Selim Onat

My main occupation is Neuroscience.

In the past, I have been interested on how the visual system processes natural scenes. To this end, I have recorded naturalistic movies using micro-cameras carried by cats while they were actively exploring a natural environment. I used these movies to train neuronal networks in an unsupervised manner and compared learnt features to the known properties of neurons in visual cortex. I also used these videos as stimuli during physiological recordings to gain insights on the principles of natural signal processing in the visual cortex.

Recently, I started working on how humans make generalizations based on what they have previously learnt. To this end, 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.

Just submitted the following short paper to the 1st Cognitive Computational Neuroscience meeting. I am looking forward to participate!

Model-Based Fixation-Pattern Similarity Analysis Reveals Adaptive Changes in Face-Viewing Strategies Following Aversive Learning

Lea Kampermann (l.kampermann@uke.de)
Department of Systems Neuroscience
University Medical Center Hamburg-Eppendorf

Niklas Wilming (n.wilming@uke.de)
Department of Neurophysiology and Pathophysiology
University Medical Center Hamburg-Eppendorf

Arjen Alink (a.alink@uke.de)
Department of Systems Neuroscience
University Medical Center Hamburg-Eppendorf

Christian B├╝chel (c.buechel@uke.de)
Department of Systems Neuroscience
University Medical Center Hamburg-Eppendorf

Selim Onat (sonat@uke.de)
Department of Systems Neuroscience
University Medical Center Hamburg-Eppendorf


Abstract:


Learning to associate an event with an aversive outcome typically leads to generalization when similar situations are encountered. In real-world situations, generalization must be based on the sensory evidence collected through active exploration. However, our knowledge on how exploration can be adaptively tailored during generalization is scarce. Here, we investigated learning-induced changes in eye movement patterns using a similarity-based multivariate fixation-pattern analysis. Humans learnt to associate an aversive outcome (a mild electric shock) with one face along a circular perceptual continuum, whereas the most dissimilar face on this continuum was kept neutral. Before learning, eye-movement patterns mirrored the similarity characteristics of the stimulus continuum, indicating that exploration was mainly guided by subtle physical differences between the faces. Aversive learning increased the dissimilarity of exploration patterns. In particular, this increase occurred specifically along the axis separating the shock predicting face from the neutral one. We suggest that this separation of patterns results from an internal categorization process for the newly learnt harmful and safe facial prototypes.
Keywords: Eye movements; Generalization; Categorization; Face Perception; Aversive Learning; Multivariate Pattern Analysis; Pattern Similarity

To avoid costly situations, animals must be able to rapidly predict future adversity based on actively harvested information from the environment. In humans, a central part of active exploration involves eye movements, which can rapidly determine what information is available in a scene. However, we currently do not know the extent to which eye movement strategies are flexible and can be adaptive following aversive learning.

We investigated how aversive learning influences exploration strategies during viewing of faces that were designed to form a circular perceptual continuum (Fig. 1A). One randomly chosen face along this continuum (CS+; Fig. 1, red, see colorwheel) was paired with a mild electric shock, which introduced an adversity gradient based on  physical  similarity  to  the


Figure 1: (A) 8 exploration patterns (FDMs, colored frames) from a representative individual overlaid on 8 face stimuli (numbered 1 to 8) calibrated to span a circular similarity continuum across two dimensions. A pair of maximally dissimilar faces was randomly selected as CS+ (red border) and CS– (cyan border; see color wheel for color code). The similarity relationships among the 8 faces and the resulting exploration patterns are depicted as two 8×8 matrices. (B-E) Multidimensional-scaling representations (top row) and the corresponding dissimilarity matrices (bottom row) depicting four possible scenarios on how learning could change the similarity geometry between the exploration maps (same color scheme; red: CS+; cyan: CS–). These matrices are decomposed onto covariate components (middle row) centered either on the CS+/CS– (specific component) or +90°/–90° faces (unspecific component). A third component is uniquely centered on the CS+ face (adversity component). These components were fitted to the observed dissimilarity matrices, and model selection procedure was carried out.

CS+ face. The most dissimilar face (CS–; Fig. 1, cyan) separated by 180° on the circular continuum was not reinforced and thus stayed neutral. Using this paradigm, we were able to investigate how exploration strategies were modified by both the physical similarity relationships between faces, and the adversity gradient introduced through aversive learning.

We used a variant of representational similarity analysis (Kriegeskorte, Mur, & Bandettini, 2008) that we term “fixation-pattern similarity analysis” (FPSA). FPSA considers exploration patterns as multivariate entities and assesses between-condition dissimilarity of fixation patterns for individual participants (Fig. 1A). We formulated 4 different hypotheses (Bottom-up saliency, increased arousal, adversity categorization, adversity tuning) based on how aversive learning might alter the similarity relationships between exploration patterns when one face on the continuum started to predict adversity (Fig. 1B-E).

Before learning, eye movement patterns mirrored the similarity characteristics of the stimulus continuum, indicating that exploration was mainly guided by subtle physical differences between the faces. Aversive learning resulted in a global increase in dissimilarity of eye movement patterns following learning. Model-based analysis of the similarity geometry indicated that this increase was specifically driven by a separation of patterns along the adversity gradient, in agreement with the adversity categorization model (Fig. 1D). These findings show that aversive learning can introduce substantial remodeling of exploration patterns in an adaptive manner during viewing of faces. In particular, we suggest that separation of patterns for harmful and safe prototypes results from an internal categorization process operating along the perceptual continuum following learning.

References

Kriegeskorte, N., Mur, M., & Bandettini, P. (2008). Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience. Frontiers in Systems Neuroscience, 2. https://doi.org/10.3389/neuro.06.004.2008