Biology subjects

Manohar, S. G.

Publications and source records attributed to Manohar, S. G..

At least 19 recordsLinked to original sources

bioRxiv2026

Motivational incentives improve working memory performance by enhancing memory-based representations rather than altering response strategies

Motivational incentives are known to improve working memory (WM) when items differ in value, but whether they enhance performance when all items are equally valued, and if so, by what mechanism, remains unclear. Improvements could reflect enhanced memory-based representations or speed-accuracy trade-offs, whereby incentives promote slower, more cautious responding without improving memory itself. Here, we tested these alternatives across two experiments using an auditory WM task in which incentives varied across trials, but all items on a trial were equally incentivised. In Experiment 1,we allowed participants to resubmit their responses if desired. Reward-related improvements in WM performance were selectively observed when memory load was high, and only at serial positions associated with greater interference. These improvements were also dissociated from systematic response slowing and resubmission behaviour. This indicated that the benefits of incentives were unlikely to be attributable to response caution. Critically, in Experiment 2, motivational benefits persisted under strict response deadlines, where speed cannot be traded for accuracy. Computational modelling showed that reward selectively improved the precision of memory-based responses without reducing guessing. Together, these findings indicate that motivational incentives improve WM by enhancing the quality of memory-based representations, rather than by strategic slowing or altering response strategies.

neuroscience↗

bioRxiv2026

Why Speech Motor Blocks Emerge in a Communicative Context: An Active Inference Model of Stuttering

Adults who stutter can speak fluently when speech is not addressed to another person, but stuttering emerges when they aim to convey information to a listener. The value of the information being conveyed to the listener also affects the likelihood of stuttering. Why should the mere absence of a listener neutralise a profound motor deficit, and why does a word's predictability affect whether it is spoken fluently? To resolve this socio-motor paradox, we develop a computational model of stuttering within an active inference architecture. The model represents the communicative context, including whether a listener is present and whether the agent is speaking or listening. It was designed around two candidate mechanisms for stuttering, a prior for silence and rigid phoneme sequencing precision. Using both, the model produced fluent private speech and more stuttering-like events during social speech. In the same parameter regime, the model also showed more stuttering-like events on words with higher information value, and produced a word-length effect, in which disfluency increased with longer words. To our knowledge, this is the first model of stuttering to generate both the private speech and the information-value effect from inferred communicative context. By representing the listener as a hidden state that makes the sensory consequences of resuming speech ambiguous, the model offers a computational link between social cognition and speech-motor instability, and suggests that speech fluency depends on whether the speaker believes anyone is present. Clinically, it may offer testable hypotheses and a route to personalising treatment, since the same overt severity can arise from different combinations of parameters.

neuroscience↗

bioRxiv2025

A plastic attractor model of flexible rule-based selective attention

A defining feature of human cognition is the ability to select and respond to stimuli flexibly in different circumstances. Here we show that a recently proposed class of small associative neural network, plastic attractors, can perform such flexible cognitive functions through the rapid formation of task-based attractors. We simulated a rule-based selective attention paradigm, in which agents respond to one task-relevant feature of a visual stimulus, while ignoring another, irrelevant feature, and respond to the attended feature according to a predefined stimulus-response mapping rule. The model consists of a broadly tuned prefrontal population with rapidly changing recurrent connections to sensory neurons that compete via lateral inhibition. In this framework, the rules governing the focus of selective attention are not coded explicitly, but arise as an emergent property of temporary associations between stimulus features and motor responses. The model exhibited activation properties that embody cornerstone concepts in current attentional theory including mixed selectivity, adaptive coding and biased competition, and reproduced a number of classic behavioral and neural findings. A causal test of the model using non-invasive brain stimulation concurrent with functional magnetic resonance imaging (TMS-fMRI) in humans showed that network perturbation reproduced neural decoding and behavioural data. When features were task-relevant, they rapidly formed synaptic connections with frontal cortex binding them into an attracting state, which manifest as prioritized representation of attended information, but this state was readily corrupted by brain stimulation. The model shows mechanistically that rapid synaptic changes could explain flexible rule-based control of attention.

animal behavior and cognition↗

bioRxiv2025

Dynamic updating of spatial working memory across eye movements: a computational investigation of transsaccadic integration

The brain continuously integrates rapidly changing visual input across eye movements to maintain stable perception, yet the precise mechanisms underpinning dynamic working memory and how these break down in brain diseases remain unclear. We developed a novel eye-tracking paradigm and computational models to investigate how spatial and colour information are updated across saccades. Our findings reveal that saccades selectively impair spatial but not colour memory. Computational modelling identified that spatial representations are maintained in a dual eye-centred frame of reference which is actively updated by a noisy memory of saccades but is vulnerable to interference. Using this model, we found that specific mechanistic failures in initial encoding and memory decay, rather than the saccadic updating process itself, account for spatial working memory deficits in Alzheimers and Parkinsons disease. These results provide a mechanistic understanding of how dynamic spatial memory operates in health and its disruption in neurodegenerative disorders.

neuroscience↗

bioRxiv2025

Human whole epigenome modelling for clinical applications with Pleiades

Gene regulation in humans extends beyond the four-letter genetic code. DNA methylation, in particular, functions as a critical epigenetic regulator, dynamically programming cellular identity, adapting gene expression in response to environmental cues, and underpinning the onset and progression of numerous diseases. Here we present Pleiades, a series of whole-genome epigenetic foundation models spanning three sizes: 90M, 600M, and 7B parameters. Pleiades is trained upon an extensive proprietary corpus of human methylation and genomic data, totalling 1.9T tokens. We introduce alignment embeddings and stacked hierarchical attention techniques to provide precise epigenetic modelling without the need for extended context lengths. Collectively, these advances enable Pleiades to perform a diverse range of down-stream biological and clinical tasks, including genomic regulatory prediction, realistic generation of cell-free DNA fragments and fragment-level cell-type-of-origin classification, within a unified and scalable computational framework. We specifically apply Pleiades to the early detection of clinical Alzheimers disease and Parkinsons disease from plasma cell-free DNA, achieving high-accuracy detection (AUROC 0.89 for AD and 0.84 for PD) using a minimally invasive blood test. Combined with plasma pTau-217, Pleiades reaches an AUROC of 0.97 for AD, underscoring the promise of multimodal epigenomic and proteomic approaches. Using mechanistic interpretability, we ground Pleiades latent features in interpretable biological signals, relating its Alzheimers predictions to fragmentomic and epigenomic properties of cfDNA. These findings demonstrate the potential of genome-wide epigenomic modelling as a clinically translatable paradigm for diagnostics and precision medicine, though prospective validation in larger, more diverse cohorts will be required to confirm clinical utility.

genomics↗

bioRxiv2024

A computational approach to understanding effort-based decision-making in depression

BackgroundMotivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. MethodsEffort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. ResultsFour fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity. ConclusionsThis work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

neuroscience↗

bioRxiv2024

Differential effects of aging and epilepsy in discriminating and reactivating memories

How are memories stored and subsequently retrieved? Pattern separation and pattern completion are distinct neurocognitive hippocampal processes that are thought to facilitate encoding and retrieval of memories. However, the way in which these operations affect the quality of memory remains unclear and there is currently no single behavioural task in humans to measure both mechanisms simultaneously. Here, we describe a novel paradigm which provides a distinct measure of each process and apply it to people with focal epilepsy, a condition associated with hippocampal dysfunction, comparing them with healthy young and older participants. Pattern separation and pattern completion were observed in healthy younger individuals. A specific deficit in pattern completion was identified in people with focal epilepsy, while the healthy older cohort showed degraded ability to perform pattern separation. To understand the underlying mechanisms, we simulated human performance using an auto-associative neural network. Modelling indicated that disruption in different populations of neurons could explain the observed memory profiles. These findings show that pattern separation and pattern completion are distinct processes that can be measured from a single behavioural task and are differentially affected by focal epilepsy and aging.

neuroscience↗

bioRxiv2024

Working memory as a representational template for reinforcement learning

Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm, participants learned stimulus-action combinations, mapping four stimuli onto two feature dimensions to one of two actions through probabilistic feedback. In parallel, participants retained the RL stimulus in WM and were asked to recall this stimulus after each trial. Crucially, the format of representation probed in WM was manipulated, with blocks encouraging either separate features or bound objects to be remembered. Incentivising a feature-based WM representation facilitated feature-based learning, shown by an improved choice strategy. This reveals a role of WM in providing sustained internal representations that are harnessed by RL, providing a framework by which these two cognitive processes cooperate.

neuroscience↗

bioRxiv2023

Syntax through rapid synaptic changes

Syntax is a central organizing component of human language but few models explain how it may be implemented in neurons. We combined two rapid synaptic rules to demonstrate how neurons can implement a simple grammar without accounting for the hierarchical property of syntax. Words bind to syntactic roles (e.g. "dog" as subject or object) and the roles obey ordering rules (e.g. subject [->] verb [->] object), guided by predefined syntactic knowledge. We find that, like humans, the model recalls sentences better than shuffled word-lists, and when given the permitted role orderings, and a set of words, the model can select a grammatical ordering and serialize the words to form a sentence influenced by the priming effect (e.g. producing a sentence in the passive voice after input of a different sentence also in the passive voice). The model also supports languages reliant on affixes, rather than word order, to define grammatical roles, exhibits syntactic priming and demonstrates typical patterns of aphasia when damaged. Crucially, it achieves these using an intuitive representation where words fill roles, allowing structured cognition.

neuroscience↗

bioRxiv2023

Hierarchical encoding of reward and effort across the cortex and basal ganglia during cost-benefit decision making

Adaptive value-guided decision-making requires weighing up the costs and benefits of pursuing an available opportunity. Though neurons across frontal cortical-basal ganglia circuits have been repeatedly shown to represent decision-related parameters, it is unclear whether and how this information is coordinated. To address this question, we performed large-scale single unit recordings simultaneously across 5 medial/orbital frontal and basal ganglia regions as rats decided whether to pursue varying reward payoffs available at different effort costs. We found that single neurons encoding combinations of the canonical decision variables (reward, effort and choice) were represented within all recorded brain regions. Co-active cell assemblies - ensembles of neurons that repeatedly co-activated within short time windows (<25ms) within and across structures - were able to provide representations of the same decision variables through the synchronisation of individual neurons with different coding properties. Together, these findings demonstrate a hierarchical encoding structure for cost-benefit computations, where individual neurons with diverse encoding properties are coordinated into larger, low-dimensional spaces within and across brain regions that can signal decision parameters on the millisecond timescale.

neuroscience↗

bioRxiv2023

Dopamine encoding of novelty facilitates efficient uncertainty-driven exploration

When facing an unfamiliar environment, animals need to explore to gain new knowledge about which actions provide reward, but also put the newly acquired knowledge to use as quickly as possible. Optimal reinforcement learning strategies should therefore assess the uncertainties of these action- reward associations and utilise them to inform decision making. We propose a novel model whereby direct and indirect striatal pathways act together to estimate both the mean and variance of reward distributions, and mesolimbic dopaminergic neurons provide transient novelty signals, facilitating effective uncertainty-driven exploration. We utilised electrophysiological recording data to verify our model of the basal ganglia, and we fitted exploration strategies derived from the model to data from behavioural experiments. We also compared the performance of directed exploration strategies inspired by our basal ganglia model with classic variants of upper confidence bound (UCB) strategy in simulation. The exploration strategies inspired by the basal ganglia model performed better than the classic algorithms in simulation in some cases, and we found qualitatively similar results in fitting model to behavioural data compared with the fitting of more idealised normative models with less implementation level detail. Overall, our results suggest that transient dopamine levels in the basal ganglia that encode novelty could contribute to an uncertainty representation which efficiently drives exploration in reinforcement learning. Author summaryHumans and other animals learn from rewards and losses resulting from their actions to maximise their chances of survival. In many cases, a trial-and-error process is necessary to determine the most rewarding action in a certain context. During this process, determining how much resource should be allocated to acquiring information ("exploration") and how much should be allocated to utilising the existing information to maximise reward ("exploitation") is key to the overall effectiveness, i.e., the maximisation of total reward obtained with a certain amount of effort. We propose a theory whereby an area within the mammalian brain called the basal ganglia integrates current knowledge about the mean reward, reward uncertainty and novelty of an action in order to implement an algorithm which optimally allocates resources between exploration and exploitation. We verify our theory using behavioural experiments and electrophysiological recording, and show in simulations that the model also achieves good performance in comparison with established benchmark algorithms.

neuroscience↗

bioRxiv2023

VmPFC supports persistence during goal pursuit through selective attention

Striking the balance between persistence with a goal and flexibility in the face of better options is critical for effectively organizing behaviour across time. While people are often biased towards completing their current goal (e.g. sunk cost biases), it is unclear how these biases occur at a mechanistic level, still allowing for some flexibility for goal abandonment. We propose that ventromedial prefrontal cortex (vmPFC) plays a critical role in orienting attention towards a current goal, prioritising goal completion but allowing for abandonment, particularly when the current goal fails. We developed a novel incremental goal pursuit task to study goal-directed attention and action in healthy individuals with functional magnetic resonance imaging (fMRI), and in an independent group of individuals with brain lesions. The task required participants to make sequential decisions between continuing to persist with a current goal (commitment), versus abandoning progress for a better alternative goal (flexibility). We show that individuals who persist more show greater goal-oriented attention outside the decision period. Increasing attentional capture by the current goal is also revealed in decision-making: people remain more likely to abandon from frustration (collapse of value of the current goal) than from temptation (attraction from valuable alternative goals). Strikingly, we find that our stable inter-individual metrics of persistence and goal-oriented attention were both predicted by baseline activity in vmPFC, tracking goal progress. We present converging evidence from an independent lesion patient study demonstrating the causal involvement of vmPFC in goal persistence: damage to the vmPFC reduces biases to over-persist with the current goal, leading to a performance benefit in our task.

neuroscience↗

bioRxiv2021

Hypersensitivity to uncertainty is key feature of subjective cognitive impairment

With an increasingly ageing global population, more people are presenting with concerns about their cognitive function, but not all have an underlying neurodegenerative diagnosis. Subjective cognitive impairment (SCI) is a common condition describing self-reported deficits in cognition without objective evidence of cognitive impairment. Many individuals with SCI suffer from depression and anxiety, which have been hypothesised to account for their cognitive complaints. Despite this association between SCI and affective features, the cognitive and brain mechanisms underlying SCI are poorly understood. Here, we show that people with SCI are hypersensitive to uncertainty and that this might be a key mechanism accounting for their affective burden. Twenty-seven individuals with SCI performed an information sampling task, where they could actively gather information prior to decisions. Across different conditions, SCI participants sampled faster and obtained more information than matched controls to resolve uncertainty. Remarkably, despite their urgent sampling behaviour, SCI participants were able to maintain their efficiency. Hypersensitivity to uncertainty indexed by this sampling behaviour correlated with the severity of affective burden including depression and anxiety. Analysis of MRI resting functional connectivity revealed that both uncertainty hypersensitivity and affective burden were associated with stronger insular-hippocampal connectivity. These results suggest that altered uncertainty processing is a key mechanism underlying the psycho-cognitive manifestations in SCI and implicate a specific brain network target for future treatment.

neuroscience↗

bioRxiv2021

Neural reinstatement tracks object-based attentional selection in working memory

Attention can be allocated in working memory (WM) to select and privilege relevant content. It is unclear whether attention selects individual features or whole objects in WM. Here, we used behavioural measures, eye-tracking and electroencephalography (EEG) to test the hypothesis that attention spreads between an objects features in WM. Twenty-six participants completed a WM task that asked them to recall the angle of one of two oriented, coloured bars after a delay while EEG and eye-tracking data was collected. During the delay, an orthogonal incidental task cued the colour of one item for a match/mismatch judgement. On congruent trials (50%), the cued item was probed for subsequent orientation recall; on incongruent trials (50%), the other memory item was probed. As predicted, selecting the colour of an object in WM brought other features of the cued object into an attended state as revealed by EEG decoding, oscillatory -power, gaze bias and improved orientation recall performance. Together, the results show that attentional selection spreads between an objects features in WM, consistent with object-based attentional selection. Analyses of neural processing at recall revealed that the selected object was automatically compared with the probe, whether it was the target for recall or not. This provides a potential mechanism for the observed benefits of non-predictive cueing in WM, where a selected item is prioritised for subsequent decision-making.

neuroscience↗

bioRxiv2021

Muscarinic receptors mediate motivation via preparatory neural activity in humans

Motivation depends on dopamine, but might be modulated by acetylcholine which influences dopamine release in the striatum, and amplifies motivation in animal studies. A corresponding effect in humans would be important clinically, since anticholinergic drugs are frequently used in Parkinsons disease, a condition that can also disrupt motivation. Reward and dopamine make us more ready to respond, as indexed by reaction times (RT), and move faster, sometimes termed vigour. These effects may be controlled by preparatory processes that can be tracked using electroencephalography (EEG). We measured vigour in a placebo-controlled, double-blinded study of trihexyphenidyl (THP), a muscarinic antagonist, with an incentivised eye movement task and EEG. Participants responded faster and with greater vigour when incentives were high, but THP blunted these motivation effects, suggesting that muscarinic receptors facilitate invigoration by reward. Preparatory EEG build-up (contingent negative variation; CNV) was strengthened by high incentives and by muscarinic blockade, although THP reduced the incentive-effect. The amplitude of preparatory activity predicted both vigour and RT, although over distinct scalp regions; frontal activity predicted vigour, whereas a larger, earlier, central component predicted RT. The incentivisation of RT was partly mediated by the CNV, though vigour was not. Moreover, the CNV mediated the drugs effect on dampening incentives, suggesting that muscarinic receptors underlie the motivational influence on this preparatory activity. Taken together, these findings show that a muscarinic blocker impairs motivated action in healthy people, and that medial frontal preparatory neural activity mediates this for RT.

neuroscience↗

bioRxiv2021

Nucleus accumbens D1-receptors regulate and focus transitions to reward-seeking action

While it is well established that dopamine transmission is integral in mediating the influence of reward expectations on reward-seeking actions, the precise causal role of dopamine transmission in moment-to-moment cue-driven behavioural control remains contentious. This is a particular issue in situations where it is necessary to refrain from responding to achieve a beneficial outcome. To examine this, we manipulated dopamine transmission pharmacologically as rats performed a Go/No-Go task that required them to either make or withhold action to gain either a small or large reward. Stimulation of D1Rs, both globally and locally in the nucleus accumbens core (NAcC) region consistently disrupted No-Go performance, potentiating inappropriate responses that clustered strongly just after cue presentation. D1R blockade did not, however, improve rats ability to withhold responses, but instead primarily disrupted performance on Go trials. While global D1R blockade caused a general reduction of invigoration of reward seeking actions, intra-NAcC administration of the D1R antagonist by contrast increased the likelihood that Go trial performance was in an "unfocused" state. Such a state was characterised, both on and off drug, by a reduction in the precision and speed of responding even though the appropriate action sequence was often executed. These findings suggests that the balance of activity at NAcC D1Rs plays a key role in enabling the rapid activation of a focused, reward-seeking state to enable animals to efficiently and accurately achieve their goal.

neuroscience↗

bioRxiv2021

Hunger improves reinforcement-driven but not planned action

Human decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free systems primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalised effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits. Significance statementThe prevalence of obesity and eating disorder is steadily increasing. To counteract problems related to eating, people need to make rational decisions. However, appetite may switch us to a different decision mode, making it harder to achieve long-term goals. Here we show that planned and reinforcement-driven actions are differentially sensitive to hunger. Hunger specifically affected reinforcement-driven actions, and did not affect the planning of actions. Our data shows that people behave differently when they are hungry. We also provide a computational model of how the behavioural changes might arise.

neuroscience↗

bioRxiv2021

Gambling on an empty stomach: Hunger modulates preferences for learned but not described risks

We assess risks differently when they are explicitly described, compared to when we learn directly from experience, suggesting dissociable decision-making systems. Our needs, such as hunger, could globally affect our risk preferences, but do they affect described and learned risks equally? On one hand, explicit decision-making is often considered flexible and contextsensitive, and might therefore be modulated by metabolic needs. On the other hand, implicit preferences learned through reinforcement might be more strongly coupled to biological drives. To answer this, we asked participants to choose between two options with different risks, where the probabilities of monetary outcomes were either described or learned. In agreement with previous studies, rewarding contexts induced risk-aversion when risks were explicitly described, but risk-seeking when they were learned through experience. Crucially, hunger attenuated these contextual biases, but only for learned risks. The results suggest that our metabolic state determines risk-taking biases when we lack explicit descriptions.

neuroscience↗