A model for recognition memory: REM—retrieving effectively from memory

Shiffrin, Richard M.; Steyvers, Mark

doi:10.3758/bf03209391

Cited by 859 publications

(1,302 citation statements)

References 25 publications

(37 reference statements)

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“…The experiments in this paper have applied the MCMC method to categorization tasks, but it could also be used in a variety of other domains as well. Many formal models of human cognition (Oaksford & Chater, 1998;Chater, Tenenbaum, & Yuille, 2006;Anderson, 1990;Shiffrin & Steyvers, 1997) assume that there is a range of degrees of belief in different hypothetical outcomes. The MCMC method is designed to estimate these subjective functions.…”

Section: Resultsmentioning

confidence: 99%

Uncovering mental representations with Markov chain Monte Carlo

Sanborn

Griffiths

Shiffrin

2010

Cognitive Psychology

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A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. Richard M. Shiffrin Indiana University, Bloomington Abstract A key challenge for cognitive psychology is the investigation of mental representations, such as object categories, subjective probabilities, choice utilities, and memory traces. In many cases, these representations can be expressed as a non-negative function defined over a set of objects. We present a behavioral method for estimating these functions. Our approach uses people as components of a Markov chain Monte Carlo (MCMC) algorithm, a sophisticated sampling method originally developed in statistical physics. Experiments 1 and 2 verified the MCMC method by training participants on various category structures and then recovering those structures. Experiment 3 demonstrated that the MCMC method can be used estimate the structures of the real-world animal shape categories of giraffes, horses, dogs, and cats. Experiment 4 combined the MCMC method with multidimensional scaling to demonstrate how different accounts of the structure of categories, such as prototype and exemplar models, can be tested, producing samples from the categories of apples, oranges, and grapes.

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Section: Resultsmentioning

confidence: 99%

Uncovering mental representations with Markov chain Monte Carlo

Sanborn

Griffiths

Shiffrin

2010

Cognitive Psychology

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“…At a behavioral level, establishing that one particular type of encoding task retards memory in a distinct manner is challenging. In our view, the best way to make progress on such issues is by leveraging multiple sources of data including insight from cognitive neuroscience about the role of semantic memory in episodic encoding (Tse et al, 2007;Gliga, Volein, & Csibra, 2010), and by building and testing detailed computational models that triangulate between the multiple factors that influence memory performance (Shiffrin & Steyvers, 1997).…”

Section: Discussionmentioning

confidence: 99%

Does category labeling lead to forgetting?

Blanco

Gureckis

2012

Cogn Process

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In this paper, we evaluate the "representational shift" hypothesis (Lupyan, 2008) which argues that the act of explicitly labeling an object as a member of a familiar semantic category alters the trace of the encoded memory in the direction of the category prototype. The typical procedure for such experiments has been to compare category labeling to a non-categorization encoding task such as a preference judgement. In a series of experiments, we examine alternative comparison tasks that attempt to control the depth of encoding and the degree to which category information is explicitly recruited at the time of study. The results appear most consistent with a depth of processing (Craik & Lockhart, 1972) (Exp. 1) or distinctiveness (Exp. 2) explanation for the pattern of memory effects found in previous studies.

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“…This comparison can take many forms, including a dot product (Hintzman, 1988) or likelihood ratio (Shiffrin & Steyvers, 1997;McClelland & Chappell, 1998); in any case, the comparison results in a single value reflecting the degree to which each memory trace is similar to the memory probe. This comparison is often assumed to take place in parallel across all memory traces (Gillund & Shiffrin, 1984;Hintzman, 1988;Shiffrin & Steyvers, 1997;McClelland & Chappell, 1998), but may take place over the course of a serial search among the various memory traces (Sternberg, 1966) or via a combination of parallel matching and serial search (Atkinson & Juola, 1974). Finally, these various match values are treated as evidence for whether the test item was or was not experienced in the target study context:…”

Section: Separate and Composite Representationsmentioning

confidence: 99%

“…However, alternative feature representations may be used that allow for much richer information to be encoded without altering the general structure of the model, such as the assumption of a geometric rather than Bernoulli distribution of feature values in the original REM model (Shiffrin & Steyvers, 1997). The SLiM model (McClelland & Chappell, 1998), like ours, assumes that probe features are binary, but allows for memory traces to contain continuous feature values in the range (0, 1).…”

Section: Alternative Memory Representationsmentioning

confidence: 99%

A dynamic approach to recognition memory.

Cox

Shiffrin

2017

Psychological Review

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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A model for recognition memory: REM—retrieving effectively from memory

Cited by 859 publications

References 25 publications

Uncovering mental representations with Markov chain Monte Carlo

Uncovering mental representations with Markov chain Monte Carlo

Does category labeling lead to forgetting?

A dynamic approach to recognition memory.

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