Merav Ahissar


Human Perceptual and cognitive  learning

A cognitive neuroscience lab which studies perceptual learning, task and stimulus driven, in various populations – typically developing, individuals with dyslexia and individuals with autism.

We use computational, behavioral, ERP and imaging tools to understand the cognitive nature of processes and their relation to brain structures and mechanisms.



COVID-19 guidelines for experiments with human participants in the lab

Recent publications

Lab Members

Postdoctoral researcher
PhD Student
PhD Student



1. ERC - Advanced  -  NeuroCompSkill 

Understanding the failure of acquiring reading and social expertise (dyslexia and autism) within a unified framework of skill acquisition

Why do most people acquire expertise with practice whereas others fail to master the same tasks? NeuroCompSkill offers a neuro-computational framework that explains failure in acquiring verbal and non-verbal communication skills. It focuses on individual ability in using task-relevant regularities, postulating that efficient use of such regularities is crucial for acquiring expertise. Specifically, it proposes that using stable temporal regularities, acquired across long time windows (> 3 sec to days) is crucial for the formation of linguistic (phonological, morphological and orthographic) skills. In contrast, fast updating of recent events (within ~ .3- 3 sec), is crucial for the formation of predictions in interactive, social communication. Based on this, we propose that individuals with difficulties in retaining regularities will have difficulties in verbal communication, whereas individuals with difficulties in fast updating will have difficulties in social non-verbal communications



2. ISF

Perceptual learning: the role of learning stimulus statistics

Practice based improvement is a main characteristic of human behavior. This applies even to basic perceptual processes. But what is it that improves? Is it the accuracy of our sensory representations, or the validity of our implicit predictions? Since perception is an integrative process, which implicitly merges information from incoming stimuli with experience-based knowledge of the world, improvement can stem from either, or both. Recent conceptual developments, such as introduction of Bayesian framework to cognitive neuroscience, offer new tools and insights for addressing this question. The Bayesian framework views perception as the posterior outcome of integrating (multiplying) the sensory response (likelihood) with knowledge about statistics of stimuli in the world (prior), allowing quantitative estimates of the contribution of each component to the resulting perceptual performance. However, the Bayesian framework does not specify how the prior is learned. We now aim to study the dynamics of learning the prior, which is not initially available to the performer, and may itself change. Here we use the term - “prediction”, referring to participants’ “belief” regarding the statistics of stimuli in the world. We shall characterize these predictions as a function of the trained conditions, at the behavioral and neural levels, in three different populations: general adult population, adult dyslexics and children, whose pattern of forming predictions is expected to differ. 

Contact information


Phone: 02-5883409