Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control DOI Creative Commons
Michael Freund, Ruiqi Chen, Gang Chen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 28, 2024

Understanding individual differences in cognitive control is a central goal psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures neuroscience such as response times or task-based fMRI activity. While prior work pinpointed the source of issue - vast amount cross-trial variability within solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding trial-level data. Using this longitudinal data from Dual Mechanisms Cognitive Control project, estimated individuals' neural responses associated color-word Stroop task, then assessed reliability across time interval several months. We show many prefrontal parietal brain regions, test-retest was near maximal, only models were able to reveal state affairs. Further, compared traditional univariate contrasts, enabled individual-level correlations be significantly greater precision. specifically link improvements precision optimized suppression decoding. Together, findings not indicate control-related individuate people highly stable manner time, but also suggest integrating provides powerful approach for investigating control, can effectively address high-variability measures.

Language: Английский

Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control DOI Creative Commons
Michael Freund, Ruiqi Chen, Gang Chen

et al.

Imaging Neuroscience, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 1, 2025

Abstract Understanding individual differences in cognitive control is a central goal psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures neuroscience such as response times or task-based fMRI activity. While prior work pinpointed the source of issue—the vast amount cross-trial variability within measures—solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding trial-level data. Using this longitudinal data from Dual Mechanisms Cognitive Control project, estimated individuals’ neural responses associated color-word Stroop task, then assessed reliability across time interval several months. We show many prefrontal parietal brain regions, test–retest was near maximal, only models were able to reveal state affairs. Further, compared traditional univariate contrasts, enabled individual-level correlations be significantly greater precision. specifically link improvements precision optimized suppression decoding. Together, findings not indicate control-related individuate people highly stable manner time, but also suggest integrating provides powerful approach for investigating control, can effectively address issue high-variability measures.

Language: Английский

Citations

1
Group-to-Individual Generalizability and Individual-Level Inferences in Cognitive Neuroscience DOI
Matthew Mattoni, Aaron J. Fisher, Kathleen M. Gates

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106024 - 106024

Published: Jan. 1, 2025

Language: Английский

Citations

0
Origins of food selectivity in human visual cortex DOI
Margaret Henderson, Michael J. Tarr, Leila Wehbe

et al.

Trends in Neurosciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
The Oomplet dataset toolkit as a flexible and extensible system for large-scale, multi-category image generation DOI Creative Commons

John P. Kasarda,

Angela Zhang,

Hua Tong

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 18, 2025

The modern study of perceptual learning across humans, non-human animals, and artificial agents requires large-scale datasets with flexible, customizable, controllable features for distinguishing between categories. To support this research, we developed the Oomplet Dataset Toolkit (ODT), an open-source, publicly available toolbox capable generating 9.1 million unique visual stimuli ten feature dimensions. Each stimulus is a cartoon-like humanoid character, termed "Oomplet," designed to be instance within clearly defined categories that are engaging suitable use diverse groups, including children. Experiments show adults can four five dimensions as single classification criteria in simple discrimination tasks, underscoring toolkit's flexibility. With ODT, researchers dynamically generate large, novel sets biological contexts.

Language: Английский

Citations

0
Individual differences in prefrontal coding of visual features DOI Creative Commons
Qi Lin, Hakwan Lau

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Abstract Each of us perceives the world differently. What may underlie such individual differences in perception? Here, we characterize lateral prefrontal cortex’s role vision using computational models, with a specific focus on differences. Using 7T fMRI dataset, found that encoding models relating visual features extracted from deep neural network to brain responses natural images robustly predict patches LPFC. We then explored representational structures and screened for high predicted observed more substantial coding schemes LPFC compared regions. Computational modeling suggests amplified could result random projection between sensory high-level regions underlying flexible working memory. Our study demonstrates under-appreciated processing idiosyncrasies how different individuals experience world.

Language: Английский

Citations

1
Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control DOI Creative Commons
Michael Freund, Ruiqi Chen, Gang Chen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 28, 2024

Understanding individual differences in cognitive control is a central goal psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures neuroscience such as response times or task-based fMRI activity. While prior work pinpointed the source of issue - vast amount cross-trial variability within solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding trial-level data. Using this longitudinal data from Dual Mechanisms Cognitive Control project, estimated individuals' neural responses associated color-word Stroop task, then assessed reliability across time interval several months. We show many prefrontal parietal brain regions, test-retest was near maximal, only models were able to reveal state affairs. Further, compared traditional univariate contrasts, enabled individual-level correlations be significantly greater precision. specifically link improvements precision optimized suppression decoding. Together, findings not indicate control-related individuate people highly stable manner time, but also suggest integrating provides powerful approach for investigating control, can effectively address high-variability measures.

Language: Английский

Citations

0