Deep learning and predictive coding architectures commonly assume that inference in
neural networks is hierarchical. However, largely neglected in deep learning and predictive …

Trial history biases and lapses are two of the most common suboptimalities observed during
perceptual decision-making. These suboptimalities are routinely assumed to arise from …

Perception is known to cycle through periods of enhanced and reduced sensitivity to
external information. Here, we asked whether such slow fluctuations arise as a noise-related …

A key challenge in neuroscience is understanding how neurons in hundreds of
interconnected brain regions integrate sensory inputs with prior expectations to initiate …

Perceptual decisions should depend on sensory evidence. However, such decisions are
also influenced by past choices and outcomes. These choice history biases may reflect …

When observers have prior knowledge about the likely outcome of their perceptual
decisions, they exhibit robust behavioural biases in reaction time and choice accuracy …

Active reinforcement learning enables dynamic prediction and control, where one should not
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …

Computational psychiatry has suggested that humans within the autism spectrum disorder
(ASD) inflexibly update their expectations (ie, Bayesian priors). Here, we leveraged high …

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks,
even when the task rule is not explicitly cued but must be inferred through trial and error. The …

Across the life sciences, an ongoing effort over the last 50 years has made data and
methods more reproducible and transparent. This openness has led to transformative …