Perception, memory, and reasoning have traditionally been studied separately. I am interested in the relations between these mental operations and in the dynamics of these relations as people practice and become experts. I am further interested in deciphering which mechanisms malfunction in the cases where practice does not lead to expertise, like the case of reading disability (dyslexia) in spite of huge amount of practice.
Conceptual frameworks for learning
1.Top-down: task driven learning
The Reverse Hierarchy Theory of perception and learning, which explains learning processes as a top-down driven processes, which proceeds backwards – and task-induced learning processes that begin at the “top” of the hierarchies and proceed by a backward search for the most informative inputs (neural populations).
2.Bottom-up: Statistical Bayesian learning
The poor-anchoring (and faster memory decay) theory of dyslexia, which explains the difficulties of dyslexics in acquiring reading expertise as a consequence of faster decay of perceptual traces, which leads to a shallower long-term learning slope, manifesting slower accumulation of linguistics (and non-linguistic) regularities.
The slow-update theory of autism, which proposes that high-functioning individuals with autism (no language difficulties) are slow in updating their perceptual predictions and their motor plans – which impedes online interactions, both social and non-social.
We use behavioral task, and use computational models to decipher and quantify underlying mechanisms.
We use ERP to assess within-trial dynamics of task preparation, neuronal adaptation and their relation to behavior.
We use fMRI to assess cross-trial dynamics and “division of labor” between various cortical and sub-cortical regions.