CNP X BSI Colloquium

Date: 08-12-2025

About CNP X BSI Colloquium
This one-day colloquium is a collaboration between the BSI and the CNP network. Integrating neuroscience, psychology, and computational modeling, this session explores how meta-learning and neuromodulatory mechanisms shape motivation in healthy functioning and psychopathology (e.g., ADHD, depression).
First, Dr. Massimo Silvetti (Institute of Cognitive Sciences and Technologies, Rome, Italy) will discuss how computational modeling can be used to model the brain’s meta-learning processes, focusing particularly on motivation, effort, and volatility.
The colloquium will be followed by a 2-part practical workshop (sponsored by the Computational NeuroPsychiatry Platform; CNP).

Program Overview:

13:45 - 14:45| BSI Colloquium: “Learning How the Brain Learns: A Computational Framework for Effort-based Decision-Making and Volatility and implications for Mental Health” - Dr. Massimo Silvetti (Institute of Cognitive Sciences and Technologies, Rome, Italy)
15:00 – 15:45| Computational Modeling Workshop (Part I): Theory, architecture and mechanisms of Reinforcement Meta-Learning - Tim Vriens & Rares Radulescu (Motivation Effort & Decision-making (MED) Lab, Behavioural Science Institute)
16:00 – 17:15| Computational Modeling Workshop (Part II): Application of the Reinforcement Meta-Learner model - Tim Vriens & Rares Radulescu (Motivation Effort & Decision-making Lab, Behavioural Science Institute)

Program Abstracts

Colloquium
Dr. Massimo Silvetti
Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy

Every day, our brains make countless decisions—not just about what action to take, but also about how much effort to invest, how much attention to allocate, and how quickly to adapt when the environment changes. Recent findings in cognitive and computational neuroscience suggest that this process is best understood as meta-learning—the brain’s ability to tune its own learning strategies to maximize outcomes (e.g., reward and information).

In this talk, Massimo Silvetti, an expert in computational modeling, will introduce the Reinforcement Meta-Learner (RML). The RML is a neurocomputational model that bridges decision-making, motivation, effort allocation, uncertainty and volatility management within a system-level neuroscience perspective. This model proposes that adaptive control of neuromodulators (i.e., dopamine and norepinephrine) allows the brain to flexibly adjust its learning and decision-making in real time, creating a feedback loop between brain dynamics and behaviour. Importantly, by tuning parameters corresponding to these key neuromodulators, the RML can simulate behaviours typically seen in disorders such as major depressive disorder (MDD; e.g., low effort allocation for reward).

Specifically, Massimo Silvetti will show how the RML explains core processes such as cognitive control and effort-based decision-making, as well as volatility estimation (detecting environmental change via prediction error and adaptive gain) in foraging decisions (choosing whether to exploit or explore). Next to this, he will discuss how the RML framework can generate mechanistic hypotheses about two high-prevalence psychiatric disorders—ADHD and MDD—and present preliminary data evaluating these predictions.

This talk is designed for researchers across disciplines—neuroscience, psychology, education, work, health, and beyond. No background in modeling is required; we will introduce key concepts like meta-learning, adaptive gain, and neuromodulators in accessible terms.

Workshop

Following the colloquium talk, a workshop will provide hands-on experience with basic modeling techniques using the RML. The workshop consists of two sessions. The first session (45 mins) introduces the architecture and key mechanisms of the RML framework. The second session (1h 15 mins) will focus on a concrete example application of the RML model.