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 Büchel (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