@article{fiset_spatio-temporal_2009,

title = {The spatio-temporal dynamics of visual letter recognition},

author = {D. Fiset and C. Blais and M. Arguin and K. Tadros and C. Éthier-Majcher and D. Bub and F. Gosselin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67649525418&doi=10.1080%2f02643290802421160&partnerID=40&md5=bca7bda93d59994f2679faff9d93f46a},

doi = {10.1080/02643290802421160},

issn = {02643294},

year  = {2009},

date = {2009-01-01},

journal = {Cognitive Neuropsychology},

volume = {26},

number = {1},

pages = {23–35},

abstract = {We applied the Bubbles technique to reveal directly the spatio-temporal features of uppercase Arial letter identification. We asked four normal readers to each identify 26,000 letters that were randomly sampled in space and time; afterwards, we performed multiple linear regressions on the participant's response accuracy and the space-time samples. We contend that each cluster of connected significant regression coefficients is a letter feature. To bridge the gap between the letter identification literature and this experiment, we also determined the relative importance of the features proposed in the letter identification literature. Results show clear modulations of the relative importance of the letter features of some letters across time, demonstrating that letter features are not always extracted simultaneously at constant speeds. Furthermore, of all the feature classes proposed in the literature, line terminations and horizontals appear to be the two most important for letter identification. © 2008 Psychology Press, an imprint of the Taylor & Francis Group.},

keywords = {article, bootstrapping, Bubbles technique, Discrimination Learning, human, Humans, Image analysis, linear regression analysis, methodology, Models, Nonlinear Dynamics, nonlinear system, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, Psychological, psychological model, reaction time, recognition, Recognition (Psychology), task performance, temporal summation, time, Time Factors, Visual, word recognition},

pubstate = {published},

tppubtype = {article}

}