Cited by 89Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on BehaviorNeurobiology of Language and Bilingualism

Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals

Open access
Cohen, Jessica·2010·Frontiers in Human Neuroscience

Abstract

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9-30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnhum.2010.00047

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The available body of research supports the proposed developmental model.

    “Although this hypothesis requires some speculation informed by an admittedly sparse literature, the available body of research supports this proposed developmental model [28, 68, 98, 107–109].”

    From The role of the anterior insula in adolescent decision making (2014) · cites this paper as [109]

  • These studies highlight the role of the anterior insula during the interplay of cognitive and affective processes.

    “These studies highlight the role of the AIC during the interplay of cognitive and affective processes.”

    From The role of the anterior insula in adolescent decision making (2014) · cites this paper as [109]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 51 · Cited by 89

Research works printed but not linked: 2

  1. Rasmussen C. E. Williams C. K. I. (2006). Gaussian Processes for Machine Learning Cambridge, MA: MIT Press
  2. Scholkopf B. Smola A. J. (2000). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) Cambridge, MA: MIT Press

Authors
Cohen, J.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Cohen, Jessica
Venue
Frontiers in Human Neuroscience
Published
2010
Volume
4
Pages
47-47
ISSN
1662-5161 (ISSN-L)
OpenAlex
W2148178834
Cited by
89
References
51
Citations by year
2026: 2 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 4 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 7 · 2016: 5 · 2015: 7 · 2014: 6 · 2013: 8 · 2012: 7
FWCI
4.46
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institute of Mental Health (R24MH072697)
  • National Institute on Drug Abuse (F31DA024534)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
Keywords
response inhibition, individual differences, developmental differences, age-related changes, stop-signal task, stop-signal reaction time, reaction time variability, response speed, anterior cingulate cortex, striatum, high-dimensional regression, predictive analytics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review