Abstract Under the Predictive Processing Framework, perception is guided by internal
models that map the probabilistic relationship between sensory states and their causes …

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 …

Surprising events trigger measurable brain activity and influence human behavior by
affecting learning, memory, and decision-making. Currently there is, however, no consensus …

Learning in the brain is poorly understood and learning rules that respect biological
constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose …

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

To understand the dysfunctional mechanisms underlying maladaptive reasoning of
psychosis, computational models of decision making have widely been applied over the …

While navigating a fundamentally uncertain world, humans and animals constantly evaluate
the probability of their decisions, actions or statements being correct. When explicitly elicited …

We must often infer latent properties of the world from noisy and changing observations.
Complex, probabilistic approaches to this challenge such as Bayesian inference are …

In this study, we examined the relationship between physiological encoding of surprise and
the learning of anticipatory eye movements. Active inference portrays perception and action …

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 …