Elsevier

Cognitive Science

Volume 27, Issue 3, May–June 2003, Pages 341-378
Cognitive Science

Modeling memory and perception

https://doi.org/10.1016/S0364-0213(03)00027-2Get rights and content

Abstract

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.

Section snippets

Overview

Events and memory traces are represented as vectors of feature values. The traces are stored separately, although some are formed through accumulation of multiple events. Traces of new events are stored in memory as incomplete and error prone vectors. Such traces are termed episodic, and contain context, physical, and meaning features. When an event is similar to one or more earlier events (e.g., a nominal repetition is taking place) there is a tendency to store a new episodic trace, but also a

Recognition tasks

In Shiffrin and Steyvers (1997), we made a number of simplifying assumptions enabling Bayesian analysis to be applied to single-item episodic recognition—i.e., presentation of a list of words followed by a list of test words, half of which were on the list and half new (a very similar episodic recognition model was developed in parallel by McClelland & Chappell, 1998). It was assumed that all study and test words were randomly generated vectors chosen according to a geometric distribution with

Retrieval of knowledge and priming

The REM system can and ought to be applied to the retrieval of knowledge: A memory framework can hardly be complete without modeling knowledge retrieval, both in its own right, and because such retrieval is critical to explain episodic storage and retrieval. By knowledge we refer not only to facts, and verbal knowledge, but perception generally, motor programs, and everything else that is learned over development. Furthermore, our view is that such learning lies on a continuum: Although we have

Extensions

The studies and modeling described in this article, of course, represent only a current and personal snapshot of a continuing journey of research that is just beginning and will not near completion for many lifetimes. I hope and believe this theory represents systematic progress that builds on the great mass of collected data and many earlier theories, including my own (e.g., Atkinson & Shiffrin, 1968, Shiffrin, 1970, Raaijmakers & Shiffrin, 1980, Raaijmakers & Shiffrin, 1981, Gillund &

Final note

This article was meant to summarize the present status of a long-term research project through a conceptual overview. Space limitations required omissions such as almost all formal analysis, even though the article’s genesis was the award of the David E. Rumelhart Prize for contributions to the ‘the formal analysis of human cognition.’ I want to assure the reader that formal analysis is available in the typically much longer publications cited. The present overview emphasizes the role of

Acknowledgments

This research was supported by NIMH Grants 12717 and 63996, and numerous students, postdoctoral visitors, and research collaborators.

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