The computational roots of positivity and confirmation biases in reinforcement learning
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 …
value and evidence confirming one's own prior belief are overweighed. Until recently …
Understanding the development of reward learning through the lens of meta-learning
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 …
individual differences in goal-directed behaviour. Studies of the effects of early-life adversity …
Deciding fast and slow: The role of cognitive biases in ai-assisted decision-making
Several strands of research have aimed to bridge the gap between artificial intelligence (AI)
and human decision-makers in AI-assisted decision-making, where humans are the …
and human decision-makers in AI-assisted decision-making, where humans are the …
Why and when beliefs change
Why people do or do not change their beliefs has been a long-standing puzzle. Sometimes
people hold onto false beliefs despite ample contradictory evidence; sometimes they …
people hold onto false beliefs despite ample contradictory evidence; sometimes they …
Computational mechanisms underlying latent value updating of unchosen actions
Current studies suggest that individuals estimate the value of their choices based on
observed feedback. Here, we ask whether individuals also update the value of their …
observed feedback. Here, we ask whether individuals also update the value of their …
Linking confidence biases to reinforcement-learning processes.
We systematically misjudge our own performance in simple economic tasks. First, we
generally overestimate our ability to make correct choices—a bias called overconfidence …
generally overestimate our ability to make correct choices—a bias called overconfidence …
Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory
As individuals learn through trial and error, some are more influenced by good outcomes,
while others weight bad outcomes more heavily. Such valence biases may also influence …
while others weight bad outcomes more heavily. Such valence biases may also influence …
The description–experience gap: a challenge for the neuroeconomics of decision-making under uncertainty
The experimental investigation of decision-making in humans relies on two distinct types of
paradigms, involving either description-or experience-based choices. In description-based …
paradigms, involving either description-or experience-based choices. In description-based …
Humans actively sample evidence to support prior beliefs
No one likes to be wrong. Previous research has shown that participants may underweight
information incompatible with previous choices, a phenomenon called confirmation bias. In …
information incompatible with previous choices, a phenomenon called confirmation bias. In …
Biased belief updating and suboptimal choice in foraging decisions
Deciding which options to engage, and which to forego, requires develo** accurate
beliefs about the overall distribution of prospects. Here we adapt a classic prey selection …
beliefs about the overall distribution of prospects. Here we adapt a classic prey selection …