Biology subjects

Vassena, E.

Publications and source records attributed to Vassena, E..

11 recordsLinked to original sources

bioRxiv2024

Meta-Reinforcement Learning reconciles surprise, value and control in the anterior cingulate cortex.

The role of the dorsal anterior cingulate cortex (dACC) in cognition is a frequently studied yet highly debated topic in neuroscience. Most authors agree that the dACC is involved in either cognitive control (e.g. voluntary inhibition of automatic responses) or monitoring (e.g. comparing expectations with outcomes, detecting errors, tracking surprise). A consensus on which theoretical perspective best explains dACC contribution to behaviour is still lacking, as two distinct sets of studies report dACC activation in tasks requiring surprise tracking for performance monitoring and cognitive control without involving surprise monitoring, respectively. This creates a theoretical impasse, as no single current account can reconcile these findings. Here we propose a novel hypothesis on dACC function that integrates both the monitoring and the cognitive control perspectives in a unifying, meta-Reinforcement Learning framework, in which cognitive control is optimized by meta-learning based on tracking Bayesian surprise. We tested the quantitative predictions from our theory in two different functional neuroimaging studies at the basis of the current theory crisis. We show that the meta-Reinforcement Learning perspective successfully captures all the neuroimaging results by predicting both cognitive control and monitoring functions, resolving the theoretical impasse about dACC function within an integrative framework. In sum, our results suggest that dACC function can be framed as a meta-learning optimisation of cognitive control, providing an integrative perspective on its roles in cognitive control, surprise tracking, and performance monitoring. Significance statementAn important debate in cognitive neuroscience concerns the role of the anterior cingulate cortex (ACC) in cognition. Two effective and competing frameworks suggest a role for the ACC in surprise monitoring or optimizing cognitive control, respectively. So far, none of these frameworks has succeeded as a unified theory of ACC function. In this study, we reanalyzed previous neuroimaging data on ACC activity during cognitive tasks using a novel computational perspective: meta-Reinforcement Learning. We show that this computational framework can explain a variety of data on ACC function that, globally, could not be captured by any of the previous models. We propose that meta-Reinforcement Learning offers a unified theory of ACC cognitive and computational function.

neuroscience↗

bioRxiv2024

Mind the instructions: reward cues are liked first, wanted later

Current theories propose that mental effort is invested only when the anticipated benefits, such as rewards, outweigh the associated costs, like task difficulty. Yet, it remains unclear whether this motivational and mitigating aspect of reward processing is reflected in the evaluation of reward/difficulty cues as such, and to what extent it depends on task experience. In a pre-registered experiment (N=84), we used the affect misattribution procedure (AMP) to gauge affective evaluations of nonword cues predicting reward and task difficulty levels. Contrary to previous studies, the AMP was administered at the outset, after cue instructions, and after the cues were used in a random dot motion (RDM) task. Compared to baseline, cues predicting a larger reward were evaluated more positively after RDM task experience, and most importantly, already after cue instructions, with no difference between the two phases. This evaluative effect manifested in increased performance after larger reward cues in the RDM task. Our results suggest that AMP effects may generally capture performance expectations which are independent of task experience. Importantly, these instructed expectations of reward and difficulty play a crucial role in the evaluation and subsequent investment of mental effort.

animal behavior and cognition↗

bioRxiv2023

Fatigue during acute systemic inflammation is associated with reduced mental effort expenditure while task accuracy is preserved

BackgroundEarlier work within the physical domain showed that acute inflammation changes motivational prioritization and effort allocation rather than physical abilities. It is currently unclear whether a similar motivational framework accounts for the mental fatigue and cognitive symptoms of acute sickness. Accordingly, this study aimed to assess the relationship between fatigue, cytokines and mental effort-based decision making during acute systemic inflammation. MethodsEighty-five participants (41 males; 18-30 years (M=23.0, SD=2.4)) performed a mental effort-based decision-making task before, 2 hours after, and 5 hours after intravenous administration of 1 ng/kg bacterial lipopolysaccharide (LPS) to induce systemic inflammation. Plasma concentrations of cytokines (interleukin (IL)-6, IL-8 and tumor necrosis factor (TNF)) and fatigue levels were assessed at similar timepoints. In the task, participants decided whether they wanted to perform (i.e., accepted) arithmetic calculations of varying difficulty (3 levels: easy, medium, hard) in order to obtain rewards (3 levels: 5, 6 or 7 points). Acceptance rates were analyzed using a binomial generalized estimated equation (GEE) approach with effort, reward and time as independent variables. Arithmetic performance was measured per effort level prior to the decisions and included as a covariate. Associations between acceptance rates, fatigue (self-reported) and cytokine concentrations levels were analyzed using partial correlation analyses. ResultsPlasma cytokine concentrations and fatigue were increased at 2 hours post-LPS compared to baseline and 5 hours post-LPS administration. Acceptance rates decreased for medium, but not for easy or hard effort levels at 2 hours post-LPS versus baseline and 5 hours post-LPS administration, irrespective of reward level. This reduction in acceptance rates occurred despite improved accuracy on the arithmetic calculations itself. Reduced acceptance rates for medium effort were associated with increased fatigue, but not with increased cytokines. ConclusionFatigue during acute systemic inflammation is associated with alterations in mental effort allocation, similarly as observed previously for physical effort-based choice. Specifically, willingness to exert mental effort depended on effort and not reward information, while task accuracy was preserved. These results extend the motivational account of inflammation to the mental domain and suggest that inflammation may not necessarily affect domain-specific mental abilities, but rather affects domain-general effort-allocation processes.

neuroscience↗

bioRxiv2023

Changes in large-scale neural networks under stress are linked to affective reactivity to stress in real life

Controlled laboratory stress induction procedures are very effective in inducing physiological and subjective stress. However, whether such stress responses are representative for stress reactivity in real life is not clear. Using a combined within-subject functional MRI laboratory stress and ecological momentary assessment stress paradigm, we investigated dynamic shifts in large-scale neural network configurations under stress and how these relate to affective reactivity to stress in real life. Laboratory stress induction resulted in significantly increased cortisol levels, and shifts in task-driven neural activity including increased salience network (SN) activation in an oddball task and decreased default mode network activity in a memory retrieval task. Crucially, individuals showing increased SN reactivity specifically in the early phase of the acute stress response also expressed increased affective reactivity in real life. Our findings provide (correlational) evidence that real-life affective stress reactivity is driven primarily by vigilant attentional reorienting mechanisms associated with SN.

neuroscience↗

bioRxiv2022

The neural basis of cost-benefit trade-offs in effort investment: a quantitative activation likelihood estimation meta-analysis

Influential theories of cognitive effort-based decision-making suggest that a cost-benefit trade-off guides mental effort allocation and that this trade-off may be reflected in shared neural activity in circuits tracking potential reward and task demand, supporting the idea that. While the dorsal medial prefrontal cortex (mPFC) - and particularly the anterior cingulate cortex (dACC) - has been proposed as a candidate region implementing this computation, it remains unclear whether mPFC/dACC activity tracks rewards and task demand independently or integrates them to reflect effort intensity. Recent accounts posit that the dACC plays a key role in mediating cost-benefit trade-offs. However, empirical evidence remains inconsistent. We conducted a systematic meta-analysis of neuroimaging studies, using the activation-likelihood estimation method to quantify brain activity across 45 studies (N = 1273 participants) investigating choices and task performance in reward-guided cognitive control. We observed significant recruitment of the mPFC/dACC, putamen, and anterior insula for processing larger rewards and higher task demands. The mPFC/dACC clusters sensitive to task demands and rewards were anatomically distinct: caudal mPFC/dACC activity tracked increasing task demands, while rostral mPFC/dACC activity tracked increasing reward. Interestingly, caudal mPFC/dACC activity tracked the integration of reward and task-demand, compatible with cost-benefit trade-off theories of dACC function. These findings provide evidence for distinct signals for mental demand and reward in the mPFC/dACC, which are integrated to support the decision to invest mental effort.

neuroscience↗

bioRxiv2021

Using wearable biosensors and ecological momentary assessments for the detection of prolonged stress in real life

BackgroundIncreasing efforts toward prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. MethodsUsing ecological momentary assessments (EMA) combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (i.e., an exam week) on physiological arousal and affect. With this paradigm we investigated whether we could use wearable devices to detect stress states using machine learning models. ResultsDuring stressful high-stake exam (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal was decreased on average during the exam week. Time-resolved analyses revealed peaks in physiological arousal associated with both self-reported stress and self-reported positive affect, while the overall decrease in physiological arousal was mediated by lower positive affect during the stress period. We then used machine learning to show that a combination of EMA and physiology yields optimal identification of stress states. ConclusionsOur findings highlight the potential of wearable biosensors in stress-related mental-health monitoring, but critically show that psychological context is essential for interpreting physiological arousal detected using these devices.

physiology↗

bioRxiv2019

Conjunction or co-activation? A multi-level MVPA approach to task set representations

While representing and maintaining rules in order to govern behavior is a critical function of the brain, it remains an open question as to how collections of rules - task sets - are represented in cortex. One possibility is that task sets are represented as the co-activation of representations of the simple rules from which a task set is composed. Alternatively, task sets could be encoded in a conjunctive manner as the unique combination of rules that belong to a task set. Using a novel multi-level MVPA approach in combination with fMRI, we attempted to answer both \"where\" and \"how\" task sets are represented in the brain. Subjects performed a delayed match-to-sample task using task sets composed of multiple, partially overlapping rules that governed which feature dimensions subjects should attend to, and MVPA was used to identify regions that encoded task set information. We identified voxels most relevant for classifying task sets, and, using these voxels as input to a second MVPA analysis, were able to identify regions in prefrontal cortex with activity consistent with co-active representation, while activity in visual cortex was consistent with conjunctive representation. These results highlight the utility of feature selection methods in neuroimaging analyses.

neuroscience↗

bioRxiv2018

Testing competing models of dorsal anterior cingulate

Recent theories have attempted to provide unifying accounts of dorsal anterior cingulate cortex (dACC), a region routinely observed in studies of cognitive control and decision-making. Despite the proliferation of frameworks, rigorous empirical testing has lagged behind theory. Here we test competing predictions of three accounts of dACC using a simple value-based decision-making task. We find that the Predicted Response-Outcome model provides an integrative and parsimonious account of our results. Our results highlight the need for increased emphasis on empirical tests of theoretical frameworks.

neuroscience↗

bioRxiv2017

Preparation for mental effort recruits Dorsolateral Prefrontal Cortex: an fNIRS investigation

Preparing for a mentally demanding task calls upon cognitive and motivational resources. The underlying neural implementation of these mechanisms is receiving growing attention, given the implications for professional, social, and medical contexts. While several fMRI studies converge in assigning a crucial role to a cortico-subcortical network including Anterior Cigulate Cortex (ACC) and striatum, the involvement of Dorsolateral Prefrontal Cortex (DLPFC) during mental effort anticipation has yet to be replicated. This study was designed to target DLPFC contribution using functional Near Infrared Spectroscopy (fNIRS), as a more cost-effective tool measuring cortical hemodynamics. We adapted a validated mental effort task, where participants performed easy and difficult mental calculation, while measuring DLPFC activity during the anticipation phase. As hypothesized, DLPFC activity increased during preparation for a hard task as compared to an easy task. Besides replicating a previous fMRI study, these results establish fNIRS as an effective tool to investigate cortical contributions to preparation for effortful behavior. This is especially useful if one requires testing large samples (e.g., to target individual differences), populations with contraindication for functional MRI (e.g., infants or patients with metal implants), or subjects in more naturalistic environments (e.g., work or sport).

neuroscience↗

bioRxiv2017

Predicting motivation: computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted in order to obtain potential benefits. Medial prefrontal cortex (MPFC) and dorsolateral prefrontal cortex (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward, and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (PRO model, Alexander and Brown, 2011, HER Model, Alexander and Brown, 2015). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations, and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions which describe how effort and reward information is coded in PFC, and how changing the configuration of such environmental information might affect decision-making and task-performance involving motivation.

neuroscience↗

bioRxiv2017

Dorsal Anterior Cingulate-Midbrain Ensemble As A Reinforcement Meta-Learner

The dorsal anterior cingulate cortex (dACC) is central in higher-order cognition and behavioural flexibility. The computational nature of this region, however, has remained elusive. Here we propose a new model - the Reinforcement Meta Learner (RML) - based on the bidirectional anatomical connections of the ACC with midbrain catecholamine nuclei (VTA and LC). In this circuit, dACC learns which actions are valuable and acts accordingly. Crucially, this mechanism is optimized by recurrent connectivity with the midbrain: Midbrain catecholamines provide modulatory signals to dACC, controlling its internal parameters (e.g. learning rate), while these parameter modulations are in turn optimized by dACC afferents to the midbrain. This closed-loop system generates emergent (i.e., homunculus-free) control and supports learning to solve hierarchical decision problems without having an intrinsic hierarchical structure itself. Further, it can be combined with other cortical modules to optimize the processing of these modules. We outline how the RML solves the current theoretical stalemate on dACC by assimilating various previous proposals on ACC functioning, and how it captures critical empirical findings from an unprecedented range of domains (stability/plasticity balance, effort processing, working memory, and higher-order classical and instrumental conditioning).

neuroscience↗