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Serial Memory: Putting Chains and Position Codes in Context
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AI
DOI：10.1037/rev0000327.png)
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Abstract
En 中文
From the beginning of research on serial memory, chaining theories and position coding theories have been pitted against each other. The central question is whether items are associated with each other or with a set of position codes that are independent of the items. Around the turn of this century, the debate focused on serial recall tasks and patterns of error data that chaining models could not accommodate. Consequently, theories based on other ideas flourished and position coding models became prominent. We present an analysis of a retrieved context model that integrates chains and position codes. Under some parameter values, it produces classic chains. Under most parameter values, it produces context representations that contain information sufficient to specify the position codes in position coding theories. We suggest three ways to extract position codes from context representations and show the codes they produce are mathematically equivalent to the codes in position coding models. The extracted position codes can be substituted for the position codes in position coding models and run through their machinery to mimic their predictions exactly. We suggest that chains, position codes, and retrieved contexts may reflect different strategies for extracting desired information from a common set of memory representations, and we emphasize the value of considering item-dependent context representations that are made from fading traces of past items encoded or retrieved.
Keywords:
serial recall
start-end model
context retrieval and updating
retrieved context model
mimicry
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