Selim Onat

I am a neuroscientist working currently on how humans make generalizations based on what they have previously learnt. To do so, I am using a variety of methodologies including fMRI (1), autonomous (2), as well as eye-movement recordings (3).

This research emanates from the well-established field of "stimulus generalization" following mainly the "lineage" of Hovland, Hull and Roger Shepard (4), and including the more recent computational work of Josua Tenenbaum (5). Furthermore, it integrates work on anxiety disorders, as it is believed that these mechanisms are impaired in people suffering from anxiety problems.

In the past, I have been working on how the nervous system processes natural scenes both at the electrophysiological and sensory-motor level. Since the times of Hubel and Wiesel, visual processing had been
overwhelmingly studied with artificial stimuli such as moving edges. However this type of stimuli suffer from an ecological validity problem, as they only rarely occur in real-life. We therefore investigated cortical processing during viewing of natural movies. This previous work focused on visual processing using mostly the technique of voltage-sensitive dye imaging and eye-tracking.

Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex

Freedman et al. generated pictures of complex objects using parametric combinations of 6 base images. These base images represented different kinds of felines or dogs, therefore their combinations gave rise to images that were graded in their category membership (e.g. whereas some combinations were clearly dog- or feline-like, others pictures were somewhere in between) while guaranteeing diversity within each category. These images were shown in a delayed-match-to-category task to monkeys. Solving this task requires a level of abstraction from the sheer appearances of the stimuli. Even at category boundaries where the discrimination is most difficult, the performance was high. They recorded activity of single neurons from prefrontal cortex, more precisely from the ventral part of the principal sulcus. Their results show evidence for neurons that are able to distinguish between these two, supposedly learnt categories. That is the responses are 1/ not gradual as the stimuli  and 2/ characterized by a sharp step-like function at the category boundary. The data is clear, the interpretation is inline with the data. I find it unfortunately that pictures which are at the category boundary were not presented. And the study would gain if the similarity measure was in the perceptual space rather than in the stimulus domain. The 6 base images could in principle be tested on humans using perceptual mapping techniques.