: Lee
authors.forename: Pei-Ying
authors.initials: PY
authors.lastname: Shih
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface