October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
The influence of long-term memory on working memory performance
Author Affiliations
  • Stephanie Saltzmann
    Louisiana State University
  • Melissa Beck
    Louisiana State University
Journal of Vision October 2020, Vol.20, 292. doi:https://doi.org/10.1167/jov.20.11.292
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      Stephanie Saltzmann, Melissa Beck; The influence of long-term memory on working memory performance. Journal of Vision 2020;20(11):292. doi: https://doi.org/10.1167/jov.20.11.292.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The current research examined if previously stored representations in LTM necessarily aid working memory performance. Research has suggested that long-term memory representations for abstract colored shapes can facilitate WM performance when they are identical to representations in WM. A second goal of the study was to determine whether increasing interference in LTM limits facilitation from LTM on WM. Remembering multiple stimuli from the same semantic category can create interference in LTM that may decreases the accessibility of LTM representations during a WM task. In two experiments, participants completed an initial study phase in which objects were categorically (i.e., semantically) related or unrelated. Participants then completed a change detection task that included both previously studied and unstudied objects. In Experiment 1, an object changed into another object from a novel category. We found no evidence of facilitation from LTM on WM performance, as displayed by similar change detection accuracy for studied and unstudied pictures. Furthermore, we found no effect of semantic-relatedness. Change detection was similarly accurate when the studied objects were all semantically-unrelated, as when the studied objects were semantically-related, demonstrating that interference in LTM did not affect WM. In Experiment 2, we attempted to increase reliance on LTM representations by increasing difficulty in the WM task. The changed object on the post-change array came from the same category as the pre-change object. Like in Experiment 1, we did not find any evidence of LTM facilitation on WM performance: change detection accuracy was similar for studied and unstudied objects. Additionally, there was no effect of interference in LTM on WM performance: change detection accuracy was similar for semantically-related objects and semantically-unrelated objects. Therefore, for WM tasks with real-world objects identical to objects previously encoded into LTM, we can conclude that the LTM representations were not used to improve WM performance.

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