Biology subjects

Burk, D. C.

Publications and source records attributed to Burk, D. C..

5 recordsLinked to original sources

bioRxiv2026

Consensus across limbic areas predicts motivational disengagement

AbstractMotivational disengagement is widely attributed to low expected reward, yet animals and humans disengage even when reward expectations are constant. This implies a latent mechanism beyond reward. After accounting for expected reward, we found a neural signal across multiple limbic areas that predicted disengagement. As animals disengaged, neural activity across areas converged onto a shared, low-dimensional axis. Consensus across areas predicted whether disengagement persisted, suggesting why single brain area perturbations have produced inconsistent effects. We showed that engagement and disengagement operated as distinct attractor states whose strength scaled with expected reward, revealing that value modulates, but does not determine, motivational state. These findings reframe motivation as a collective computation and suggest that effective interventions for motivational deficits may require targeting multiple areas simultaneously.

neuroscience↗

bioRxiv2026

Long-term Learning Induces Plastic Changes in Frontostriatal Circuits

Neural activity in frontal-striatal circuits underlies reinforcement learning. Traditional theories suggest that reinforcement signals, which drive learning, strengthen connections within the basal ganglia. This strengthening is believed to shift information processing from cortical regions to subcortical regions as learning becomes established over time. To examine this hypothesis, we trained macaques to associate multiple sets of images with their values. Selecting different images led to either an increase (+2, +1) or a decrease (-1, -2) in the number of tokens, which subsequently determined the amount of juice reward the macaques received. We simultaneously recorded neuronal activity from orbitofrontal cortex, ventral striatum, amygdala, and dorsomedial thalamic nucleus, analyzing the dynamic changes in these brain regions during both the initial learning and overlearned stages. The results indicated that as learning progressed from the initial stage to the overlearned stage, information processing shifted from the ventral striatum to the orbitofrontal cortex, corresponding to the abstraction from stimulus value to state value. This finding challenges traditional theories and provides a new perspective on the neural circuit mechanisms of learning.

neuroscience↗

bioRxiv2024

Cross-species real time detection of trends in pupil size fluctuation

Pupillometry is a popular method because pupil size is easily measured, sensitive to central neural activity, and associated with behavior, cognition, emotion, and perception. Currently, there is no method for online monitoring phases of pupil size fluctuation. We introduce rtPupilPhase - an open source software that automatically detects trends in pupil size in real time, enabling novel implementations of real time pupillometry towards achieving numerous research and translational goals. We validated the performance of rtPupilPhase on human, rodent, and monkey pupil data and propose future applications of real time pupillometry.

neuroscience↗

bioRxiv2023

Computational mechanisms underlying motivation to earn symbolic reinforcers

Reinforcement learning (RL) is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g. money, points) that can later be exchanged for primary reinforcers (e.g. food, drink). Although symbolic reinforcers are motivating, little is understood about the neural or computational mechanisms underlying the motivation to earn them. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g. current number of accumulated tokens, choice options, task epoch, trials since last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to capture the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task (n=5 monkeys). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement. Significance statementSymbolic reinforcers, like money and points, play a critical role in our lives. Like rewards, symbolic reinforcers can be motivating and can even lead to compulsive behaviors like gambling addiction. However, we lack an understanding of how symbolic reinforcement can drive fluctuations in motivation. Here we investigated the effect of symbolic reinforcers on behaviors related to motivation during a token reinforcement learning task, using a novel reinforcement learning model and data from five monkeys. Our findings suggest that the value of a task state can affect willingness to initiate a trial, speed to choose, and persistence to complete a trial. Our model makes testable predictions for within trial fluctuations of neural activity related to values of task states.

neuroscience↗

bioRxiv2022

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal cortex, an area traditionally known for shape processing, has access to motion information during degraded shape recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons preferred blurry shapes over clear ones. Further, many of the neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion pattern could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multi-feature recognition tasks to understand the role of area IT in naturalistic visual object recognition. (Word count: 199)

neuroscience↗