ArticleNeuroImage2021
Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 32 citations in OpenAlex.
- Effects of 12-week free sprint training and speed, agility, and quickness training (SAQT) on reaction time and lower limb muscle activation during crouch start.European journal of applied physiology · 2025Trial
- Planning-related hubs and brain network organization during motor decision-making.Brain structure & function · 2026Article
- Auditory Emotional Prosody Perception Using Pseudo-Speech Stimuli in Native and Non-Native Listeners.Brain and behavior · 2025Article
- Human response times are governed by dual anticipatory processes with distinct neural signatures.Communications biology · 2025Article
- Potentiation of Motor Adaptation Via Cerebellar tACS: Characterization of the Stimulation Frequency.Cerebellum (London, England) · 2024Article
- Mapping causal links between prefrontal cortical regions and intra-individual behavioral variability.Nature communications · 2024Article
- Exome-wide analysis reveals role ofHGG advances · 2023Article
- Article
- Motor-induced oscillations in choice response performance.Psychophysiology · 2023Article
- Heksor: the central nervous system substrate of an adaptive behaviour.The Journal of physiology · 2022Article
- Nonlinear Dynamics of Reaction Time and Time Estimation during Repetitive Test.International journal of environmental research and public health · 2022Article
- Modulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing.NeuroImage · 2021Article
- Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
Abstract
Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.