[Lisa_seminaires] Fwd: UdeM-McGill-MITACS machine learning seminar Fri Nov. 26 at 14h30, AA-3195

Dumitru Erhan erhandum at iro.umontreal.ca
Ven 26 Nov 10:30:50 EST 2010


Reminder!

Dumitru

---------- Forwarded message ----------
From: Dumitru Erhan <erhandum at iro.umontreal.ca>
Date: Monday, November 22, 2010
Subject: UdeM-McGill-MITACS machine learning seminar Fri Nov. 26 at 14h30, AA-3195
To: lisa_seminaires at iro.umontreal.ca


The UdeM-McGill-MITACS machine learning seminar series is continuing
this Friday:

The use of visual information during face recognition and reading

by Frédéric Gosselin
Département de psychologie
Université de Montréal

Location: Pavillon André-Aisenstadt (UdeM), room AA-3195
Time: Friday, Nov 26, 14:30

Abstract: I will present a number of studies that were carried out in
my laboratory using Bubbles, a technique that I pioneered almost 10
years ago (Gosselin & Schyns, 2001).  Bubbles is a generic
psychophysical technique designed to reveal the effective information
in any recognition task. I will talk about Bubbles experiments on the
use of information to discriminate the six basic facial expressions
(happiness, fear, sadness, surprise, disgust, and anger) in normal
humans (Smith et al., 2005) and in SM, a woman with a brain lesion
encompassing all nuclei of the amygdala bilaterally, which has
difficulty discriminating fearful from happy faces (Adolphs et al.,
2005).  For example, I’ve shown that SM’s difficulty co-occurs with a
failure to use high-spatial frequencies (90-22.5 cycles per face)
within the eye region of the face stimuli like normal controls.
Relatedly, I've discovered that unilateral amygdala lesions induce
ipsilateral biases in a facial expression discrimination task but not
in a facial gender discrimination task (Gosselin, et al., in press).
I'll briefly discuss other face recognition tasks that were examined
using Bubbles include gender (e.g., Dupuis-Roy et al., 2009), identity
(Willenbockel et al., 2010), and age.  Furthermore, I’ll talk about
the application of Bubbles to reveal which areas of letters are
efficient for the identification of letters (Fiset et al., 2008).
I’ve provided the first empirical evidence that line terminations are
the most important features for letter identification.  Finally, I’ll
present the space-time use of letter information while reading using
the Bubbles technique (Blais et al., 2009).



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