Understanding forgetting from working memory, the memory used in ongoing cognitive processing, is critical to understanding human cognition. In the past decade, a number of conflicting findings have been reported regarding the role of time in forgetting from working memory. This has led to a debate concerning whether longer retention intervals necessarily result in more forgetting. An obstacle to directly comparing conflicting reports is a divergence in methodology across studies. Studies that find no forgetting as a function of retention-interval duration tend to use sequential presentation of memory items, while studies that find forgetting as a function of retention-interval duration tend to use simultaneous presentation of memory items. Here, we manipulate the duration of retention and the presentation method of memory items, presenting items either sequentially or simultaneously. We find that these differing presentation methods can lead to different rates of forgetting because they tend to differ in the time available for consolidation into working memory. The experiments detailed here show that equating the time available for working memory consolidation equates the rates of forgetting across presentation methods. We discuss the meaning of this finding in the interpretation of previous forgetting studies and in the construction of working memory models.
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http://dx.doi.org/10.1037/a0034301 | DOI Listing |
Environ Monit Assess
December 2024
Faculty of Information Technology, University of Engineering and Technology, Vietnam National University Hanoi, E3 Building, 144 Xuan Thuy Street, Dich Vong Hau Ward, Cau Giay District, Ha Noi, 100000, Vietnam.
PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is crucial for assessing air quality. However, Vietnam's state-of-the-art (SOTA) dataset with a 3 km resolution needs to be revised to depict spatial variation in smaller regions accurately.
View Article and Find Full Text PDFFatigue is a state of exhaustion that influences our willingness to engage in effortful tasks. While both physical and cognitive exertion can cause fatigue, there is a limited understanding of how fatigue in one exertion domain (e.g.
View Article and Find Full Text PDFIdentifying neural markers of clinical symptom fluctuations is prerequisite to developing more precise brain-targeted treatments in psychiatry. We have recently shown that working memory (WM) in healthy adults is dependent on the rise and fall interplay between alpha/beta and gamma bursts within frontoparietal regions, and that deviations in these patterns lead to WM performance errors. However, it is not known whether such bursting deviations underlie clinically relevant WM-related symptoms or clinical status in individuals with WM deficits.
View Article and Find Full Text PDFBrain Commun
December 2024
Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29208, USA.
Syntactic processing and verbal working memory are both essential components to sentence comprehension. Nonetheless, the separability of these systems in the brain remains unclear. To address this issue, we performed causal-inference analyses based on lesion and connectome network mapping using MRI and behavioural testing in two groups of individuals with chronic post-stroke aphasia.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Research Centre of Mathematics, University of Minho, Guimarães, Portugal.
Continuous bump attractor networks (CANs) have been widely used in the past to explain the phenomenology of working memory (WM) tasks in which continuous-valued information has to be maintained to guide future behavior. Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning. We address both challenges in a two-dimensional (2D) network model formalized by two coupled neural field equations of Amari type.
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