A Dynamic Stimulus-Driven Model of Signal Detection Brandon M. Turner and Trisha Van Zandt The Ohio State University Scott Brown University of Newcastle Signal detection theory forms the core of many current models of cognition, including memory, choice, and categorization. However, the classic signal detection model presumes the a priori existence of fixed stimulus representations— usually Gaussian distributions— even when the observer has no experience with the task. Furthermore, the classic signal detection model requires the observer to place a response criterion along the axis of stimulus strength, and without theoretical elaboration, this criterion is fixed and independent of the observer’s experience. We present a dynamic, adaptive model that addresses these 2 long-standing issues. Our model describes how the stimulus representation can develop from a rough subjective prior and thereby explains changes in signal detection performance over time. The model structure also provides a basis for the signal detection decision that does not require the placement of a criterion along the axis of stimulus strength. We present simulations of the model to examine its behavior and several experiments that provide data to test the model. We also fit the model to recognition memory data and discuss the role that feedback plays in establishing stimulus representations. Keywords: signal detection theory, recognition memory, cognitive modeling, dynamic models of infor- mation processing Signal detection theory (SDT) is crucial to many important theories in cognitive psychology, especially those theories that deal with performance in two-choice tasks. In such tasks, an observer is presented with a series of trials in which he or she must respond to a stimulus. The stimulus is one of two types, either “noise” (requiring a response of “no”) or “signal” (requiring a response of “yes”). What constitutes noise or signal can be very flexible. The SDT framework assumes that the presentation of a stimulus gives rise to a perception of some sensory effect in the cognitive apparatus of the observer. The magnitude of the effect, conceived on some relevant experiential scale such as “loudness” or “famil- iarity,” is used as the basis of the “yes” or “no” decision. Random noise in the observer’s perceptual system (or in the stimulus itself) results in varying magnitudes of effects over different stimulus presentations but, on average, signals result in larger effects than noise. Variability in sensory effects is represented by two random variables that often are assumed to follow equal-variance Gaussian distributions, though this assumption is not strictly necessary. To make a decision, traditional SDT assumes that observers place a criterion along the axis of sensory effect. The “yes” or “no” response is determined by whether the perceived effect is above or below this criterion (see Macmillan & Creelman, 2005, for a review). SDT is not confined to the relatively simple problem of detect- ing the presence of signals. Any two-choice task that can be recast as a magnitude judgment can be shoehorned into the SDT frame- work. Consider, for example, a lab technician whose job is to examine Pap smears and decide which are normal and which show signs of disease. The technician looks at a number of features, such as the number of white blood cells, lymphocytes, squamous cells, presence of bacteria, and so forth. Some of these features may be more diagnostic than others of disease, but collapsing these fea- tures onto a single dimension (which we might label “abnormal- ity”) permits us to apply the SDT machinery to the problem (Beck & Shultz, 1986; Metz, Herman, & Shen, 1998). There are two distinct ways in which SDT is used in psychol- ogy. The first use is as an analytic tool to measure discriminability (usually estimated by a statistic like d') and response bias (usually estimated by a statistic like ). The second use is as a psycholog- ical model for how people structure discrimination problems and make simple choices. When SDT is used as a model, the two distributions of sensory effect serve as the mental representations of the different stimulus classes. The decision rule is the selection of a criterion () separating noise from signals at some satisfactory location within the representation. Although the analytic contribu- tion of SDT to quantifying discrimination performance cannot be disputed, there are several long-standing theoretical problems associated with the use of SDT as a model of choice that, This article was published Online First September 5, 2011. Brandon M. Turner and Trisha Van Zandt, Department of Psychology, The Ohio State University; Scott Brown, School of Psychology, University of Newcastle, Callaghan, Australia. This work was made possible by National Science Foundation Grants SES-0437251, BCS-0738059, and SES-1024709 and by Australian Re- search Council Project DP0878858. Portions of this work were presented at the 42nd Annual Meeting of the Society for Mathematical Society, Amsterdam; the 2010 annual meeting of the Psychonomics Society, St. Louis; and the 2011 Annual Context and Episodic Memory Symposium, Philadelphia. Portions of this work were submitted by Brandon M. Turner in partial fulfillment of the requirements for the master’s degree in psy- chology at The Ohio State University. We would like to thank Rob Nosofsky for helpful comments on experimental design and modeling and John Dunn and Michael D. Lee for helpful comments that improved an earlier version of this article. Correspondence concerning this article should be addressed to Trisha Van Zandt, Department of Psychology, The Ohio State University, Co- lumbus, OH 43202. E-mail: van-zandt.2@osu.edu Psychological Review © 2011 American Psychological Association 2011, Vol. 118, No. 4, 583– 613 0033-295X/11/$12.00 DOI: 10.1037/a0025191 583