Attentional expectancies in the human brain.
|Date||18 March 2016|
|Time||16:00 - 17:00|
There is now accumulating evidence that the inference of stimulus likelihood can plausibly be described by Bayesian models that provide a principled prescription of how predictions are updated after new observations. These models can be regarded as variants of predictive coding – in which updates are determined by prediction errors that are weighted by their salience or expected precision.
In my talk I will present a series of studies employing a novel variant of Posner’s location-cueing paradigm in which the proportion of valid trials (percentage of cue validity) was manipulated over the course of the experiment to create volatile contingencies. Behavioural computational modelling results as well as functional magnetic resonance imaging (fMRI) data will be presented to elucidate the computational and neural mechanisms underlying flexible attentional control in relation to Bayesian inference.
Moreover, data from a psychopharmacological study with the cholinergic agonist galantamine will be shown to illustrate how psychopharmacological agents may affect the updating of probabilistic beliefs.
These findings shall provide further insights into how trial-wise inference on environmental statistics governs attentional selection.