Biology subjects

Ebitz, B.

Publications and source records attributed to Ebitz, B..

10 recordsLinked to original sources

bioRxiv2025

NMDA receptor ablation in medial prefrontal cortex disrupts value updating and reward history integration

Schizophrenia, a serious mental illness, is associated with evidence of NMDA receptor (NMDAR) dysfunction and characterized by cognitive impairments that reflect impaired value updating and feedback-driven control; however the cellular and circuit-level mechanisms underlying these disruptions remain unclear. Here we test how NMDA receptor (NMDAR) signaling in the medial prefrontal cortex (mPFC) contributes to adaptive decision-making by combining targeted genetic ablation in mice and systemic pharmacology. Using a CRISPR-Cas9 approach to eliminate the obligate GluN1 subunit, we induced spatially confined NMDAR hypofunction in mPFC and compared its effects to systemic pharmacological blockade with the NMDAR antagonist MK-801 during performance of a touchscreen-based restless bandit task. Prefrontal NMDAR ablation impaired value discrimination, weakened the use of negative feedback, and reduced mutual information between recent outcomes and current choices, indicating disrupted reward-history integration. Reinforcement-learning models incorporating a choice-kernel term best captured behavior and revealed that NMDAR ablation selectively dampened learning and choice-history parameters governing flexible updating. Systemic MK-801 produced broad impairments in control animals, reducing accuracy, mutual information, and outcome sensitivity, yet exerted only modest additional effects after NMDAR ablation, suggesting that prefrontal NMDAR loss occluded much of the pharmacological disruption. Simulations using fitted RLCK parameters reproduced these patterns, showing convergent flattening of choice dynamics under MK-801 and persistent deficits in GluN1 ablated animals. Together, these findings demonstrate that prefrontal NMDAR signaling is necessary for effective value updating and feedback-driven learning, and that its loss recapitulates core features of systemic NMDAR hypofunction. This work establishes a mechanistic bridge between localized cortical glutamatergic dysfunction and the reinforcement-learning disturbances characteristic of schizophrenia.

neuroscience↗

bioRxiv2025

Individual differences in sequential decision-making

1People differ widely in how they make decisions in uncertain environments. While many studies leverage this variability to measure differences in specific cognitive processes and parameters, the key dimension(s) of individual variability in uncertain decision-making tasks has not been identified. Here, we analyzed behavioral data from 1001 participants performing a restless three-armed bandit task, where reward probabilities fluctuated unpredictably over time. Using a novel analytical approach that controlled for the stochasticity in this tasks, we identified a dominant nonlinear axis of individual variability. We found that this primary axis of variability was strongly and selectively correlated with the probability of exploration, as inferred by latent state modeling. This suggests that the major factor shaping individual differences in bandit task performance is the tendency to explore (versus exploit), rather than personality characteristics, reinforcement learning model parameters, or low-level strategies. Certain demographic characteristics also predicted variance along this principle axis: participants at the exploratory end tended to be younger than participants at the exploitative end, and self-identified men were overrepresented at both extremes. Together, these findings offer a principled framework for understanding individual differences in task behavior while highlighting the cognitive and demographic factors that shape individual differences in decision-making under uncertainty.

neuroscience↗

bioRxiv2024

Humans forage for reward in reinforcement learning tasks

How do we make good decisions in uncertain environments? In psychology and neuroscience, the classic view is that we calculate the value of each option, compare them, and choose the most rewarding modulo exploratory noise. An ethologist, conversely, would argue that we commit to one option until its value drops below a threshold and then explore alternatives. Because the fields use incompatible methods, it remains unclear which view better describes human decision-making. Here, we found that humans use compare-to-threshold computations in classic compare-alternative tasks. Because compare-alternative computations are central to the reinforcement-learning (RL) models typically used in the cognitive and brain sciences, we developed a novel compare-to-threshold model ("foraging"). Compared to previous RL models, the foraging model better fit participant behavior, better predicted the tendency to repeat choices, and predicted held-out participants that were almost impossible under comparealternative models. These results suggest that humans use compare-to-threshold computations in sequential decision-making.

neuroscience↗

bioRxiv2024

Individual differences in uncertainty evaluation explain opposing exploratory behaviors in anxiety and apathy

Decision-making in uncertain environments often leads to varied outcomes. Understanding how individuals interpret the causes of unexpected feedback is crucial for adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility is about how quickly conditions change, impacting results. Stochasticity, on the other hand, refers to outcomes affected by random chance or "luck". Understanding these factors enables individuals to have more effective environmental analysis and strategy implementation (explore or exploit) for future decisions. This study investigates how anxiety and apathy, two prevalent affective states, influence the perceptions of uncertainty and exploratory behavior. Participants (N = 1001) completed a restless three-armed bandit task that was analyzed using latent state models. Anxious individuals perceived uncertainty as more volatile, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

neuroscience↗

bioRxiv2024

"Value" emerges from imperfect memory

Whereas computational models of value-based decision-making generally assume that past rewards are perfectly remembered, biological brains regularly forget, fail to encode, or misremember past events. Here, we ask how realistic memory retrieval errors would affect decision-making. We build a simple decision-making model that systematically misremembers the timing of past rewards but performs no other value computations. We call these agents "Imperfect Memory Programs" (IMPs) and their single free parameter optimizes the trade-off between the magnitude of error and the complexity of imperfect recall. Surprisingly, we found that IMPs perform better than a simple agent with perfect memory in multiple classic decision-making tasks. IMPs also generated multiple behavioral signatures of value-based decision-making without ever calculating value. These results suggest that mnemonic errors (1) can improve, rather than impair decision-making, and (2) provide a plausible alternative explanation for some behavioral correlates of "value".

animal behavior and cognition↗

bioRxiv2023

Learning attentional templates for value-based decision-making

Attention filters sensory inputs to enhance task-relevant information. It is guided by an attentional template that represents the stimulus features that are relevant for the current task. To understand how the brain learns and uses new templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found templates were represented across prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template towards rewarded features. Finally, we found attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide new insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

neuroscience↗

bioRxiv2023

A low dimensional manifold of human exploration and exploitation behavior reveals behavioral states of apathy and anxiety

Exploration-exploitation decision-making is a feature of daily life that is altered in a number of neuropsychiatric conditions. Humans display a range of exploration and exploitation behaviors, which can be affected by apathy and anxiety. It remains unknown how factors underlying decision-making generate the spectrum of observed exploration-exploitation behavior and how they relate to states of anxiety and apathy. Here, we report a latent structure underlying sequential exploration and exploitation decisions that explains variation in anxiety and apathy. 1001 participants in a gender-balanced sample completed a three-armed restless bandit task along with psychiatric symptom surveys. Using dimensionality reduction methods, we found that decision sequences reduced to a low-dimensional manifold. The axes of this manifold explained individual differences in the balance between states of exploration and exploitation and the stability of those states, as determined by a statistical mechanics model of decision-making. Position along the balance axis was correlated with opposing symptoms of behavioral apathy and anxiety, while position along the stability axis correlated with the level of emotional apathy. This result resolves a paradox over how these symptoms can be correlated in samples but have opposite effects on behavior. Furthermore, this work provides a basis for using behavioral manifolds to reveal relationships between behavioral dynamics and affective states, with important implications for behavioral measurement approaches to neuropsychiatric conditions.

neuroscience↗

bioRxiv2021

Subspace alignment as a mechanism for binding

To choose between options, we must solve two important binding problems. First, the features that determine each options values must be appropriately combined and kept separate from the corresponding features of other options. Second, options must be associated with the specific actions needed to select them. We hypothesized that the brain solves these problems through use of aligned (for bound dimensions) and orthogonal (for separated dimensions) population subspaces. We examined responses of single neurons in six putative value-coding regions in rhesus macaques performing a risky choice task. In all areas, single neurons encode the features that define the value of each option (stakes and probability) but only very weakly encode value per se. However, the coding dimensions associated with these features are aligned on a single subspace, from which a strong emergent value signal can be read out. Moreover, all six regions use nearly orthogonal subspaces for the left and right options, thereby linking options to their position in space, implementing functional partitioning, and reducing the possibility of misbinding. These results provide a new solution to the neuroeconomic binding problems and suggest that other forms of binding may work through similar principles.

neuroscience↗

bioRxiv2020

Human dorsal anterior cingulate neurons signal conflict by amplifying task-relevant information

Hemodynamic activity in dorsal anterior cingulate cortex (dACC) correlates with conflict, suggesting it contributes to conflict processing. This correlation could be explained by multiple neural processes that can be disambiguated by population firing rates patterns. We used targeted dimensionality reduction to characterize activity of populations of single dACC neurons as humans performed a task that manipulates two forms of conflict. Although conflict enhanced firing rates, this enhancement did not come from a discrete population of domain-general conflict-encoding neurons, nor from a distinct conflict-encoding response axis. Nor was it the epiphenomenal consequence of simultaneous coactivation of action plans. Instead, conflict amplified the task-relevant information encoded across the neuronal population. Effects of conflict were weaker and more heterogeneous in the dorsolateral prefrontal cortex (dlPFC), suggesting that dACCs role in conflict processing may be somewhat specialized. Overall, these results support the theory that conflict biases competition between sensorimotor transformation processes occurring in dACC.

neuroscience↗

bioRxiv2019

Rule adherence warps decision-making

We have the capacity to follow arbitrary stimulus-response rules, meaning policies that determine how we will behave across circumstances. Yet, it is not clear how rules guide sensorimotor decision-making in the brain. Here, we recorded from neurons in three regions linked to decision-making, the orbitofrontal cortex, ventral striatum, and dorsal striatum, while macaques performed a rule-based decision-making task. We found that different rules warped the neural representations of chosen options by expanding rule-relevant coding dimensions relative to rule-irrelevant ones. Some cognitive theories suggest that warping could increase processing efficiency by facilitating rule-relevant computations at the expense of irrelevant ones. To test this idea, we modeled rules as the latent causes of decisions and identified a set of "rule-free" choices that could not be explained by simple rules. Contrasting these with rule-based choices revealed that following rules decreased the energetic cost of decision-making while warping the representational geometry of choice. SIGNIFICANCE STATEMENTOne important part of our ability to adapt flexibly to the world around us is our ability to implement arbitrary stimulus-response mappings, known as "rules". Many studies have shown that when we follow a rule, its identity is encoded in neuronal firing rates. However, it remains unclear how rules regulate behavior. Here, we report that rules warp the way that sensorimotor information is represented in decision-making circuits: enhancing information that is relevant to the current rule at the expense of information that is irrelevant. These results imply that rules are implemented as a kind of attentional gate on what information is available for decision-making.

neuroscience↗