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, yet graded--single-image activation profiles of human category-selective cortical regions.

Mur M et al. investigated the selectivity of activity levels in parahippocampal place area (PPA) and fusiform face area (FFA) evoked by single images. They focus here only on the average BOLD activity within carefully selected ROIs.

The paper is very creative in terms of new analyses methods, relies heavily on rank orders and hypothesis testing with bootstrapping.

First it establishes the fact that PPA and FFA behaves as expected, that is face stimuli for FFA and place stimuli for PPA rank highest in terms of evoked activity. Overall PPA responses are more selective than FFA responses, reaching AUC values of 1 in both hemispheres. This results from the fact that faces evokes really high activity levels in the FFA, whereas, in the case of PPA inactivation by faces contribute to the PPA selectivity.

The rest of the report focuses on characterizing the category selectivity of these areas.

If an area is category selective in an ideal sense, non-preferred stimuli should never evoke higher activity levels than any other preferred stimulus, and if so, then only by chance due to noise.

The number of inverted pairs measures exactly the number of times one could identify violation of this rule by counting the number of times a stimulus from outside the category is ranked higher than a stimulus from within category. If these inverted pairs survive across multiple sessions (as measured by PRIP metric), this would be an evidence against ideal category selectivity. However as such, PRIP is not a very sensitive metric. For example one single preferred stimulus failing by chance to evoke any activity at all would be sufficient to generate very many inverted pairs, thus the metric seems to fluctuate highly non-linearly with respect to distance between inverted pairs. Therefore the authors, used the first sessions to identify preferred-nonpreferred pairs with largest activity difference, with the idea that an inversion with the largest activity difference would be the observation with least chance level. If these pairs survive across sessions, the difference in activity would then decrease only marginally and remain positive, thus providing evidence for stable inversions (as such, however this measure is also influenced by the noise on both the preferred and non-preferred stimulus). These analyses provide supporting evidence that FFA and PPA behave like an ideal category selective area, with the exception of left FFA, in line with the fact that left FFA is the ROI where smallest AUC values were observed (only though at ROI size of 128 voxels).