VTA dopamine neuron activity encodes social interaction and promotes reinforcement learning through social prediction error.
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ABSTRACT:
SUBMITTER: Solie C
PROVIDER: S-EPMC7612196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Like ventral tegmental area (VTA) dopamine (DA) neurons, VTA glutamate neuron activity can support positive reinforcement. However, a subset of VTA neurons co-release DA and glutamate, and DA release might be responsible for behavioral reinforcement induced by VTA glutamate neuron activity. To test this, we used optogenetics to stimulate VTA glutamate neurons in which tyrosine hydroxylase (TH), and thus DA biosynthesis, was conditionally ablated using either floxed Th mice or viral-based CRISPR/Cas9. Both approaches led to loss of TH expression in VTA glutamate neurons and loss of DA release from their distal terminals in nucleus accumbens (NAc). Despite loss of the DA signal, optogenetic activation of VTA glutamate cell bodies or axon terminals in NAc was sufficient to support reinforcement. These results suggest that glutamate release from VTA is sufficient to promote reinforcement independent of concomitant DA co-release, establishing a non-DA mechanism by which VTA activity can support reward-seeking behaviors.
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Project description:Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.
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Project description:Acute stress is pervasive in everyday modern life and is thought to affect how people make choices and learn from them. Reinforcement learning, which implicates learning from the unexpected rewarding and punishing outcomes of our choices (i.e., prediction errors), is critical for adjusted behaviour and seems to be affected by acute stress. However, the neural mechanisms by which acute stress disrupts this type of learning are still poorly understood. Here, we investigate whether and how acute stress blunts neural signalling of prediction errors during reinforcement learning using model-based functional magnetic resonance imaging. Male participants completed a well-established reinforcement-learning task involving monetary gains and losses whilst under stress and control conditions. Acute stress impaired participants' (n = 23) behavioural performance towards obtaining monetary gains (p < 0.001), but not towards avoiding losses (p = 0.57). Importantly, acute stress blunted signalling of prediction errors during gain and loss trials in the dorsal striatum (p = 0.040) - with subsidiary analyses suggesting that acute stress preferentially blunted signalling of positive prediction errors. Our results thus reveal a neurocomputational mechanism by which acute stress may impair reward learning.
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Project description:Dopamine (DA) transmission in the nucleus accumbens (NAc) facilitates cue-reward associations and appetitive action. Reward-related accumbal DA release dynamics are traditionally ascribed to ventral tegmental area (VTA) DA neurons. Activation of VTA to NAc DA signaling is thought to reinforce action and transfer reward-related information to predictive cues, allowing cues to guide behavior and elicit dopaminergic activity. Here, we use optogenetics to control DA neuron activity and voltammetry to simultaneously record accumbal DA release in order to quantify how reinforcer-evoked dopaminergic activity shapes conditioned mesolimbic DA transmission. We find that cues predicting access to DA neuron self-stimulation elicit conditioned responding and NAc DA release. However, cue-evoked DA release does not reflect the cost or magnitude of DA neuron activation. Accordingly, conditioned accumbal DA release selectively tracks the expected availability of DA-neuron-mediated reinforcement. This work provides insight into how mesolimbic DA transmission drives and encodes appetitive action.
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Project description:We used 2-photon calcium imaging to determine whether the same or different dopamine neurons of the ventral tegmental area (VTADA) encode food and social stimuli, and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience increased the proportion of neurons that respond to both stimuli, implying that increasing motivation for one stimulus further increases overlap. We used single-nucleus RNA sequencing (snRNA-seq) to examine cell type-specific gene expression changes across different hunger states and the level of co-expression of feeding- and social-hormone related genes in individual VTADA neurons.
2023-08-21 | GSE235149 | GEO
Project description:Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.
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Project description:Reinforcement learning (RL) uses sequential experience with situations ("states") and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior.
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Project description:RATIONALE:Depression is a disorder characterized by sustained negative affect and blunted positive affect, suggesting potential abnormalities in reward learning and its interaction with episodic memory. OBJECTIVES:This study investigated how reward prediction errors experienced during learning modulate memory for rewarding events in individuals with depressive and non-depressive symptoms. METHODS:Across three experiments, participants learned the average values of two scene categories in two learning contexts. Each learning context had either high or low outcome variance, allowing us to test the effects of small and large prediction errors on learning and memory. Participants were later tested for their memory of trial-unique scenes that appeared alongside outcomes. We compared learning and memory performance of individuals with self-reported depressive symptoms (N?=?101) to those without (N?=?184). RESULTS:Although there were no overall differences in reward learning between the depressive and non-depressive group, depression severity within the depressive group predicted greater error in estimating the values of the scene categories. Similarly, there were no overall differences in memory performance. However, in depressive participants, negative prediction errors enhanced episodic memory more so than did positive prediction errors, and vice versa for non-depressive participants who showed a larger effect of positive prediction errors on memory. These results reflected differences in memory both within group and across groups. CONCLUSIONS:Individuals with self-reported depressive symptoms showed relatively intact reinforcement learning, but demonstrated a bias for encoding events that accompanied surprising negative outcomes versus surprising positive ones. We discuss a potential neural mechanism supporting these effects, which may underlie or contribute to the excessive negative affect observed in depression.
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Project description:Dopamine neurons in the ventral tegmental area (VTA) are strongly implicated in cognitive and affective processing as well as in psychiatric disorders, including schizophrenia, depression, attention-deficit hyperactivity disorder and substance abuse disorders. In human studies, dopamine-related functions are routinely assessed using functional magnetic resonance imaging (fMRI) measures of blood oxygenation-level-dependent (BOLD) signals during the performance of dopamine-dependent tasks. There is, however, a critical void in our knowledge about whether and how activation of VTA dopamine neurons specifically influences regional or global fMRI signals. Here, we used optogenetics in Th::Cre rats to selectively stimulate VTA dopamine neurons while simultaneously measuring global hemodynamic changes using BOLD and cerebral blood volume-weighted (CBVw) fMRI. Phasic activation of VTA dopamine neurons increased BOLD and CBVw fMRI signals in VTA-innervated limbic regions, including the ventral striatum (nucleus accumbens). Surprisingly, basal ganglia regions that receive sparse or no VTA dopaminergic innervation, including the dorsal striatum and the globus pallidus, were also activated. In fact, the most prominent fMRI signal increase in the forebrain was observed in the dorsal striatum that is not traditionally associated with VTA dopamine neurotransmission. These data establish causation between phasic activation of VTA dopamine neurons and global fMRI signals. They further suggest that mesolimbic and non-limbic basal ganglia dopamine circuits are functionally connected and thus provide a potential novel framework for understanding dopamine-dependent functions and interpreting data obtained from human fMRI studies.
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Project description:Midbrain dopamine neurons are commonly thought to report a reward prediction error (RPE), as hypothesized by reinforcement learning (RL) theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here, we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signalling errors in both sensory and reward predictions, dopamine supports a form of RL that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and RPEs, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.
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