@poster{2090,
  title = {Study of Human Classification using Psychophysics and Machine Learning},
  abstract = {We attempt to reach a better understanding of classication in humans using both
  psychophysical and machine learning techniques. In our psychophysical paradigm the
  stimuli presented to the human subjects are modied using machine learning algorithms
  according to their responses. Frontal views of human faces taken from a processed
  version of the MPI face database are employed for a gender classication task. The
  processing assures that all heads have same mean intensity, same pixel-surface area
  and are centered. This processing stage is followed by a smoothing of the database
  in order to eliminate, as much as possible, scanning artifacts. Principal Component
  Analysis is used to obtain a low-dimensional representation of the faces in the database.
  A subject is asked to classify the faces and experimental parameters such as class (i.e.
  female/male), condence ratings and reaction times are recorded. A mean classication
  error of 14.5% is measured and, on average, 0.5 males are classied as females
  and 21.3females as males. The mean reaction time for the correctly classied faces is
  1229 +- 252 [ms] whereas the incorrectly classied faces have a mean reaction time of
  1769 +- 304 [ms] showing that the reaction times increase with the subject's classi-
  cation error. Reaction times are also shown to decrease with increasing condence,
  both for the correct and incorrect classications. Classication errors, reaction times
  and condence ratings are then correlated to concepts of machine learning such as
  separating hyperplane obtained when considering Support Vector Machines, Relevance
  Vector Machines, boosted Prototype and K-means Learners. Elements near the separating
  hyperplane are found to be classied with more errors than those away from
  it. In addition, the subject's condence increases when moving away from the hyperplane.
  A preliminary analysis on the available small number of subjects indicates that
  K-means classication seems to re
  ect the subject's classication behavior best. The
  above learnersare then used to generate \special" elements, or representations, of the
  low-dimensional database according to the labels given by the subject. A memory experiment
  follows where the representations are shown together with faces seen or unseen
  during the classication experiment. This experiment aims to assess the representations
  by investigating whether some representations, or special elements, are classied
  as \seen before" despite that they never appeared in the classication experiment,
  possibly hinting at their use during human classication.},
  volume = {6},
  pages = {149},
  editors = {H.H. B{\"u}lthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2003},
  author = {Graf, ABA. and Wichmann, FA. and B{\"u}lthoff, HH. and Sch{\"o}lkopf, B.},
  month_numeric = {2}
}