Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

This paper concerns the distributed intelligence or federated inference that emerges under
belief-sharing among agents who share a common world—and world model. Imagine, for …

Previous research has stressed the importance of uncertainty for controlling the speed of
learning, and how such control depends on the learner inferring the noise properties of the …

Clinical anxiety impairs decision making, and high trait anxiety interferes with learning. Less
understood are the effects of temporary anxious states on learning and decision making in …

In conditions of constant illumination, the eye pupil diameter indexes the modulation of
arousal state and responds to a large breadth of cognitive processes, including mental effort …

A key feature of sequential decision making under uncertainty is a need to balance between
exploiting—choosing the best action according to the current knowledge, and exploring …

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence
of external reward or when the environment changes. Here, we employ a deep sequential …

Learning in a changing, uncertain environment is a difficult problem. A popular solution is to
predict future observations and then use surprising outcomes to update those predictions …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective changes through time, or where all the training data and …

Bipolar disorder (BD) involves altered reward processing and decision-making, with
inconsistencies across studies. Here, we integrated hierarchical Bayesian modelling with …