Dorsal Striatal–midbrain Connectivity in Humans Predicts How Reinforcements Are Used to Guide Decisions

2009 ◽  
Vol 21 (7) ◽  
pp. 1332-1345 ◽  
Author(s):  
Thorsten Kahnt ◽  
Soyoung Q Park ◽  
Michael X Cohen ◽  
Anne Beck ◽  
Andreas Heinz ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Functional Connectivity ◽  
Ventral Striatum ◽  
Critical Role ◽  
Prediction Errors ◽  
Midbrain Neurons ◽  
Reward Prediction ◽  
Future Behavior ◽  
The Impact

It has been suggested that the target areas of dopaminergic midbrain neurons, the dorsal (DS) and ventral striatum (VS), are differently involved in reinforcement learning especially as actor and critic. Whereas the critic learns to predict rewards, the actor maintains action values to guide future decisions. The different midbrain connections to the DS and the VS seem to play a critical role in this functional distinction. Here, subjects performed a dynamic, reward-based decision-making task during fMRI acquisition. A computational model of reinforcement learning was used to estimate the different effects of positive and negative reinforcements on future decisions for each subject individually. We found that activity in both the DS and the VS correlated with reward prediction errors. Using functional connectivity, we show that the DS and the VS are differentially connected to different midbrain regions (possibly corresponding to the substantia nigra [SN] and the ventral tegmental area [VTA], respectively). However, only functional connectivity between the DS and the putative SN predicted the impact of different reinforcement types on future behavior. These results suggest that connections between the putative SN and the DS are critical for modulating action values in the DS according to both positive and negative reinforcements to guide future decision making.

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.

Emotion prediction errors guide socially adaptive behavior

2021 ◽  
Author(s):  
Joseph Heffner ◽  
Jae-Young Son ◽  
Oriel FeldmanHall
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Adaptive Behavior ◽  
Emotional Response ◽  
New Method ◽  
Prediction Errors ◽  
Emotional Experiences ◽  
Past Work ◽  
Reward Prediction ◽  
Expected Outcomes

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.

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 Positive reward prediction errors during decision-making strengthen memory encoding

2019 ◽  
Vol 3 (7) ◽  
pp. 719-732 ◽  
Author(s):  
Anthony I. Jang ◽  
Matthew R. Nassar ◽  
Daniel G. Dillon ◽  
Michael J. Frank
Keyword(s):  
Decision Making ◽  
Prediction Errors ◽  
Memory Encoding ◽  
Reward Prediction

Imaginative Reinforcement Learning: Computational Principles and Neural Mechanisms

2017 ◽  
Vol 29 (12) ◽  
pp. 2103-2113 ◽  
Author(s):  
Samuel J. Gershman ◽  
Jimmy Zhou ◽  
Cody Kommers
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Internal Model ◽  
Learning Model ◽  
Neural Mechanisms ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
The Impact ◽  
To Receive ◽  
Reinforcement Learning Model

Imagination enables us not only to transcend reality but also to learn about it. In the context of reinforcement learning, an agent can rationally update its value estimates by simulating an internal model of the environment, provided that the model is accurate. In a series of sequential decision-making experiments, we investigated the impact of imaginative simulation on subsequent decisions. We found that imagination can cause people to pursue imagined paths, even when these paths are suboptimal. This bias is systematically related to participants' optimism about how much reward they expect to receive along imagined paths; providing feedback strongly attenuates the effect. The imagination effect can be captured by a reinforcement learning model that includes a bonus added onto imagined rewards. Using fMRI, we show that a network of regions associated with valuation is predictive of the imagination effect. These results suggest that imagination, although a powerful tool for learning, is also susceptible to motivational biases.

scholarly journals The contribution of striatal pseudo-reward prediction errors to value-based decision-making

NeuroImage ◽  
2019 ◽  
Vol 193 ◽  
pp. 67-74 ◽  
Author(s):  
Ernest Mas-Herrero ◽  
Guillaume Sescousse ◽  
Roshan Cools ◽  
Josep Marco-Pallarés
Keyword(s):  
Decision Making ◽  
Prediction Errors ◽  
Reward Prediction

scholarly journals Increased fronto-striatal reward prediction errors moderate decision making in obsessive–compulsive disorder

2017 ◽  
Vol 47 (7) ◽  
pp. 1246-1258 ◽  
Author(s):  
T. U. Hauser ◽  
R. Iannaccone ◽  
R. J. Dolan ◽  
J. Ball ◽  
J. Hättenschwiler ◽  
...  
Keyword(s):  
Decision Making ◽  
Obsessive Compulsive Disorder ◽  
Late Onset ◽  
Anterior Cingulate ◽  
Prediction Errors ◽  
Learning Mechanisms ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Reward Prediction ◽  
Reinforcement Learning Models

BackgroundObsessive–compulsive disorder (OCD) has been linked to functional abnormalities in fronto-striatal networks as well as impairments in decision making and learning. Little is known about the neurocognitive mechanisms causing these decision-making and learning deficits in OCD, and how they relate to dysfunction in fronto-striatal networks.MethodWe investigated neural mechanisms of decision making in OCD patients, including early and late onset of disorder, in terms of reward prediction errors (RPEs) using functional magnetic resonance imaging. RPEs index a mismatch between expected and received outcomes, encoded by the dopaminergic system, and are known to drive learning and decision making in humans and animals. We used reinforcement learning models and RPE signals to infer the learning mechanisms and to compare behavioural parameters and neural RPE responses of the OCD patients with those of healthy matched controls.ResultsPatients with OCD showed significantly increased RPE responses in the anterior cingulate cortex (ACC) and the putamen compared with controls. OCD patients also had a significantly lower perseveration parameter than controls.ConclusionsEnhanced RPE signals in the ACC and putamen extend previous findings of fronto-striatal deficits in OCD. These abnormally strong RPEs suggest a hyper-responsive learning network in patients with OCD, which might explain their indecisiveness and intolerance of uncertainty.

scholarly journals Frontal Theta Oscillatory Activity Is a Common Mechanism for the Computation of Unexpected Outcomes and Learning Rate

2014 ◽  
Vol 26 (3) ◽  
pp. 447-458 ◽  
Author(s):  
Ernest Mas-Herrero ◽  
Josep Marco-Pallarés
Keyword(s):  
Prediction Error ◽  
Learning Task ◽  
Learning Rate ◽  
Oscillatory Activity ◽  
Common Mechanism ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Medial Pfc ◽  
The Impact

In decision-making processes, the relevance of the information yielded by outcomes varies across time and situations. It increases when previous predictions are not accurate and in contexts with high environmental uncertainty. Previous fMRI studies have shown an important role of medial pFC in coding both reward prediction errors and the impact of this information to guide future decisions. However, it is unclear whether these two processes are dissociated in time or occur simultaneously, suggesting that a common mechanism is engaged. In the present work, we studied the modulation of two electrophysiological responses associated to outcome processing—the feedback-related negativity ERP and frontocentral theta oscillatory activity—with the reward prediction error and the learning rate. Twenty-six participants performed two learning tasks differing in the degree of predictability of the outcomes: a reversal learning task and a probabilistic learning task with multiple blocks of novel cue–outcome associations. We implemented a reinforcement learning model to obtain the single-trial reward prediction error and the learning rate for each participant and task. Our results indicated that midfrontal theta activity and feedback-related negativity increased linearly with the unsigned prediction error. In addition, variations of frontal theta oscillatory activity predicted the learning rate across tasks and participants. These results support the existence of a common brain mechanism for the computation of unsigned prediction error and learning rate.

scholarly journals Positive reward prediction errors strengthen incidental memory encoding

2018 ◽  
Author(s):  
Anthony I. Jang ◽  
Matthew R. Nassar ◽  
Daniel G. Dillon ◽  
Michael J. Frank
Keyword(s):  
Prediction Error ◽  
Memory Systems ◽  
Prediction Errors ◽  
Memory Encoding ◽  
Dopamine System ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Incidental Memory ◽  
Episodic Memories ◽  
The Impact

AbstractThe dopamine system is thought to provide a reward prediction error signal that facilitates reinforcement learning and reward-based choice in corticostriatal circuits. While it is believed that similar prediction error signals are also provided to temporal lobe memory systems, the impact of such signals on episodic memory encoding has not been fully characterized. Here we develop an incidental memory paradigm that allows us to 1) estimate the influence of reward prediction errors on the formation of episodic memories, 2) dissociate this influence from other factors such as surprise and uncertainty, 3) test the degree to which this influence depends on temporal correspondence between prediction error and memoranda presentation, and 4) determine the extent to which this influence is consolidation-dependent. We find that when choosing to gamble for potential rewards during a primary decision making task, people encode incidental memoranda more strongly even though they are not aware that their memory will be subsequently probed. Moreover, this strengthened encoding scales with the reward prediction error, and not overall reward, experienced selectively at the time of memoranda presentation (and not before or after). Finally, this strengthened encoding is identifiable within a few minutes and is not substantially enhanced after twenty-four hours, indicating that it is not consolidation-dependent. These results suggest a computationally and temporally specific role for putative dopaminergic reward prediction error signaling in memory formation.

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