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Cognitive categories of emotional and conversational facial expressions are influenced by dynamic information

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84005

Kaulard,  K
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84298

Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83877

de la Rosa,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kaulard, K., Wallraven, C., de la Rosa, S., & Bülthoff, H. (2010). Cognitive categories of emotional and conversational facial expressions are influenced by dynamic information. Talk presented at 11th Conference of Junior Neuroscientists of Tübingen (NeNa 2010). Heiligkreuztal, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BE1A-B
Abstract
Most research on facial expressions focuses on static, ’emotional’ expressions. Facial expressions, however, are also important in interpersonal communication (’conversational’ expressions). In addition, communication is a highly dynamic phenomenon and previous evidence suggests that dynamic presentation of stimuli facilitates recognition. Hence, we examined the categorization of emotional and conversational expressions using both static and dynamic stimuli. In a between-subject design, 40 participants were asked to group 55 different facial expressions (either static or dynamic) of ten actors in a free categorization task. Expressions were to be grouped according to their overall similarity. The resulting confusion matrix was used to determine the consistency with which facial expressions were categorized. In the static condition, emotional expressions were grouped as separate categories while participants confused conversational expressions. In the dynamic condition, participants uniquely categorized basic and sub-ordinate emotional, as well as several conversational facial expressions. Furthermore, a multidimensional scaling analysis suggests that the same potency and valence dimensions underlie the categorization of both static and dynamic expressions. Basic emotional expressions represent the most effective categories when only static information is available. Importantly, however, our results show that dynamic information allows for a much more fine-grained categorization and is essential in disentangling conversational expressions.