Moving beyond the monosyllable in models of skilled reading: Mega-study of disyllabic nonword reading

TY - JOUR

T1 - Moving beyond the monosyllable in models of skilled reading

T2 - Mega-study of disyllabic nonword reading

AU - Mousikou, Petroula

AU - Sadat, Jasmin

AU - Lucas, Rebecca

AU - Rastle, Kathleen

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Most English words are polysyllabic, yet research on reading aloud typically focuses on monosyllables. Forty-one skilled adult readers read aloud 915 disyllabic nonwords that shared important characteristics with English words. Stress, pronunciation, and naming latencies were analyzed and compared to data from three computational accounts of disyllabic reading, including a rule-based algorithm (Rastle & Coltheart, 2000) and connectionist approaches (the CDP++ model of Perry, Ziegler, & Zorzi, 2010, and the print-to-stress network of Ševa, Monaghan, & Arciuli, 2009). Item-based regression analyses revealed orthographic and phonological influences on modal human stress assignment, pronunciation variability, and naming latencies, while human and model data comparisons revealed important strengths and weaknesses of the opposing accounts. Our dataset provides the first normative nonword corpus for British English and the largest database of its kind for any language; hence, it will be critical for assessing generalization performance in future developments of computational models of reading.© 2016 The Author(s). Published by Elsevier Inc. This is an open access article. The attached document (embargoed until 20/10/2017) is the author produced version of the paper  uploaded in accordance with the publisher’s self- archiving policy.

AB - Most English words are polysyllabic, yet research on reading aloud typically focuses on monosyllables. Forty-one skilled adult readers read aloud 915 disyllabic nonwords that shared important characteristics with English words. Stress, pronunciation, and naming latencies were analyzed and compared to data from three computational accounts of disyllabic reading, including a rule-based algorithm (Rastle & Coltheart, 2000) and connectionist approaches (the CDP++ model of Perry, Ziegler, & Zorzi, 2010, and the print-to-stress network of Ševa, Monaghan, & Arciuli, 2009). Item-based regression analyses revealed orthographic and phonological influences on modal human stress assignment, pronunciation variability, and naming latencies, while human and model data comparisons revealed important strengths and weaknesses of the opposing accounts. Our dataset provides the first normative nonword corpus for British English and the largest database of its kind for any language; hence, it will be critical for assessing generalization performance in future developments of computational models of reading.© 2016 The Author(s). Published by Elsevier Inc. This is an open access article. The attached document (embargoed until 20/10/2017) is the author produced version of the paper  uploaded in accordance with the publisher’s self- archiving policy.

KW - mega-study

KW - reading aloud

KW - computational models of reading

KW - generalisation

KW - stress assignment

KW - pronounciation

U2 - 10.1016/j.jml.2016.09.003

DO - 10.1016/j.jml.2016.09.003

M3 - Article

SN - 0749-596X

VL - 93

SP - 169

EP - 192

JO - JOURNAL OF MEMORY AND LANGUAGE

JF - JOURNAL OF MEMORY AND LANGUAGE

ER -