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.

Talk given at the EMHFC Conference


I gave this talk at the European Meeting on Human Fear Conditioning about "Temporal Dynamics of Aversive Generalization".

Abstract:
Temporal Dynamics of Aversive Learning and Generalization in Amygdala
The amygdala is thought to orchestrate coordinated bodily responses important for the survival
of the organism during threatening situations. However its contribution to the generalization of
previously learnt aversive associations is not well understood. As amygdala responses in the
context of fear conditioning are temporally phasic and adapt quickly, we designed a new
paradigm to investigate its temporal dynamics during fear generalization. We used faces that
formed a perceptual circular similarity continuum, allowing us to gather two-sided generalization
gradients. While one face predicted an aversive outcome (UCS), the most dissimilar face was
kept neutral. Importantly, participants were compelled to learn these associations throughout the
fMRI recording which they started naive, allowing us to collect temporally resolved
generalization gradients for BOLD and skin-conductance responses. Following fMRI, we
evaluated subjective likelihood for single faces to be associated with UCS, and complemented
these with behavioral measurements using eye-movement recordings to assess how the saliency
associated with faces were modified. Aversive generalization in the amygdala emerged late
during the task, and temporal dynamics were characterized by low learning rates. We observed
significant differences in amygdala responses for participants who exhibited a behavioral effect
in addition to verbal ratings of UCS likelihood. Amygdalar responses contrasted with temporal
dynamics in the insula where generalization gradients emerged earlier and gradually increased
with higher learning rates, similar to skin-conductance responses. Overall our results imply a
weak and late contribution of the amygdala to aversive generalization, in comparison to insular
responses that are stronger and contribute early during learning.