Abstract
The Bayesian brain theory is currently having a large influence in experimental psychology and cognitive (neuro)science. Thought to originate from the 1867 von Helmholtz’s notion of “unconscious inference”, this theory posits that there are three basic elements to perform perceptual inference: 1) sampling of sensory evidence, 2) extraction of statistical regularities to create models about the environment, and 3) exploitation of such predictive models. These three building blocks do not work as a serial chain but interact dynamically to optimize the decision-making process (e.g., if sensory evidence points in a direction opposite to what predicted, models must be readjusted through new learning). The current literature shows a plethora of behavioral evidence about the potential role of predictive coding in perception, while there are very little systematic attempts to understand their precise psychophysiological mechanisms, in particular within an oscillatory framework. This is of particular relevance if we consider that the constituent blocks of Bayesian inference and their dynamic integration appear to be altered in the autistic spectrum disorder (ASD) and schizotypal spectrum disorder (SSD) continuum: while sensory evidence would be predominant in ASD-like perception, SSD would rely excessively on priors to guide perception and behavior. Notably, signatures of ‘oscillopathies’ are evident in both ASD and SSD, but their influence in the context of predictive perception remains unknown. In the present project we propose to conduct the first systematic investigation of the psychophysiological mechanisms underlying human predictive processes, with a specific focus on rhythmic oscillatory activity, and test the interindividual differences as a function of ASD and SSD traits. Specifically, the project will delineate the constituent building blocks of predictive perception and their dynamics interplay to reach a deep understanding of: (I) how sensory evidence is weighted in perceptual decisions and how it can give rise to low-level priors, (II) the temporal dynamics underlying statistical priors learning and the interplay between low-level priors and implicitly learned prior models, (III) the impact that explicit high-level prior has on perception and its interplay with low-level priors. The project will benefit from a large data collection (N=420) and from complementary and synergistic expertise of two research groups in methods including electroencephalography, psychophysics, computational modeling, as well as complementary expertise in ASD and SSD. The experimental settings we propose are tuned to create innovative contexts able to isolate the building blocks of Bayesian inference (sensory evidence sampling, prior formation and exploitation), but where also dynamic decision-making and flexible response are required, mimicking more ecological contexts and allowing to shed lights on fundamental individual differences in the predictive brain.
Project details
Unibo Team Leader: Vincenzo Romei
Unibo involved Department/s:
Dipartimento di Psicologia "Renzo Canestrari"
Coordinator:
ALMA MATER STUDIORUM - Università di Bologna(Italy)
Total Eu Contribution: Euro (EUR) 204.772,00
Total Unibo Contribution: Euro (EUR) 103.090,00
Project Duration in months: 24
Start Date:
05/10/2023
End Date:
28/02/2026
