In 2017 I received an FNRS-grant to pursue a PhD within the ACTE and Co3 research centers. My thesis focuses on predictive coding and associative learning in autistic individuals.
The theory of predictive coding has recently been applied to autism and provides new insight into the understanding of information processing in autistic individuals. Based on this framework, authors frequently argue for a difficulty to produce fruitful predictive statistical learning in uncertain or volatile environments and/or for a weaker influence of prior learning during predictive processing in autistic individuals.
However, to date, there are still inconsistencies among studies and there is no clear empirical evidence of statistical learning impairment or concrete dysfunctional predictive processing in autism spectrum disorder.
The main objective of my thesis is to study the possible existence of an efficient albeit atypical associative learning mechanism in autistic adults and children. To that aim, I implemented a new eye tracking paradigm that allows participants to associate different stimuli with each other in two distinct ways according to their precision weighing of prediction errors during oddball events. My main hypothesis is that an atypical processing of prediction errors should lead autistic participants to associate stimuli in a peculiar way and therefore drive them to predict events differently from non-autistic participants. I am also interested in whether an atypical predictive coding in autistic children may correlate with lexical acquisition and categorization performance known to be delayed or divergent in the condition.
Mikhail Kissine (https://acte.ulb.be/index.php/fr/l-equipe-acte/team/1)
Arnaud Destrebecqz (https://crcn.ulb.ac.be/members/?q=16)