Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Janine Thome; Mathieu Pinger; Daniel Durstewitz; Wolfgang H Sommer; Peter Kirsch; Georgia Koppe

doi:10.3389/fnins.2022.1077735

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Front Neurosci. 2023 Jan 9:16:1077735. doi: 10.3389/fnins.2022.1077735. eCollection 2022.

Authors

Janine Thome^{1

2}, Mathieu Pinger³, Daniel Durstewitz^{1

4}, Wolfgang H Sommer⁵, Peter Kirsch^{3

6}, Georgia Koppe^{1

2}

Affiliations

¹ Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
² Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
³ Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
⁴ Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
⁵ Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
⁶ Institute of Psychology, Heidelberg University, Heidelberg, Germany.

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.

Keywords: adaptive design; behavioral model; computational models; computational psychiatry; delay discounting; design optimization; homogenizing behavior; reward discounting.

Grants and funding

This study was supported by the German Research Foundation (DFG) within the collaborative research center TRR 265, subproject B08, granted to GK, PK, and WS, and by the WIN-Kolleg 8 of the Heidelberg Academy of Sciences and Humanities granted to GK.