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.

OHBM Hamburg 2014 Abstract: Precision of Neuronal Representations during Fear Generalization

Precision of Neuronal Representations during Fear Generalization

Onat S., Büchel C.
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany

Introduction

Fear generalization is usually conceived as resulting from a lack of precision in the neuronal representations about the aversive stimulus. Therefore the lack of certainty is believed to be the source of generalization that is observed on the behavioral level. Alternatively, fear generalization might be the result of an active neuronal process, whereby the nervous system tries to optimally control behavior based on what is already known. This scenario predicts that hyper-precise neuronal representations about the aversive stimulus would co-exist with a broader behavioral tuning. Using an event-related fMRI paradigm, we analyzed the precision of neuronal signals in different brain regions and compared these with different behavioral measurements.

Methods

We created 8 computer generated faces that were organized along a circular similarity gradient (Fig. 1, dashed line). A maximum-likelihood based multi-dimensional scaling method (Maloney et al., 2003) was used to confirm the circularity of the perceptual organization of these stimuli (Fig. 1, solid line). This gradual change in stimulus similarity was translated to an aversiveness gradient using a classical Pavlovian conditioning paradigm. To this end, one randomly selected face (CS+) was partially associated with an aversive electric shock. The most dissimilar face was kept as neutral (CS-). BOLD responses were recorded before and after the conditioning phase together with changes in skin conductance, as well as aversiveness ratings (n = 29).



Results

We identified a set of neuronal clusters that were significantly modulated as a function of increasing dissimilarity to the CS+ face. The average amplitude of evoked responses by the CS+, CS- and all intermediate faces is shown in Fig. 2 (mean ± SEM, red for CS+, cyan for CS-) for two clusters located in hippocampus and insula. The effect of conditioning is clearly seen as a modulation of responses centered on the CS+ face following the conditioning (bottom panels). These responses were fit with a Gaussian function, yielding parameterized fear-tuning profiles (Fig. 2, black curves), where alpha (𝛼) and sigma (𝜎) parameters characterized the strength and the width of the tuning profiles, respectively (Fig. 2).





Almost all clusters within this identified fear generalization network including a set of prefrontal, cingular, hippocampal, and face selective sensory sites exhibited a strong deactivation in response to CS+ face (Fig. 3, left panel). The right insula was the sole exception to this pattern (p < 0.001), showing a fear-tuning profile that was characterized by a net activation (Fig. 3, left panel, top bar). Among all the clusters investigated, the insula showed the sharpest fear tuning (Fig. 3, right panel, bottom bar). We next compared, the precision of insular aversive tuning to the fear tuning of skin conductance and aversiveness ratings. The width of insular tuning was even sharper than the tuning of any behavioral measure i.e. ratings (t(28)= -2.67, p = 0.0062) and skin conductance (t(28)= -2.23, p = 0.017) responses (Fig. 4).  

 

Conclusion

These results show that the representation of the aversive stimulus is present in a hyper-precise manner within the neuronal networks responsible for fear-generalization. The imprecision of the tuning that is observed in other neuronal sites and at the behavior level seems to be mediated by a mechanism that actively “blurs” the source of the aversive event, rather than resulting from a lack of precision in the neuronal representations. Our results therefore suggest that a controlled imprecision rather than an imprecision in the control, is responsible for the generalization of fear in the healthy humans.