scholarly journals Neural Signatures of Prediction Errors in a Decision-Making Task Are Modulated by Action Execution Failures

2018 ◽  
Author(s):  
Samuel D. McDougle ◽  
Peter A. Butcher ◽  
Darius Parvin ◽  
Fasial Mushtaq ◽  
Yael Niv ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Learning Task ◽  
Prediction Errors ◽  
Action Execution ◽  
Model Driven ◽  
Assignment Of Credit ◽  
Level Information ◽  
High Level

AbstractDecisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should not only be sensitive to whether the choice itself was suboptimal, but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this scenario, we used a modified version of a classic reinforcement learning task in which feedback indicated if negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful but the reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction error in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes.

scholarly journals Pupil responses as indicators of value-based decision-making

2018 ◽  
Author(s):  
Joanne C. Van Slooten ◽  
Sara Jahfari ◽  
Tomas Knapen ◽  
Jan Theeuwes
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Computational Model ◽  
Cognitive Processes ◽  
Learning Task ◽  
Brain Regions ◽  
Computational Approach ◽  
Prediction Errors ◽  
Reward Prediction ◽  
Exciting Possibility

AbstractPupil responses have been used to track cognitive processes during decision-making. Studies have shown that in these cases the pupil reflects the joint activation of many cortical and subcortical brain regions, also those traditionally implicated in value-based learning. However, how the pupil tracks value-based decisions and reinforcement learning is unknown. We combined a reinforcement learning task with a computational model to study pupil responses during value-based decisions, and decision evaluations. We found that the pupil closely tracks reinforcement learning both across trials and participants. Prior to choice, the pupil dilated as a function of trial-by-trial fluctuations in value beliefs. After feedback, early dilation scaled with value uncertainty, whereas later constriction scaled with reward prediction errors. Our computational approach systematically implicates the pupil in value-based decisions, and the subsequent processing of violated value beliefs, ttese dissociable influences provide an exciting possibility to non-invasively study ongoing reinforcement learning in the pupil.

scholarly journals Neural basis of decision making guided by emotional outcomes

2015 ◽  
Vol 113 (9) ◽  
pp. 3056-3068 ◽  
Author(s):  
Kentaro Katahira ◽  
Yoshi-Taka Matsuda ◽  
Tomomi Fujimura ◽  
Kenichi Ueno ◽  
Takeshi Asamizuya ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Prediction Errors ◽  
Neural Basis ◽  
Decision Outcomes ◽  
Gain Loss ◽  
Emotional Events

Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures.

scholarly journals Behavioural and computational evidence for memory consolidation biased by reward-prediction errors

2019 ◽  
Author(s):  
Emma L. Roscow ◽  
Matthew W. Jones ◽  
Nathan F. Lepora
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Learning Task ◽  
Male Rats ◽  
Adaptive Behaviour ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Per Se ◽  
Reinforcement Learning Model

AbstractNeural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of the corresponding memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based rein-forcement learning task designed to dissociate reward outcomes from reward-prediction errors. Four variations of a reinforcement learning model were fitted to the rats’ behaviour over multiple days. Behaviour was best predicted by a model incorporating replay biased by reward-prediction error, compared to the same model with no replay; random replay or reward-biased replay produced poorer predictions of behaviour. This insight disentangles the influences of salience on replay, suggesting that reinforcement learning is tuned by post-learning replay biased by reward-prediction error, not by reward per se. This work therefore provides a behavioural and theoretical toolkit with which to measure and interpret replay in striatal, hippocampal and neocortical circuits.

scholarly journals Reward prediction errors induce risk-seeking

2020 ◽  
Author(s):  
Moritz Moeller ◽  
Jan Grohn ◽  
Sanjay Manohar ◽  
Rafal Bogacz
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Risk Preferences ◽  
Behavioral Effect ◽  
Learning Task ◽  
Expected Value ◽  
Learning Theories ◽  
Prediction Errors ◽  
Risk Seeking ◽  
The Difference

AbstractReinforcement learning theories propose that humans choose based on the estimated values of available options, and that they learn from rewards by reducing the difference between the experienced and expected value. In the brain, such prediction errors are broadcasted by dopamine. However, choices are not only influenced by expected value, but also by risk. Like reinforcement learning, risk preferences are modulated by dopamine: enhanced dopamine levels induce risk-seeking. Learning and risk preferences have so far been studied independently, even though it is commonly assumed that they are (partly) regulated by the same neurotransmitter. Here, we use a novel learning task to look for prediction-error induced risk-seeking in human behavior and pupil responses. We find that prediction errors are positively correlated with risk-preferences in imminent choices. Physiologically, this effect is indexed by pupil dilation: only participants whose pupil response indicates that they experienced the prediction error also show the behavioral effect.

scholarly journals Surprise acts as a reducer of outcome value in human reinforcement learning

2019 ◽  
Author(s):  
Motofumi Sumiya ◽  
Kentaro Katahira
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Simulation Analysis ◽  
Learning Task ◽  
Risk Averse ◽  
Choice Preference ◽  
Proposed Model ◽  
Modulation Parameter ◽  
Reinforcement Learning Models

Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome itself; studies have indicated that increasing surprise, as absolute value of prediction error, decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggested that, on the assumption that surprise decreases the outcome value, agents will increase their risk averse choices when an outcome is often surprisal. Here, we propose the surprise-sensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision-making. To investigate the assumption, we compared this model with previous reinforcement learning models on a risky probabilistic learning task with simulation analysis, and model selection with two experimental datasets with different tasks and population. We further simulated a simple decision-making task to investigate how parameters within the proposed model modulate the choice preference. As a result, we found the proposed model explains the risk averse choices in a manner similar to the previous models, and risk averse choices increased as the surprise-based modulation parameter of outcome value increased. The model fits these datasets better than the other models, with same free parameters, thus providing a more parsimonious and robust account for risk averse choices. These findings indicate that surprise acts as a reducer of outcome value and decreases the action value for risky choices in which prediction error often occurs.

Transfer Reinforcement Learning for Autonomous Driving

2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Problem ◽  
Autonomous Driving ◽  
High Fidelity ◽  
Rule Based ◽  
High Level ◽  
Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

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 Attenuated directed exploration during reinforcement learning in gambling disorder

2019 ◽  
Author(s):  
A. Wiehler ◽  
K. Chakroun ◽  
J. Peters
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Gambling Disorder ◽  
Brain Activity ◽  
Clinical Status ◽  
Classical Problem ◽  
Behavioral Flexibility ◽  
Network Connectivity ◽  
Prediction Errors ◽  
Reward Contingencies

AbstractGambling disorder is a behavioral addiction associated with impairments in decision-making and reduced behavioral flexibility. Decision-making in volatile environments requires a flexible trade-off between exploitation of options with high expected values and exploration of novel options to adapt to changing reward contingencies. This classical problem is known as the exploration-exploitation dilemma. We hypothesized gambling disorder to be associated with a specific reduction in directed (uncertainty-based) exploration compared to healthy controls, accompanied by changes in brain activity in a fronto-parietal exploration-related network.Twenty-three frequent gamblers and nineteen matched controls performed a classical four-armed bandit task during functional magnetic resonance imaging. Computational modeling revealed that choice behavior in both groups contained signatures of directed exploration, random exploration and perseveration. Gamblers showed a specific reduction in directed exploration, while random exploration and perseveration were similar between groups.Neuroimaging revealed no evidence for group differences in neural representations of expected value and reward prediction errors. Likewise, our hypothesis of attenuated fronto-parietal exploration effects in gambling disorder was not supported. However, during directed exploration, gamblers showed reduced parietal and substantia nigra / ventral tegmental area activity. Cross-validated classification analyses revealed that connectivity in an exploration-related network was predictive of clinical status, suggesting alterations in network dynamics in gambling disorder.In sum, we show that reduced flexibility during reinforcement learning in volatile environments in gamblers is attributable to a reduction in directed exploration rather than an increase in perseveration. Neuroimaging findings suggest that patterns of network connectivity might be more diagnostic of gambling disorder than univariate value and prediction error effects. We provide a computational account of flexibility impairments in gamblers during reinforcement learning that might arise as a consequence of dopaminergic dysregulation in this disorder.

scholarly journals Strategic Tasks for Explainable Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 10007-10008 ◽  
Author(s):  
Rey Pocius ◽  
Lawrence Neal ◽  
Alan Fern
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Environment ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Level Control ◽  
Mini Games ◽  
High Level

Commonly used sequential decision making tasks such as the games in the Arcade Learning Environment (ALE) provide rich observation spaces suitable for deep reinforcement learning. However, they consist mostly of low-level control tasks which are of limited use for the development of explainable artificial intelligence(XAI) due to the fine temporal resolution of the tasks. Many of these domains also lack built-in high level abstractions and symbols. Existing tasks that provide for both strategic decision-making and rich observation spaces are either difficult to simulate or are intractable. We provide a set of new strategic decision-making tasks specialized for the development and evaluation of explainable AI methods, built as constrained mini-games within the StarCraft II Learning Environment.

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