Proceedings of the Annual Meeting of the Cognitive Science Society

In a caricature drawing, the artist exaggerates the facial features of a person in proportion to their deviations from the average face. Empirically, it has been shown that caricature drawings are more quickly recognized than veridical drawings (Rhodes, Brennan, & Carey, 1987). Two competing hypotheses have been postulated to account for the caricature advantage. The caricature hypothesis claims that the caricature drawing finds a more similar match in memory than the veridical drawing because the underlying face representation is stored as an exaggeration. The distinctive features hypothesis claims that the caricature drawing produces speeded recognition by graphically emphasizing the distinctive properties that serve to individuate that face from other faces stored in memory. A computational test of the two hypotheses was performed by training a neural network model to recognize individual face vectors and then testing the model's ability to recognize both caricaturized and veridical versions of the face vectors. It was found that the model produced a higher level of activation to caricature face vectors than to the non-distorted face vectors. The obtained caricature advantage stems from the model's ability to abstract the distinctive features from a learned set of inputs. Simulation results were therefore interpreted as support for the distinctive features hypothesis.