Biology subjects

Silvetti, M.

Publications and source records attributed to Silvetti, M..

5 recordsLinked to original sources

bioRxiv2025

EEG reveals how space acts as a late heuristic of time

To compensate for its sensory intangibility, humans often rely on spatial metaphors, gestures, and visual tools to represent the passage of time. These spatial tools, i.e. heuristics, range from everyday practices--such as directional hand gestures to indicate past or future events--to more abstract scientific conceptualizations, such as the "curving of space-time" in the theory of relativity. Despite this widespread spatialization of time, it remains unclear to what extent space is an inherent component of the brains representation of time and its role in monitoring temporal durations. Here, we combine EEG-behavioral methods and neural network models of optimal decision-making to show that space is a late compensatory mechanism of time representation recruited when faster and non-spatial timekeeping mechanisms are suboptimally engaged. EEG analyses reveal a cascade-like process: spatial engagement in timekeeping follows the insufficient non-spatial encoding of time intervals, leading to delayed decisions on their length and slower response selection. Computational modelling further indicates that trial-by-trial fluctuations in the spatialization of time are explained by stochastic variations in the activity of the dopaminergic/noradrenergic (DA/NE) system and its interaction with the anterior cingulate cortex. These findings provide the first clear evidence of when, why, and how the brain recruits spatial mechanisms in the service of temporal processing and demonstrate that non-spatial and spatial timekeeping systems can be dissociated at both behavioural and electrophysiological levels.

neuroscience↗

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↗

bioRxiv2023

The impact of Subclinical Psychotic Symptoms on Delay and Effort discounting: insights from behavioral, computational, and electrophysiological methods

BackgroundThe ability to value rewards is crucial for adaptive behavior and is influenced by the time and effort required to obtain them. Impairments in these computations have been observed in patients with schizophrenia and may be present in individuals with subclinical psychotic symptoms (PS). MethodsIn this study, we employed delay and effort-discounting tasks with food rewards in thirty-nine participants divided into high and low levels of PS. We investigated the underlying mechanisms of effort-discounting through computational modelling of dopamine prefrontal and subcortical circuits and the electrophysiological biomarker of both delay and effort-discounting alterations through resting-state frontal alpha asymmetry (FAA). ResultsResults revealed greater delay discounting in the High PS group compared to the Low PS group but no differences in the effort discounting task. However, in this task, the same levels of estimated dopamine release were associated with a lower willingness to exert effort for high-calorie food rewards in High PS participants compared to Low PS participants. Although there were no significant differences in FAA between the High PS and Low PS groups, FAA was significantly associated with the severity of participants negative symptoms. ConclusionsOur study suggests that the dysfunction in temporal and effort cost computations, seen in patients with schizophrenia, may be present in individuals with subclinical PS. These findings provide valuable insight into the early vulnerability markers (behavioral, computational, and electrophysiological) for psychosis, which may aid in the development of preventive interventions.

neuroscience↗

bioRxiv2021

A Reinforcement Meta-Learning Framework of Executive Function and Information Demand

Gathering information is crucial for maximizing fitness, but requires diverting resources from searching directly for primary rewards to actively exploring the environment. Optimal decision-making thus maximizes information while reducing effort costs, but little is known about the neural implementation of these tradeoffs. We present a Reinforcement Meta-Learning (RML) computational mechanism that solves the trade-offs between the value and costs of gathering information. We implement the RML in a biologically plausible architecture that links catecholaminergic neuromodulators, the medial prefrontal cortex and topographically organized visual maps and show that it accounts for neural and behavioral findings on information demand motivated by instrumental incentives and intrinsic utility. Moreover, the utility function used by the RML, encoded by dopamine, is an approximation of free-energy. Thus, the RML presents a biologically plausible mechanism through which coordinated motivational, executive and sensory systems generate visual information gathering policies that minimize free energy.

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↗