Pub Date : 2020-11-05DOI: 10.1093/oso/9780198842286.003.0007
Cody A. Mashburn, Jason S. Tsukahara, R. Engle
This chapter outlines the executive attention theory of higher-order cognition, which argues that individual differences in the ability to maintain information in working memory and disengage from irrelevant information is inextricably linked to variation in the ability to deploy domain-free attentional resources in a goal-directed fashion. It also summarizes recent addendums to the theory, particularly regarding the relationship between attention control, working memory capacity, and fluid intelligence. Specifically, the chapter argues that working memory capacity and fluid intelligence measures require different allocations of the same attentional resources, a fact which accounts for their strong correlation. At various points, it addresses theoretical alternatives to the executive attention theory of working memory capacity and empirical complications of the study of attention control, including difficulties deriving coherent attention control latent factors.
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Pub Date : 2020-11-05DOI: 10.1093/acprof:oso/9780198570394.003.0004
P. Barrouillet, V. Camos
The time-based resource-sharing model considers working memory as the workspace in which mental representations are built, maintained, and transformed for completing goal-oriented tasks. Its main component is made of an episodic buffer and a procedural system that form an executive loop in which processing and storage share domain-general attentional resources on a temporal basis. Because working memory representations decay with time when attention is diverted, the cognitive load of a given activity is the proportion of time during which it occupies attention and prevents it from counteracting this decay through attentional refreshing. Consequently, recall in working memory tasks is an inverse function of the cognitive load of concurrent processing. Besides this system, an independent domain-specific maintenance system exists for verbal, but not visuospatial, information. Within this framework, working memory development mainly results from increasing processing speed that affects both the duration of the distraction of attention by concurrent tasks and refreshing efficiency.
{"title":"The Time-Based Resource-Sharing Model of Working Memory","authors":"P. Barrouillet, V. Camos","doi":"10.1093/acprof:oso/9780198570394.003.0004","DOIUrl":"https://doi.org/10.1093/acprof:oso/9780198570394.003.0004","url":null,"abstract":"The time-based resource-sharing model considers working memory as the workspace in which mental representations are built, maintained, and transformed for completing goal-oriented tasks. Its main component is made of an episodic buffer and a procedural system that form an executive loop in which processing and storage share domain-general attentional resources on a temporal basis. Because working memory representations decay with time when attention is diverted, the cognitive load of a given activity is the proportion of time during which it occupies attention and prevents it from counteracting this decay through attentional refreshing. Consequently, recall in working memory tasks is an inverse function of the cognitive load of concurrent processing. Besides this system, an independent domain-specific maintenance system exists for verbal, but not visuospatial, information. Within this framework, working memory development mainly results from increasing processing speed that affects both the duration of the distraction of attention by concurrent tasks and refreshing efficiency.","PeriodicalId":344464,"journal":{"name":"Working Memory","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116704031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main objective of this chapter is to introduce concepts of dynamic field theory, a continuous attractor neural network, and its implementation of visual working memory. In dynamic field theory, working memory is an attractor state where representations are self-sustained through strong interactions between self-excitation and lateral inhibition. The chapter discusses a visual working memory model with fields represented by stabilized attractor states. Using this model, it demonstrates how encoding, consolidation, maintenance, and comparison occur in correct and incorrect, same and different trials in a change detection task. Further, the model captures accuracy and capacity limitations when visual working memory load is manipulated. Critically, the chapter reviews work from the authors’ research group by demonstrating how the model captures behavioural performance and makes haemodynamic predictions in early childhood, young adulthood, and older adulthood. Using the model, the chapter posits that developmental changes in visual working memory processing occur as a result of the modulation of strength and width of excitation and inhibition. Finally, the chapter describes how the dynamic field theory account compares with current views on a domain-general account and distributed nature of working memory processing.
{"title":"A Dynamic Field Theory of Visual Working Memory","authors":"Sobanawartiny Wijeakumar, J. Spencer","doi":"10.31234/osf.io/hkc4e","DOIUrl":"https://doi.org/10.31234/osf.io/hkc4e","url":null,"abstract":"The main objective of this chapter is to introduce concepts of dynamic field theory, a continuous attractor neural network, and its implementation of visual working memory. In dynamic field theory, working memory is an attractor state where representations are self-sustained through strong interactions between self-excitation and lateral inhibition. The chapter discusses a visual working memory model with fields represented by stabilized attractor states. Using this model, it demonstrates how encoding, consolidation, maintenance, and comparison occur in correct and incorrect, same and different trials in a change detection task. Further, the model captures accuracy and capacity limitations when visual working memory load is manipulated. Critically, the chapter reviews work from the authors’ research group by demonstrating how the model captures behavioural performance and makes haemodynamic predictions in early childhood, young adulthood, and older adulthood. Using the model, the chapter posits that developmental changes in visual working memory processing occur as a result of the modulation of strength and width of excitation and inhibition. Finally, the chapter describes how the dynamic field theory account compares with current views on a domain-general account and distributed nature of working memory processing.","PeriodicalId":344464,"journal":{"name":"Working Memory","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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