Presenter:  Michael Pratte
Presentation type:  Talk
Presentation date/time:  7/27  1:15-1:40
 
Modeling participant and item effects in the theory of signal detection
 
Michael Pratte, University of Missouri - Columbia
Jeffrey Rouder, University of Missouri - Columbia
Richard Morey, University of Missouri - Columbia
 
Recognition memory has been conventionally modeled with the theory of signal detection by assuming that the memory strength of studied target words is increased over non-studied distractors. Previous research (e.g., Ratcliff, Sheu, & Gronlund, 1992; Glanzer, Kim, Hilford, & Adams, 1999) has indicated that study not only increases the mean memory strength of targets, but increases the standard deviation of their strength as well. We highlight a potential problem in these findings---analysis is predicated on aggregating data over items. Whereas these items may vary systematically (some are more memorable than others), there is unaccounted variance. We show how this variance distorts conventional measures leading to an asymptotic underestimation of mean strength (d') and an asymptotic overestimation of standard deviation. To provide for accurate estimation, we propose a Bayesian hierarchical model which simultaneously models participant and item effects without recourse to aggregation.