title: Separating decision and encoding noise in signal detection tasks.
abstract: In this article we develop an extension to the signal detection theory framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains signal detection theory models with decision noise by combining a multipass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with 3 and 5 response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009; Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks.
authors.affiliation: Laboratory of Brain Processes (LOBES), Center for Cognitive and Brain Sciences, Department of Psychology, The Ohio State University.
authors.forename: Carlos Alexander
authors.initials: CA
authors.lastname: Cabrera
authors.affiliation: Laboratory of Brain Processes (LOBES), Center for Cognitive and Brain Sciences, Department of Psychology, The Ohio State University.
authors.forename: Zhong-Lin
authors.initials: ZL
authors.lastname: Lu
authors.affiliation: Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine.
authors.forename: Barbara Anne
authors.initials: BA
authors.lastname: Dosher
mesh_terms: D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D017603:Signal Detection, Psychological / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*