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Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.

Niek StevensonReilly J InnesRussell J BoagSteven MiletićScott J S IsherwoodAnne C TruttiAndrew HeathcoteBirte U Forstmann
Published in: Computational brain & behavior (2024)
Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.
Keyphrases
  • decision making
  • working memory
  • healthcare
  • social media