Document Type

Article

Publication Date

11-2013

Abstract

Faces are an important visual category for many taxa, and the human face is no exception to this. Because faces differ in subtle ways and possess many idiosyncratic features, they provide a rich source of perceptual cues. A fair amount of those cues are learned through social interactions and are used for future identification of individual humans. These effects of individual experience can be studied particularly well in hetero-specific face perception. Domestic dogs represent a perfect model in this respect, due to their proved ability to extract important information from the human face in socio-communicative interactions. There is also suggestive evidence that dogs can identify their owner or other familiar human individuals by using visual information from the face. However, most studies have used only dogs’ looking behavior to examine their visual processing of human faces and it has been demonstrated only that dogs can differentiate between familiar and unknown human faces. Here, we examined the dog's ability to discriminate the faces of two familiar persons by active choice (approach and touch). Furthermore, in successive stages of the experiment we investigated how well dogs discriminate humans in different representations by systematically reducing the informational richness and the quality of the stimuli. We found a huge inter-individual and inter-stage variance in performance, indicating differences across dogs in their learning ability as well as their selection of discriminative cues. On a group level, the performance of dogs significantly decreased when they were presented with pictures of human heads after having learned to discriminate the real heads, and when – after relearning – confronted with the same pictures showing only the inner parts of the heads. However, as two dogs quickly mastered all stages, we conclude that dogs are in principle able to discriminate people on the basis of visual information from their faces and by making active choices.

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open access article under a Creative Commons license

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