scholarly journals Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

2021 ◽  
Author(s):  
Korleki Akiti ◽  
Iku Tsutsui-Kimura ◽  
Yudi Xie ◽  
Alexander Mathis ◽  
Jeffrey Markowitz ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Behavioral Responses ◽  
Individual Variability ◽  
Dopamine Neuron ◽  
Striatal Dopamine ◽  
Prediction Errors ◽  
Characteristic Sequence ◽  
Point Tracking ◽  
Behavioral Dynamics ◽  
Influence Behavior

Animals exhibit diverse behavioral responses, such as exploration and avoidance, to novel cues in the environment. However, it remains unclear how dopamine neuron-related novelty responses influence behavior. Here, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability were explained by a novel reinforcement learning (RL) model of threat prediction, in which behavior arises from a novelty-induced initial threat prediction (akin to shaping bonus), and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.

scholarly journals Velocity estimation in reinforcement learning

2018 ◽  
Author(s):  
Carlos Velazquez ◽  
Manuel Villarreal ◽  
Arturo Bouzas
Keyword(s):  
Reinforcement Learning ◽  
Hierarchical Structure ◽  
Predictive Accuracy ◽  
Information Criterion ◽  
Individual Variability ◽  
Velocity Estimation ◽  
Hyperbolic Function ◽  
Prediction Errors ◽  
Signal To Noise ◽  
Learning Rates

The current work aims to study how people make predictions, under a reinforcement learning framework, in an environment that fluctuates from trial to trial and is corrupted with Gaussian noise. A computer-based experiment was developed where subjects were required to predict the future location of a spaceship that orbited around planet Earth. Its position was sampled from a Gaussian distribution with the mean changing at a variable velocity and four different values of variance that defined our signal-to-noise conditions. Three error-driven algorithms using a Bayesian approach were proposed as candidates to describe our data. The first is the standard delta-rule. The second and third models are delta rules incorporating a velocity component which is updated using prediction errors. The third model additionally assumes a hierarchical structure where individual learning rates for velocity and decision noise come from Gaussian distributions with means following a hyperbolic function. We used leave-one-out cross-validation and the Widely Applicable Information Criterion to compare the predictive accuracy of these models. In general, our results provided evidence in favor of the hierarchical model and highlight two main conclusions. First, when facing an environment that fluctuates from trial to trial, people can learn to estimate its velocity to make predictions. Second, learning rates for velocity and decision noise are influenced by uncertainty constraints represented by the signal-to-noise ratio. This higher order control was modeled using a hierarchical structure, which qualitatively accounts for individual variability and is able to generalize and make predictions about new subjects on each experimental condition.

scholarly journals Neuro-computational mechanisms of action-outcome learning under moral conflict

2020 ◽  
Author(s):  
Alessandra D. Nostro ◽  
Kalliopi Ioumpa ◽  
Riccardo Paracampo ◽  
Selene Gallo ◽  
Laura Fornari ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Social Functioning ◽  
Computational Models ◽  
Computational Modelling ◽  
Mechanisms Of Action ◽  
Individual Variability ◽  
Moral Conflict ◽  
Prediction Errors ◽  
Conflict Task ◽  
Financial Gain

AbstractLearning to predict how our actions result in conflicting outcomes for self and others is essential for social functioning, but remains poorly understood. We test whether Reinforcement Learning Theory captures how participants learn to choose between two symbols that define a moral conflict between financial gain to self and pain for others. Computational modelling and fMRI imaging show that participants have dissociable representations for self-gain and pain to others. Signals in dorsal rostral cingulate and insulae track more closely with outcomes than prediction errors, while the opposite is true for the ventral rostral cingulate. Cognitive computational models estimated a valuational preference parameter that captured individual variability of choice in this moral conflict task. Participants’ valuational preferences predicted how much they chose to spend to reduce another person’s pain in an independent task. Learning separate representations for self and others allows participants to rapidly adapt to changes in contingencies during conflicts.

scholarly journals Language statistical learning responds to reinforcement learning principles rooted in the striatum

PLoS Biology ◽  
2021 ◽  
Vol 19 (9) ◽  
pp. e3001119
Author(s):  
Joan Orpella ◽  
Ernest Mas-Herrero ◽  
Pablo Ripollés ◽  
Josep Marco-Pallarés ◽  
Ruth de Diego-Balaguer
Keyword(s):  
Reinforcement Learning ◽  
Language Learning ◽  
Statistical Learning ◽  
Dorsal Striatum ◽  
Rule Learning ◽  
Prediction Errors ◽  
Neural Basis ◽  
Structural Rules ◽  
Learning Principles ◽  
Striatal Function

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

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 A nonlinear relationship between prediction errors and learning rates in human reinforcement learning

2019 ◽  
Author(s):  
Erdem Pulcu
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear Relationship ◽  
Prediction Errors ◽  
Learning Rates ◽  
The Face ◽  
In The Wild ◽  
Actual Outcome ◽  
Update Rules ◽  
Different Sources

AbstractWe are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty1 (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This is a type of value-based decision-making which is very important for survival in the wild and long-term evolutionary fitness. Consequently, reinforcement learning (RL) models describing cognitive/computational processes underlying learning-based adaptations have been pivotal in behavioural2,3 and neural sciences4–6, as well as machine learning7,8. This paper demonstrates the suitability of novel update rules for RL, based on a nonlinear relationship between prediction errors (i.e. difference between the agent’s expectation and the actual outcome) and learning rates (i.e. a coefficient with which agents update their beliefs about the environment), that can account for learning-based adaptations in the face of environmental uncertainty. These models illustrate how learners can flexibly adapt to dynamically changing environments.

scholarly journals Variability in Action Selection Relates to Striatal Dopamine 2/3 Receptor Availability in Humans: A PET Neuroimaging Study Using Reinforcement Learning and Active Inference Models

2020 ◽  
Vol 30 (6) ◽  
pp. 3573-3589 ◽  
Author(s):  
Rick A Adams ◽  
Michael Moutoussis ◽  
Matthew M Nour ◽  
Tarik Dahoun ◽  
Declan Lewis ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Model Comparison ◽  
Behavioral Model ◽  
Action Selection ◽  
Striatal Dopamine ◽  
Active Inference ◽  
Inference Models ◽  
Positron Emission ◽  
Dopamine Signaling

Abstract Choosing actions that result in advantageous outcomes is a fundamental function of nervous systems. All computational decision-making models contain a mechanism that controls the variability of (or confidence in) action selection, but its neural implementation is unclear—especially in humans. We investigated this mechanism using two influential decision-making frameworks: active inference (AI) and reinforcement learning (RL). In AI, the precision (inverse variance) of beliefs about policies controls action selection variability—similar to decision ‘noise’ parameters in RL—and is thought to be encoded by striatal dopamine signaling. We tested this hypothesis by administering a ‘go/no-go’ task to 75 healthy participants, and measuring striatal dopamine 2/3 receptor (D2/3R) availability in a subset (n = 25) using [11C]-(+)-PHNO positron emission tomography. In behavioral model comparison, RL performed best across the whole group but AI performed best in participants performing above chance levels. Limbic striatal D2/3R availability had linear relationships with AI policy precision (P = 0.029) as well as with RL irreducible decision ‘noise’ (P = 0.020), and this relationship with D2/3R availability was confirmed with a ‘decision stochasticity’ factor that aggregated across both models (P = 0.0006). These findings are consistent with occupancy of inhibitory striatal D2/3Rs decreasing the variability of action selection in humans.

scholarly journals The Tortoise and the Hare: Interactions between Reinforcement Learning and Working Memory

2018 ◽  
Vol 30 (10) ◽  
pp. 1422-1432 ◽  
Author(s):  
Anne G. E. Collins
Keyword(s):  
Working Memory ◽  
Reinforcement Learning ◽  
Computational Modeling ◽  
Behavioral Experiment ◽  
Prediction Errors ◽  
Multiple Systems ◽  
Memory Interference ◽  
Neural Representations ◽  
The Cost

Learning to make rewarding choices in response to stimuli depends on a slow but steady process, reinforcement learning, and a fast and flexible, but capacity-limited process, working memory. Using both systems in parallel, with their contributions weighted based on performance, should allow us to leverage the best of each system: rapid early learning, supplemented by long-term robust acquisition. However, this assumes that using one process does not interfere with the other. We use computational modeling to investigate the interactions between the two processes in a behavioral experiment and show that working memory interferes with reinforcement learning. Previous research showed that neural representations of reward prediction errors, a key marker of reinforcement learning, were blunted when working memory was used for learning. We thus predicted that arbitrating in favor of working memory to learn faster in simple problems would weaken the reinforcement learning process. We tested this by measuring performance in a delayed testing phase where the use of working memory was impossible, and thus participant choices depended on reinforcement learning. Counterintuitively, but confirming our predictions, we observed that associations learned most easily were retained worse than associations learned slower: Using working memory to learn quickly came at the cost of long-term retention. Computational modeling confirmed that this could only be accounted for by working memory interference in reinforcement learning computations. These results further our understanding of how multiple systems contribute in parallel to human learning and may have important applications for education and computational psychiatry.

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