Header

UZH-Logo

Maintenance Infos

How does semantic knowledge impact working memory maintenance? Computational and behavioral investigations


Kowialiewski, Benjamin; Lemaire, Benoît; Portrat, Sophie (2021). How does semantic knowledge impact working memory maintenance? Computational and behavioral investigations. Journal of Memory & Language, 117:104208.

Abstract

It is now firmly established that long-term memory knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in Working Memory (WM). This support from semantic knowledge is well-explained by models assuming that verbal items are directly activated in long-term memory, and that this activation provides the representational basis for WM maintenance. However, the exact mechanisms underlying semantic influence on WM performance remain poorly understood. We manipulated the presence of between-item semantic relatedness in an immediate serial recall task, by mixing triplets composed of semantically related and unrelated items (e.g. leaf – tree – branch – wall – beer – dog; hand – father – truck – cloud – sky – rain). Compared to unrelated items, related items were better recalled, as had been classically observed. Critically, semantic relatedness also impacted WM maintenance in a complex manner, as observed by the presence of proactive benefit effects on subsequent unrelated items, and the absence of retroactive effects. The complexity of these interactions is well-captured by TBRS*-S, a decay-based computational architecture in which the activation occurring in long-term memory is described. The present study suggests that semantic knowledge can be used to free up WM resources that can be reallocated for maintenance purposes, and supports models postulating that long-term memory knowledge constrains WM maintenance processes.

Abstract

It is now firmly established that long-term memory knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in Working Memory (WM). This support from semantic knowledge is well-explained by models assuming that verbal items are directly activated in long-term memory, and that this activation provides the representational basis for WM maintenance. However, the exact mechanisms underlying semantic influence on WM performance remain poorly understood. We manipulated the presence of between-item semantic relatedness in an immediate serial recall task, by mixing triplets composed of semantically related and unrelated items (e.g. leaf – tree – branch – wall – beer – dog; hand – father – truck – cloud – sky – rain). Compared to unrelated items, related items were better recalled, as had been classically observed. Critically, semantic relatedness also impacted WM maintenance in a complex manner, as observed by the presence of proactive benefit effects on subsequent unrelated items, and the absence of retroactive effects. The complexity of these interactions is well-captured by TBRS*-S, a decay-based computational architecture in which the activation occurring in long-term memory is described. The present study suggests that semantic knowledge can be used to free up WM resources that can be reallocated for maintenance purposes, and supports models postulating that long-term memory knowledge constrains WM maintenance processes.

Statistics

Citations

Dimensions.ai Metrics
4 citations in Web of Science®
4 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 03 May 2022
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Scopus Subject Areas:Social Sciences & Humanities > Neuropsychology and Physiological Psychology
Social Sciences & Humanities > Language and Linguistics
Social Sciences & Humanities > Experimental and Cognitive Psychology
Social Sciences & Humanities > Linguistics and Language
Physical Sciences > Artificial Intelligence
Uncontrolled Keywords:Artificial Intelligence, Linguistics and Language, Experimental and Cognitive Psychology, Language and Linguistics, Neuropsychology and Physiological Psychology
Language:English
Date:1 April 2021
Deposited On:03 May 2022 15:24
Last Modified:04 May 2022 20:00
Publisher:Elsevier
ISSN:0749-596X
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.jml.2020.104208
Project Information:
  • : FunderAgence Nationale de la Recherche
  • : Grant ID
  • : Project Title