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UID:20260913T061149EDT-7720l5luVM@132.216.98.100
DTSTAMP:20260913T101149Z
DESCRIPTION:Abstract: Decades of research have amassed an impressive body o
 f knowledge on sources of variability in eye-movement control in reading. 
 Major sources include text characteristics (i.e. properties of letters\, m
 orphemes\, words\, sentences\, or passages) and participant characteristic
 s (clinical status\, age\, reading experience\, IQ\, working memory\, etc.
 ). As a result\, word length\, frequency of occurrence and predictability 
 in context – and more recently\, component skills of reading (Reichle et a
 l.\, 2013) – are routinely used as benchmark predictors of eye-movements a
 nd core parameters of computational models of eye-movement control (Reichl
 e et al.\, 2006\; Engbert\, 2005). However\, little effort has been alloca
 ted to establishing  how important individual predictors or (sets of predi
 ctors) of eye-movements are relative to other predictors (or other sets). 
 Yet such information is crucial for highlighting which aspects of linguist
 ic complexity and individual ability and skill are central for efficient r
 eading and when in the time-course of reading they are engaged.\n	I will pr
 esent a study in which the non-parametric machine-learning technique of ra
 ndom forests evaluates the relative importance of a large set of text-rela
 ted and participant-related variables as predictors of eye-movements and c
 omprehension scores observed during text reading. I will demonstrate the u
 tility of this method both for the comprehensive description of individual
  differences and language-driven variability in reading behavior unfolding
  over time\, and for the generation of specific hypotheses that can be pur
 sued with the confirmatory analysis.\n
DTSTART:20150630T173000Z
DTEND:20150630T190000Z
LOCATION:Room S3/4\, Stewart Biology Building\, CA\, QC\, Montreal\, H3A 1B
 1\, 1205 avenue du Docteur-Penfield
SUMMARY:Random forests as an exploratory tool for eye-movement control in r
 eading\, Victor Kuperman\, PhD
URL:https://www.mcgill.ca/channels/event/random-forests-exploratory-tool-ey
 e-movement-control-reading-victor-kuperman-phd-253754
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