Humans do not integrate new information objectively: outcomes carrying a positive affective
value and evidence confirming one's own prior belief are overweighed. Until recently …

The past decade has seen the emergence of the use of reinforcement learning models to
study developmental change in value-based learning. It is unclear, however, whether these …

When forming and updating beliefs about future life outcomes, people tend to consider good
news and to disregard bad news. This tendency is assumed to support the optimism bias …

Determining how environments shape how people learn is central to understanding
individual differences in goal-directed behaviour. Studies of the effects of early-life adversity …

Previous studies suggest that factual learning, that is, learning from obtained outcomes, is
biased, such that participants preferentially take into account positive, as compared to …

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences,
promising to explain behavior from simple conditioning to complex problem solving, to shed …

We systematically misjudge our own performance in simple economic tasks. First, we
generally overestimate our ability to make correct choices—a bias called overconfidence …

The valence of new information influences learning rates in humans: good news tends to
receive more weight than bad news. We investigated this learning bias in four experiments …

The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but
unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation …

Studies of reinforcement learning have shown that humans learn differently in response to
positive and negative reward prediction errors, a phenomenon that can be captured …