scholarly journals Identifying Model-Based and Model-Free Patterns in Behavior on Multi-Step Tasks

2016 ◽  
Author(s):  
Kevin J. Miller ◽  
Carlos D. Brody ◽  
Matthew M. Botvinick
Keyword(s):  
Model Fitting ◽  
Decision Task ◽  
Sequential Decision ◽  
Agent Based Model ◽  
Agent Based ◽  
Model Based ◽  
Model Free ◽  
Analysis Technique ◽  
Analyze Data

Recent years have seen a surge of research into the neuroscience of planning. Much of this work has taken advantage of a two-step sequential decision task developed by Daw et al. (2011), which gives the ability to diagnose whether or not subjects’ behavior is the result of planning. Here, we present simulations which suggest that the techniques most commonly used to analyze data from this task may be confounded in important ways. We introduce a new analysis technique, which suffers from fewer of these issues. This technique also presents a richer view of behavior, making it useful for characterizing patterns in behavior in a theory-neutral manner. This allows it to provide an important check on the assumptions of more theory-driven analysis such as agent-based model-fitting.

scholarly journals Analysis of the Evacuation Capacity of Parallel Double Running Stairs in Different Merging Form Based on Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xia-zhong Zheng ◽  
Xue-ling Xie ◽  
Dan Tian ◽  
Jian-lan Zhou ◽  
Ming Zhang
Keyword(s):  
Computer Simulation ◽  
Evaluation Model ◽  
Pedestrian Flow ◽  
Agent Based Model ◽  
Evacuation Time ◽  
Agent Based ◽  
Positive Correlation ◽  
Model Based ◽  
Evacuation Model ◽  
Merging Behavior

In order to analyze the evacuation capacity of parallel double running stairs, a dozen stairs merging forms are set by investigation and statistics, and the improved agent-based evacuation model that considers the merging behavior is used to simulate the process of merging and evacuation in the stairs. The stairs evacuation capacity is related to the evacuation time and the robustness of stairs, and the evacuation time can be calculated by using the improved agent-based model based on computer simulation. The robustness of each merging form can be obtained according to the fluctuation degree of evacuation time under the different pedestrian flow. The evaluation model of stairs evacuation capacity is established by fusing the evacuation time and the robustness of stairs. Combined with the specific example to calculate the evacuation capacity of each stairs form, it is found that every merging form has different evacuation time and different robustness, and the evacuation time has not positive correlation with the robustness for the same form stairs. Meanwhile, the evacuation capacity of stairs is not related to the number of the floor entrances. Finally, the results show that the evacuation capacity of stairs is optimal when the floor entrances are close to out stairs in parallel double running stairs and suitable to the case where pedestrian flow and the change of pedestrian flow are large.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

scholarly journals Increased temporal discounting and reduced model-based control in problem gambling are not substantially modulated by exposure to virtual gambling environments.

2021 ◽  
Author(s):  
Luca Rene Bruder ◽  
Ben Wagner ◽  
David Mathar ◽  
Jan Peters
Keyword(s):  
Pathological Gambling ◽  
Electrodermal Activity ◽  
Temporal Discounting ◽  
Sports Betting ◽  
Reduced Model ◽  
Sequential Decision ◽  
Model Based Control ◽  
Model Based ◽  
Model Free ◽  
Environment Exposure

High-performance virtual reality (VR) technology has opened new possibilities for the examination of the reactivity towards addiction-related cues (cue-reactivity) in addiction. In this preregistered study (https://osf.io/4mrta), we investigated the subjective, physiological, and behavioral effects of gambling-related VR environment exposure in participants reporting frequent or pathological gambling (n=31) as well as non-gambling controls (n=29). On two separate days, participants explored two rich and navigable VR-environments (neutral: cafe vs. gambling-related: casino/sports-betting facility), while electrodermal activity and heart rate were continuously measured using remote sensors. Within VR, participants performed a temporal discounting task and a sequential decision-making task designed to assess model-based and model-free contributions to behavior. Replicating previous findings, we found strong evidence for increased temporal discounting and reduced model-based control in participants reporting frequent or pathological gambling. Although VR gambling environment exposure increased subjective craving, there was if anything inconclusive evidence for further behavioral or physiological effects. Instead, VR exposure substantially increased physiological arousal (electrodermal activity), across groups and conditions. VR is a promising tool for the investigation of context effects in addiction, but some caution is warranted since effects of real gambling environments might not generally replicate in VR. Future studies should delineate how factors such as cognitive load and ecological validity could be balanced to create a more naturalistic VR experience.

scholarly journals Credit assignment to state-independent task representations and its relationship with model-based decision making

2019 ◽  
Vol 116 (32) ◽  
pp. 15871-15876 ◽  
Author(s):  
Nitzan Shahar ◽  
Rani Moran ◽  
Tobias U. Hauser ◽  
Rogier A. Kievit ◽  
Daniel McNamee ◽  
...  
Keyword(s):  
Decision Task ◽  
Credit Assignment ◽  
Free System ◽  
Assigned Value ◽  
Model Based ◽  
Independent Task ◽  
Model Free ◽  
Motor Representations ◽  
The Impact ◽  
Task Representations

Model-free learning enables an agent to make better decisions based on prior experience while representing only minimal knowledge about an environment’s structure. It is generally assumed that model-free state representations are based on outcome-relevant features of the environment. Here, we challenge this assumption by providing evidence that a putative model-free system assigns credit to task representations that are irrelevant to an outcome. We examined data from 769 individuals performing a well-described 2-step reward decision task where stimulus identity but not spatial-motor aspects of the task predicted reward. We show that participants assigned value to spatial-motor representations despite it being outcome irrelevant. Strikingly, spatial-motor value associations affected behavior across all outcome-relevant features and stages of the task, consistent with credit assignment to low-level state-independent task representations. Individual difference analyses suggested that the impact of spatial-motor value formation was attenuated for individuals who showed greater deployment of goal-directed (model-based) strategies. Our findings highlight a need for a reconsideration of how model-free representations are formed and regulated according to the structure of the environment.

scholarly journals Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making

2015 ◽  
Vol 112 (5) ◽  
pp. 1595-1600 ◽  
Author(s):  
Lorenz Deserno ◽  
Quentin J. M. Huys ◽  
Rebecca Boehme ◽  
Ralph Buchert ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Ventral Striatum ◽  
Behavioral Control ◽  
Striatal Dopamine ◽  
Quantitative Relation ◽  
Sequential Decision ◽  
Model Based Control ◽  
Model Based ◽  
Model Free ◽  
Neuropsychiatric Diseases ◽  
Neural Signatures

Dual system theories suggest that behavioral control is parsed between a deliberative “model-based” and a more reflexive “model-free” system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [18F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.

scholarly journals Hunger improves reinforcement-driven but not planned action

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.

scholarly journals Metacontrol of decision-making strategies in human aging

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.

scholarly journals Metacontrol of decision-making strategies in human aging

eLife ◽  
2019 ◽  
Vol 8 ◽  
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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