Edward H Patzelt1, Wouter Kool2, Alexander J Millner2, Samuel J Gershman3. 1. Department of Psychology, Harvard University, Cambridge, Massachusetts; Center for Brain Science, Harvard University, Cambridge, Massachusetts. Electronic address: patzelt@g.harvard.edu. 2. Department of Psychology, Harvard University, Cambridge, Massachusetts. 3. Department of Psychology, Harvard University, Cambridge, Massachusetts; Center for Brain Science, Harvard University, Cambridge, Massachusetts.
Abstract
BACKGROUND: Human decision making exhibits a mixture of model-based and model-free control. Recent evidence indicates that arbitration between these two modes of control ("metacontrol") is based on their relative costs and benefits. While model-based control may increase accuracy, it requires greater computational resources, so people invoke model-based control only when potential rewards exceed those of model-free control. We used a sequential decision task, while concurrently manipulating performance incentives, to ask if symptoms and traits of psychopathology decrease or increase model-based control in response to incentives. METHODS: We recruited a nonpatient population of 839 online participants using Amazon Mechanical Turk who completed transdiagnostic self-report measures encompassing symptoms, traits, and factors. We fit a dual-controller reinforcement learning model and obtained a computational measure of model-based control separately for small incentives and large incentives. RESULTS: None of the constructs were related to a failure of large incentives to boost model-based control. In fact, for the sensation seeking trait and anxious-depression factor, higher scores were associated with a larger incentive effect, whereby greater levels of these constructs were associated with larger increases in model-based control. Many constructs showed decreases in model-based control as a function of severity, but a social withdrawal factor was positively correlated; alcohol use and social anxiety were unrelated to model-based control. CONCLUSIONS: Our results demonstrate that model-based control can reliably be improved independent of construct severity for most measures. This suggests that incentives may be a useful intervention for boosting model-based control across a range of symptom and trait severity.
BACKGROUND:Human decision making exhibits a mixture of model-based and model-free control. Recent evidence indicates that arbitration between these two modes of control ("metacontrol") is based on their relative costs and benefits. While model-based control may increase accuracy, it requires greater computational resources, so people invoke model-based control only when potential rewards exceed those of model-free control. We used a sequential decision task, while concurrently manipulating performance incentives, to ask if symptoms and traits of psychopathology decrease or increase model-based control in response to incentives. METHODS: We recruited a nonpatient population of 839 online participants using Amazon Mechanical Turk who completed transdiagnostic self-report measures encompassing symptoms, traits, and factors. We fit a dual-controller reinforcement learning model and obtained a computational measure of model-based control separately for small incentives and large incentives. RESULTS: None of the constructs were related to a failure of large incentives to boost model-based control. In fact, for the sensation seeking trait and anxious-depression factor, higher scores were associated with a larger incentive effect, whereby greater levels of these constructs were associated with larger increases in model-based control. Many constructs showed decreases in model-based control as a function of severity, but a social withdrawal factor was positively correlated; alcohol use and social anxiety were unrelated to model-based control. CONCLUSIONS: Our results demonstrate that model-based control can reliably be improved independent of construct severity for most measures. This suggests that incentives may be a useful intervention for boosting model-based control across a range of symptom and trait severity.
Authors: Eva R Pool; Rani Gera; Aniek Fransen; Omar D Perez; Anna Cremer; Mladena Aleksic; Sandy Tanwisuth; Stephanie Quail; Ahmet O Ceceli; Dylan A Manfredi; Gideon Nave; Elizabeth Tricomi; Bernard Balleine; Tom Schonberg; Lars Schwabe; John P O'Doherty Journal: Learn Mem Date: 2021-12-15 Impact factor: 2.460