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