Publication
Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Related publications (112)
Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study
Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...
EPFL2024Unveiling the complexity of learning and decision-making
Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...
EPFL2024Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life
A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...
EPFL2024Butterfly effects in perceptual development: A review of the 'adaptive initial degradation' hypothesis
Lukas Vogelsang, Marin Vogelsang
Human perceptual development evolves in a stereotyped fashion, with initially limited perceptual capabilities maturing over the months or years following the commencement of sensory experience into robust proficiencies. This review focuses on the functiona ...
San Diego2024Computational models of intrinsic motivation for curiosity and creativity
Wulfram Gerstner, Alireza Modirshanechi, Sophia Becker
We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...
2024Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes
Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...
PUBLIC LIBRARY SCIENCE2023Deep Learning Meets Sparse Regularization
Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...
2023The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry
While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...
2023Survey experiments on economic expectations
In this chapter, we discuss field experiments in surveys that are conducted with the purpose of learning about expectation formation and the link between expectations and behavior. We begin by reviewing the rationale for conducting experiments within surve ...
Academic Press2023Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning
Meritxell Bach Cuadra, Hamza Kebiri
Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...
Springer2023