Preexisting semantic representation improves working memory performance in the visuospatial domain.

Mem Cognit

Linnaeus Centre HEAD and Swedish Institute for Disability Research, Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden.

Published: May 2016

Working memory (WM) for spoken language improves when the to-be-remembered items correspond to preexisting representations in long-term memory. We investigated whether this effect generalizes to the visuospatial domain by administering a visual n-back WM task to deaf signers and hearing signers, as well as to hearing nonsigners. Four different kinds of stimuli were presented: British Sign Language (BSL; familiar to the signers), Swedish Sign Language (SSL; unfamiliar), nonsigns, and nonlinguistic manual actions. The hearing signers performed better with BSL than with SSL, demonstrating a facilitatory effect of preexisting semantic representation. The deaf signers also performed better with BSL than with SSL, but only when WM load was high. No effect of preexisting phonological representation was detected. The deaf signers performed better than the hearing nonsigners with all sign-based materials, but this effect did not generalize to nonlinguistic manual actions. We argue that deaf signers, who are highly reliant on visual information for communication, develop expertise in processing sign-based items, even when those items do not have preexisting semantic or phonological representations. Preexisting semantic representation, however, enhances the quality of the gesture-based representations temporarily maintained in WM by this group, thereby releasing WM resources to deal with increased load. Hearing signers, on the other hand, may make strategic use of their speech-based representations for mnemonic purposes. The overall pattern of results is in line with flexible-resource models of WM.

Download full-text PDF

Source
http://dx.doi.org/10.3758/s13421-016-0585-zDOI Listing

Publication Analysis

Top Keywords

preexisting semantic
16
deaf signers
16
semantic representation
12
hearing signers
12
signers performed
12
performed better
12
working memory
8
visuospatial domain
8
signers
8
hearing nonsigners
8

Similar Publications

Frontotemporal Dementia Differential Diagnosis in Clinical Practice: A Single-Center Retrospective Review of Frontal Behavioral Referrals.

Neurol Clin Pract

February 2025

Eastern Cognitive Disorders Clinic (NK, AS, M. Christensen, KAR, CK, DGD, AB), Department of Neurosciences, Box Hill Hospital; Eastern Health Clinical School (NK, M. Christensen, DGD, AB); Alfred Health (M. Chew, M. Christensen, DGD, AB), Monash University, Melbourne; Austin Health (AS, AB), University of Melbourne, Heidelberg; Calvary Health Care Bethlehem (KAR), Caulfield; Wimmera Health Care Group (FI), Horsham; Central Clinical School (DGD, AB), Monash University, Melbourne; and Melbourne Health Cognitive Neurology Service (AB), Royal Melbourne Hospital, Parkville, Australia.

Article Synopsis
  • The study examines the types of diagnoses given to patients referred for frontal network impairments at a cognitive neurology clinic in Melbourne, specifically looking into cases suspected of frontotemporal dementia (FTD).
  • Out of 161 patients analyzed over a decade, the most common final diagnosis was an FTD syndrome, with behavioral variant FTD being the most frequent sub-type.
  • Other diagnoses included primary psychiatric disorders, vascular cognitive impairment, and Alzheimer's disease, with behavioral variant FTD patients showing higher rates of medical comorbidities.
View Article and Find Full Text PDF

Although episodic memory is typically impaired in older adults (OAs) compared to young adults (YAs), this deficit is attenuated when OAs can leverage their rich semantic knowledge, such as their knowledge of schemas. Memory is better for items consistent with pre-existing schemas and this effect is larger in OAs. Neuroimaging studies have associated schema use with the ventromedial prefrontal cortex (vmPFC) and hippocampus (HPC), but most of this research has been limited to YAs.

View Article and Find Full Text PDF
Article Synopsis
  • Early diagnosis and monitoring of eye diseases are crucial, and this study focuses on using computer-aided detection (CAD) techniques for effective diagnosis through pixel-level classification via semantic segmentation.
  • The research introduces three U-Net models for multi-class semantic segmentation, utilizing transfer learning and combining outputs from MSU-Net and BU-Net to create binary masks for key retinal regions like the optic disc and cup.
  • Results from training nine pre-trained models on the HRF dataset, which includes various annotated regions, show high accuracy (87.7% pixel accuracy, 87% IoU), aiding in the early diagnosis of glaucoma and related optic nerve disorders.
View Article and Find Full Text PDF

Semantic structures facilitate threat memory integration throughout the medial temporal lobe and medial prefrontal cortex.

Curr Biol

August 2024

Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA; Department of Neuroscience, University of Texas at Austin, Austin, TX, USA. Electronic address:

Emotional experiences can profoundly impact our conceptual model of the world, modifying how we represent and remember a host of information even indirectly associated with that experienced in the past. Yet, how a new emotional experience infiltrates and spreads across pre-existing semantic knowledge structures (e.g.

View Article and Find Full Text PDF

Background: Awake craniotomy is the standard of care for treating language eloquent gliomas. However, depending on preoperative functionality, it is not feasible in each patient and selection criteria are highly heterogeneous. Thus, this study aimed to identify broadly applicable predictor variables allowing for a more systematic and objective patient selection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!