Retaining social interactions in working memory (WM) for further social activities is vital for a successful social life. Researchers have noted a social chunking phenomenon in WM: WM involuntarily uses the social interaction cues embedded in the individual actions and chunks them as one unit. Our study is the first to examine whether the social chunking in WM is an automatic process, by asking whether social chunking of agent actions in WM is resource-demanding, a key hallmark of automaticity. We achieved this by probing whether retaining agent interactions in WM as a chunk required more attention than retaining actions without interaction. We employed a WM change-detection task with actions containing social interaction cues as memory stimuli, and required participants only memorizing individual actions. As domain-general attention and object-based attention are suggested playing a key role in retaining chunks in WM, a secondary task was inserted in the WM maintenance phase to consume these two types of attention. We reestablished the fact that the social chunking in WM required no voluntary control (Experiments 1 and 2). Critically, we demonstrated substantial evidence that social chunking in WM did not require extra domain-general attention (Experiment 1) or object-based attention (Experiment 2). These findings imply that the social chunking of agent actions in WM is not resource-demanding, supporting an automatic view of social chunking in WM.
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http://dx.doi.org/10.1016/j.cognition.2022.105249 | DOI Listing |
Front Psychol
December 2024
Department of Psychology, Emory University, Atlanta, GA, United States.
Introduction: Implicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is 'grammatical' or 'ungrammatical'. By contrast, 'processing-based' tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
Department of Learning Health Sciences, School of Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Black men are more likely to be diagnosed with type 2 diabetes (T2D) compared to non-Hispanic White men, especially those over 55 years of age. Although there is ample evidence around the efficacy of peer-led diabetes self-management and support (PLDSMS) programs in improving diabetes health outcomes, Black men living with T2D experience several barriers to meaningful participation in peer-led programs and program developers face barriers to implementation. This qualitative study aimed to identify perspectives from collaborators on barriers and facilitators that impact the implementation of a PLDSMS intervention for older Black men with T2D.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Center for Technology Experience, AIT - Austrian Institute of Technology, Vienna, Austria.
Proc Natl Acad Sci U S A
September 2024
Centre of Excellence in Music, Mind, Body and Brain, Department of Music, Arts and Culture Studies, University of Jyväskylä, Jyväskylä 40014, Finland.
The perception of musical phrase boundaries is a critical aspect of human musical experience: It allows us to organize, understand, derive pleasure from, and remember music. Identifying boundaries is a prerequisite for segmenting music into meaningful chunks, facilitating efficient processing and storage while providing an enjoyable, fulfilling listening experience through the anticipation of upcoming musical events. Expanding on Sridharan et al.
View Article and Find Full Text PDFEffective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage.
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