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Abstract:
High-level after-effects have been reported for the recognition of static faces [1,2]. It has been
shown that the presentation of static ‘anti-faces’ biases the perception of neutral test faces
temporarily towards the perception of specific identities. Recent studies have demonstrated
high-level after-effects also for point-light walkers, resulting in shifts of perceived gender [3,4].
We present an experiment showing for the first time high-level after-effects in the recognition
of dynamic facial expressions.
Facial expressions were generated as a morph animation based on a weighted sum of 3D
shapes derived from scans of facial action units [5]. With this technique we were able to define
a metric space of dynamic expressions by morphing, similar to face spaces for static stimuli.
Morphing between prototypical expressions (happy and disgust) and a neutral face without
intrinsic facial motion we generated ‘anti-expressions’ by choosing negative weights for the
prototypes. In addition, for testing we generated expressions with reduced recognizability
choosing small positive weights of the prototypes. The morphing space was equilibrated for
recognizability by measuring the psychometric functions that map the morphing weights on
the recognition rates of the two expressions (happy and disgust) in a 2 AFC task. Only the
non-rigid intrinsic face motion was morphed. In addition, a meaningless 3D head motion
was added in order to minimize the influence of low-level adaptation effects. Subjects were
adapted for 8s with 5 repetitions of the anti-expressions. They were tested with happy and
disgust expressions with reduced expression strength. Adaptation stimuli were simulated with
2 facial identities and were shown either in forward or reverse time order.
We found strong expression-related after-effects (increased and decreased recognition for
matching and non-matching expression, respectively, p < 0.05, N=13). We investigated the
influence of static vs. dynamic representations in the observed after-effect. The temporal order
of the adapting stimuli does not have a significant influence on the strength of the observed
after-effect. The analysis of the 2D optic flow patterns of adaptation and test stimuli rules out
the possibility that the observed after-effects reflect classical low-level motion after effects.
Instead, the results seem compatible with the adaptation of neural representations of ‘snapshot
keyframes’ [6] that arise during the presentation of dynamic facial expressions.
