A Bayesian brain model of adaptive behavior

• Verfasst von: D'Alessandro, Marco [VerfasserIn]
• Verfasst von: Radev, Stefan [VerfasserIn]
• Verfasst von: Voß, Andreas [VerfasserIn]
• Verfasst von: Lombardi, Luigi [VerfasserIn]
• Titel: A Bayesian brain model of adaptive behavior
• Titelzusatz: an application to the Wisconsin Card Sorting Task
• Verf.angabe: Marco D’Alessandro, Stefan T. Radev, Andreas Voss and Luigi Lombardi
• E-Jahr: 2020
• Jahr: 30 November 2020
• Fussnoten: Gesehen am 13.01.2021
• Titel Quelle: Enthalten in: PeerJ
• Ort Quelle: London [u.a.] : PeerJ, Inc., 2013
• Jahr Quelle: 2020
• Band/Heft Quelle: 8(2020) Artikel-Nummer e10316, 32 Seiten
• ISSN Quelle: 2167-8359
• DOI: doi:10.7717/peerj.10316
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1744511888

Abstract

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.