Attention has been found to sample visual information periodically, in a wide range of frequencies below 20 Hz. This periodicity may be supported by brain oscillations at corresponding frequencies. We propose that part of the discrepancy in periodic frequencies observed in the literature is due to differences in attentional demands, resulting from heterogeneity in tasks performed. To test this hypothesis, we used visual search and manipulated task complexity, i.e., target discriminability (high, medium, low) and number of distractors (set size), while electro-encephalography was simultaneously recorded. We replicated previous results showing that the phase of pre-stimulus low-frequency oscillations predicts search performance. Crucially, such effects were observed at increasing frequencies within the theta-alpha range (6-18 Hz) for decreasing target discriminability. In medium and low discriminability conditions, correct responses were further associated with higher post-stimulus phase-locking than incorrect ones, in increasing frequency and latency. Finally, the larger the set size, the later the post-stimulus effect peaked. Together, these results suggest that increased complexity (lower discriminability or larger set size) requires more attentional cycles to perform the task, partially explaining discrepancies between reports of attentional sampling. Low-frequency oscillations structure the temporal dynamics of neural activity and aid top-down, attentional control for efficient visual processing.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035177PMC
http://dx.doi.org/10.1038/s41598-022-10647-5DOI Listing

Publication Analysis

Top Keywords

set size
12
visual search
8
target discriminability
8
medium low
8
low-frequency oscillations
8
larger set
8
periodic attention
4
attention operates
4
operates faster
4
faster complex
4

Similar Publications

A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations.

View Article and Find Full Text PDF

Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.

Design: Retrospective observational study.

View Article and Find Full Text PDF

Algae extract-based nanoemulsions for photoprotection against UVB radiation: an electrical impedance spectroscopy study.

Sci Rep

January 2025

Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Cra. 30 N° 45-03, Bogotá D.C., Colombia.

Skin cancer is one of the most common types of cancer worldwide, with exposure to UVB radiation being a significant risk factor for its development. To prevent skin cancer, continuous research efforts have focused on finding suitable photoprotective ingredients from natural sources that are also environmentally friendly. This study aimed to develop oil-in-water photoprotective nanoemulsions containing marine macroalgae extract.

View Article and Find Full Text PDF

Owing to its topographic variations, Ethiopia is a biodiversity-rich country. However, the long-term degradation of resources has resulted in isolated forest patches largely around sacred places. Thus, this work was aimed to evaluate the plant community formation and structural dynamics of the Abraham Sacred Forest patch.

View Article and Find Full Text PDF

The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.

J Neuroophthalmol

December 2024

Division of Ophthalmology (EB-S, AS, AA-A, AS-B, DW, SS, FC), Department of Surgery, University of Calgary, Calgary, Canada; Department of Biomedical Engineering (CN), University of Calgary, Calgary, Canada; Departments of Neurology (LBDL) and Ophthalmology (LBDL), University of Michigan, Ann Arbor, Michigan; and Department of Clinical Neurosciences (SS, FC), University of Calgary, Calgary, Canada.

Background: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss.

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!