Biology subjects

Hunt, L. T.

Publications and source records attributed to Hunt, L. T..

At least 19 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↗

bioRxiv2026

Valence-specific representation of uncertainty in the anterior cingulate is impaired in those with affective symptoms

The ability to seek reward and avoid punishment is a fundamental survival instinct. In natural environments, however, the statistics of rewards and punishments can change independently of one another. In addition, individuals experiencing anxiety and depression may be selectively biased to process rewards and punishments differently. Here, we examine how humans adapt their behavior in such dynamic environments, using a task where cues are associated with independently changing probabilities of rewards (wins) and punishments (losses). We demonstrate that participants dynamically adjust their learning rates based on the relative volatility of each valence. Neuroimaging reveals that this behavioral flexibility is supported by valence-specific segregation of volatility signals within distinct subregions of the anterior cingulate cortex (ACC: perigenual and dorsal). Furthermore, individuals with higher levels of anxiety and depression exhibit a relative reduction in loss-volatility learning adaptation, accompanied by less distinct neural encoding of win and loss volatility in both ACC subregions. Together, our findings indicate that flexible adaptation to separate reward and punishment contingencies relies on distinct, valence-specific tracking of volatility within the prefrontal cortex. These findings suggest a potential computational and neuroanatomical framework for understanding maladaptive learning in affective disorders.

neuroscience↗

bioRxiv2025

Environmental dynamics shape human learning: change points versus random walks

Adaptive learning requires interpreting prediction errors in light of environmental dynamics. However, environments may not only differ in terms of when changes occur, but also how they occur: some nonstationary processes in nature exhibit slow drifts over time, while others show more abrupt changes. This raises the question as to whether humans can adapt their learning to reflect the generative structure of their environment. Here, we compared how humans learn in two canonical nonstationary environments: abrupt change points versus gradual random walks. Using a predictive inference task and a unified Bayesian framework, we show across two behavioral experiments that humans adapt their learning normatively between these two environments. Notably, identical prediction errors were interpreted differently across them. Large prediction errors triggered sharp increases in learning rate under change-point but not random-walk dynamics, where small and large errors were more equally weighted. This matched the predictions of a normative Bayesian model, which itself adopted the appropriate generative model for each environment. In addition, we found that humans could dissociate two latent variables that needed to be jointly inferred: the mean and variance (or stochasticity). This was demonstrated by explicit uncertainty reports that closely tracked the current variance, and only showed sustained changes following change points in variance but not mean. Together, these results show that humans adapt their learning strategy to both how and what aspects of the environment are changing. They establish a unified computational account of adaptive learning across different environmental dynamics.

neuroscience↗

bioRxiv2025

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering

It has been suggested that humans and other animals are driven by a fundamental desire to acquire information about opportunities available in their environments. Not only might such a desire explain pathological behaviors, but it may be needed to account for how everyday decisions are resolved. Here, we combine artificial neural networks (ANNs) with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice. This model accounts for behavior in our own data and in previous work, outperforming existing accounts of information sampling such as the Upper Confidence Bound heuristic. This modelling approach has broad potential for uncovering novel patterns in behavior and cognitive processes, while also specifying them in human-interpretable formats. We then used the value of information derived by our model, together with ultra-high field neuroimaging, to examine activity across a suite of subcortical neuromodulatory nuclei and two cortical regions that influence these nuclei. This established roles for midbrain dopaminergic nuclei, anterior cingulate cortex, and anterior insula in mediating the influence of value of information on behavior.

neuroscience↗

bioRxiv2025

Working memory shapes neural geometry in human EEG over learning

Working memory has been traditionally studied as a passive storage for information. However, recent advances have suggested that working memory is prospective rather than retrospective, meaning that its content undergoes transformations that will support future behaviour. One perspective that underscores this notion conceptualises memory processes as a computational resource that can be used to reduce the complexity of computation at decision time. Here, we explore this perspective by examining whether the process of maintenance shapes neural geometry and leads to low-dimensional representations during storage and later decision time. We recorded EEG in 25 human participants who learnt to solve a XOR task. We hypothesised that separating task features by a working memory delay would result in participants temporally decomposing the XOR computation, by prospectively processing one of the task features early in trial time. In line with our predictions, participants transformed the first feature from a sensory to an abstract format and maintained this pre-processed information throughout the delay. This process was related to the low-dimensional representation required at decision time early in learning, a representation that has recently been shown to support later cross-generalisation. These results demonstrate that low-dimensional representations, elsewhere associated with slow learning, might also provide a mechanism for maintenance processes in working memory.

neuroscience↗

bioRxiv2024

Indecision and recency-weighted evidence integration in non-clinical and clinical settings

Biases in information gathering are common in the general population and can reach pathological extremes in paralysing indecisiveness, as in obsessive-compulsive disorder (OCD). Here, we adopt a new perspective on information gathering and demonstrate an information integration bias where most recent information is over-weighted by means of evidence strength updates ({Delta}ES). In a large, crowd-sourced sample (N=5,237), we find reduced {Delta}ES-weighting drives indecisiveness along an OC spectrum. We replicate the attenuated {Delta}ES- weighting in a second lab-based study (N=105) that includes a transdiagnostic OC spectrum encompassing OCD and generalised anxiety patients. Using magnetoencephalography (MEG), we trace {Delta}ES signals to a late neural signal peaking around 920 ms. Critically, high-OC participants show an attenuated neural {Delta}ES signal in medio-frontal areas while other decision- relevant processes remain intact. Our findings establish biased information-weighting as a key driver of information gathering, where attenuated {Delta}ES can lead to indecisiveness across an OC spectrum.

neuroscience↗

bioRxiv2024

Dynamic Network Analysis of Electrophysiological Task Data

An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. Whilst this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches, can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks. HighlightsO_LIWe show how oscillatory task response analysis can be carried out at the network level using two state-of-the-art methods: the Hidden Markov Model (HMM) and Dynamics Network Modes (DyNeMo). C_LIO_LIThe HMM and DyNeMo can identify oscillatory task responses that conventional time-frequency methods fail to detect. C_LIO_LIDyNeMo provides a more interpretable and precise network decomposition of the data compared to the HMM. C_LIO_LIThe dataset and (Python) scripts used to perform dynamic network analysis on task data are made publicly available. C_LI

neuroscience↗

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.

neuroscience↗

bioRxiv2023

Reward positivity biases interval production in a continuous timing task

The neural circuits of reward processing and interval timing (including perception and production) are functionally intertwined, suggesting that it might be possible for momentary reward processing to influence subsequent timing behavior. Previous animal and human studies have mainly focused on the effect of reward on interval perception, whereas its impact on interval production is less clear. In this study, we examined whether feedback, as an example of performance-contingent reward, biases interval production. We recorded EEG from 20 participants while they engaged in a continuous drumming task with different realistic tempos (1728 trials per participant). Participants received color-coded feedback after each beat about whether they were correct (on time) or incorrect (early or late). Regression-based EEG analysis was used to unmix the rapid occurrence of a feedback response called the reward positivity (RewP), which is traditionally observed in more slow-paced tasks. Using linear mixed modelling, we found that RewP amplitude predicted timing behavior for the upcoming beat. This performance-biasing effect of the RewP was interpreted as reflecting the impact of fluctuations in dopaminergic activities on timing, and the necessity of continuous paradigms to make such observations was highlighted.

neuroscience↗

bioRxiv2023

Learning shapes neural geometry in the prefrontal cortex

The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience1-6. The primate prefrontal cortex (PFC) is a primary focus of inquiry, as it can encode information with geometries that either rely on past experience7-13 or are experience agnostic3,14-16. One hypothesis is that PFC representations should evolve with learning4,17,18, from a format that supports exploration of all possible task rules to a format that minimises the encoding of task-irrelevant features4,17,18 and supports generalisation7,8. Here we test this idea by recording neural activity from PFC when learning a new rule ( XOR rule) from scratch. We show that PFC representations progress from being high dimensional, nonlinear and randomly mixed to low dimensional and rule selective. Upon generalising the rule to novel stimuli, these representations further evolve into an abstract, stimulus-invariant geometry. These findings reconcile previously conflicting accounts of PFC function by demonstrating how neural representations adapt across distinct stages of learning.

neuroscience↗

bioRxiv2022

The Neural Correlates of Continuous Feedback Processing

Feedback processing is commonly studied by analyzing the brains response to discrete rather than continuous events. Such studies have led to the hypothesis that rapid phasic midbrain dopaminergic activity tracks reward prediction errors (RPEs), the effects of which are measurable at the scalp via electroencephalography (EEG). Although studies using continuous feedback are sparse, recent animal work suggests that moment-to-moment changes in reward are tracked by slowly ramping midbrain dopaminergic activity. Some have argued that these ramping signals index state values rather than RPEs. Our goal here was to develop an EEG measure of continuous feedback processing in humans, then test whether its behaviour could be accounted for by the RPE hypothesis. Participants completed a stimulus-response learning task in which a continuous reward cue gradually increased or decreased over time. A regression-based unmixing approach revealed EEG activity with a topography and timecourse consistent with the stimulus-preceding negativity (SPN), a scalp potential previously linked to reward anticipation and tonic dopamine release. Importantly, this reward-related activity depended on outcome expectancy: as predicted by the RPE hypothesis, activity for expected reward cues was reduced compared to unexpected reward cues. These results demonstrate the possibility of using human scalp-recorded potentials to track continuous feedback processing, and test candidate hypotheses of this activity.

neuroscience↗

bioRxiv2022

Decision-making in dynamic, continuously evolving environments: quantifying the flexibility of human choice

During perceptual decision-making tasks, centroparietal EEG potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision making remain unclear. Here, we use a novel continuous task design to study how the Centroparietal Positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.

neuroscience↗

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.

neuroscience↗

bioRxiv2021

Computational specialization within the cortical eye movement system

Animals actively sample their environment through orienting actions such as saccadic eye movements. Saccadic targets are selected based both on sensory evidence immediately preceding the saccade, and a salience map or prior built up over multiple saccades. In the primate cortex, the selection of each individual saccade depends on competition between target-selective cells that ramp up their firing rate to saccade release. However it is less clear how a cross-saccade prior might be represented, either in neural firing or through an activity-silent mechanism such as modification of synaptic weights on sensory inputs. Here we present evidence from magnetoencephalography for two distinct processes underlying the selection of the current saccade, and the representation of the prior, in human parietal cortex. While the classic ramping decision process for each saccade was reflected in neural firing rates (measured in the event related field), a prior built up over multiple saccades was represented via modulation of the gain on sensory inputs from the preferred target, as evidenced by rapid frequency tagging. A cascade of computations over time (initial representation of the prior, followed by evidence accumulation and then updating) provides a mechanism by which a salience map may be built up across saccades in parietal cortex. It also provides insight into why evidence accumulation signals are present in parietal cortex, when inactivation of the region has been shown not to affect performance on single-trial tasks.

neuroscience↗

bioRxiv2020

Temporal Scaling of Human Scalp-Recorded Potentials During Interval Estimation

Much of human behaviour is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single unit recordings in animals have revealed neural activity scales to span different durations during behaviours demanding flexible timing. Here, we employed a general linear modelling approach using a novel combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent EEG datasets, including interval perception, production, prediction and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behaviour. Our results provide a general approach for studying flexibly timed behaviour in the human brain. Significance StatementNeural activity is traditionally thought to occur over fixed time scales. However, recent animal work has suggested that some neural responses occur over varying timescales. We extended this animal result to humans by detecting temporally scaled signals non-invasively at the scalp in four different tasks. Our results suggest that temporal scaling is an important feature of cognitive processes known to unfold over varying timescales.

neuroscience↗

bioRxiv2020

Medial frontal cortex activity predicts information sampling in economic choice

Decision-making not only requires agents to decide what to choose, but also how much information to sample before committing to a choice. Previously established frameworks for economic choice argue for a deliberative process of evidence accumulation across time. These tacitly acknowledge a role of information sampling, in that decisions are only made once sufficient evidence is acquired, yet few experiments have explicitly placed information sampling under the participants control. Here, we use functional MRI to investigate the neural basis of information sampling in economic choice, by allowing participants to actively sample information in a multi-step decision task. We show that medial frontal cortex (MFC) activity is predictive of further information sampling prior to choice. Choice difficulty (inverse value difference) was also encoded in MFC, but this effect was explained away by the inclusion of information sampling as a co-regressor in the general linear model. A distributed network of regions across prefrontal cortex encoded key features of the sampled information at the time it was presented. We propose that MFC is an important controller of the extent to which information is gathered before committing to an economic choice. This role may explain why MFC activity has been associated with evidence accumulation in previous studies, in which information sampling was an implicit rather than explicit feature of the decision.

neuroscience↗

bioRxiv2020

Projections of non-invasive human recordings into state space show unfolding of spontaneous and over-trained choice

Choices rely on a transformation of sensory inputs into motor responses. Using invasive single neuron recordings, the evolution of a choice process has been tracked by projecting population neural responses into state spaces. Here we develop an approach that allows us to recover state space trajectories on a millisecond timescale in non-invasive human recordings. We selectively suppress activity related to relevant and irrelevant sensory inputs and response direction in magnetoencephalography data acquired during context-dependent choices. Recordings from premotor cortex show a smooth progression from sensory input encoding to response encoding. In contrast to previous macaque recordings, information related to choice-irrelevant features is represented more weakly than choice-relevant sensory information. To test whether this mechanistic difference between species is caused by extensive overtraining common in non-human primate studies, we trained humans on >20,000 trials of the task. Choice-irrelevant features were still weaker than relevant features in premotor cortex after overtraining.

neuroscience↗

bioRxiv2019

A circuit mechanism for irrationalities in decision-making and NMDA receptor hypofunction: behaviour, computational modelling, and pharmacology

Decision-making biases can be systematic features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases in health and disease. Monkeys performed an evidence integration task in which they showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a neural mechanism for this behaviour. Because NMDA receptor (NMDA-R) hypofunction is a leading hypothesis for neuropathology in schizophrenia, we simulated behavioural effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were tested experimentally using the NMDA-R antagonist ketamine, yielding changes in decision-making consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent.\n\nSignificancePeople can make apparently irrational decisions because of underlying features in their decision circuitry. Deficits in the same neural circuits may also underlie debilitating cognitive symptoms of neuropsychiatric patients. Here, we reveal a neural circuit mechanism explaining an irrationality frequently observed in healthy humans making binary choices - the pro-variance bias. Our circuit model could be perturbed by introducing deficits in either excitatory or inhibitory neuron function. These two perturbations made specific, dissociable predictions for the types of irrational decisionmaking behaviour produced. We used the NMDA-R antagonist ketamine, an experimental model for schizophrenia, to test if these predictions were relevant to neuropsychiatric pathophysiology. The results were consistent with impaired excitatory neuron function, providing important new insights into the pathophysiology of schizophrenia.

neuroscience↗