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Mikhael, J. G.

Publications and source records attributed to Mikhael, J. G..

5 recordsLinked to original sources

Dopamine mediates the bidirectional update of interval timing

The role of dopamine as a reward prediction error signal in reinforcement learning tasks has been well-established over the past decades. Recent work has shown that the reward prediction error interpretation can also account for the effects of dopamine on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a read-out of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of reinforcement learning and interval timing.

neuroscience↗

Impulsivity as Bayesian inference under dopaminergic control

Bayesian models successfully account for several of dopamine (DA)s effects on contextual calibration in interval timing and reward estimation. In these models, tonic levels of DA control the precision of stimulus encoding, which is weighed against contextual information when making decisions. When DA levels are high, the animal relies more heavily on the (highly precise) stimulus encoding, whereas when DA levels are low, the context affects decisions more strongly. Here, we extend this idea to intertemporal choice and probability discounting tasks. In intertemporal choice tasks, agents must choose between a small reward delivered soon and a large reward delivered later, whereas in probability discounting tasks, agents must choose between a small reward that is always delivered and a large reward that may be omitted with some probability. Beginning with the principle that animals will seek to maximize their reward rates, we show that the Bayesian model predicts a number of curious empirical findings in both tasks. First, the model predicts that higher DA levels should normally promote selection of the larger/later option, which is often taken to imply that DA decreases impulsivity, and promote selection of the large/risky option, often taken to imply that DA increases risk-seeking. However, if the temporal precision is sufficiently decreased, higher DA levels should have the opposite effect--promoting selection of the smaller/sooner option (higher impulsivity) and the small/safe option (lower risk-seeking). Second, high enough levels of DA can result in preference reversals. Third, selectively decreasing the temporal precision, without manipulating DA, should promote selection of the larger/later and large/risky options. Fourth, when a different post-reward delay is associated with each option, animals will not learn the option-delay contingencies, but this learning can be salvaged when the post-reward delays are made more salient. Finally, the Bayesian model predicts correlations among behavioral phenotypes: Animals that are better timers will also appear less impulsive.

neuroscience↗

Rational Inattention and Tonic Dopamine

Slow-timescale (tonic) changes in dopamine (DA) contribute to a wide variety of processes in reinforcement learning, interval timing, and other domains. Furthermore, changes in tonic DA exert distinct effects depending on when they occur (e.g., during learning vs. performance) and what task the subject is performing (e.g., operant vs. classical conditioning). Two influential theories of tonic DA--the average reward theory and the Bayesian theory in which DA controls precision--have each been successful at explaining a subset of empirical findings. But how the same DA signal performs two seemingly distinct functions without creating crosstalk is not well understood. Here we reconcile the two theories under the unifying framework of rational inattention, which (1) conceptually links average reward and precision, (2) outlines how DA manipulations affect this relationship, and in so doing, (3) captures new empirical phenomena. In brief, rational inattention asserts that agents can increase their precision in a task (and thus improve their performance) by paying a cognitive cost. Crucially, whether this cost is worth paying depends on average reward availability, reported by DA. The monotonic relationship between average reward and precision means that the DA signal contains the information necessary to retrieve the precision. When this information is needed after the task is performed, as presumed by Bayesian inference, acute manipulations of DA will bias behavior in predictable ways. We show how this framework reconciles a remarkably large collection of experimental findings. In reinforcement learning, the rational inattention framework predicts that learning from positive and negative feedback should be enhanced in high and low DA states, respectively, and that DA should tip the exploration-exploitation balance toward exploitation. In interval timing, this framework predicts that DA should increase the speed of the internal clock and decrease the extent of interference by other temporal stimuli during temporal reproduction (the central tendency effect). Finally, rational inattention makes the new predictions that these effects should be critically dependent on the controllability of rewards, that post-reward delays in intertemporal choice tasks should be underestimated, and that average reward manipulations should affect the speed of the clock--thus capturing empirical findings that are unexplained by either theory alone. Our results suggest that a common computational repertoire may underlie the seemingly heterogeneous roles of DA. Author SummaryThe roles of tonic dopamine (DA) have been the subject of much speculation, partly due to the variety of processes it has been implicated in. For instance, tonic DA modulates how we learn new information, but also affects how previously learned information is used. DA affects the speed of our internal timing mechanism, but also modulates the degree to which our temporal estimates are influenced by context. DA improves performance in some tasks, but seems only to affect confidence in others. Are there common principles that govern the role of DA across these domains? In this work, we introduce the concept of rational inattention, originally borrowed from economics, to the DA literature. We show how the rational inattention account of DA unites two influential theories that are seemingly at odds: the average reward theory and the Bayesian theory of tonic DA. We then show how this framework reconciles the diverse roles of DA, which cannot be addressed by either theory alone.

neuroscience↗

Ramping and State Uncertainty in the Dopamine Signal

Reinforcement learning models of the basal ganglia map the phasic dopamine signal to reward prediction errors (RPEs). Conventional models assert that, when a stimulus predicts a reward with fixed delay, dopamine activity during the delay should converge to baseline through learning. However, recent studies have found that dopamine ramps up before reward in certain conditions even after learning, thus challenging the conventional models. In this work, we show that sensory feedback causes an unbiased learner to produce RPE ramps. Our model predicts that, when feedback gradually decreases during a trial, dopamine activity should resemble a bump, whose ramp-up phase should furthermore be greater than that of conditions where the feedback stays high. We trained mice on a virtual navigation task with varying brightness, and both predictions were empirically observed. In sum, our theoretical and experimental results reconcile the seemingly conflicting data on dopamine behaviors under the RPE hypothesis.

neuroscience↗

A unified framework for dopamine signals across timescales

Rapid phasic activity of midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. Recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independently from somatic spiking activity. Here, we developed novel experimental paradigms using virtual reality that disambiguate RPEs from values. We examined the dopamine circuit activity at various stages including somatic spiking, axonal calcium signals, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all the stages examined. We further show that ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.

neuroscience↗