Biology subjects

Bari, B. A.

Publications and source records attributed to Bari, B. A..

8 recordsLinked to original sources

bioRxiv2022

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of entropy-based metrics in studying adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

neuroscience↗

bioRxiv2022

Undermatching is a consequence of policy compression

The matching law describes the tendency of agents to match the ratio of choices allocated to the ratio of rewards received when choosing among multiple options (Herrnstein, 1961). Perfect matching, however, is infrequently observed. Instead, agents tend to undermatch, or bias choices towards the poorer option. Overmatching, or the tendency to bias choices towards the richer option, is rarely observed. Despite the ubiquity of undermatching, it has received an inadequate normative justification. Here, we assume agents not only seek to maximize reward, but also seek to minimize cognitive cost, which we formalize as policy complexity (the mutual information between actions and states of the environment). Policy complexity measures the extent to which an agents policy is state-dependent. Our theory states that capacity-constrained agents (i.e., agents that must compress their policies to reduce complexity), can only undermatch or perfectly match, but not overmatch, consistent with the empirical evidence. Moreover, we validate a novel prediction about which task conditions exaggerate undermatching. Finally, we argue that a reduction in undermatching with higher dopamine levels in patients with Parkinsons disease is consistent with an increased policy complexity. Significance statementThe matching law describes the tendency of agents to match the ratio of choices allocated to different options to the ratio of reward received. For example, if option A yields twice as much reward as option B, matching states that agents will choose option A twice as much. However, agents typically undermatch: they choose the poorer option more frequently than expected. Here, we assume that agents seek to simultaneously maximize reward and minimize the complexity of their action policies. We show that this theory explains when and why undermatching occurs. Neurally, we show that policy complexity, and by extension undermatching, is controlled by tonic dopamine, consistent with other evidence that dopamine plays an important role in cognitive resource allocation.

animal behavior and cognition↗

bioRxiv2021

Novel entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching. To address this, we developed novel metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct a more accurate model of choice. Together, our novel entropy-based metrics provide a powerful, model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

neuroscience↗

bioRxiv2021

Reinforcement learning modeling reveals a reward-history-dependent strategy underlying reversal learning in squirrel monkeys

Insight into psychiatric disease and development of therapeutics relies on behavioral tasks that study similar cognitive constructs in multiple species. The reversal learning task is one popular paradigm that probes flexible behavior, aberrations of which are thought to be important in a number of disease states. Despite widespread use, there is a need for a high-throughput primate model that can bridge the genetic, anatomic, and behavioral gap between rodents and humans. Here, we trained squirrel monkeys, a promising preclinical model, on an image-guided deterministic reversal learning task. We found that squirrel monkeys exhibited two key hallmarks of behavior found in other species: integration of reward history over many trials and a side-specific bias. We adapted a reinforcement learning model and demonstrated that it could simulate monkey-like behavior, capture training-related trajectories, and provide insight into the strategies animals employed. These results validate squirrel monkeys as a model in which to study behavioral flexibility.

animal behavior and cognition↗

bioRxiv2020

Serotonin neurons modulate learning rate through uncertainty

Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change is difficult in environments with noisy relationships between actions and outcomes. Under these conditions, theories propose that uncertainty can be used to modulate learning rates ("meta-learning"). We show that mice behaving in a dynamic foraging task exhibit choice behavior that varied as a function of two forms of uncertainty estimated from a meta-learning model. The activity of dorsal raphe serotonin neurons tracked both types of uncertainty in the foraging task, as well as in a dynamic Pavlovian task. Reversible inhibition of serotonin neurons in the foraging task reproduced changes in learning predicted by a simulated lesion of meta-learning in the model. We thus provide a quantitative link between serotonin neuron activity, learning, and decision making.

neuroscience↗

bioRxiv2020

Locus coeruleus spiking differently correlates with somatosensory cortex activity and pupil diameter

We examined the relationships between activity in the locus coeruleus (LC), activity in the primary somatosensory cortex (S1), and pupil diameter in mice performing a tactile detection task. While LC spiking consistently preceded S1 membrane potential depolarization and pupil dilation, the correlation between S1 and pupil was more heterogeneous. Furthermore, the relationships between LC, S1 and pupil varied on timescales of sub-seconds to seconds within trials. Our data suggest that pupil diameter can be dissociated from LC spiking and cannot be used as a stationary index of LC activity.

neuroscience↗

bioRxiv2020

Punishment history biases corticothalamic responses to motivationally-significant stimuli

Making predictions about future rewards or punishments is fundamental to adaptive behavior. These processes are influenced by prior experience. For example, prior exposure to aversive stimuli or stressors changes behavioral responses to negative- and positive-value predictive cues. Here, we demonstrate a role for medial prefrontal cortex (mPFC) neurons projecting to the paraventricular nucleus of the thalamus (PVT; mPFC[->]PVT) in this process. We found that a history of punishments negatively biased behavioral responses to motivationally-relevant stimuli in mice and that this negative bias was associated with hyperactivity in mPFC[->]PVT neurons during exposure to those cues. Furthermore, artificially mimicking this hyperactive response with selective optogenetic excitation of the same pathway recapitulated the punishmentinduced negative behavioral bias. Together, our results highlight how information flow within the mPFC[->]PVT circuit is critical for making predictions about imminent motivationally-relevant outcomes as a function of prior experience.

neuroscience↗

bioRxiv2019

A history-derived reward prediction error signal in ventral pallidum

Learning from past interactions with the environment is critical for adaptive behavior. Within the framework of reinforcement learning, the nervous system builds expectations about future reward by computing reward prediction errors (RPEs), the difference between actual and predicted rewards. Correlates of RPEs have been observed in the midbrain dopamine system, which is thought to locally compute this important variable in service of learning. However, the extent to which RPE signals may be computed upstream of the dopamine system is largely unknown. Here, we quantify history-based RPE signals in the ventral pallidum (VP), an input region to the midbrain dopamine system implicated in reward-seeking behavior. We trained rats to associate cues with future delivery of reward and fit computational models to predict individual neuron firing rates at the time of reward delivery. We found that a subset of VP neurons encoded RPEs and did so more robustly than nucleus accumbens, an input to VP. VP RPEs predicted trial-by-trial task engagement, and optogenetic inhibition of VP reduced subsequent task-related reward seeking. Consistent with reinforcement learning, activity of VP RPE cells adapted when rewards were delivered in blocks. We further found that history- and cue-based RPEs were largely separate across the VP neural population. The presence of behaviorally-instructive RPE signals in the VP suggests a pivotal role for this region in value-based computations.

neuroscience↗