BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ILCB - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ILCB
X-ORIGINAL-URL:https://www.ilcb.fr
X-WR-CALDESC:Events for ILCB
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20130331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20131027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20140330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20141026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20150329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20151025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20141128T110000
DTEND;TZID=Europe/Paris:20141128T130000
DTSTAMP:20260424T212146
CREATED:20190212T175107Z
LAST-MODIFIED:20190212T175110Z
UID:2291-1417172400-1417179600@www.ilcb.fr
SUMMARY:Probabilistic Graphical Models of Dyslexia by Yaïr LAKRETZ
DESCRIPTION:Probabilistic Graphical Models of Dyslexia by Yaïr LAKRETZ (‘Sagol’ school of neuroscience\, Tel-Aviv University)\nReading is a complex cognitive faculty\, errors in which assume diverse forms. To capture the complex structure of reading errors\, we propose a novel way of analyzing these errors using probabilistic graphical models. Our study focuses on three inquiries. (a) We examine which graphical model best captures the hidden structure of reading errors. (b) We draw on the results of (a) to resolve a theoretical debate on whether dyslexia is a monolithic or heterogeneous disorder. (c) We examine whether a graphical model can diagnose dyslexia closely to how experts do. We explore three different models: an LDA-based model and two Naïve Bayes models which differ by their assumptions about the generation process of reading errors. The models are trained on a large corpus of reading errors. Our results show that the LDA-based model best captures patterns of reading errors and may therefore contribute to the understanding of dyslexia and to the diagnostic procedure. We also demonstrate that patterns of reading errors are best described by a model assuming multiple dyslexia subtypes\, therefore supporting the heterogeneous approach to dyslexia. Finally\, a Naïve Bayes model\, which shares assumptions with diagnostic practice\, best replicates labels given by clinicians and can be therefore used for automation of the diagnosis process.
URL:https://www.ilcb.fr/event/probabilistic-graphical-models-of-dyslexia-by-yair-lakretz/
LOCATION:Salle de conférences\, 5 avenue Pasteur\, Aix-en-Provence\, 13100\, France
CATEGORIES:Seminars
END:VEVENT
END:VCALENDAR