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Abstract:
We attempt to understand visual classication in humans using both psychophysical and machine
learning techniques. Frontal views of human faces were used for a gender classication
task. Human subjects classied the faces and their gender judgment, reaction time (RT) and
condence rating (CR) were recorded for each face. RTs are longer for incorrect answers than
for correct ones, high CRs are correlated with low classication errors and RTs decrease as the
CRs increase. This results suggest that patterns difcult to classify need more computation by
the brain than patterns easy to classify. Hyperplane learning algorithms such as Support Vector
Machines (SVM), Relevance Vector Machines (RVM), Prototype learners (Prot) and K-means
learners (Kmean) were used on the same classication task using the Principal Components
of the texture and oweld representation of the faces. The classication performance of the
learning algorithms was estimated using the face database with the true gender of the faces as
labels, and also with the gender estimated by the subjects. Kmean yield a classication performance
close to humans while SVM and RVM are much better. This surprising behaviour
may be due to the fact that humans are trained on real faces during their lifetime while they
were here tested on articial ones, while the algorithms were trained and tested on the same
set of stimuli. We then correlated the human responses to the distance of the stimuli to the
separating hyperplane (SH) of the learning algorithms. On the whole stimuli far from the SH
are classied more accurately, faster and with higher condence than those near to the SH if
we pool data across all our subjects and stimuli. We also nd three noteworthy results. First,
SVMs and RVMs can learn to classify faces using the subjects' labels but perform much better
when using the true labels. Second, correlating the average response of humans (classication
error, RT or CR) with the distance to the SH on a face-by-face basis using Spearman's rank
correlation coefcients shows that RVMs recreate human performance most closely in every
respect. Third, the mean-of-class prototype, its popularity in neuroscience notwithstanding, is
the least human-like classier in all cases examined.
