Descrizione del progetto
Ricompense nelle decisioni umane basate sulla statistica
Gli esseri umani e altri animali devono apprendere a contenere in maniera efficiente i propri input sensoriali, sotto la guida delle regolarità statistiche nell’ambiente e delle ricompense che esso offre. Recenti studi psicologici e neuroscientifici dimostrano che i segnali di ricompensa modulano adeguatamente le rappresentazioni apprese. Tuttavia, esse determinano come riusciamo a riconoscere oggetti e concetti, e l’influenza indiretta delle ricompense su tali rappresentazioni rimane poco chiara. Il progetto RELEARN parte dall’ipotesi che gli esseri umani utilizzino risorse di calcolo limitate per indirizzare le rappresentazioni apprese a loro vantaggio, al fine di ottenere una ricompensa; il progetto propone dunque un modello matematico che integra l’apprendimento per rinforzo e l’apprendimento rappresentativo probabilistico. La ricerca metterà alla prova previsioni comportamentali e neurali, nel corso di esperimenti in cui gli esseri umani imparano a ottenere ricompense sulla base delle statistiche di alcune caratteristiche di un ambiente simulato.
Obiettivo
Learning the statistical buildup of the environment serves the purpose of making good decisions, thus what regularities humans learn and what ones they neglect depends on the relevance towards maximizing reward. Recent studies characterise reward-based modulation of feature representations built by humans and animals both on the behavioural and neural level, but the effect of reward on learning higher-order environmental statistics is unknown. Our hypothesis is that humans do not learn to represent feature co-occurrence statistics if it does not help to predict reward due to resource constraints on computation and storage. We propose a mathematical framework based on Bayesian hierarchical modelling and reinforcement learning to predict the modulatory effect of reward on learned representations. We will test the predictions of the model in a series of experiments where humans need to learn to associate precisely controlled statistical aspects of a naturalistic simulated environment to reward both in the lab and online, in reactive and planning-based tasks. Additional to behaviour, the model will predict the structure of neural representations and their changes over the course of the experiment as well. We will test those predictions using magnetoencephalography during the learning phase of the experiments and decoding analysis to compare model variables to neural responses. The results will contribute to the understanding of representational learning in humans, with potential implications in psychiatry and economics as well as supply the community with novel analytical tools and data. The unique mentoring at the host institution together with the extensive training program including international visits to world-leading collaborators will establish my independent research program in computational neuroscience.
Campo scientifico
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Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
80539 Munchen
Germania