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A Distributed Representation of Temporal Context (2002)
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BibTeX
@MISC{Howard02adistributed,
author = {Marc W. Howard and Michael J. Kahana},
title = { A Distributed Representation of Temporal Context},
year = {2002}
}
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Abstract
The principles of recency and contiguity are two cornerstones of the theoretical and empirical analysis of human memory. Recency has been alternatively explained by mechanisms of decay, displacement, and retroactive interference. Another account of recency is based on the idea of variable context (Estes, 1955; Mensink & Raaijmakers, 1989). Such notions are typically cast in terms of a randomly fluctuating population of elements reflective of subtle changes in the environment or in the subjects ’ mental state. This random context view has recently been incorporated into distributed and neural network memory models (Murdock, 1997; Murdock, Smith, & Bai, 2001). Here we propose an alternative model. Rather than being driven by random fluctuations, this formulation, the temporal context model (TCM), uses retrieval of prior contextual states to drive contextual drift. In TCM, retrieved context is an inherently asymmetric retrieval cue. This allows the model to provide a principled explanation of the widespread advantage for forward recalls in free and serial recall. Modeling data from single-trial free recall, we demonstrate that TCM can simultaneously explain recency and
Keyphrases
temporal context distributed representation neural network memory model temporal context model subtle change human memory serial recall alternative model empirical analysis retroactive interference subject mental state random fluctuation single-trial free recall principled explanation widespread advantage variable context mensink raaijmakers random context view prior contextual state contextual drift forward recall asymmetric retrieval cue
