US-German Collaboration: Computational and Neural Mechanisms of Inference over Decision-Structure

California Institute Of Technology, Pasadena CA

Investigators

Abstract

US-German Collaboration: Computational and Neural Mechanisms of Inference over Decision-Structure PI: John O?Doherty, Co PI: Peter Bossaerts ABSTRACT In this project the Principal Investigators will determine how the brain is able to identify the relevant rules that apply to a given decision-making problem in order to effectively make decisions. In most cases, features of the decision structure are hidden variables, i.e. they can be inferred only through discrete observations of outcome variables (such as reward feedback). Understanding how inferences over decision-structure are performed in a noisy and partially observable environment is therefore a fundamental yet almost unaddressed issue in the computational neurobiology of human decision-making. Here, we conceive of inferences over decision problems as a form of hierarchical inference in which the higher level of the hierarchy represents probabilistic beliefs over which decision structure is currently in place, while the lower level of the hierarchy encodes beliefs over which actions are currently rewarded within a specific decision structure. We will compare and contrast a variety of computational models deploying different strategies to solve this problem. We will combine these models with behavioral and functional magnetic resonance imaging (fMRI) data from human participants in order to address whether dynamic signals are present in the brain pertaining to the implementation of such hierarchical models, and whether different brain regions are involved in performing inference at different levels of the hierarchy. This project could potentially lead to a new understanding of the contribution of the prefrontal cortex and other brain regions in decision-making. This project will also provide insight into the neural implementation of a fundamental missing part of the picture concerning the neurobiology of human decision-making: decision-structure inference. In terms of broader impacts, this research could provide fundamental new insights into understanding situations where human learning or decision-making fails or breaks down. Sometimes poor learning or decision-making may be due to a failure to infer the correct rules governing a decision-problem rather than a difficulty in learning or deciding per se. Such insights will not only impact on academic fields studying decision making but could also be used to develop novel methods to help individuals and organizations make better decisions (by focusing on improving inference over structure). The findings could provide relevant data for the development of artificial agents capable of autonomous, flexible and adaptive decisions. The proposal also has high potential clinical relevance: disorders with delusional beliefs such as schizophrenia and borderline personality disorder might involve in part a difficulty in performing inference over decision structure so as to rule out inappropriate (decision) structures in lieu of more appropriate ones. The present research might yield novel tools to study this question in clinical populations. Furthermore, there are substantial impacts on teaching and training. The PI and co-PI teach courses at undergraduate and graduate level and involve undergraduate researchers directly in their research programs. The work proposed here could lead to the development of new software to enable the analysis of brain imaging data using computational models. A companion project is being funded by the German Ministry of Education and Research (BMBF).