Chernoff faces (Chernoff, 1973) are an early attempt at large scale multivariate data representation andare based on underlying assumptions that have not been empirically tested. This study investigated i)whether data coded as Chernoff faces benefit from face perception, and ii) whether each feature of aChernoff face is equally salient. We tested four pairs of oppositely coded Chernoff faces (e.g. smile, frown)in an oddball search paradigm with set sizes of 5, 10 and 15. To evaluate whether face perception aidedsearch, we used a control condition with inverted faces, a manipulation known to diminish holistic faceprocessing. Equivalent search efficiencies for upright and inverted Chernoff faces demonstrates that theydo not receive any significant benefit from face perception. Additionally, none of the features testedtogether produced significantly different search efficiencies from one another. It also appears that overallChernoff faces do not allow for particularly efficient visual search.
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