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 information 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 "familiarity," 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 detecting the presence of signals. Any two-choice task that can be recast as a magnitude judgment can be shoehorned into the SDT framework. Consider, for example, a lab technician whose job is to examine Pap smears an...