Model-based decision making and model-free learning

Decision making in a complex world, characterized both by predictable regularities and by frequent departures from the norm, requires dynamic switching between rapid habit-like, automatic processes and slower, more flexible evaluative processes. These strategies, formalized as “model-free” and “model-based” reinforcement learning algorithms, respectively, can lead to divergent behavioral outcomes, requiring a mechanism to arbitrate between them in a context-appropriate manner. Recent data suggest that individuals with obsessive-compulsive disorder (OCD) rely excessively on inflexible habit-like decision making during reinforcement-driven learning. We propose that inflexible reliance on habit in OCD may reflect a functional weakness in the mechanism for context-appropriate dynamic arbitration between model-free and model-based decision making. Support for this hypothesis derives from emerging functional imaging findings. A deficit in arbitration in OCD may help reconcile evidence for excessive reliance on habit in rewarded learning tasks with an older literature suggesting inappropriate recruitment of circuitry associated with model-based decision making in unreinforced procedural learning. The hypothesized deficit and corresponding circuitry may be a particularly fruitful target for interventions, including cognitive remediation.

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Higher Meta-cognitive Ability Predicts Less Reliance on Over Confident Habitual Learning System

10.1101/650556 ◽

2019 ◽

Author(s):

Sara Ershadmanesh ◽

Mostafa Miandari ◽

Abdol-hossein Vahabie ◽

Majid Nili Ahmadabadi

Keyword(s):

Decision Making ◽

Cognitive Ability ◽

Learning Strategies ◽

Learning Style ◽

Learning Systems ◽

Learning System ◽

Variable Environment ◽

Free System ◽

Model Based ◽

Model Free

AbstractMany studies on human and animals have provided evidence for the contribution of goal-directed and habitual valuation systems in learning and decision-making. These two systems can be modeled using model-based (MB) and model-free (MF) algorithms in Reinforcement Learning (RL) framework. Here, we study the link between the contribution of these two learning systems to behavior and meta-cognitive capabilities. Using computational modeling we showed that in a highly variable environment, where both learning strategies have chance level performances, model-free learning predicts higher confidence in decisions compared to model-based strategy. Our experimental results showed that the subjects’ meta-cognitive ability is negatively correlated with the contribution of model-free system to their behavior while having no correlation with the contribution of model-based system. Over-confidence of the model-free system justifies this counter-intuitive result. This is a new explanation for individual difference in learning style.

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S146. ASSOCIATION OF IMPAIRED MODEL-FREE DECISION-MAKING WITH ABERRANT STRIATAL DOPAMINE, BRAIN ACTIVATION, AND COGNITIVE DIFFICULTIES IN PATIENTS WITH SCHIZOPHRENIA DURING PSYCHOTIC REMISSION

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.212 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S91-S92

Author(s):

Felix Brandl ◽

Mihai Avram ◽

Jorge Cabello ◽

Mona Mustafa ◽

Claudia Leucht ◽

...

Keyword(s):

Decision Making ◽

Cognitive Impairments ◽

Dorsal Striatum ◽

Striatal Dopamine ◽

Dopamine Synthesis ◽

Cognitive Difficulties ◽

Model Based ◽

Model Free ◽

Trail Making ◽

Task Activation

Abstract Background Human decision-making ranges between the extremes of automatic and fast model-free behavior (i.e., relying only on previous outcomes) and more flexible, but computationally demanding model-based behavior (i.e., implementing cognitive models). Model-based/model-free decision-making can be investigated using sequential decision tasks and has been shown to be associated with presynaptic striatal dopamine synthesis. During phases of psychotic remission in schizophrenia, dopamine synthesis in the dorsal striatum is reduced. We hypothesized that particularly model-free decision-making is impaired in schizophrenia during psychotic remission and is associated with (i) abnormal dopamine synthesis in dorsal striatum, (ii) aberrant task-activation in dorsal striatum, and (iii) cognitive difficulties in patients (e.g., reduced speed). Methods 26 patients with chronic schizophrenia, currently in psychotic remission, and 22 healthy controls (matched by age and gender) were enrolled in the study. Model-based/model-free decision-making was evaluated with a two-stage Markov decision task, followed by computational modeling of subjects’ learning behavior. Presynaptic dopamine synthesis was assessed by 18F-DOPA positron emission tomography and subsequent graphical Patlak analysis. Task-activation was measured by functional magnetic resonance imaging. Cognitive impairments were quantified by Trail-Making-Test A (among others). Associations between decision-making parameters, dopamine synthesis, task-activation, and cognitive impairments were tested by correlation analyses. Results Patients with schizophrenia showed selectively impaired model-free decision-making. 18F-DOPA uptake (i.e., presynaptic dopamine synthesis capacity) in the dorsal striatum was decreased in patients. Impaired model-free decision-making in patients correlated with (i) decreased dopamine synthesis in dorsal striatum, (ii) abnormal task-activation in dorsal striatum, and (iii) lower speed in Trail-Making-Test A. Discussion Results demonstrate an association of reduced dorsal striatal dopamine synthesis and brain activity with impaired model-free decision-making in schizophrenia, which potentially contributes to cognitive difficulties.

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Hunger improves reinforcement-driven but not planned action

10.1101/2021.03.24.436435 ◽

2021 ◽

Author(s):

Maaike M.H. van Swieten ◽

Rafal Bogacz ◽

Sanjay G. Manohar

Keyword(s):

Decision Making ◽

Sequential Decision ◽

Context Sensitive ◽

Decision Systems ◽

Model Based ◽

Model Free ◽

Prevalence Of Obesity ◽

Model Free Control ◽

Decision Mode

AbstractHuman decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free system’s primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalised effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits.Significance statementThe prevalence of obesity and eating disorder is steadily increasing. To counteract problems related to eating, people need to make rational decisions. However, appetite may switch us to a different decision mode, making it harder to achieve long-term goals. Here we show that planned and reinforcement-driven actions are differentially sensitive to hunger. Hunger specifically affected reinforcement-driven actions, and did not affect the planning of actions. Our data shows that people behave differently when they are hungry. We also provide a computational model of how the behavioural changes might arise.

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How Does Drug Use Shift the Balance Between Model-Based and Model-Free Control of Decision Making?

Biological Psychiatry ◽

10.1016/j.biopsych.2019.04.016 ◽

2019 ◽

Vol 85 (11) ◽

pp. 886-888

Author(s):

Kurt M. Fraser ◽

Patricia H. Janak

Keyword(s):

Decision Making ◽

Drug Use ◽

Model Based ◽

Model Free ◽

Model Free Control

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Model based planners reflect on their model-free propensities

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008552 ◽

2021 ◽

Vol 17 (1) ◽

pp. e1008552

Author(s):

Rani Moran ◽

Mehdi Keramati ◽

Raymond J. Dolan

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Drug Abuse ◽

Learning Theory ◽

Planning Model ◽

Present Evidence ◽

Model Based ◽

Model Free ◽

Short And Long Term

Dual-reinforcement learning theory proposes behaviour is under the tutelage of a retrospective, value-caching, model-free (MF) system and a prospective-planning, model-based (MB), system. This architecture raises a question as to the degree to which, when devising a plan, a MB controller takes account of influences from its MF counterpart. We present evidence that such a sophisticated self-reflective MB planner incorporates an anticipation of the influences its own MF-proclivities exerts on the execution of its planned future actions. Using a novel bandit task, wherein subjects were periodically allowed to design their environment, we show that reward-assignments were constructed in a manner consistent with a MB system taking account of its MF propensities. Thus, in the task participants assigned higher rewards to bandits that were momentarily associated with stronger MF tendencies. Our findings have implications for a range of decision making domains that includes drug abuse, pre-commitment, and the tension between short and long-term decision horizons in economics.

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Metacontrol of decision-making strategies in human aging

10.31234/osf.io/xw53v ◽

2019 ◽

Author(s):

Florian Bolenz ◽

Wouter Kool ◽

Andrea M.F. Reiter ◽

Ben Eppinger

Keyword(s):

Older Adults ◽

Decision Making ◽

Cognitive Abilities ◽

Reduced Model ◽

Task Structure ◽

Costs And Benefits ◽

Sequential Decision ◽

Model Based Control ◽

Model Based ◽

Model Free

When making decisions, humans employ different strategies which are commonly formalized as model-free and model-based reinforcement learning. While previous research has reported reduced model-based control with aging, it remains unclear whether this is due to limited cognitive capacities or a reduced willingness to engage in an effortful strategy. Moreover, it is not clear how aging affects the metacontrol of decision making, i.e. the dynamic adaptation of decision-making strategies to varying situational demands. To this end, we tested younger and older adults in a sequential decision-making task that dissociates model-free and model-based control. In contrast to previous research, in this study we applied a task in which model-based control led to higher payoffs in terms of monetary reward. Moreover, we manipulated the costs and benefits associated with model-based control by varying reward magnitude as well as the stability of the task structure. Compared to younger adults, older adults showed reduced reliance on model-based decision making and less adaptation of decision-making strategies to varying costs and benefits of model-based control. Our findings suggest that aging affects the dynamic metacontrol of decision-making strategies and that reduced model-based control in older adults is due to limited cognitive abilities to represent the structure of the task.

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Metacontrol of decision-making strategies in human aging

eLife ◽

10.7554/elife.49154 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 3

Author(s):

Florian Bolenz ◽

Wouter Kool ◽

Andrea MF Reiter ◽

Ben Eppinger

Keyword(s):

Older Adults ◽

Decision Making ◽

Cognitive Abilities ◽

Cross Sectional Study ◽

Reduced Model ◽

Task Structure ◽

Sequential Decision ◽

Cross Sectional ◽

Model Based ◽

Model Free

Humans employ different strategies when making decisions. Previous research has reported reduced reliance on model-based strategies with aging, but it remains unclear whether this is due to cognitive or motivational factors. Moreover, it is not clear how aging affects the metacontrol of decision making, that is the dynamic adaptation of decision-making strategies to varying situational demands. In this cross-sectional study, we tested younger and older adults in a sequential decision-making task that dissociates model-free and model-based strategies. In contrast to previous research, model-based strategies led to higher payoffs. Moreover, we manipulated the costs and benefits of model-based strategies by varying reward magnitude and the stability of the task structure. Compared to younger adults, older adults showed reduced model-based decision making and less adaptation of decision-making strategies. Our findings suggest that aging affects the metacontrol of decision-making strategies and that reduced model-based strategies in older adults are due to limited cognitive abilities.

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Model-based decision-making and its metacontrol in childhood

10.31234/osf.io/ervsb ◽

2020 ◽

Cited By ~ 1

Author(s):

Claire Rosalie Smid ◽

Wouter Kool ◽

Tobias U. Hauser ◽

Nikolaus Steinbeis

Keyword(s):

Decision Making ◽

Computational Cost ◽

Cost Benefit ◽

Optimal Decision ◽

Developmental Studies ◽

Free System ◽

Model Based ◽

Model Free ◽

Human Decision ◽

Optimal Decision Making

Human decision-making is underpinned by distinct systems that differ in their flexibility and associated computational cost. A widely accepted dichotomy distinguishes a flexible but costly model-based system and a cheap but rigid model-free system. Optimal decision-making requires adaptive arbitration between these two systems depending on environmental demands. Previous developmental studies suggest that model-based decision-making only emerges in adolescence. Here, we show that when using a paradigm more conducive to model-based decision-making, children as young as 5 years show contributions from a model-based system to their behaviour. Furthermore, we find that between the ages 5 to 11, children demonstrate increasing metacontrol, which is the engagement of cost-benefit arbitration over decision-making systems on a trial-by-trial basis. Our results suggest that model-based decision-making emerges much earlier than previously believed, while adaptive arbitration between computationally cheap and costly systems continues to undergo developmental changes during childhood.

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