Interindividual differences in Pavlovian influence on learning are consistent DOI Creative Commons
Sepehr Saeedpour,

Mostafa Minadari Hossein,

Ophélia Deroy

et al.

Royal Society Open Science, Journal Year: 2023, Volume and Issue: 10(9)

Published: Sept. 1, 2023

Pavlovian influences impair instrumental learning. It is easier to learn approach reward-predictive signals and avoid punishment-predictive cues than their contrary. Whether the interindividual variability in this influence consistent across time has been examined by a number of recent studies met with mixed results. Here we introduce an open-source, web-based instance well-established Go-NoGo paradigm for measuring influence. We closely replicated previous laboratory-based Moreover, differences were two-week window at level (i) raw measures learning (i.e. performance accuracy), (ii) linear, descriptive estimates bias (test-retest reliability: 0.40), (iii) parameters obtained from reinforcement model fitting selection 0.25). Nonetheless, correlations reported here are still lower standards 0.7) employed psychometrics self-reported measures. Our results provide support trusting as relatively stable individual characteristic using its measure computational understanding human mental health.

Language: Английский

Dynamic computational phenotyping of human cognition DOI Creative Commons
Roey Schurr, Daniel Reznik, Hanna Hillman

et al.

Nature Human Behaviour, Journal Year: 2024, Volume and Issue: 8(5), P. 917 - 931

Published: Feb. 8, 2024

Abstract 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 behavioural data. However, the interpretation these hinges critically on their psychometric properties, which are rarely studied. To 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. examine influence state 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

Citations

23
Dynamic computational phenotyping of human cognition DOI Open Access
Roey Schurr, Daniel Reznik, Hanna Hillman

et al.

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

Citations

12
Interindividual differences in Pavlovian influence on learning are consistent DOI Creative Commons
Sepehr Saeedpour,

Mostafa Minadari Hossein,

Ophélia Deroy

et al.

Royal Society Open Science, Journal Year: 2023, Volume and Issue: 10(9)

Published: Sept. 1, 2023

Pavlovian influences impair instrumental learning. It is easier to learn approach reward-predictive signals and avoid punishment-predictive cues than their contrary. Whether the interindividual variability in this influence consistent across time has been examined by a number of recent studies met with mixed results. Here we introduce an open-source, web-based instance well-established Go-NoGo paradigm for measuring influence. We closely replicated previous laboratory-based Moreover, differences were two-week window at level (i) raw measures learning (i.e. performance accuracy), (ii) linear, descriptive estimates bias (test-retest reliability: 0.40), (iii) parameters obtained from reinforcement model fitting selection 0.25). Nonetheless, correlations reported here are still lower standards 0.7) employed psychometrics self-reported measures. Our results provide support trusting as relatively stable individual characteristic using its measure computational understanding human mental health.

Language: Английский

Citations

3