Biology subjects

Nassar, M. R.

Publications and source records attributed to Nassar, M. R..

At least 19 recordsLinked to original sources

bioRxiv2025

A parietal grid-like code rotates with cognitive maps but lags rapid behavioral transfer

The neural grid code has been proposed to provide a mechanism for generalization and transfer of relational knowledge between situations enabling rapid adaptation of behavior in novel circumstances. However, to date, very little is known about the dynamics with which grid representations change at context transitions, or how such dynamics relate to downstream behavioral adaptation. Here we tested whether grid representations measured with fMRI rotate to match behavioral goals at context transitions and whether such rotations underlie knowledge transfer. Human participants performed a task that included unsignaled state changes at which the position of multiple target locations abruptly and synchronously rotated by the same degree. After state changes, participants were able to leverage the relative positions of the targets to rapidly infer locations, even novel ones, constituting a form of zero-shot transfer. We observed a cognitive grid-like code in the right posterior parietal cortex with a consistent phase angle that rotated with the relative positions of the targets. However, this rotation was too slow to account for rapid improvements in performance after a state change, and instead these improvements were more closely related to representations of the identity and location of spatial targets in the frontoparietal and orbitofrontal cortex, respectively. Our results highlight the ability of humans to rapidly transfer knowledge and demonstrate that a parietal grid-like code rotates into behaviorally relevant reference frames, but raise questions about the function of such rotations, pointing instead to alternate neural mechanisms for rapid knowledge transfer.

neuroscience↗

bioRxiv2025

Altered Decision-Making Across Acute and Chronic Pain States: A Bias Towards Context-Dependent Valuations

Chronic pain is often characterized by the entrenchment of maladaptive behaviors -- avoidance, inactivity, and hypervigilance -- that outlast tissue damage. A central but mechanistically underspecified question is how behaviors acquired during acute pain become locked in as pain chronifies. Influential accounts, including the fear-avoidance model, suggest that behavioral patterns learned in a specific context -- such as avoidance during acute injury -- overgeneralize to situations where they are no longer advantageous. Whether this reflects altered learning or changes in how learned information guides decisions has remained unclear. To address this, we administered a probabilistic reinforcement learning task to 239 individuals with chronic pain, acute pain, or no pain, designed to dissociate two decision strategies: reliance on recent reinforcement history -- what was reinforced in a prior context -- versus global expected value -- the objective worth of an option regardless of context. Learning performance was comparable across all groups, confirming intact associative learning. However, groups differed significantly in decision-making: individuals with chronic pain favored options with stronger context-dependent reinforcement history over those with higher global expected value; however, those without pain showed no preference for one over the other, with the acute pain group showing an intermediate pattern. Computational modeling confirmed this, with chronic pain patients showing significantly reduced weighting toward global expected value. Critically, this shift tracked pain duration rather than intensity -- suggesting prolonged pain exposure gradually biases decisions away from global expected value towards a context-dependent history. These findings offer a computational explanation for behavioral persistence in chronic pain, and this duration-dependent shift, already evident in the acute pain group, may represent an early cognitive signature of chronification.

neuroscience↗

bioRxiv2025

Absence of Systematic Effects of Internalizing Psychopathology on Learning Under Uncertainty

Difficulties in adapting learning to meet the challenges of uncertain and changing environments are widely thought to play a central role in internalizing psychopathology, including anxiety and depression. This view stems from findings linking trait anxiety and transdiagnostic internalizing symptoms to learning impairments in laboratory tasks often used as proxies for real-world behavioral flexibility. These tasks typically require learners to adjust learning rates dynamically in response to uncertainty, for instance, increasing learning from prediction errors in volatile environments. However, prior studies have produced inconsistent and sometimes contradictory findings regarding the nature and extent of learning impairments in populations with internalizing disorders. To address this, we conducted eight experiments (N = 820) using predictive inference and reversal learning tasks, and applied a bi-factor analysis to capture internalizing symptom variance shared across and differentiated between anxiety and depression. While we observed robust evidence for adaptive learning-rate modulation across participants, we found no convincing evidence of a systematic relationship between internalizing symptoms and either learning rates or task performance. These findings challenge prominent claims that learning difficulties are a hallmark feature of internalizing psychopathology and suggest that the relationship between these traits and adaptive behavior under uncertainty may be more subtle than previously thought.

neuroscience↗

bioRxiv2025

Adaptive learning via BG-thalamo-cortical circuitry

People adjust their use of feedback over time through a process referred to as adaptive learning. We have recently proposed that the underlying mechanisms of adaptive learning are rooted in how the brain organizes time into similarly credited units, which we refer to as latent states. Here we develop a basal ganglia-thalamo-cortical circuit model of this process and show that it captures both the commonalities and heterogeneity in human adaptive learning behavior. Our model learns incrementally through synaptic plasticity in prefrontal-basal ganglia (PFC-BG) connections, but upon observing discordant information, produces thalamocortical reset signals that alter PFC connectivity, driving attractor state transitions that facilitate rapid updating of behavioral policy. We demonstrate that this mechanism can give rise to optimized learning dynamics in the context of either change-points or reversals, and that under reasonable biological assumptions the model is able to generalize efficiently across these conditions, adjusting behavior in a context-appropriate manner. Taken together, our results provide a biologically plausible mechanistic model for adaptive learning that explains existing behavioral data and makes testable predictions about the computational roles of different brain regions in complex learning behaviors.

neuroscience↗

bioRxiv2025

Fluctuations in arousal reflect latent state transitions that facilitate behavioral optimization

People are often faced with surprising events that defy expectations. Such events elicit transient activity in the locus coeruleus/norepinephrine system and elevation of peripheral arousal markers including pupil dilation and the EEG P300, but the function of these signals remains unclear. We propose that they reflect latent state transitions that dynamically control the mental context governing learning and perception. We tested and confirmed five preregistered predictions of this theory using EEG and pupil measurements collected from people performing a color prediction and reproduction task. Task latent state transitions elicited pupil dilation and amplified event-related potentials including the P300. These EEG and pupil measures related to behavioral signatures of latent state updating, including reduced bias and bidirectional adjustment of learning, both across trials and individuals. Our findings support the theory that LC/NE-linked arousal systems optimize behavior by signaling environmental transitions to facilitate rapid adjustments of mental context.

neuroscience↗

bioRxiv2024

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.

The ability to calibrate learning according to new information is a fundamental component of an organisms ability to adapt to changing conditions. Yet, the exact neural mechanisms guiding dynamic learning rate adjustments remain unclear. Catecholamines appear to play a critical role in adjusting the degree to which we use new information over time, but individuals vary widely in the manner in which they adjust to changes. Here, we studied the effects of a low dose of methamphetamine (MA), and individual differences in these effects, on probabilistic reversal learning dynamics in a within-subject, double-blind, randomized design. Participants first completed a reversal learning task during a drug-free baseline session to provide a measure of baseline performance. Then they completed the task during two sessions, one with MA (20 mg oral) and one with placebo (PL). First, we showed that, relative to PL, MA modulates the ability to dynamically adjust learning from prediction errors. Second, this effect was more pronounced in participants who performed moderately low at baseline. These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.

neuroscience↗

bioRxiv2024

Enhancement of Mediodorsal Thalamus Rescues Aberrant Belief Dynamics in a Novel Mouse Model for Schizophrenia

Optimizing behavioral strategy requires belief updating based on new evidence, a process that engages higher cognition. In schizophrenia, aberrant belief dynamics may lead to psychosis, but the mechanisms underlying this process are unknown, in part, due to lack of appropriate animal models and behavior readouts. Here, we address this challenge by taking two synergistic approaches. First, we generate a mouse model bearing patient-derived point mutation in Grin2a (Grin2aY700X+/-), a gene that confers high-risk for schizophrenia and recently identified by large-scale exome sequencing. Second, we develop a computationally trackable foraging task, in which mice form and update belief-driven strategies in a dynamic environment. We found that Grin2aY700X+/- mice perform less optimally than their wild-type (WT) littermates, showing unstable behavioral states and a slower belief update rate. Using functional ultrasound imaging, we identified the mediodorsal (MD) thalamus as hypofunctional in Grin2aY700X+/- mice, and in vivo task recordings showed that MD neurons encoded dynamic values and behavioral states in WT mice. Optogenetic inhibition of MD neurons in WT mice phenocopied Grin2aY700X+/- mice, and enhancing MD activity rescued task deficits in Grin2aY700X+/- mice. Together, our study identifies the MD thalamus as a key node for schizophrenia-relevant cognitive dysfunction, and a potential target for future therapeutics.

neuroscience↗

bioRxiv2022

Age-related decline in prefrontal glutamate predicts failure to efficiently deploy working memory in the service of learning

The ability to use past experience to effectively guide decision making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision making deficits using an RL-WM task to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision making in older adulthood.

animal behavior and cognition↗

bioRxiv2022

Two distinct value-related patterns of EEG activity emerge during value-based choice, neither related to evidence accumulation

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here, we test whether these signatures of the choice process can be temporally dissociated from additional, choice-independent value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, neither of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can mimic an evidence accumulation process when aligned to the response. Re-analyzing four previous studies - including three perceptual decision-making studies - we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation. Significance StatementTo choose, people must evaluate their options and select between them. Selection is well described by a process of accumulating evidence up to some threshold, with an electrophysiological signature in the centroparietal positivity (CPP). However, decision-making also gives rise to value signals reflecting affective reactions and other selection-unrelated processes. Measuring EEG while participants made value-based choices, we identified two spatiotemporally distinct value signals, neither reflecting evidence accumulation. Instead, we show that evidence accumulation signals found in the CPP can arise artifactually from overlapping stimulus- and response-related activity. These findings call for a significant reexamination of established links between neural and computational mechanisms of choice, while inviting deeper consideration of the array of cognitive and affective processes that occur in parallel.

neuroscience↗

bioRxiv2021

Dynamic Representation of the Subjective Value of Information

To improve future decisions, people should seek information based on the value of information (VOI), which depends on the current evidence and the reward structure of the upcoming decision. When additional evidence is supplied, people should update VOI to adjust subsequent information seeking, but the neurocognitive mechanisms of this updating process remain unknown. We used a modified beads task to examine how the VOI is represented and updated in the human brain. We theoretically derived, and empirically verified, a normative prediction that the VOI depends on decision evidence and is biased by reward asymmetry. Using fMRI, we found that the subjective VOI is represented in right dorsolateral prefrontal cortex (DLPFC). Critically, this VOI representation was updated when additional evidence was supplied, showing that DLPFC dynamically tracks the up-to-date VOI over time. These results provide new insights into how humans adaptively seek information in the service of decision making.

neuroscience↗

bioRxiv2020

Noise correlations for faster and more robust learning.

Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of overall population signal-to-noise ratio in order to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning whereas those across irrelevant feature dimensions slowed it. Our results complement existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions. Significance statementPositive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal to noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.

neuroscience↗

bioRxiv2020

Adaptive learning through temporal dynamics of state representation

People adjust their learning rate rationally according to local environmental statistics and calibrate such adjustments based on the broader statistical context. To date, no theory has captured the observed range of adaptive learning behaviors or the complexity of its neural correlates. Here, we attempt to do so using a neural network model that learns to map an internal context representation onto a behavioral response via supervised learning. The network shifts its internal context upon receiving supervised signals that are mismatched to its output, thereby changing the "state" to which feedback is associated. A key feature of the model is that such state transitions can either increase learning or decrease learning depending on the duration over which the new state is maintained. Sustained state transitions that occur after changepoints facilitate faster learning and mimic network reset phenomena observed in the brain during rapid learning. In contrast, state transitions after one-off outlier events are short-lived, thereby limiting the impact of outlying observations on future behavior. State transitions in our model provide the first mechanistic interpretation for bidirectional learning signals, such the p300, that relate to learning differentially according to the source of surprising events and may also shed light on discrepant observations regarding the relationship between transient pupil dilations and learning. Taken together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning across different statistical environments. Significance StatementHow humans adjust their sensitivity to new information in a changing world has remained largely an open question. Bridging insights from normative accounts of adaptive learning and theories of latent state representation, here we propose a feed-forward neural network model that adjusts its learning rate online by controlling the speed of transitioning its internal state representations. Our model proposes a mechanistic framework for explaining learning under different statistical contexts, explains previously observed behavior and brain signals, and makes testable predictions for future experimental studies.

neuroscience↗

bioRxiv2020

The stability flexibility tradeoff and the dark side of detail

Learning in dynamic environments requires integrating over stable fluctuations to minimize the impact of noise (stability) but rapidly responding in the face of fundamental changes (flexibility). Achieving one of these goals often requires sacrificing the other to some degree, producing a stability-flexibility tradeoff. Individuals navigate this tradeoff in different ways, with some people learning rapidly (emphasizing flexibility) and others relying more heavily on historical information (emphasizing stability). Despite the prominence of such individual differences in learning tasks, the degree to which they relate to broader characteristics of real-world behavior or pathologies has not been well explored. Here we relate individual differences in learning behavior to self-report measures thought to collectively capture characteristics of the Autism spectrum. We show that that young adults who learn most slowly tend to integrate more effective samples into their beliefs about the world making them more robust to noise (more stability), but are more likely to integrate information from previous contexts (less flexibility). We show that individuals who report paying more attention to detail tend to use high flexibility and low stability information processing strategies. We demonstrate the robustness of this inverse relationship between attention to detail and formation of stable beliefs in a heterogeneous population of children that includes a high proportion of Autism diagnoses. Together, our results highlight that attention to detail reflects an information processing policy that comes with a substantial downside, namely the ability to integrate data to overcome environmental noise.

neuroscience↗

bioRxiv2019

Functional brain network reconfiguration during learning in a dynamic environment

When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

neuroscience↗

bioRxiv2018

I knew that! Confidence in outcome prediction and its impact on feedback processing and learning

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Specifically, we propose that people leverage insights from response-based performance monitoring - outcome predictions and confidence - to control learning from feedback. In line with predictions from a Bayesian inference model, we find that people who are better at calibrating their confidence to the precision of their outcome predictions learn more quickly. Further in line with our proposal, EEG signatures of feedback processing are sensitive to the accuracy of, and confidence in, post-response outcome predictions. Taken together, our results suggest that online predictions and confidence serve to calibrate neural error signals to improve the efficiency of learning.

neuroscience↗

bioRxiv2018

Dissociable forms of uncertainty-driven representational change across the human brain.

Environmental change can lead decision makers to shift rapidly among different behavioral regimes. These behavioral shifts can be accompanied by rapid changes in the firing pattern of neural networks. However, it is unknown what the populations of neurons that participate in such \"network reset\" phenomena are representing. Here we examined 1) whether and where rapid changes in multivariate activity patterns are observable with fMRI during periods of rapid behavioral change, and 2) what types of representations give rise to these phenomena. We did so by examining fluctuations in multi-voxel patterns of BOLD activity from human subjects making sequential inferences about the state of a partially observable and discontinuously changing variable. We found that, within the context of this sequential inference task, the multivariate patterns of activity in a number of cortical regions contain representations that change more rapidly during periods of uncertainty following a change in behavioral context. In motor cortex, this phenomenon was indicative of discontinuous change in behavioral outputs, whereas in visual regions the same basic phenomenon was evoked by tracking of salient environmental changes. In most other cortical regions, including dorsolateral prefrontal and anterior cingulate cortex, the phenomenon was most consistent with directly encoding the degree of uncertainty. However, in a few other regions, including orbitofrontal cortex, the phenomenon was best explained by representations of a shifting context that evolve more rapidly during periods of rapid learning. These representations may provide a dynamic substrate for learning that facilitates rapid disengagement from learned responses during periods of change.

neuroscience↗

bioRxiv2017

A control theoretic model of adaptive behavior in dynamic environments

To behave adaptively in environments that are noisy and non-stationary, humans and other animals must monitor feedback from their environment and adjust their predictions and actions accordingly. An under-studied approach for modeling these adaptive processes comes from the engineering field of control theory, which provides general principles for regulating dynamical systems, often without requiring a generative model. The proportional-integral-derivative (PID) controller is one of the most popular models of industrial process control. The proportional term is analogous to the \"delta rule\" in psychology, adjusting estimates in proportion to each successive error in prediction. The integral and derivative terms augment this update to simultaneously improve accuracy and stability. Here, we tested whether the PID algorithm can describe how people sequentially adjust their predictions in response to new information. Across three experiments, we found that the PID controller was an effective model of participants decisions in noisy, changing environments. In Experiment 1, we re-analyzed a change-point detection experiment, and showed that participants behavior incorporated elements of PID updating. In Experiments 2-3 we developed a task with gradual transitions that we optimized to detect PID-like adjustments. In both experiments, the PID model offered better descriptions of behavioral adjustments than both the classical delta-rule model and its more sophisticated variant, the Kalman filter. We further examined how participants weighted different PID terms in response to salient environmental events, finding that these control terms were modulated by reward, surprise, and outcome entropy. These experiments provide preliminary evidence that adaptive behavior in dynamic environments resembles PID control.

neuroscience↗

bioRxiv2017

Chunking as a rational strategy for lossy data compression in visual working memory tasks.

The nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a \"chunking\" process to optimize performance on visual working memory tasks. We show that such chunking can: 1) facilitate performance improvements for abstract capacity-limited systems, 2) be optimized through reinforcement, 3) be implemented by center-surround dynamics, and 4) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, inter-item dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for re-evaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects.

neuroscience↗