Biology subjects

Veselic, S.

Publications and source records attributed to Veselic, S..

8 recordsLinked to original sources

bioRxiv2026

Rapid value learning reveals generalized and context-dependent codes in frontal cortex

Studying how the brain represents value spans distinct methods and training histories, from neuroimaging in task-naive humans to single-neuron recordings in extensively trained non-human primates. Similar findings across fields have encouraged the untested assumption that rapidly emerging and overtrained value representations are equivalent. Here we recorded single-neuron activity in anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC) as macaques learned novel cue values and chose between novel and overtrained cues. Value responses emerged within 4-7 cue presentations, matching behavioural adaptation. Yet ACC and OFC used distinct codes. ACC encoded value in a common format that generalised across training history. OFC coding was more context-dependent, with distinct subpopulations recruited by choice experience. Choice novelty was also encoded independently of value before chosen value signals emerged. During learning, ACC responses shifted from overtrained secondary reinforcers to newly predictive cues. These findings establish the necessary (rapid acquisition) and sufficient (generalised format) conditions for comparing value representations across methods, species, and training histories.

neuroscience↗

bioRxiv2025

Reasoning with programs in replay

Reasoning flexibly composes known elements to solve novel problems. Recent theories suggest the brain uses the axis of time to compose elements for reasoning. In this view, elements are packaged into fast neural sequences, with each sequence exploring the implications of a different composition. Using magnetoencephalography, we tested this idea while participants mentally executed programs. Each program contained a set of steps linked to operations, the implications of which had to be computed. We found that behavioral performance scaled with program complexity, and by the end of execution, inferred program solutions were represented in prefrontal and parietal cortices. We identified a possible mechanism by which these solutions were computed. During reasoning, representations of steps in the program reactivated in fast sequences, consistent with sampling candidate partial solutions. Further evidence suggested these reactivations were accompanied by representations of their operations, and were followed by neural patterns reflecting their computed implications. Together, these results suggest replaying sequences supports program execution and reveal a highly organized temporal microarchitecture of reasoning.

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bioRxiv2024

A neural mechanism for compositional generalization of structure in humans

A human ability to adapt to the dynamics of novel environments relies on abstracting and generalizing from past experiences. Previous research has focused on how humans generalize from isolated sequential processes, yet we know little about mechanisms that enable adaptation to more complex dynamics, including those that govern much everyday experience. Here, using a novel sequence learning task based on graph factorization, coupled with simultaneous magnetoencephalography (MEG) recordings, we ask whether reuse of experiential "building blocks" enable inference and generalization. Behavioral results were consistent with participants decomposing task experience into subprocesses, abstracting their dynamical structure away from their sensory specifics and transferring these to a new task environment. Neurally, this transfer was associated with a learning-induced, condition-specific, increase in neural similarity among stimuli conditional on their structural role in abstract subprocesses that were shared across task phases. Consistent with this, a complementary dynamical role decoding analysis revealed enhanced neural decodability for stimuli sharing identical structural transition types across experiential contexts, specifically when prior experience included the relevant graph structure. Decoding strength for these role representations was positively related to behavioral success in subprocess knowledge transfer, a relationship that will require future independent replication. These findings provide neural evidence consistent with the idea that a structural scaffolding mechanism supports generalization of experience to new contexts.

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bioRxiv2023

A cognitive map for value-guided choice in ventromedial prefrontal cortex

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

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bioRxiv2021

Covert valuation for information sampling and choice

We use our eyes to assess the value of objects around us and carefully fixate options that we are about to choose. Neurons in the prefrontal cortex reliably encode the value of fixated options, which is essential for decision making. Yet as a decision unfolds, it remains unclear how prefrontal regions determine which option should be fixated next. Here we show that anterior cingulate cortex (ACC) encodes the value of options in the periphery to guide subsequent fixations during economic choice. In an economic decision-making task involving four simultaneously presented cues, we found rhesus macaques evaluated cues using their peripheral vision. This served two distinct purposes: subjects were more likely to fixate valuable peripheral cues, and more likely to choose valuable options whose cues were never even fixated. ACC, orbitofrontal cortex, dorsolateral prefrontal cortex, and ventromedial prefrontal cortex neurons all encoded cue value post-fixation. ACC was unique, however, in also encoding the value of cues before fixation and even cues that were never fixated. This pre-saccadic value encoding by ACC predicted which cue was next fixated during the decision process. ACC therefore conducts simultaneous processing of peripheral information to guide information sampling and choice during decision making.

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bioRxiv2021

Distributional reinforcement learning in prefrontal cortex

Prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories centre on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. Here we show distributional RL also better explains prefrontal cortical responses, suggesting it is a ubiquitous mechanism for reward-guided learning.

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bioRxiv2020

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Designing robotic assistance devices for manipulation tasks is challenging. This work aims at improving accuracy and usability of physical human-robot interaction (pHRI) where a user interacts with a physical robotic device (e.g., a human operated manipulator or exoskeleton) by transmitting signals which need to be interpreted by the machine. Typically these signals are used as an open-loop control, but this approach has several limitations such as low take-up and high cognitive burden for the user. In contrast, a control framework is proposed that can respond robustly and efficiently to intentions of a user by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision making on how to assist the user. Context-awareness is achieved by creating a set of candidate grasp targets and reach-to grasp trajectories in a cluttered scene. User-awareness is implemented as a linear time-variant feedback controller (TV-LQR) over the generated trajectories to facilitate the motion towards the most likely intention of a user. The system also dynamically recovers from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting the users intention, the proposed controller allows the subject to achieve superhuman performance in terms of accuracy and thereby usability.

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bioRxiv2020

A causal role for estradiol in human reinforcement learning

The sex hormone estrogen is hypothesized to play a key role in human cognition via its interactions with the dopaminergic system. Work in rodents has shown that estrogens most potent form, estradiol, impacts striatal dopamine functioning predominately via increased D1-receptor signalling, while human work has suggested that high estradiol levels are associated with altered reward sensitivity. Here, we addressed two fundamental questions: 1) whether estradiol causally alters reward sensitivity in men, and 2) whether this effect of estradiol is moderated by individual variation in polymorphisms of dopaminergic genes. To test this, we performed a double-blind placebo-controlled administration study in which hundred men received either a single dose of estradiol (2 mg) or placebo. We found that estradiol administration increased reward sensitivity, which was moderated by baseline dopamine. This was observed in choice behaviour and increased learning rates. These results confirm a causal role of estradiol in reinforcement learning in men that is moderated by the striatal dopaminergic pathway.

neuroscience↗