Biology subjects

Boorman, E. D.

Publications and source records attributed to Boorman, E. D..

7 recordsLinked to original sources

bioRxiv2026

Extended amygdala function is governed by threat probability, not uncertainty

Anxiety is a universal experience that arises as we anticipate negative events that may or may not happen. When extreme, anxiety is a major cause of distress, suffering, and disability-adjusted life years, with nearly one in four individuals experiencing clinically relevant anxiety in their lifetime1-3. Elucidating the biological basis of anxiety has the potential to help people understand, cope with, and ultimately receive improved treatments for their anxiety. The canonical neuroscientific model of anxiety posits that distinct regions of the extended amygdala (EA) elicit distress that is conditional on threat uncertainty4. Here, we refute this model using a precise computational framework to disambiguate specific dimensional facets of threat uncertainty in a racially diverse sample of 104 individuals. We show that self-report, psychophysiological, and neural correlates of threat anticipation are much more strongly associated with threat probability than uncertainty per se. Importantly, EA regions, including the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST), were most reliably associated with threat probability--contrary to the canonical model. Re-examination of the pioneering experiments that motivated the canonical model reveal a previously unappreciated confound between probability and uncertainty, suggesting the novel hypothesis that EA regions differ in their sensitivity to low-probability threats. Our fMRI data support this, showing that BST is more sensitive to low-probability threats than Ce. Moreover, lesion experiments in rhesus monkeys (Macaca mulatta) demonstrate that BST causally contributes to defensive behavior in low-probability threat contexts. Together, these data reconcile previous empirical inconsistencies, provide a fresh theoretical perspective on the neurobiology of anxiety, and underscore the theoretical importance of a formal lingua franca for coordinated cross-species emotion research.

neuroscience↗

bioRxiv2026

A cognitive map of subjective value space for human risky choice

Individuals are thought to make choices based on subjective valuations of options that integrate multiple attributes into a unified subjective value signal in the brains "value coding system." How these attributes are transformed into a subjective valuation remains poorly understood. One candidate is the "cognitive mapping system," which efficiently represents relational, multi-dimensional information and has been proposed to afford novel inferences. We develop a risky decision-making task and use fMRI to show that a two-dimensional (2D) subjective value space of reward probability and amount is represented in the cognitive mapping system as both a grid-like representation of decision vectors and a 2D "positional" code in entorhinal cortex (EC) and medial prefrontal cortex (mPFC). The strength of grid-like and subjective value representations are positively correlated, suggesting the cognitive mapping and value coding systems work in tandem. These findings bridge these systems and support a new framework for how the brain constructs values.

neuroscience↗

bioRxiv2025

Fronto-Hippocampal Synchronization in Rapid Spatial Learning in Humans

Achieving goals in real-life situations--from fetching a glass of water to landing a dream job--often requires planning based on experience and executing a sequence of actions. Neurophysiological research in animal models has indicated that the orbitofrontal cortex (OFC) mediates relationships between memory, actions, and outcomes and the hippocampus and other medial temporal lobe (MTL) regions are known to be critical for rapid learning, but little is known about how these areas interact to support rapid learning and retrieval of goal-directed action sequences in humans. Here, we leverage a rare opportunity to investigate human OFC gamma oscillations and examine the coordination between the OFC and MTL during a continuous multi-step task that requires applying recently acquired experience to guide behavior. We used multisite intracranial electroencephalography (iEEG) recordings while participants searched for a hidden goal in an animated game to study neural activity in both brain areas during goal-directed behavior. Hippocampal ripples--brief high-frequency oscillations reflecting synchronized neuronal firing--are known to support memory consolidation during sleep, but their role during active memory retrieval and updating remains unclear. We found that OFC gamma activity was modulated by both memory demands and ripples in the hippocampus and adjacent structures. Notably, ripple-coupled OFC gamma during exploration was associated with subsequent task performance. We propose that hippocampal ripples mark a narrow window, supporting hippocampal-cortical communication required for successful goal encoding for future behaviors.

neuroscience↗

bioRxiv2023

The representational geometry of cognitive maps under dynamic cognitive control

Recent work has shown that the brain abstracts non-spatial relationships between entities or task states into representations called cognitive maps. Here, we investigate how cognitive control enables flexible top-down selection of goal-relevant information from multidimensional cognitive maps retrieved from memory. We examine the relationship between cognitive control and representational geometry by conducting parallel analyses of functional magnetic resonance imaging data and recurrent neural network models trained to perform the same task. We find both stable map-like representations in a medial temporal lobe and orbitofrontal cortical network that reflect both task-relevant and irrelevant dimensions and context-dependent, orthogonal representations of only task-relevant dimensions in a frontoparietal network. These representational motifs also emerge with distinct temporal profiles over the course of training in the recurrent neural network, with map-like representations appearing first. We further show that increasing control demands due to incongruence (conflicting responses) between current task-relevant and irrelevant dimensions impact the geometry of subjective representations, and the degree of this effect further accounts for individual differences in cognitive control. Taken together, our findings show how complementary representational geometries balance stability and behavioral flexibility, and reveal an intricate relationship between cognitive control and cognitive map geometry.

neuroscience↗

bioRxiv2021

Neural mechanisms of credit assignment for inferred relationships in a structured world

Animals have been proposed to abstract compact representations of a tasks structure that could, in principle, support accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. Here, we develop a novel hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, mPFC and lateral orbital frontal cortex track the inferred current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance both of tracking the current position in an abstracted task space and efficient, generalizable representations in prefrontal cortex for supporting flexible learning and inference in structured environments.

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bioRxiv2020

Novel Inferences in a Multidimensional Social Network Use a Grid-like Code

Generalizing experiences to guide decision making in novel situations is a hallmark of flexible behavior. It has been hypothesized such flexibility depends on a cognitive map of an environment or task, but directly linking the two has proven elusive. Here, we find that discretely sampled abstract relationships between entities in an unseen two-dimensional (2-D) social hierarchy are reconstructed into a unitary 2-D cognitive map in the hippocampus and entorhinal cortex. We further show that humans utilize a grid-like code in several brain regions, including entorhinal cortex and medial prefrontal cortex, for inferred direct trajectories between entities in the reconstructed abstract space during discrete decisions. Moreover, these neural grid-like codes in the entorhinal cortex are associated with neural decision value computations in the medial prefrontal cortex and temporoparietal junction area during choice. Collectively, these findings show that grid-like codes are used by the human brain to infer novel solutions, even in abstract and discrete problems, and suggest a general mechanism underpinning flexible decision making and generalization.

neuroscience↗

bioRxiv2019

Map making: Constructing, combining, and navigating abstract cognitive maps

Cognitive maps are thought to enable model-based inferences from limited experience that can guide novel decisions-a hallmark of behavioral flexibility. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel inferences. Subjects learned the status of people in two separate unseen 2-D social hierarchies defined by competence and popularity piecemeal from binary comparisons, with each dimension learned on a separate day. Although only one dimension was ever behaviorally relevant, multivariate activity patterns in HC, EC and vmPFC/mOFC were linearly related to the Euclidean distance between people in the mentally reconstructed 2-D space. Hubs created unique comparisons between the two hierarchies, enabling inferences between novel pairs of people. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidean distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, combined, and enable novel inferences in the human brain.

neuroscience↗