Phonetic vowel training for child second language learners: the role of input variability and training task Gwen Brekelmans A thesis submitted for the degree of Doctor of Philosophy Department of Language & Cognition Division of Psychology and Language Sciences University College London 2020 1 Declaration I, Gwen Brekelmans confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 2 Acknowledgements First and foremost, thank you so much to Liz Wonnacott for being an amazing supervisor. Thank you for your help with pretty much anything, for all your hard work, and your statistical knowledge; I’ve learnt so much over the past four years. Thanks also for littering my writing drafts with helpful comments and restructuring suggestions, which have made this thesis a lot easier to follow. Any remaining structural randomness is entirely my own, and any typos have earned their right to be printed by managing to slip under the radar. Many thanks as well to Bronwen Evans for being a fantastic second supervisor: thank you for all your phonetic wisdom, helpful advice, inspiration, and incredible kindness. I’m eternally grateful to all the participants, adults and children alike, with extra thanks to the clever and wonderful children for all the hair I braided, laces I tied, stickers I got to hand out, and stories I was told, making testing anything but boring. Particular thanks also to all the teachers and head teachers at the schools I tested at, who let me wreak havoc on their schedule by taking children out of class in between sports days, Roman History projects, and play performances of Matilda. Thanks also to Anna, Cathy, Claire, Lewis, Matthew, Natasha, Rachel, Harrie, Karin, Lex, Maria, and Riek for being the voices in my experiments, and in doing so eliciting delightful comments from some very puzzled 7-year-olds. I‘m very grateful to Stuart Rosen and Kathleen McCarthy for providing me with the category boundary task for Study 2, even if I didn’t end up using it because of spectacularly timed technical difficulties during the first day of testing. Thanks to Jason Shaw for providing the code for the production analysis of Study 2, and to Andrew Clark for the LaTeX code to make the vowel diagrams. Many thanks to my fellow language learning lab members: Hanyu Dong, Anna Samara, Cat Silvey, Daniela Singh, Maša Vujović, and Liz Wonnacott, for being such a supportive lab. Thanks also to the various other labs and journal clubs I was adopted into, and to the wonderful people in Chandler House who made creating this thesis a little easier, in particular to Anna Casey, Andrew Clark, Richard 3 Jardine, Nadine Lavan, Merle Mahon, Carolyn McGettigan, Caroline Newton, David Newton, Steve Newton, Courtenay Norbury, Rachel Rees, Tim Schoof, Gisela Tomé Lourido, Outi Tuomainen, Rosemary Varley, and Vitor Zimmerer. Thanks to all the wonderful people in the LangCog PhD office for making it such a fun place to work, particularly to Ria Bernard, Lena Blott, Claudia Bruns, Sabrina Mahmood, Vanessa Meitanis, Claire Murphy, Jo Saul, Daniela Singh, Anna Volkmer, Maša Vujović, and Lydia Yeomans. Special thanks to Vanessa, Anna V, Lena, and Claudia for many more adventures outside of the office as well. A million thanks to Marina for your support and shared ‘phon phon’ enthusiasm. Thanks so much to Nadine for being equally baffled by the variability literature, and happy to rant about it (and many other things since) over tea. Thanks also to Giulia, Max, Vanessa, and Anna E for the regular lunch breaks, Shiran and Anna K for the coffee chats, and Julie for the ‘last PhD year panic coffees’ that never happened but the many dinners that did. Many thanks to Andrew, Anna V, Carolyn, Cat, Isabel, Nadine, and Vanessa for regularly letting me shove books at you and indulging my passionate raving about whatever latest quirky scifi/YA novel had struck my fancy, and to all the writers of those same books for providing a very welcome distraction to my research. Huge thanks to Željka and Tori for being the best RavenPuffIn housemates, to Alessa, Bas, Cüyén, Eliza, Maya, and Mengfan for the fun and friendship despite the multiple countries between us, and to Isabel for the theatre breaks. Kiitos especially to Sanni for keeping me sane and being there to answer the most random questions at all times, from ponderings on the pronunciation of ‘ooh’ versus ‘uhh’, to grammar checking odd sentences (including these acknowledgements). And finally, to my parents Riek and Harrie, my grandma Miet, and my brother Lex, for their loving support throughout the whole PhD: hartstikke bedankt, ik had het niet zonder jullie gekund! De laatste twee jaar zijn zeker niet makkelijk geweest maar het is ons toch maar gelukt. Pap, ik weet dat je keitrots zou zijn geweest. 4 Dedication Vur ons pap 2 October 1959 – 24 December 2017 5 Abstract Acquiring a second language speech contrast that does not exist in the native language is often difficult. High variability phonetic training (HVPT) is a well- established method used to train learners on specific non-native phoneme contrasts: it critically uses high variability (HV) input after earlier attempts using low variability (LV) input had proved unsuccessful. HVPT has since been successfully applied in many different adult studies. However, there is no consensus on the effect of input variation on children’s learning of non-native phoneme contrasts. This thesis aims to further investigate the effect of input variability on phonetic training for children, and examining whether they show the same HV benefit which has been argued to hold for adults. In the first set of studies, native English speaking adults and children were taught Dutch vowels in a single computerised training session, during which they received either HV or LV input. Additionally, the traditional HVPT paradigm was adapted to see if mapping vowels to orthography-like symbols representing phoneme categories was more or less effective than a vocabulary training method without such representations. Learning was stronger with training most akin to vocabulary learning, particularly for children, suggesting a benefit for a more meaningful learning context. Crucially, there was no evidence of a HV benefit for either children or adults. 6 The second study was a two-week training study in which Dutch children of two age groups were trained on Standard Southern British English vowel contrasts. Since picture-based training had proved beneficial, this study combined both orthography and pictures in training. Potential effects of HV or LV input in training were investigated using a pre/post-test design. Older children outperformed younger children throughout, and again no evidence for a variability benefit was found. This indicates children might not benefit from high input variability. 7 Impact Statement The majority of the world’s population learns at least one language in addition to their native language(s). However, learning to perceive and produce the sounds of a non-native language is often difficult. Part of this is due to the complexity of speech: the input a learner receives is inherently variable, and learning which cues to pay attention to takes time and practice. The studies in this thesis investigate how children acquire second language speech in the context of a phonetic training paradigm. The research in this thesis particularly focusses on the role of variability and the type of training task used in acquiring non-native speech sounds. Although there is a sizable literature with adult learners in this area, few previous studies have investigated these questions working with children. The research in this thesis contributes towards a better understanding of the role of variability in second language speech learning, as well as providing a foundation for future research into the role of variability in second language speech learning in children. This thesis also makes an important methodological contribution by being the first in the phonetic training literature to use Bayes Factors as the key method of inference throughout the thesis. This is an advance because with traditional frequentist methods of inference, it is not possible to say whether a non-significant result actually provides evidence against the set hypothesis, or is ambiguous. Bayes Factors differentiate these two possibilities. Further, the second study presented here was pre-registered, which means the analyses and hypotheses were planned 8 in advance and the reader can compare the planned analyses and hypotheses with the ones presented in this thesis. This is important in preventing publication bias which is known to be a problem in the field. This thesis also has more practical implications for second language learning in naturalistic settings. The finding that variability is not beneficial for children learning a second language means that schools might want to focus on providing varied content while potentially not needing to put as much effort and resources in providing content from a large number of talkers. Rather, this thesis suggests that the choice of task used for learning seems to play a more important role. This also extends to language learning software development and computer assisted language learning, where the choice of task used in the programs could prove more influential more than the amount of talker variability provided in the input. However, for all of these implications, it is important to keep in mind that this thesis is limited to Dutch learners of English and English learners of Dutch, learning specific vowel contrasts, and that caution should be exercised in extending them to other groups of learners or other types of training. 9 Table of contents Declaration .................................................................................................................