Individual differences in sequential decision-making DOI Creative Commons
Mojtaba Abbaszadeh,

Erica Ozanick,

Noa Magen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

1People differ widely in how they make decisions uncertain environments. While many studies leverage this variability to measure differences specific cognitive processes and parameters, the key dimension(s) of individual decision-making tasks has not been identified. Here, we analyzed behavioral data from 1001 participants performing a restless three-armed bandit task, where reward probabilities fluctuated unpredictably over time. Using novel analytical approach that controlled for stochasticity tasks, identified dominant nonlinear axis variability. We found primary was strongly selectively correlated with probability exploration, as inferred by latent state modeling. This suggests major factor shaping task performance is tendency explore (versus exploit), rather than personality characteristics, reinforcement learning model or low-level strategies. Certain demographic characteristics also predicted variance along principle axis: at exploratory end tended be younger exploitative end, self-identified men were overrepresented both extremes. Together, these findings offer principled framework understanding behavior while highlighting factors shape under uncertainty.

Language: Английский

Individual differences in sequential decision-making DOI Creative Commons
Mojtaba Abbaszadeh,

Erica Ozanick,

Noa Magen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

1People differ widely in how they make decisions uncertain environments. While many studies leverage this variability to measure differences specific cognitive processes and parameters, the key dimension(s) of individual decision-making tasks has not been identified. Here, we analyzed behavioral data from 1001 participants performing a restless three-armed bandit task, where reward probabilities fluctuated unpredictably over time. Using novel analytical approach that controlled for stochasticity tasks, identified dominant nonlinear axis variability. We found primary was strongly selectively correlated with probability exploration, as inferred by latent state modeling. This suggests major factor shaping task performance is tendency explore (versus exploit), rather than personality characteristics, reinforcement learning model or low-level strategies. Certain demographic characteristics also predicted variance along principle axis: at exploratory end tended be younger exploitative end, self-identified men were overrepresented both extremes. Together, these findings offer principled framework understanding behavior while highlighting factors shape under uncertainty.

Language: Английский

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