This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture–word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term (...) declarative learning, and between sequential sampling and task control. In a traditional sequential sampling model, the onset of the process within the task is unclear, as is the number of sampling processes. RACE/A provides a theoretical basis for estimating the onset of sequential sampling processes during task execution and allows for easy modeling of multiple sequential sampling processes within a task. (shrink)

Light and Kennison proposed that bias effects in the forced-choice perceptual identification of words result from a strategy engaged in by subjects to retrieve explicit information about the words. This article enumerates several problems with this proposal and presents new experimental data against it. It is concluded that subjects do not ordinarily employ an explicit retrieval strategy. The data are discussed in the context of the general problem of separating implicit from explicit influences on performance.

I present a framework for modeling memory, retrieval, perception, and their interactions. Recent versions of the models were inspired by Bayesian induction: We chose models that make optimal decisions conditioned on a memory/perceptual system with inherently noisy storage and retrieval. The resultant models are, fortunately, largely consistent with my models dating back to the 1960s, and are therefore natural successors. My recent articles have presented simplified models in order to focus on particular applications. This article takes a larger perspective and (...) places the individual models in a more global framework. I will discuss (1) the storage of episodic traces, the accumulation of these into knowledge (e.g., lexical/semantic traces in the case of words), and the changes in knowledge caused by learning; (2) the retrieval of information from episodic memory and from general knowledge; (3) decisions concerning storage, retrieval, and responding. Examples of applications include episodic recognition and cued and free recall, perceptual identification (naming, yes–no and forced‐choice), lexical decision, and long‐term and short‐term priming. (shrink)