Biology subjects

Zenon, A.

Publications and source records attributed to Zenon, A..

13 recordsLinked to original sources

bioRxiv2025

Transcutaneous Vagus Nerve Stimulation boosts evidence accumulation during perceptual decision-making

The locus coeruleus-norepinephrine (LC-NE) system has been implicated in perceptual decision-making, but its causal contribution and underlying mechanisms in humans remain unclear. Here, we used transcutaneous vagus nerve stimulation (tVNS) to modulate LC-NE activity during a random dot motion task, with stimulation delivered at three distinct time points across groups, each targeting different stages of LC-NE engagement during the task. tVNS reliably increased pupil-linked LC-NE activity across all groups. Notably, early stimulation, at a time when LC-NE activity was still at baseline, elicited a more sustained pupil dilation that extended into the decision phase, resulting in comparable pupil responses during decision-making across groups. For behavior, tVNS selectively improved decision accuracy in contexts characterized by initially low performance, without affecting response times. Drift diffusion modeling revealed that this improvement was specifically associated with increased drift rate, consistent with more efficient evidence accumulation with tVNS. These effects were consistent across groups but most pronounced when tVNS was applied at the early time point. Our results provide causal evidence that tVNS enhances decision-making in a state-dependent manner, likely by stabilizing attentional engagement and facilitating evidence accumulation when endogenous control is suboptimal.

neuroscience↗

bioRxiv2025

Transfer entropy predicts pupillary response and cognitive effort during a tracking task

1.Cognitive cost plays a crucial role in determining action choices and task engagement. We have proposed that cognitive cost relates to the amount of Bayesian inference required, quantified using relative entropy. In continuous tasks, such as visuomotor tracking, this demand can be estimated using transfer entropy (TE), which accounts for the information transferred from input to output variables. This study aimed to test experimentally the relationship between TE and markers of cognitive cost. We designed a continuous tracking task, manipulating target speed, predictability, and response lag. The effects of task variables on performance, subjective effort and pupil-linked arousal were assessed using Bayesian mixed models. We disentangled the effect of TE from that of other correlated variables (predictability, acceleration, spatial error), by using model comparison and mediation models. Results showed that TE was mostly affected by target predictability and impacted significantly on subjective effort and pupil response. While the variations of pupillary response induced by task conditions were accounted for by TE only, subjective effort was only partially predicted by TE, and depended also on target predictability. These findings suggest that the cognitive demand of motor control tasks can be quantified using TE, which relates to both subjective effort and pupillary response. However, the multifaceted nature of effort in motor tasks and the limitations of subjective measures highlight the need for more nuanced tools to isolate mental and physical effort.

neuroscience↗

bioRxiv2024

Pupil size variations reveal Bayesian inference in cognitive arithmetic

The assumption that the brain relies on Bayesian inference has been successful in accounting for many behavioural and neurophysiological observations, but to date, dependence on such mechanism has not been assessed in the context of arithmetic. Bayesian inference implies the representation of uncertainty and reliance on prior beliefs. In arithmetic problem solving, it would consist in refining prior knowledge about the response range as the system progressively integrates the numerical information conveyed by the operands. Within this framework, the amount of information needed to progress from a prior to a posterior probability distribution over responses can be quantified by the information gain, which would relate to the cognitive workload of the task. To test this hypothesis, we designed three experiments in which participants computed the sum of two numbers presented one after another through headphones. In each experiment, the information about response predictability conveyed by the first operand was manipulated. The first operand was either highly informative and contributed to narrow down the response range, or poorly informative and conveyed little information about a plausible response. Throughout all experiments, we found that pupil-related arousal signalled the information gain associated with the first operand, indicating that participants already updated the probability distribution of possible responses upon hearing that first stimulus. This finding shows that Bayesian inference is central to arithmetic problem solving and that information gains consecutive to the integration of the operands can be tracked over time through pupillometry. Public significance statement- This study suggests that when we do arithmetic, our brain uses Bayesian inference, a method similar to how scientists update their theories based on new evidence. This means that when doing math, we start with hypotheses about the result and progressively narrow down possible solutions. - By observing the eye of the participants while they were solving arithmetic problems, the authors showed that their pupil size increased in proportion to the amount of processed information. - The authors also found that when participants received more informative first operands in the math problem, and when their pupil size increased more during this presentation, they were also faster to respond, indicating that greater initial information processing facilitated quicker problem-solving.

neuroscience↗

bioRxiv2024

Transcutaneous Vagus Nerve Stimulation Boosts Post-Error Accuracy During Perceptual Decision-Making

The locus coeruleus-norepinephrine (LC-NE) system is a well-established regulator of behavior, yet its precise role remains unclear. Animal studies predominantly support a "gain" hypothesis, suggesting that the LC-NE system enhances sensory processing, while human studies have proposed an alternative "urgency" hypothesis, postulating that LC-NE primarily accelerates responses. To address this discrepancy, we administered transcutaneous vagus nerve stimulation (tVNS) in two experiments involving 43 participants. In the first experiment, we showed that 4-second tVNS trains reliably induced greater pupil dilation compared to SHAM condition, indicating increased LC-NE activity. In the second experiment, we applied tVNS during a random dot motion task to assess its impact on perceptual decision-making. Notably, tVNS improved accuracy without affecting reaction times, which appears inconsistent with the "urgency" hypothesis. Drift-diffusion model analyses further supported the "gain" hypothesis, revealing that tVNS increased the drift rate, indicative of enhanced evidence accumulation. Accuracy and drift-rate improvements were especially pronounced following errors and in less proficient participants, who otherwise exhibited post-error declines in these measures under SHAM condition. Our findings suggest that the influence of the LC-NE system adapts to task demands, becoming especially beneficial in challenging contexts. Overall, this study underscores the potential of tVNS as a non-invasive tool to investigate the causal role of the LC-NE system in human behavior.

neuroscience↗

bioRxiv2024

Fronto-motor circuits linked to subclinical apathy

Apathy is a syndrome characterized by a disruption in effort-reward decision-making, accompanied by structural and functional changes in a related fronto-basal ganglia (BG) network. While activity changes in the primary motor cortex (M1) during effort and reward valuation have been repeatedly observed, previous work on apathy has largely overlooked the connections between the fronto-BG network and M1, potentially missing key circuits in the apathy network. This study addresses this gap by investigating structural and effective connectivity in fronto-M1, fronto-BG-M1, and intra-M1 circuits in relation to apathy in 45 healthy subjects. Behavior was assessed using a battery of apathy-related questionnaires and computational modeling of effort and reward valuation in a decision-making task. Fronto-motor circuits were examined through a combination of MRI-derived tractography and paired-pulse transcranial magnetic stimulation, which probed structural and effective connectivity, respectively. The data reveal that apathy scores are associated with both structural and effective connectivity in fronto-M1 and fronto-BG-M1 circuits. Circuits originating from the supplementary motor area primarily index effort valuation, while connectivity in intra-M1 GABAergic circuits correlates exclusively with reward valuation. These findings suggest that distinct fronto-motor circuits are linked to different dimensions of motivated behavior and may constitute specific neuromodulation targets for patients suffering from apathy.

neuroscience↗

bioRxiv2023

Task goals shape the relationship between decision and movement speed

The speed at which we move is linked to the speed at which we decide to make these movements. Yet, the principles guiding such relationship remain unclear: while some studies point towards a shared invigoration process boosting decision and movement speed jointly, others rather indicate a tradeoff between both levels of control, with slower movements accompanying faster decisions. Here, we aimed (1) at further investigating the existence of a shared invigoration process linking decision and movement and (2) at testing the hypothesis that such a link is masked when detrimental to the reward rate. To this aim, we tested 62 subjects who performed the tokens task in two experiments (separate sessions): Experiment 1 evaluated how changing decision speed affects movement speed while Experiment 2 assessed how changing movement speed affects decision speed. In the latter experiment, subjects were either encouraged to favor decision speed (fast decision group) or decision accuracy (slow decision group). Various mixed model analyses revealed a coregulation of decision (urgency) and movement speed in Experiment 1 and in the fast decision group of Experiment 2, but not in the slow decision group despite the fact that these same subjects displayed a coregulation effect in Experiment 1. Altogether, our findings support the idea that coregulation occurs as a default mode but that this form of control is diminished or supplanted by a tradeoff relationship, contingent on reward rate maximization. Drawing from these behavioral observations, we propose that multiple processes contribute to shaping the speed of decisions and movements. New & NoteWorhtyThe principles guiding the relationship between decision and movement speed are unclear. In the present behavioral study involving two experiments conducted on 62 human subjects, we report findings indicating a relationship that varies as a function of the task goals. Coregulation emerges as a default mode of control that fades when detrimental to the reward rate, possibly due to the influence of other processes that can selectively shape the speed of our decisions and movements.

neuroscience↗

bioRxiv2020

Information rate in humans during visuomotor tracking

While previous studies of human information rate focused primarily on discrete forced-choice tasks, we extend the scope of the investigation to the framework of sensorimotor tracking of continuous signals. We show how considering information transfer in this context sheds new light on the problem; crucially, such an analysis requires one to consider and carefully disentangle the effects due to real-time information processing of surprising inputs (feedback component) from the contribution to performance due to prediction (feedforward component). We argue that only the former constitutes a faithful representation of the true information processing rate. We provide information-theoretic measures which separately quantify these components and show that they correspond to a decomposition of the total information shared between target and tracking signals. We employ a linear quadratic regulator model to provide evidence for the validity of the measures, as well as of the estimator of visual-motor delay (VMD) from experimental data, instrumental to compute them in practice. On experimental tracking data, we show that the contribution of prediction as computed by the feedforward measure increases with the predictability of the signal, confirming previous findings. Importantly, we further find the feedback component to be modulated by task difficulty, with higher information transmission rates observed with noisier signals. Such opposite trends between feedback and feedforward point to a tradeoff of cognitive resources/effort and performance gain. Author summaryPrevious investigations concluded that the human brains information processing rate remains fundamentally constant, irrespective of task demands. However, their conclusion rested in analyses of simple discrete-choice tasks. The present contribution recasts the question of human information rate within the context of visuomotor tasks, which provides a more ecologically relevant arena, albeit a more complex one. We argue that, while predictable aspects of inputs can be encoded virtually free of charge, real-time information transfer should be identified with the processing of surprises. We formalise this intuition by deriving from first principles a decomposition of the total information shared by inputs and outputs into a feedforward, predictive component and a feedback, error-correcting component. We find that the information measured by the feedback component, a proxy for the brains information processing rate, scales with the difficulty of the task at hand, in agreement with cost-benefit models of cognitive effort.

neuroscience↗

bioRxiv2020

Neural fatigue by passive induction

Theories of mental fatigue disagree on whether performance decrement is caused by motivational or functional alterations. We tested the assumption that keeping neural networks active for an extensive period of time entrains consequences at the subjective and objective level - the defining characteristics of fatigue - when confounds such as motivation, boredom and level of skill are controlled. We reveal that passive visual stimulation affects the performance of a subsequent task that is carried out in the same portion of visual space. This outcome, consisting either in an enhancement or deterioration of performance, was determined by the participants level of cognitive load and arousal, which were manipulated through variations in the difficulty of concurrent auditory tasks. Thus, repeated stimulation of neural networks leads to their altered functional performance, a mechanism which may play a role in the development of global mental fatigue.

neuroscience↗

bioRxiv2019

Eye pupil signals information gain

In conditions of constant illumination, the eye pupil diameter indexes the modulation of arousal state and responds to a large breadth of cognitive processes, including mental effort, attention, surprise, decision processes, decision biases, value beliefs, uncertainty, volatility, exploitation/exploration trade-off or learning rate. Here, I propose an information theoretic framework that has the potential to explain the ensemble of these findings as reflecting pupillary response to information processing. In short, updates of brain internal model, quantified formally as the Kullback-Leibler (KL) divergence between prior and posterior beliefs, are the common denominator to all these instances of pupillary dilation to cognition. I show that stimulus presentation leads to pupillary response that is proportional to the amount of information the stimulus carries about itself and to the quantity of information it provides about other task variables. In the context of decision making, pupil dilation in relation to uncertainty is explained by the wandering of the evidence accumulation process, leading to large summed KL divergences. Finally, pupillary response to mental effort and variations in tonic pupil size are also formalized in terms of information theory. On the basis of this framework, I compare pupillary data from past studies to simple information theoretic simulations of task designs and show good correspondance with data across studies. The present framework has the potential to unify the large set of results reported on pupillary dilation to cognition and to provide a theory to guide future research.

neuroscience↗

bioRxiv2018

Learning and Forgetting Using Reinforced Bayesian Change Detection

Agents living in volatile environments must be able to detect changes in contingencies while refraining to adapt to unexpected events that are caused by noise. In Reinforcement Learning (RL) frameworks, this requires learning rates that adapt to past reliability of the model. The observation that behavioural flexibility in animals tends to decrease following prolonged training in stable environment provides experimental evidence for such adaptive learning rates. However, in classical RL models, learning rate is either fixed or scheduled and can thus not adapt dynamically to environmental changes. Here, we propose a new Bayesian learning model, using variational inference, that achieves adaptive change detection by the use of Stabilized Forgetting, updating its current belief based on a mixture of fixed, initial priors and previous posterior beliefs. The weight given to these two sources is optimized alongside the other parameters, allowing the model to adapt dynamically to changes in environmental volatility and to unexpected observations. This approach is used to implement the \"critic\" of an actor-critic RL model, while the actor samples the resulting value distributions to choose which action to undertake. We show that our model can emulate different adaptation strategies to contingency changes, depending on its prior assumptions of environmental stability, and that model parameters can be fit to real data with high accuracy. The model also exhibits trade-offs between flexibility and computational costs that mirror those observed in real data. Overall, the proposed method provides a general framework to study learning flexibility and decision making in RL contexts.\n\nAuthor summaryIn stable contexts, animals and humans exhibit automatic behaviour that allows them to make fast decisions. However, these automatic processes exhibit a lack of flexibility when environmental contingencies change. In the present paper, we propose a model of behavioural automatization that is based on adaptive forgetting and that emulates these properties. The model builds an estimate of the stability of the environment and uses this estimate to adjust its learning rate and the balance between exploration and exploitation policies. The model performs Bayesian inference on latent variables that represent relevant environmental properties, such as reward functions, optimal policies or environment stability. From there, the model makes decisions in order to maximize long-term rewards, with a noise proportional to environmental uncertainty. This rich model encompasses many aspects of Reinforcement Learning (RL), such as Temporal Difference RL and counterfactual learning, and accounts for the reduced computational cost of automatic behaviour. Using simulations, we show that this model leads to interesting predictions about the efficiency with which subjects adapt to sudden change of contingencies after prolonged training.

neuroscience↗

bioRxiv2017

Variational Treatment of Trial-by-Trial Drift-Diffusion Models of Behaviour

The Full Drift Diffusion Model (DDM) is challenging to fit to behavioural data. Precision of the fits are usually poor for some if not all parameters, and computationally expensive to obtain. Moreover, inference at the trial level for each and every parameters, threshold included, has so far been considered as impossible. Most approaches rely on strong assumptions about the model structure, such as selection of the parameters that are subject to trial-to-trial variability or prior distribution of these parameters, that usually lack precise mathematical or empirical justifications. The fact that, in most versions of the DDM, the sequence of participants choices are considered as independent and identically distributed (i.i.d.), has been mainly overlooked so far. Our contribution to the field is threefold: first, we introduce Variational Bayes as a method to fit the full DDM. Second, we relax the i.i.d. assumption, and propose a data-driven algorithm based on a Recurrent Auto-Encoder, that estimates the local posterior probability of the DDM parameters at each trial based on the sequence of parameters and data preceding the data. Finally, we show that inference at the trial level can be achieved efficiently for each and every parameter of the DDM, threshold included. This data-driven approach is highly generic and self-contained, in the sense that no external input (e.g. regressors or physiological measure) is necessary to fit the data. Using simulations and real-world examples, we show that this method outperforms by several order of magnitude the i.i.d.-based ones, either Markov Chain Monte Carlo or i.i.d.-VB.

animal behavior and cognition↗

bioRxiv2017

An information-theoretic perspective on the costs of cognition

In statistics and machine learning, model accuracy is traded off with complexity, which can be viewed as the amount of information extracted from the data. Here, we discuss how cognitive costs can be expressed in terms of similar information costs, i.e. as a function of the amount of information required to update a persons prior knowledge (or internal model) to effectively solve a task. We then examine the theoretical consequences that ensue from this assumption. This framework naturally explains why some tasks - for example, unfamiliar or dual tasks - are costly and permits to quantify these costs using information-theoretic measures. Finally, we discuss brain implementation of this principle and show that subjective cognitive costs can originate either from local or global capacity limitations on information processing or from increased rate of metabolic alterations. These views shed light on the potential adaptive value of cost-avoidance mechanisms.

neuroscience↗

bioRxiv2017

Objective but not subjective fatigue increases cognitive task avoidance

Mentally demanding tasks feel effortful and are usually avoided. Furthermore, prolonged cognitive engagement leads to mental fatigue, consisting of subjective feeling of exhaustion and decline in performance. Despite the intuitive characterization of fatigue as an increase in subjective effort perception, the effect of fatigue on effort cost has never been tested experimentally. To this end, sixty participants in 2 separate experiments underwent a forced-choice working memory task following either a fatigue-inducing (i.e. Stroop task) or a control manipulation. We measured subjective fatigue and effort as well as their objective behavioral signatures: performance decline and task avoidance, respectively. We found that fatigue-induced performance decline was correlated with task avoidance, while the feelings of fatigue and effort were unrelated to each other. Our findings highlight the discrepancy between subjective and objective manifestations of fatigue and effort, and provide valuable evidence feeding the ongoing theoretical debate on the nature of these constructs.

neuroscience↗