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Does source memory exist for unrecognized items?

Authors
Mr. Julian William Fox
The University of Melbourne ~ Melbourne School of Psychological Sciences
Dr. Adam Osth
The University of Melbourne ~ Melbourne School of Psychological Sciences
Abstract

In episodic memory research, there is a debate concerning whether decision-making in recognition and source memory is better explained by models that assume discrete cognitive states, or continuous underlying strengths. One aspect in which these classes of models differ is their predictions regarding the ability to retrieve contextual details (or source details) of an experienced event, given that the event itself is not recognized. Discrete state models predict that when items are unrecognized, source retrieval is not possible and only guess responses can be elicited. In contrast, models assuming continuous strengths predict that it is possible to retrieve the source of unrecognized items (albeit with low accuracy). Empirically, there have been numerous studies reporting either chance accuracy or above-chance accuracy for source memory in the absence of recognition. For instance, studies presenting recognition and source judgments for the same item in immediate succession have revealed chance-level accuracy, while studies presenting a block of recognition judgments followed by a block of source judgments have revealed slightly above-chance accuracy. In the present investigation, data from two novel experiments involving multiple design manipulations were investigated using a hierarchical Bayesian signal detection model. Across most conditions it was shown that source accuracy for unrecognized items was slightly above chance. It is suggested that findings of a null effect in the prior literature may be attributable to design elements that hinder source memory as a whole, and to high degrees of uncertainty in the participant-level source data when conditioned on unrecognized items.

Tags

Keywords

recognition memory
source memory
discrete state models
signal detection models
hierarchical Bayesian modelling

Topics

Bayesian Modeling
Memory Models
Study design
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