Topic: Bayesian model

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

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

Systematic biases in perception can be either attractive or repulsive with respect to the immediate spatial or temporal context. There is an extensive amount of literature [1,2,3] on how to explain these differences in biases with a variety of mechanisms, ranging from sensory adaptation over efficient coding to various flavors of probabilistic 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