Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed 'Stochastic Resonance' (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140507PMC
http://dx.doi.org/10.3389/fnins.2023.1110714DOI Listing

Publication Analysis

Top Keywords

internal noise
16
individuals higher
12
noise
10
noise levels
8
noise neurotypical
8
neurotypical individuals
8
visual identification
8
noise individuals
8
higher internal
8
autistic traits
8

Similar Publications

Optimization of carotid CT angiography image quality with deep learning image reconstruction with high setting (DLIR-H) algorithm under ultra-low radiation and contrast agent conditions.

Radiography (Lond)

September 2025

Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China; School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China; Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, Jiangs

Introduction: Carotid artery disease is a major cause of stroke and is frequently evaluated using Carotid CT Angiography (CTA). However, the associated radiation exposure and contrast agent use raise concerns, particularly for high-risk patients. Recent advances in Deep Learning Image Reconstruction (DLIR) offer new potential to enhance image quality under low-dose conditions.

View Article and Find Full Text PDF

Objective: Effective deep brain stimulation (DBS) treatment for Parkinson's disease requires careful adjustment of stimulation parameters and targeting to avoid motor side effects caused by activation of the internal capsule. Currently, patients must self-report side effects during device programming and implantation surgery - a challenging and subjective process that could lead to suboptimal therapy or exacerbate the time needed to optimize treatment. Motor evoked potentials (mEP), the use of electromyography to record DBS-induced muscle activation, offer a promising biomarker for objective motor side effect detection.

View Article and Find Full Text PDF

Unlabelled: Adaptive behavior requires integrating information from multiple sources. These sources can originate from distinct channels, such as internally maintained latent cognitive representations or externally presented sensory cues. Because these signals are often stochastic and carry inherent uncertainty, integration is challenging.

View Article and Find Full Text PDF

Deep Learning for Segmenting Ischemic Stroke Infarction in Non-contrast CT Scans by Utilizing Asymmetry.

Clin Neuroradiol

September 2025

Department of Radiology, Beijing Chao-Yang Hospital, No. 8 GongrenTiyuchangNanlu, Chaoyang District, 100020, Beijing, China.

Background: Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. This study aims to develop a segmentation method for ischemic lesions in NCCT scans, combining symmetry-based principles with the nnUNet segmentation model.

View Article and Find Full Text PDF

This paper presents a strategy for noise suppression and stability enhancement of organic photodetectors (OPDs) by introducing pH-neutralized and transfer-laminated poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as the hole-transporting layer (HTL). Although PEDOT:PSS is widely used as an HTL material, its intrinsic acidity and structural instability hinder the performance of the OPD. Here, imidazole-induced neutralization promotes a linear entangled structure, while transfer lamination enables controlled PSS domain distribution.

View Article and Find Full Text PDF