Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models.
adaptive design
behavioral model
computational models
computational psychiatry
delay discounting
design optimization
homogenizing behavior
reward discounting
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2022
2022
Historique:
received:
23
10
2022
accepted:
14
12
2022
entrez:
26
1
2023
pubmed:
27
1
2023
medline:
27
1
2023
Statut:
epublish
Résumé
Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments. These ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error. The proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning. Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.
Identifiants
pubmed: 36699538
doi: 10.3389/fnins.2022.1077735
pmc: PMC9868576
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1077735Informations de copyright
Copyright © 2023 Thome, Pinger, Durstewitz, Sommer, Kirsch and Koppe.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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