Low sensitivities but surprisingly high efficiencies for face-gender discrimination from interattribute distances

Real-world interattribute distances lead to inefficient face gender categorization.

The processing of interattribute distances is believed to be critical for upright face categorization. A recent study by Taschereau-Dumouchel, Rossion, Schyns, and Gosselin (2010) challenged this idea by showing that participants were nearly at chance when asked to identify faces on the sole basis of real-world interattribute distances, while they were nearly perfect when all other facial cues ...

Journal of Experimental Psychology: Human Perception and Performance Real-World Interattribute Distances Lead to Inefficient Face Gender Categorization

Face adaptation improves gender discrimination

Adaptation to a visual pattern can alter the sensitivities of neuronal populations encoding the pattern. However, the functional roles of adaptation, especially in high-level vision, are still equivocal. In the present study, we performed three experiments to investigate if face gender adaptation could affect gender discrimination. Experiments 1 and 2 revealed that adapting to a male/female fac...

Distance weighted discrimination of face images for gender classification

We illustrate the advantages of distance weighted discrimination for classification and feature extraction in a High Dimension Low Sample Size (HDLSS) situation. The HDLSS context is a gender classification problem of face images in which the dimension of the data is several orders of magnitude larger than the sample size. We compare distance weighted discrimination with Fisher’s linear discrim...

Efficiencies for the statistics of size discrimination.

Different laboratories have achieved a consensus regarding how well human observers can estimate the average orientation in a set of N objects. Such estimates are not only limited by visual noise, which perturbs the visual signal of each object's orientation, they are also inefficient: Observers effectively use only √N objects in their estimates (e.g., S. C. Dakin, 2001; J. A. Solomon, 2010). M...