Bayesian model

…estimation, variably called Bayesian or predictive or optimal. Here we want to demonstrate with several examples that some reasons for attraction vs. repulsion have been overlooked, neglected, or not acknowledged for specific domains. One example for st

Multisensory processing in the brain is pivotal for forming perceptual decisions. While the primary sensory areas were traditionally considered strictly unisensory, recent rodent research has unveiled significant interactions between the auditory and visual cortices. Notably, V1 neurons were found to be hyperpolarized by auditory noise bursts or show sensitivity to the temporal coherence of audiovisual stimuli. These findings indicate that primary sensory regions possess the computational capacity for multisensory processing, although the impact of such cross-modal interactions on decision-mak

In the current virtual journal club Dr Di Luca will present findings from a series of psychophysical investigations where he measured sensitivity and bias in the perception of the timing of stimuli. He will present how improved detection with longer sequences and biases in reporting isochrony can be accounted for by optimal statistical predictions. Among his findings was also that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted to appear more regular. Such change depends on whether the context these sequences are presented is also regul

Our understanding of how the brain makes decisions has benefited greatly from normative theories that have, for example, established benchmarks for how specific forms of evidence accumulation and decision rules can optimally balance quantities like decision speed and accuracy. These theories have recently been expanded to include adaptive components that are necessary for effective decision making in dynamic environments. However, these extensions remain incomplete, because they have focused primarily on how an unconstrained agent manages single, independent decisions [1]. In contrast, real-wo

Pain perception is a warning signal enabling animals to preserve their body integrity. When pain persists over time, the brain needs to learn to predict its temporal evolution in order to minimize harm. Whereas several studies have already shown the effects of experience and expectations on responses to isolated nociceptive stimuli using axiomatic approaches, it remains largely unknown how individuals can learn to predict a stream of nociceptive events and how these processes are implemented in the brain. To clarify these aspects, we exposed healthy human participants (n = 31) to probabilistic