Dynamic computational phenotyping of human cognition

Published: June 26, 2023

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across variety of cognitive domains. An individual's computational phenotype is defined set mechanistically interpretable parameters obtained from fitting models to behavioral data. However, the interpretation these hinges critically on their psychometric properties, which are rarely studied. In order identify sources governing temporal phenotype, we carried out 12-week longitudinal study using battery seven tasks that measure aspects human learning, memory, perception, and decision making. To examine influence state-like effects, each week participants provided reports tracking mood, habits daily activities. We developed dynamic framework, allowed us tease apart time-varying effects practice internal states such affective valence arousal. Our results show many dimensions covary with factors, indicating what appears be unreliability may reflect previously unmeasured structure. These support fundamentally understanding within an individual.

Language: Английский

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Improving the reliability and validity of the IAT with a dynamic model driven by similarity

Behavior Research Methods, Journal Year: 2023, Volume and Issue: 56(3), P. 2158 - 2193

Published: July 5, 2023

Language: Английский

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Hierarchical-Model Insights for Planning and Interpreting Individual-Difference Studies of Cognitive Abilities

Current Directions in Psychological Science, Journal Year: 2024, Volume and Issue: 33(2), P. 128 - 135

Published: Feb. 22, 2024

Although individual-difference studies have been invaluable in several domains of psychology, there has less success cognitive using experimental tasks. The problem is often called one reliability: Individual differences tasks, especially cognitive-control seem too unreliable. In this article, we use the language hierarchical models to define a novel reliability measure—a signal-to-noise ratio—that reflects nature tasks alone without recourse sample sizes. Signal-to-noise may be used plan appropriately powered as well understand cause low correlations across should they occur. motivated by models, it estimated from simple calculation straightforward summary statistics.

Language: Английский

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Toward a “Standard Model” of Early Language Learning

Current Directions in Psychological Science, Journal Year: 2021, Volume and Issue: 31(1), P. 20 - 27

Published: Dec. 23, 2021

A standard model is a theoretical framework that synthesizes observables into quantitative consensus. Have researchers made progress toward this kind of synthesis for children’s early language learning? Many computational models vocabulary learning assume individual words are learned through an accumulation environmental input. This assumption also implicit in empirical work emphasizes links between input and outcomes. However, have typically focused on average performance, whereas has variability. To variability, we relate the tradition research accumulator to item response theory from psychometrics. formal connection reveals currently available data sets do not allow test resulting fully, illustrating critical need contribute shaping new collection creating testing eventual model.

Language: Английский

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The longitudinal stability of fMRI activation during reward processing in adolescents and young adults

NeuroImage, Journal Year: 2021, Volume and Issue: 232, P. 117872 - 117872

Published: Feb. 19, 2021

The use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis human reward function. This approach included identification potential neurobiological mechanisms psychiatric disease and examination environmental, experiential, biological factors that may contribute to risk via effects on system. However, a central largely unexamined assumption much this research is function individual difference characteristic relatively stable trait-like over time.

Language: Английский

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