Positive Reward Prediction Errors Research Articles

On the effects of impulsivity and compulsivity on neural correlates of model-based performance

Impaired goal-directed behavior is associated with a range of mental disorders, implicating underlying transdiagnostic factors. While compulsivity has been linked to reduced model-based (MB) control, impulsivity has rarely been studied in the context of reinforcement learning despite its links to reward processing and cognitive control. This study investigated the neural mechanisms underlying MB control and the influence of impulsivity and compulsivity, using EEG data from 238 individuals during a two-step decision making task. Single-trial analyses revealed a modulation of the feedback-related negativity (FRN), where amplitudes were higher after common transitions and positive reward prediction error (RPE), indicating a valence effect. Meanwhile, enhanced P3 amplitudes after rare transitions and both positive and negative RPE possibly reflect surprise. In a second step, we regressed the mean b values of the effect of RPE on the EEG signals onto self-reported impulsivity and compulsivity and behavioral MB control (w). The effect of RPE on FRN-related activity was mainly associated with higher w scores, linking the FRN to MB control. Crucially, the modulation of the P3 by RPE was negatively associated with compulsivity, pointing to a deficient mental model in highly compulsive individuals.

Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning

The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty analogously to reward uncertainty may affect updating from rewards after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward sensitivity. In an online between-subjects experiment with N = 141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.

Positive affect modulates memory by regulating the influence of reward prediction errors

How our decisions impact our memories is not well understood. Reward prediction errors (RPEs), the difference between expected and obtained reward, help us learn to make optimal decisions-providing a signal that may influence subsequent memory. To measure this influence and how it might go awry in mood disorders, we recruited a large cohort of human participants to perform a decision-making task in which perceptually memorable stimuli were associated with probabilistic rewards, followed by a recognition test for those stimuli. Computational modeling revealed that positive RPEs enhanced both the accuracy of memory and the temporal efficiency of memory search, beyond the contribution of perceptual information. Critically, positive affect upregulated the beneficial effect of RPEs on memory. These findings demonstrate how affect selectively regulates the impact of RPEs on memory, providing a computational mechanism for biased memory in mood disorders.

Generalized cue reactivity in dopamine neurons after opioids.

Cue reactivity is the maladaptive neurobiological and behavioral response upon exposure to drug cues and is a major driver of relapse. The leading hypothesis is that dopamine release by addictive drugs represents a persistently positive reward prediction error that causes runaway enhancement of dopamine responses to drug cues, leading to their pathological overvaluation compared to non-drug reward alternatives. However, this hypothesis has not been directly tested. Here we developed Pavlovian and operant procedures to measure firing responses, within the same dopamine neurons, to drug versus natural reward cues, which we found to be similarly enhanced compared to cues predicting natural rewards in drug-naïve controls. This enhancement was associated with increased behavioral reactivity to the drug cue, suggesting that dopamine release is still critical to cue reactivity, albeit not as previously hypothesized. These results challenge the prevailing hypothesis of cue reactivity, warranting new models of dopaminergic function in drug addiction, and provide critical insights into the neurobiology of cue reactivity with potential implications for relapse prevention.

A dopamine mechanism for reward maximization

Individual survival and evolutionary selection require biological organisms to maximize reward. Economic choice theories define the necessary and sufficient conditions, and neuronal signals of decision variables provide mechanistic explanations. Reinforcement learning (RL) formalisms use predictions, actions, and policies to maximize reward. Midbrain dopamine neurons code reward prediction errors (RPE) of subjective reward value suitable for RL. Electrical and optogenetic self-stimulation experiments demonstrate that monkeys and rodents repeat behaviors that result in dopamine excitation. Dopamine excitations reflect positive RPEs that increase reward predictions via RL; against increasing predictions, obtaining similar dopamine RPE signals again requires better rewards than before. The positive RPEs drive predictions higher again and thus advance a recursive reward-RPE-prediction iteration toward better and better rewards. Agents also avoid dopamine inhibitions that lower reward prediction via RL, which allows smaller rewards than before to elicit positive dopamine RPE signals and resume the iteration toward better rewards. In this way, dopamine RPE signals serve a causal mechanism that attracts agents via RL to the best rewards. The mechanism improves daily life and benefits evolutionary selection but may also induce restlessness and greed.

The effect of reward prediction errors on subjective affect depends on outcome valence and decision context.

The valence of an individual's emotional response to an event is often thought to depend on their prior expectations for the event: better-than-expected outcomes produce positive affect and worse-than-expected outcomes produce negative affect. In recent years, this hypothesis has been instantiated within influential computational models of subjective affect that assume the valence of affect is driven by reward prediction errors. However, there remain a number of open questions regarding this association. In this project, we investigated the moderating effects of outcome valence and decision context (Experiment 1: free vs. forced choices; Experiment 2: trials with vs. trials without counterfactual feedback) on the effects of reward prediction errors on subjective affect. We conducted two online experiments (N = 300 in total) of general-population samples recruited via Prolific to complete a risky decision-making task with an embedded high-resolution sampling of subjective affect. Hierarchical Bayesian computational modeling revealed that both outcome amount and reward prediction errors influenced subjective affect, but that the effects of reward prediction errors were moderated by both outcome valence and decision context. Specifically, we found evidence that only negative reward prediction errors (worse-than-expected outcomes) influenced subjective affect, with no significant effect of positive reward prediction errors (better-than-expected outcomes). Moreover, these effects were only apparent on trials in which participants made a choice freely (but not on forced-choice trials) and when counterfactual feedback was absent (but not when counterfactual feedback was present). These results deepen our understanding of the effects of reward prediction errors on subjective affect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Losses disguised as wins evoke the reward positivity event-related potential in a simulated machine gambling task.

Electronic gambling machines include a suite of design characteristics that may contribute to gambling-related harms and require more careful attention of regulators and policymakers. One strategy that has contributed to these concerns is the presentation of "losses disguised as wins" (LDWs), a type of salient losing outcome in which a gambling payout is less than the amount wagered (i.e., a net loss), but is nonetheless accompanied by the celebratory audio-visual stimuli that typically accompany a genuine win. These events could thereby be mistaken for gains, or otherwise act as a reward signal, reinforcing persistent gambling, despite being a loss. This study aimed to determine whether LDWs evoke a reward positivity component in a task modeled on slot machine gambling. A prominent account of the reward positivity event-related potential suggests that it is evoked during the positive appraisal of task-related feedback, relative to neutral or negative events, or that it is evoked by neural systems that implement the computation of a positive reward prediction error. We recruited 32 individuals from university recruitment pools and asked them to engage in a simple gambling task designed to mimic key features of a slot machine design. The reward positivity was identified using temporospatial principal components analysis. Results indicated a more positive reward positivity following LDWs relative to clear losses, consistent with the theory that LDWs contribute to positive reinforcement of continued gambling, despite being net losses.

Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates

Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons’ choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons’ learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.

Risk preference as an outcome of evolutionarily adaptive learning mechanisms: An evolutionary simulation under diverse risky environments.

The optimization of cognitive and learning mechanisms can reveal complicated behavioral phenomena. In this study, we focused on reinforcement learning, which uses different learning rules for positive and negative reward prediction errors. We attempted to relate the evolved learning bias to the complex features of risk preference such as domain-specific behavior manifests and the relatively stable domain-general factor underlying behaviors. The simulations of the evolution of the two learning rates under diverse risky environments showed that the positive learning rate evolved on average to be higher than the negative one, when agents experienced both tasks where risk aversion was more rewarding and risk seeking was more rewarding. This evolution enabled agents to flexibly choose more reward behaviors depending on the task type. The evolved agents also demonstrated behavioral patterns described by the prospect theory. Our simulations captured two aspects of the evolution of risk preference: the domain-specific aspect, behavior acquired through learning in a specific context; and the implicit domain-general aspect, corresponding to the learning rates shaped through evolution to adaptively behave in a wide range of environments. These results imply that our framework of learning under the innate constraint may be useful in understanding the complicated behavioral phenomena.

Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex

The signed value and unsigned salience of reward prediction errors (RPEs) are critical to understanding reinforcement learning (RL) and cognitive control. Dorsomedial prefrontal cortex (dMPFC) and insula (INS) are key regions for integrating reward and surprise information, but conflicting evidence for both signed and unsigned activity has led to multiple proposals for the nature of RPE representations in these brain areas. Recently developed RL models allow neurons to respond differently to positive and negative RPEs. Here, we use intracranially recorded high frequency activity (HFA) to test whether this flexible asymmetric coding strategy captures RPE coding diversity in human INS and dMPFC. At the region level, we found a bias towards positive RPEs in both areas which paralleled behavioral adaptation. At the local level, we found spatially interleaved neural populations responding to unsigned RPE salience and valence-specific positive and negative RPEs. Furthermore, directional connectivity estimates revealed a leading role of INS in communicating positive and unsigned RPEs to dMPFC. These findings support asymmetric coding across distinct but intermingled neural populations as a core principle of RPE processing and inform theories of the role of dMPFC and INS in RL and cognitive control.

Remembering unexpected beauty: Contributions of the ventral striatum to the processing of reward prediction errors regarding the facial attractiveness in face memory

The COVID-19 pandemic has led people to predict facial attractiveness from partially covered faces. Differences in the predicted and observed facial attractiveness (i.e., masked and unmasked faces, respectively) are defined as reward prediction error (RPE) in a social context. Cognitive neuroscience studies have elucidated the neural mechanisms underlying RPE-induced memory improvements in terms of monetary rewards. However, little is known about the mechanisms underlying RPE-induced memory modulation in terms of social rewards. To elucidate this, the present functional magnetic resonance imaging (fMRI) study investigated activity and functional connectivity during face encoding. In encoding trials, participants rated the predicted attractiveness of faces covered except for around the eyes (prediction phase) and then rated the observed attractiveness of these faces without any cover (outcome phase). The difference in ratings between these phases was defined as RPE in facial attractiveness, and RPE was categorized into positive RPE (increased RPE from the prediction to outcome phases), negative RPE (decreased RPE from the prediction to outcome phases), and non-RPE (no difference in RPE between the prediction and outcome phases). During retrieval, participants were presented with individual faces that had been seen and unseen in the encoding trials, and were required to judge whether or not each face had been seen in the encoding trials. Univariate activity in the ventral striatum (VS) exhibited a linear increase with increased RPE in facial attractiveness. In the multivariate pattern analysis (MVPA), activity patterns in the VS and surrounding areas (extended VS) significantly discriminated between positive/negative RPE and non-RPE. In the functional connectivity analysis, significant functional connectivity between the extended VS and the hippocampus was observed most frequently in positive RPE. Memory improvements by face-based RPE could be involved in functional networks between the extended VS (representing RPE) and the hippocampus, and the interaction could be modulated by RPE values in a social context.

Prediction errors arising from switches between major and minor modes in music: An fMRI study

The major and minor modes in Western music have positive and negative connotations, respectively. The present fMRI study examined listeners’ neural responses to switches between major and minor modes. We manipulated the final chords of J. S. Bach’s keyboard pieces so that each major-mode passage ended with either the major (Major-Major) or minor (Major-Minor) tonic chord, and each minor-mode passage ended with either the minor (Minor-Minor) or major (Minor-Major) tonic chord. If the final major and minor chords have positive and negative reward values respectively, the Major-Minor and Minor-Major stimuli would cause negative and positive reward prediction errors (RPEs) respectively in a listener’s brain. We found that activity in a frontoparietal network was significantly higher for Major-Minor than for Major-Major. Based on previous research, these results support the idea that a major-to-minor switch causes negative RPE. The contrast of Minor-Major minus Minor-Minor yielded activation in the ventral insula and visual cortex, speaking against the idea that a minor-to-major switch causes positive RPE. We discuss our results in relation to executive functions and the emotional connotations of major versus minor modes.

Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms.

Obsessive-compulsive disorder (OCD) and pathological gambling (PG) are accompanied by deficits in behavioural flexibility. In reinforcement learning, this inflexibility can reflect asymmetric learning from outcomes above and below expectations. In alternative frameworks, it reflects perseveration independent of learning. Here, we examine evidence for asymmetric reward-learning in OCD and PG by leveraging model-based functional magnetic resonance imaging (fMRI). Compared with healthy controls (HC), OCD patients exhibited a lower learning rate for worse-than-expected outcomes, which was associated with the attenuated encoding of negative reward prediction errors in the dorsomedial prefrontal cortex and the dorsal striatum. PG patients showed higher and lower learning rates for better- and worse-than-expected outcomes, respectively, accompanied by higher encoding of positive reward prediction errors in the anterior insula than HC. Perseveration did not differ considerably between the patient groups and HC. These findings elucidate the neural computations of reward-learning that are altered in OCD and PG, providing a potential account of behavioural inflexibility in those mental disorders.

A computational neuroimaging study of reinforcement learning and goal-directed exploration in schizophrenia spectrum disorders.

Prior evidence indicates that negative symptom severity and cognitive deficits, in people with schizophrenia (PSZ), relate to measures of reward-seeking and loss-avoidance behavior (implicating the ventral striatum/VS), as well as uncertainty-driven exploration (reliant on rostrolateral prefrontal cortex/rlPFC). While neural correlates of reward-seeking and loss-avoidance have been examined in PSZ, neural correlates of uncertainty-driven exploration have not. Understanding neural correlates of uncertainty-driven exploration is an important next step that could reveal insights to how this mechanism of cognitive and negative symptoms manifest at a neural level. We acquired fMRI data from 29 PSZ and 36 controls performing the Temporal Utility Integration decision-making task. Computational analyses estimated parameters corresponding to learning rates for both positive and negative reward prediction errors (RPEs) and the degree to which participates relied on representations of relative uncertainty. Trial-wise estimates of expected value, certainty, and RPEs were generated to model fMRI data. Behaviorally, PSZ demonstrated reduced reward-seeking behavior compared to controls, and negative symptoms were positively correlated with loss-avoidance behavior. This finding of a bias toward loss avoidance learning in PSZ is consistent with previous work. Surprisingly, neither behavioral measures of exploration nor neural correlates of uncertainty in the rlPFC differed significantly between groups. However, we showed that trial-wise estimates of relative uncertainty in the rlPFC distinguished participants who engaged in exploratory behavior from those who did not. rlPFC activation was positively associated with intellectual function. These results further elucidate the nature of reinforcement learning and decision-making in PSZ and healthy volunteers.

Opponent Learning with Different Representations in the Cortico-Basal Ganglia Circuits.

The direct and indirect pathways of the basal ganglia (BG) have been suggested to learn mainly from positive and negative feedbacks, respectively. Since these pathways unevenly receive inputs from different cortical neuron types and/or regions, they may preferentially use different state/action representations. We explored whether such a combined use of different representations, coupled with different learning rates from positive and negative reward prediction errors (RPEs), has computational benefits. We modeled animal as an agent equipped with two learning systems, each of which adopted individual representation (IR) or successor representation (SR) of states. With varying the combination of IR or SR and also the learning rates from positive and negative RPEs in each system, we examined how the agent performed in a dynamic reward navigation task. We found that combination of SR-based system learning mainly from positive RPEs and IR-based system learning mainly from negative RPEs could achieve a good performance in the task, as compared with other combinations. In such a combination of appetitive SR-based and aversive IR-based systems, both systems show activities of comparable magnitudes with opposite signs, consistent with the suggested profiles of the two BG pathways. Moreover, the architecture of such a combination provides a novel coherent explanation for the functional significance and underlying mechanism of diverse findings about the cortico-BG circuits. These results suggest that particularly combining different representations with appetitive and aversive learning could be an effective learning strategy in certain dynamic environments, and it might actually be implemented in the cortico-BG circuits.

Effort Reinforces Learning.

Humans routinely learn the value of actions by updating their expectations based on past outcomes - a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those underpinning reward-based learning, but the behavioral relationship between these two functions remains unclear. Across two experiments, we tested healthy male and female human participants (N = 140) on a reinforcement learning task in which they registered their responses by applying physical force to a pair of hand-held dynamometers. We examined the effect of effort on learning by systematically manipulating the amount of force required to register a response during the task. Our key finding, replicated across both experiments, was that greater effort increased learning rates following positive outcomes and decreased them following negative outcomes, which corresponded to a differential effect of effort in boosting positive RPEs and blunting negative RPEs. Interestingly, this effect was most pronounced in individuals who were more averse to effort in the first place, raising the possibility that the investment of effort may have an adaptive effect on learning in those less motivated to exert it. By integrating principles of reinforcement learning with neuroeconomic approaches to value-based decision-making, we show that the very act of investing effort modulates one's capacity to learn, and demonstrate how these functions may operate within a common computational framework.SIGNIFICANCE STATEMENT Recent work suggests that learning and effort may share common neurophysiological substrates. This raises the possibility that the very act of investing effort influences learning. Here, we tested whether effort modulates teaching signals in a reinforcement learning paradigm. Our results showed that effort resulted in more efficient learning from positive outcomes and less efficient learning from negative outcomes. Interestingly, this effect varied across individuals, and was more pronounced in those who were more averse to investing effort in the first place. These data highlight the importance of motivational factors in a common framework of reward-based learning, which integrates the computational principles of reinforcement learning with those of value-based decision-making.

Disentangling performance-monitoring signals encoded in feedback-related EEG dynamics

The feedback-related negativity (FRN) is a well-established electrophysiological correlate of feedback-processing. However, there is still an ongoing debate whether the FRN is driven by negative or positive reward prediction errors (RPE), valence of feedback, or mere surprise. Our study disentangles independent contributions of valence, surprise, and RPE on the feedback-related neuronal signal including the FRN and P3 components using the statistical power of a sample of N = 992 healthy individuals. The participants performed a modified time-estimation task, while EEG from 64 scalp electrodes was recorded. Our results show that valence coding is present during the FRN with larger amplitudes for negative feedback. The FRN is further modulated by surprise in a valence-dependent way being more positive-going for surprising positive outcomes. The P3 was strongly driven by both global and local surprise, with larger amplitudes for unexpected feedback and local deviants. Behavioral adaptations after feedback and FRN just show small associations. Results support the theory of the FRN as a representation of a signed RPE. Additionally, our data indicates that surprising positive feedback enhances the EEG response in the time window of the P3. These results corroborate previous findings linking the P3 to the evaluation of PEs in decision making and learning tasks.

Pain feedback interferes with reward positivity production.

The reinforcement learning (RL) theory of the reward positivity (RewP) proposes that RewP indexes a reward prediction error (RPE) signal processed in the anterior cingulate cortex (ACC). According to this theory, RewP is an event-related potential (ERP) that is more positive-going for feedback stimuli that predict better-than-expected outcomes (positive feedback) than for feedback stimuli that predict worse-than-expected outcomes (negative feedback). Despite strong evidence for this hypothesis, findings have been equivocal for tasks involving painful outcomes. We hypothesized that the RewP is modulated by high-level task goals such that outcomes that are congruent with the goals elicit positive RPEs even if their immediate consequences are negative. Accordingly, changes in high-level task goals should modulate RewP amplitude for tasks that involve seeking pain compared to tasks that involve avoiding pain. We recorded the electroencephalogram from participants who were instructed to navigate a virtual T-Maze to find reward-predictive feedback in a reward condition and pain-predictive feedback in a pain condition. We expected more positive-going ERPs to reward feedback in the reward condition and more positive-going ERPs to pain feedback in the pain condition. Despite behavioral results indicating that participants complied with task instructions, contrary to our predictions, we did not find a RewP to pain feedback. We suggest that pain feedback interfered with the effect of high-level task goals on RewP amplitude, which is indicative of conflict at different levels of task hierarchy.

Differential effects of positive and negative reward prediction error on saccade response time adaptation in a reversal learning visual stop-signal task

Differential effects of positive and negative reward prediction error on saccade response time adaptation in a reversal learning visual stop-signal task

An association between prediction errors and risk-seeking: Theory and behavioral evidence.

Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. Based on the common neural substrate, we hypothesize that RPEs and risk preferences are linked on the level of behavior as well. Here, we develop this hypothesis theoretically and test it empirically. First, we apply a recent theory of learning in the basal ganglia to predict how RPEs influence risk preferences. We find that positive RPEs should cause increased risk-seeking, while negative RPEs should cause risk-aversion. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that our prediction was correct: participants become more risk-seeking if choices are preceded by positive RPEs, and more risk-averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates.