Biology subjects

Yoo, S. B. M.

Publications and source records attributed to Yoo, S. B. M..

At least 19 recordsLinked to original sources

bioRxiv2026

A number simplex in the human medial temporal lobe

Humans handle numbers nimbly, suggesting a richer neural manifold structure than the prevalent mental number line model. In populations of medial temporal lobe (MTL) neurons in humans performing two simple tasks (dot counting and arithmetic), we find robust neural coding of numerosity that results in high dimensional, simplex-shaped manifolds. This shape affords more flexibility than a linear manifold due to its high shattering dimensionality and expressibility. Dot arrays and Arabic numerals evoked distinct simplicial population codes, yet they were linked by a linearly transferable latent structure within the same task. We find similar simplicial geometry of number representations in large language models (LLMs). Moreover, subjects internally computed arithmetic results were decodable during the calculation period, with decoding accuracy correlating with individual mathematical capacity. Finally, linear transformations of simplicial operand representations modeled the brains conversion of operands into decodable results, suggesting that the brains arithmetic procedures have some resemblance to the attention architecture of LLMs. Together, these findings establish a high dimensional representational foundation for numerical cognition in the brain.

neuroscience↗

bioRxiv2026

The Computational and Neural Basis of Zero-Shot Control in Dynamic Pursuit

Biological agents flexibly adapt their behavior to novel goals and environmental demands without additional training, yet the computational principles enabling such control remain unclear. Here, we propose that three cognitive constructs constitute minimal computational motifs for flexible control: relational structure, spotlight attention, and affordance computation. We examine whether these constructs underpin flexible control in an embodied dynamic pursuit task requiring continuous integration of inter-entity relations, reward, and action feasibility, making it a suitable testbed for real-time control. By implementing these constructs within a multi-module graph convolutional network, we show that the model achieves zero-shot transfer across novel pursuit scenarios without additional training. Although not explicitly trained to do so, the model also exhibits change-of-mind behavior, a hallmark of flexible control exhibited by biological agents. Neural recordings from the primate dorsal anterior cingulate cortex revealed population-level signatures linking these constructs to neural dynamics, providing biological support for the proposed computational architecture.

neuroscience↗

bioRxiv2025

Shared neural geometries for bilingual semantic representations

The human brain has the remarkable ability to comprehend and express similar concepts in multiple languages. To understand how it does so, we examined responses of hippocampal neurons during passive listening, directed speaking, and spontaneous conversation, in both English and Spanish, in a small group of balanced bilinguals. We find a small number of putative cross-language neurons, whose responses to equivalent words (e.g., "tierra" and "earth") are correlated. However, neurons semantic tunings differed substantially by language, suggesting language-specific neural implementations. Instead, the crucial driver of translation was a preserved geometric organization of neural responses between the two languages, one that did not depend on neuron level functional overlap. Indeed, that geometry was implemented by a common set of neurons along distinct readout axes; this difference in readout may help prevent cross-language interference. Together, these results suggest that hippocampus encodes a language-independent internal model for meaning.

neuroscience↗

bioRxiv2025

The neural basis of emotional generalization in empathy

The essence of empathy is generalization of emotion across persons. Here, we leverage recent theoretical advances in the neuroscience of generalization to help us understand empathy. We measured brain activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one identifying emotional responses in others. We quantified the representational geometry of local field potential (LFP) high-gamma activity in four regions: the medial temporal lobe, anterior cingulate cortex, orbitofrontal cortex, and insula. We found encoding of both self- and other-emotions in all four regions, but codes for emotion and person are disentangled (that is, factorized) in the insula, but not the other regions. This factorized representation allows for cross-person generalization of emotion in a way that tangled (non-factorized) representations do not. Together, these results support the hypothesis that the insula uniquely contributes to social mirroring processes by which we understand emotions across individuals.

neuroscience↗

bioRxiv2025

Representational Geometries of Perception and Working Memory: A Pilot Study

This study examined the representational geometry of visual working memory using human 7T fMRI. Observers viewed a facescene blended sample, attended to either the face or scene, and judged whether the attended aspect matched a subsequent test image. We found that the coding dimension distinguishing faces and scenes rotated between encoding and maintenance phases, in both visual and association areas. In these regions, exclusive combinations (i.e., encoded face and maintained scene vs. encoded scene and maintained face) were linearly decodable, indicating encoding and maintenance phases represent visual information using distinct subspaces. Such high-dimensional, flexible geometry can, in principle, protect maintained visual information from incoming input. At the same time, robust crossdecoding was observed across phases, reflecting the stability and generalizability of the represented contents. The balance between representational flexibility and stability varied along the cortical hierarchy: early sensory regions emphasized flexibility, whereas transmodal regions showed greater stability.

neuroscience↗

bioRxiv2025

Selective Observation Under Limited Resources Biases Social Inference Through Hysteresis

Despite limited access to others actions and outcomes, humans excel at inferring hidden intentions. Given only partial access, how do they decide what to observe, and how does selective observation shape inference? Here, we examined how choosing what to observe can bias the inference about others intentions. Participants played a game where they pursued a fleeing target while a computerized opponent acted competitively or cooperatively. Participants overestimated the opponents competitiveness after the opponent acted more competitively than expected, whereas no such bias occurred when the opponent was more cooperative than expected. This asymmetry depended on the sequence of events, resembling hysteresis, a form of path dependence observed in physical systems. We found that these biases became stronger when participants chose to observe the opponent instead of their own avatar, and this choice came at the cost of losing precise control over their avatar. Our findings highlight the trade-off in selecting what to observe, as the resulting inference biases propagate differently depending on the interaction history.

neuroscience↗

bioRxiv2025

Complementary roles for hippocampus and anterior cingulate in composing continuous choice

Naturalistic, goal directed behavior often requires continuous actions directed at dynamically changing goals. In this context, the closest analogue to choice is a strategic reweighting of multiple goal-specific control policies in response to shifting environmental pressures. To understand the algorithmic and neural bases of choice in continuous contexts, we examined behavior and brain activity in humans performing a continuous prey-pursuit task. Using a newly developed control-theoretic decomposition of behavior, we find pursuit strategies are well described by a meta-controller dictating a mixture of lower-level controllers, each linked to specific pursuit goals. We find that hippocampal neurons encode the policy blending variable in a value-invariant manner and monitor policy switches after they occur. ACC neurons encode policy switches in a value-dependent manner, with value related modulation detectable several hundred ms before the switch, alongside a ramping increase in mean firing rate toward the switch. Meanwhile, OFC activity is consistent with an encoding of the current value structure of the task, rather than policy switching. Together these results are consistent with a tripartite functional division in which hippocampus serves as a controller over behavior, ACC serves as a meta-controller, and OFC provides a value context signal. Overall, our results shed light onto the complex processes associated with choice during naturalistic continuous interactive behavior.

neuroscience↗

bioRxiv2025

Independent Continuous Tracking of Multiple Agents in the Human Hippocampus

The pursuit of fleeing prey is a core element of many species behavioral repertoires. It poses the difficult problem of continuous tracking of multiple agents, including both self and others. To understand how this tracking is implemented neurally, we examined responses of hippocampal neurons while humans performed a joystick-controlled continuous prey-pursuit task involving two simultaneously fleeing prey (and, in some cases, a predator) in a virtual open field. We found neural maps encoding the positions of all the agents. All maps were multiplexed in single neurons and were disambiguated by the use of the population coding principle of semi-orthogonal subspaces, which can facilitate cross-agent generalization. Some neurons, more common in the posterior hippocampus, had narrow tuning functions reminiscent of place cells, lower firing rates, and high information per spike; others, which were found in both anterior and posterior hippocampus, had broad tuning functions, higher firing rates, and less information per spike. Semi-orthogonalization was selectively associated with the broadly tuned neurons. These results suggest an answer to the problem of navigational individuation, that is, how mapping codes can distinguish different agents, and establish the neuronavigational foundations of pursuit.

neuroscience↗

bioRxiv2024

State-dependent Online Reactivations for Different Learning Strategies in Foraging

Reactivation of neural responses associated with navigation is thought to facilitate learning. We wondered whether reactivation is subject to contextual control, meaning that different types of learning promote different reactivation patterns. We trained macaques to forage in a first-person virtual maze and identified two distinct learning states prioritizing reward and information using unsupervised ethogramming based on low-level features. In orbitofrontal (OFC) and retrosplenial (RSC) cortices, representations of the goal, the path towards it, and recently traveled paths were strongly reactivated - online - during reward-prioritizing choices. During learning, reactivation of optimal paths increased in RSC after reward-prioritizing choices, and reactivation of uninformative paths decreased in RSC and OFC after information-prioritizing choices. Reactivation in OFC selectively covaried with ongoing RSC activity when prioritizing information; vice versa during prioritizing reward. These results highlight that cognitive states can drive learning and reactivation patterns can be tailored to the needs of the moment.

neuroscience↗

bioRxiv2023

Hypothalamic neuronal activation in primates drives naturalistic goal-directed eating behavior

Eating addiction is the primary cause of modern obesity. Although the causal role of the lateral hypothalamic area (LHA) for eating is demonstrated in rodents, there is no evidence in primates regarding naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHAGABA) neurons in eating by chemogenetics in three macaques. LHAGABA neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, which was specific for palatable food. PET and MRS validated the chemogenetic activation. Rs-fMRI result revealed that functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after the LHAGABA neuron activation. Thus, our study elucidates the role of LHAGABA neurons in eating and obesity therapeutics for primates and humans.

neuroscience↗

bioRxiv2021

Orthogonal but linked neural codes for value

Our ability to effectively choose between dissimilar options implies that information regarding the options values must be available, either explicitly or implicitly, in the brain. Explicit realizations of value involve single neurons whose responses depend on value and not on the specific features that determine it. Implicit realizations, by contrast, come from the coordinated action of neurons that encode specific features. One signature of implicit value coding is that population responses to offers with the same value but different features should occupy semi- or fully orthogonal neural subspaces that are nonetheless linked. Here, we examined responses of neurons in six core value-coding areas in a choice task with risky and safe options. Using stricter criteria than some past studies have used, we find, surprisingly, no evidence for abstract value neurons (i.e., neurons with the response to equally valued risky and safe options) in any of these regions. Moreover, population codes for value resided in orthogonal subspaces; these subspaces were linked through a linear transform of each of their constituent subspaces. These results suggest that in all six regions, populations of neurons embed value implicitly in a distributed population.

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↗

bioRxiv2021

Differential encoding of safe and risky offers

Common currency theories in neuroeconomics hold that neurons in specific brain regions specifically encode subjective values of offers and not stimulus-specific information. The rationale behind these theories is that abstract value encoding lets the decision maker compare qualitatively different options. Alternatively, expectancy-based theories hold that the brain preferentially tracks the relationship between options and their outcomes, and thus does not abstract away details of offers. To adjudicate between these theories, we examined responses of neurons in six reward regions to risky and safe offers while macaques performed a gambling task. In all regions, responses to safe options are unrelated to responses evoked by equally preferred risky options. Nor does any region appear to contain a specialized subset of value-selective neurons. Finally, in all regions, responses to risky and safe options occupy distinct response subspaces, indicating that the organizational framework for encoding risky and safe offers is different. Together, these results argue against the idea that putative reward regions carry abstract value signals, and instead support the idea that these regions carry information that links specific options to their outcomes in support of a broader cognitive map.

neuroscience↗

bioRxiv2020

Signatures of processing complexity during global cognitive states in ventromedial prefrontal cortex

Behavioral neuroscience almost exclusively studies behavior during tasks and ignores the unstructured inter-trial interval (ITI). However, it is unlikely that the ITI is simply an idling or paused mode; instead, it is a likely time for globally focused cognition, in which attention is disengaged from the task at hand and oriented more broadly. To gain insight into the computational underpinnings of globally focused cognition, we recorded from neurons in a core decision-making region, area 14 of ventromedial prefrontal cortex (vmPFC), as macaques performed a foraging search task with long inter-trial intervals (ITIs). We find that during the ITI, ensemble firing is associated with increased discriminability of a key mnemonic variable, recent reward rate, which in turn predicts upcoming search strategy. ITI activity is also associated with increased ensemble dimensionality and faster subspace reorganization, presumed markers of processing complexity. These results demonstrate the flexible nature of mnemonic processing and support the idea that the brain makes use of ostensible downtime to engage in complex processing.

animal behavior and cognition↗

bioRxiv2020

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and several fields of medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure its behavior, specifically, its pose (position of multiple major body landmarks) in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a novel deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 precisely calibrated and synchronized machine vision cameras that encircle an open 2.45mx2.45mx2.75m enclosure. The resulting multiview image streams allow for novel data augmentation via 3D reconstruction of hand-annotated images that in turn train a robust view-invariant deep neural network model. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track two monkey social interactions without human intervention. We also make the training data (195,228 images) and trained detection model publicly available.

neuroscience↗

bioRxiv2019

Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion

Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesized that the dorsal anterior cingulate cortex (dACC), which communicates bidirectionally with both the hippocampal complex and premotor/motor areas, would serve a mapping role in this process. We recorded responses of dACC ensembles in two macaques performing a joystick-controlled continuous pursuit/evasion task. We found that dACC multiplexes two sets of signals, (1) world-centric variables that together form a representation of the position and velocity of all relevant agents (self, prey, and predator) in the virtual world, and (2) avatar-centric variables, i.e. self-prey distance and angle. Both sets of variables are multiplexed within an overlapping set of neurons. Our results suggest that dACC may contribute to pursuit and evasion by computing and continuously updating a multicentric representation of the unfolding task state, and support the hypothesis that it plays a high-level abstract role in the control of behavior.

neuroscience↗

bioRxiv2019

The Neural Basis of Predictive Pursuit

It remains unclear how and to what extent non-human animals make demanding on-the-fly predictions during pursuit. We studied this problem in a novel laboratory pursuit task that incentivizes prediction of future prey positions. We trained three macaques to perform joystick-controlled pursuit of prey that followed intelligent escape algorithms. Subjects reliably aimed towards the preys likely future positions, indicating that they generate internal predictions and use those predictions to guide behavior. We then developed a generative model that explains real-time pursuit trajectories and showed that our subjects use prey position, velocity, and acceleration to make predictions. We identified neurons in the dorsal anterior cingulate cortex (dACC) whose responses track these three variables. These neurons multiplexed prediction-related variables with a distinct and explicit representation of the preys future position. Our results provide a clear demonstration that the brain can explicitly represent future predictions and highlight the critical role of anterior cingulate cortex for future-oriented cognition.\n\nOne-sentence summaryIn a dynamic pursuit environment, monkeys actively predict future prey positions and dACC neurons encode these future positions.

neuroscience↗

bioRxiv2019

A Comparison of Non-human Primate and Deep Reinforcement Learning Agent Performance in a Virtual Pursuit-Avoidance Task

We compare the performance of non-human primates and deep reinforcement learning agents in a virtual pursuit-avoidance task, as part of an effort to understand the role that cognitive control plays in the deeply evolved skill of chase and escape behavior. Here we train two agents, a deep Q network and an actor-critic model, on a video game in which the player must capture a prey while avoiding a predator. A previously trained rhesus macaque performed well on this task, and in a manner that obeyed basic principles of Newtonian physics. We sought to compare the principles learned by artificial agents with those followed by the animal, as determined by the ability of one to predict the other. Our findings suggest that the agents learn primarily 1st order physics of motion, while the animal exhibited abilities consistent with the 2nd order physics of motion. We identify scenarios in which the actions taken by the animal and agents were consistent as well as ones in which they differed, including some surprising strategies exhibited by the agents. Finally, we remark on how the differences between how the agents and the macaque learn the task may affect their peak performance as well as their ability to generalize to other tasks.

neuroscience↗