Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. SIGNIFICANCE STATEMENT: Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies.

Dopamine Modulation of Intertemporal Decision-making: Evidence from Parkinson Disease.

Choosing between smaller prompt rewards and larger later rewards is a common choice problem, and studies widely agree that frontostriatal circuits heavily innervated by dopamine are centrally involved. Understanding how dopamine modulates intertemporal choice has important implications for neurobiological models and for understanding the mechanisms underlying maladaptive decision-making. However, the specific role of dopamine in intertemporal decisions is not well understood. Dopamine may play a role in multiple aspects of intertemporal choices--the valuation of choice outcomes and sensitivity to reward delays. To assess the role of dopamine in intertemporal decisions, we tested Parkinson disease patients who suffer from dopamine depletion in the striatum, in either high (on medication, PDON) or low (off medication, PDOFF) dopaminergic states. Compared with both PDOFF and healthy controls, PDON made more farsighted choices and reduced their valuations less as a function of increasing time to reward. Furthermore, reduced discounting in the high dopaminergic state was robust across multiple measures, providing new evidence for dopamine's role in making decisions about the future.

Experiential reward learning outweighs instruction prior to adulthood.

Throughout our lives, we face the important task of distinguishing rewarding actions from those that are best avoided. Importantly, there are multiple means by which we acquire this information. Through trial and error, we use experiential feedback to evaluate our actions. We also learn which actions are advantageous through explicit instruction from others. Here, we examined whether the influence of these two forms of learning on choice changes across development by placing instruction and experience in competition in a probabilistic-learning task. Whereas inaccurate instruction markedly biased adults' estimations of a stimulus's value, children and adolescents were better able to objectively estimate stimulus values through experience. Instructional control of learning is thought to recruit prefrontal-striatal brain circuitry, which continues to mature into adulthood. Our behavioral data suggest that this protracted neurocognitive maturation may cause the motivated actions of children and adolescents to be less influenced by explicit instruction than are those of adults. This absence of a confirmation bias in children and adolescents represents a paradoxical developmental advantage of youth over adults in the unbiased evaluation of actions through positive and negative experience.

Multiple memory systems as substrates for multiple decision systems.

It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an "internal model." Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. Here we investigated the relationship between model-based RL and relational memory by comparing individual differences across behavioral tasks designed to measure either capacity. Human subjects performed two tasks, a learning and generalization task (acquired equivalence) which involves relational encoding and depends on the hippocampus; and a sequential RL task that could be solved by either a model-based or model-free strategy. We assessed the correlation between subjects' use of flexible, relational memory, as measured by generalization in the acquired equivalence task, and their differential reliance on either RL strategy in the decision task. We observed a significant positive relationship between generalization and model-based, but not model-free, choice strategies. These results are consistent with the hypothesis that model-based RL, like acquired equivalence, relies on a more general-purpose relational memory system.

Reduced susceptibility to confirmation bias in schizophrenia.

Patients with schizophrenia (SZ) show cognitive impairments on a wide range of tasks, with clear deficiencies in tasks reliant on prefrontal cortex function and less consistently observed impairments in tasks recruiting the striatum. This study leverages tasks hypothesized to differentially recruit these neural structures to assess relative deficiencies of each. Forty-eight patients and 38 controls completed two reinforcement learning tasks hypothesized to interrogate prefrontal and striatal functions and their interaction. In each task, participants learned reward discriminations by trial and error and were tested on novel stimulus combinations to assess learned values. In the task putatively assessing fronto-striatal interaction, participants were (inaccurately) instructed that one of the stimuli was valuable. Consistent with prior reports and a model of confirmation bias, this manipulation resulted in overvaluation of the instructed stimulus after its true value had been experienced. Patients showed less susceptibility to this confirmation bias effect than did controls. In the choice bias task hypothesized to more purely assess striatal function, biases in endogenously and exogenously chosen actions were assessed. No group differences were observed. In the subset of participants who showed learning in both tasks, larger group differences were observed in the confirmation bias task than in the choice bias task. In the confirmation bias task, patients also showed impairment in the task conditions with no prior instruction. This deficit was most readily observed on the most deterministic discriminations. Taken together, these results suggest impairments in fronto-striatal interaction in SZ, rather than in striatal function per se.

Dopaminergic genes predict individual differences in susceptibility to confirmation bias.

The striatum is critical for the incremental learning of values associated with behavioral actions. The prefrontal cortex (PFC) represents abstract rules and explicit contingencies to support rapid behavioral adaptation in the absence of cumulative experience. Here we test two alternative models of the interaction between these systems, and individual differences thereof, when human subjects are instructed with prior information about reward contingencies that may or may not be accurate. Behaviorally, subjects are overly influenced by prior instructions, at the expense of learning true reinforcement statistics. Computational analysis found that this pattern of data is best accounted for by a confirmation bias mechanism in which prior beliefs--putatively represented in PFC--influence the learning that occurs in the striatum such that reinforcement statistics are distorted. We assessed genetic variants affecting prefrontal and striatal dopaminergic neurotransmission. A polymorphism in the COMT gene (rs4680), associated with prefrontal dopaminergic function, was predictive of the degree to which participants persisted in responding in accordance with prior instructions even as evidence against their veracity accumulated. Polymorphisms in genes associated with striatal dopamine function (DARPP-32, rs907094, and DRD2, rs6277) were predictive of learning from positive and negative outcomes. Notably, these same variants were predictive of the degree to which such learning was overly inflated or neglected when outcomes are consistent or inconsistent with prior instructions. These findings indicate dissociable neurocomputational and genetic mechanisms by which initial biases are strengthened by experience.

Beyond reversal: a critical role for human orbitofrontal cortex in flexible learning from probabilistic feedback.

Damage to the orbitofrontal cortex (OFC) has been linked to impaired reinforcement processing and maladaptive behavior in changing environments across species. Flexible stimulus-outcome learning, canonically captured by reversal learning tasks, has been shown to rely critically on OFC in rats, monkeys, and humans. However, the precise role of OFC in this learning remains unclear. Furthermore, whether other frontal regions also contribute has not been definitively established, particularly in humans. In the present study, a reversal learning task with probabilistic feedback was administered to 39 patients with focal lesions affecting various sectors of the frontal lobes and to 51 healthy, demographically matched control subjects. Standard groupwise comparisons were supplemented with voxel-based lesion-symptom mapping to identify regions within the frontal lobes critical for task performance. Learning in this dynamic stimulus-reinforcement environment was considered both in terms of overall performance and at the trial-by-trial level. In this challenging, probabilistic context, OFC damage disrupted both initial and reversal learning. Trial-by-trial performance patterns suggest that OFC plays a critical role in interpreting feedback from a particular trial within the broader context of the outcome history across trials rather than in simply suppressing preexisting stimulus-outcome associations. The findings show that OFC, and not other prefrontal regions, plays a necessary role in flexible stimulus-reinforcement learning in humans.

The Straw That Broke the Camel's Back: Natural Variations in 17ß-Estradiol and COMT-Val158Met Genotype Interact in the Modulation of Model-Free and Model-Based Control.

The sex hormone estradiol has recently gained attention in human decision-making research. Animal studies have already shown that estradiol promotes dopaminergic transmission and thus supports reward-seeking behavior and aspects of addiction. In humans, natural variations of estradiol across the menstrual cycle modulate the ability to learn from direct performance feedback ("model-free" learning). However, it remains unclear whether estradiol also influences more complex "model-based" contributions to reinforcement learning. Here, 41 women were tested twice - in the low and high estradiol state of the follicular phase of their menstrual cycle - with a Two-Step decision task designed to separate model-free from model-based learning. The results showed that in the high estradiol state women relied more heavily on model-free learning, and accomplished reduced performance gains, particularly during the more volatile periods of the task that demanded increased learning effort. In contrast, model-based control remained unaltered by the influence of hormonal state across the group. Yet, when accounting for individual differences in the genetic proxy of the COMT-Val158Met polymorphism (rs4680), we observed that only the participants homozygote for the methionine allele (n = 12; with putatively higher prefrontal dopamine) experienced a decline in model-based control when facing volatile reward probabilities. This group also showed the increase in suboptimal model-free control, while the carriers of the valine allele remained unaffected by the rise in endogenous estradiol. Taken together, these preliminary findings suggest that endogenous estradiol may affect the balance between model-based and model-free control, and particularly so in women with a high prefrontal baseline dopamine capacity and in situations of increased environmental volatility.

Seeing is believing: trustworthiness as a dynamic belief.

Recent efforts to understand the mechanisms underlying human cooperation have focused on the notion of trust, with research illustrating that both initial impressions and previous interactions impact the amount of trust people place in a partner. Less is known, however, about how these two types of information interact in iterated exchanges. The present study examined how implicit initial trustworthiness information interacts with experienced trustworthiness in a repeated Trust Game. Consistent with our hypotheses, these two factors reliably influence behavior both independently and synergistically, in terms of how much money players were willing to entrust to their partner and also in their post-game subjective ratings of trustworthiness. To further understand this interaction, we used Reinforcement Learning models to test several distinct processing hypotheses. These results suggest that trustworthiness is a belief about probability of reciprocation based initially on implicit judgments, and then dynamically updated based on experiences. This study provides a novel quantitative framework to conceptualize the notion of trustworthiness.

More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning.

People often perceive configurations rather than the elements they comprise, a bias that may emerge because configurations often predict outcomes. But how does the brain learn to associate configurations with outcomes and how does this learning differ from learning about individual elements? We combined behavior, reinforcement learning models, and functional imaging to understand how people learn to associate configurations of cues with outcomes. We found that configural learning depended on the relative predictive strength of elements versus configurations and was related to both the strength of BOLD activity and patterns of BOLD activity in the hippocampus. Configural learning was further related to functional connectivity between the hippocampus and nucleus accumbens. Moreover, configural learning was associated with flexible knowledge about associations and differential eye movements during choice. Together, this suggests that configural learning is associated with a distinct computational, cognitive, and neural profile that is well suited to support flexible and adaptive behavior.

The expanding role of dopamine.

Evidence increasingly suggests that dopaminergic neurons play a more sophisticated role in predicting rewards than previously thought.

Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala.

Socially-conveyed rules and instructions strongly shape expectations and emotions. Yet most neuroscientific studies of learning consider reinforcement history alone, irrespective of knowledge acquired through other means. We examined fear conditioning and reversal in humans to test whether instructed knowledge modulates the neural mechanisms of feedback-driven learning. One group was informed about contingencies and reversals. A second group learned only from reinforcement. We combined quantitative models with functional magnetic resonance imaging and found that instructions induced dissociations in the neural systems of aversive learning. Responses in striatum and orbitofrontal cortex updated with instructions and correlated with prefrontal responses to instructions. Amygdala responses were influenced by reinforcement similarly in both groups and did not update with instructions. Results extend work on instructed reward learning and reveal novel dissociations that have not been observed with punishments or rewards. Findings support theories of specialized threat-detection and may have implications for fear maintenance in anxiety.

Instrumental learning of traits versus rewards: dissociable neural correlates and effects on choice.

Humans learn about people and objects through positive and negative experiences, yet they can also look beyond the immediate reward of an interaction to encode trait-level attributes. We found that perceivers encoded both reward and trait-level information through feedback in an instrumental learning task, but relied more heavily on trait representations in cross-context decisions. Both learning types implicated ventral striatum, but trait learning also recruited a network associated with social impression formation.

Model-based choices involve prospective neural activity.

Decisions may arise via 'model-free' repetition of previously reinforced actions or by 'model-based' evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional magnetic resonance imaging and a sequential reward-learning task in which paths contained decodable object categories, we found that humans' model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection.

Older Adults are Highly Responsive to Recent Events During Decision-Making.

Recent work suggests that older adults' decision-making behavior is highly affected by recent events. In the present work younger and older adults performed a two-choice task where one option provided a larger average reward, but there was a large amount of noise around the mean reward for each option which led to sharp improvements or declines in rewards over trials. Older adults showed greater responsiveness to recent events than younger adults as evidenced by fits of Reinforcement Learning (RL) models. Older adults were particularly sensitive to recent negative events, which was evidenced by a strong tendency for older adults to switch to the other option following steep declines in reward. This tendency led to superior performance for older adults in one condition where heightened sensitivity to recent negative events was advantageous. These results extend prior work that has found an older adult bias toward negative feedback, and suggest that older adults engage in more abrupt switching in response to negative outcomes than younger adults.

The ubiquity of model-based reinforcement learning.

The reward prediction error (RPE) theory of dopamine (DA) function has enjoyed great success in the neuroscience of learning and decision-making. This theory is derived from model-free reinforcement learning (RL), in which choices are made simply on the basis of previously realized rewards. Recently, attention has turned to correlates of more flexible, albeit computationally complex, model-based methods in the brain. These methods are distinguished from model-free learning by their evaluation of candidate actions using expected future outcomes according to a world model. Puzzlingly, signatures from these computations seem to be pervasive in the very same regions previously thought to support model-free learning. Here, we review recent behavioral and neural evidence about these two systems, in attempt to reconcile their enigmatic cohabitation in the brain.

Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration.

How do individuals decide to act based on a rewarding status quo versus an unexplored choice that might yield a better outcome? Recent evidence suggests that individuals may strategically explore as a function of the relative uncertainty about the expected value of options. However, the neural mechanisms supporting uncertainty-driven exploration remain underspecified. The present fMRI study scanned a reinforcement learning task in which participants stop a rotating clock hand in order to win points. Reward schedules were such that expected value could increase, decrease, or remain constant with respect to time. We fit several mathematical models to subject behavior to generate trial-by-trial estimates of exploration as a function of relative uncertainty. These estimates were used to analyze our fMRI data. Results indicate that rostrolateral prefrontal cortex tracks trial-by-trial changes in relative uncertainty, and this pattern distinguished individuals who rely on relative uncertainty for their exploratory decisions versus those who do not.

Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation.

The basal ganglia support learning to exploit decisions that have yielded positive outcomes in the past. In contrast, limited evidence implicates the prefrontal cortex in the process of making strategic exploratory decisions when the magnitude of potential outcomes is unknown. Here we examine neurogenetic contributions to individual differences in these distinct aspects of motivated human behavior, using a temporal decision-making task and computational analysis. We show that two genes controlling striatal dopamine function, DARPP-32 (also called PPP1R1B) and DRD2, are associated with exploitative learning to adjust response times incrementally as a function of positive and negative decision outcomes. In contrast, a gene primarily controlling prefrontal dopamine function (COMT) is associated with a particular type of 'directed exploration', in which exploratory decisions are made in proportion to Bayesian uncertainty about whether other choices might produce outcomes that are better than the status quo. Quantitative model fits reveal that genetic factors modulate independent parameters of a reinforcement learning system.

Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.