Word Processing in Languages Using Non-Alphabetic Scripts
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Word processing in languages using non-alphabetic scripts. The cases of Japanese and Chinese Published by LOT Phone: +31 30 253 6006 Trans 10 3512 JK Utrecht e-mail: [email protected] The Netherlands http://www.lotschool.nl Cover photo by Rinus Verdonschot ISBN: 978-94-6093-059-1 NUR 616 Copyright © 2011: Rinus Verdonschot. All rights reserved. Word processing in languages using non-alphabetic scripts. The cases of Japanese and Chinese PROEFSCHRIFT Ter verkrijging van de graad van Doctor aan de Universiteit Leiden op gezag van Rector Magnificus prof. mr. P.F. van der Heijden, volgens besluit van het College voor Promoties te verdedigen op donderdag 12 mei 2011 klokke 13.45 uur door Rinus Verdonschot Geboren te Geldrop in 1977 Promotie commissie Promotor: Prof. Dr. N.O. Schiller Co-Promotor: Dr. W. La Heij Overige Leden: Prof. Dr. B. Hommel Prof. Dr. L.L. Cheng Prof. Dr. K. Tamaoka (Nagoya University) Dr. C.C. Levelt Dr. P.A. Starreveld (Universiteit van Amsterdam) Acknowledgements I am indebted to my supervisor (Prof. Niels Schiller) and co- supervisor (dr. Wido La Heij) for allowing me to explore my fascination for the processing of logographic scripts. I have learned a lot from both of you and our discussions were always held in a constructive and pleasant way. I am also very grateful to the LUCL and the Cognitive Psychology department for supporting me when I was collecting data in Japan and China. In this respect I want to especially mention the involvement of Albertien Olthoff and Gea Hakker. Without you I could not have efficiently planned and carried out the work necessary for this dissertation. I also want to thank Prof. Katsuo Tamaoka, Prof. Qingfang Zhang, dr. Sachiko Kiyama, dr. John Phillips, Nanae Murata, Xuebing Zhu, Haoran Zhao and Chikako Ara for their invaluable assistance in recruiting and testing participants in Japan and China. During my PhD period I was lucky to have met so many nice, smart and crazy colleagues here in the Netherlands as well as in Japan and China, it would unfortunately take up too much space to personally acknowledge everybody I have a good connection with but I do want to mention my two office-mates (Marieke and Jun) for making our time together in (and outside) the office so enjoyable. Last but not least I am grateful to my friends, my two ‘paranimfen’ (Johan and Vincent) and above all to my family for their constant support! ϕ5ϟ3ϙϧϗ) ኽ Contents Acknowledgements Chapter 1: General Introduction........................................................... 7 Chapter 2: Japanese kanji primes facilitate naming of multiple katakana targets.................................................................................. 25 Experiment 1: Reading aloud katakana targets preceded by kanji primes with Equal Reading Preference...................................... 33 Experiment 2: Reading aloud katakana strings preceded by kanji primes with Equal and high-ON/high-KUN readings................ 38 Chapter 3: Semantic context effects when naming Japanese kanji, but not Chinese hànzì ............................................................................... 53 Experiment: Semantic context effects in Japanese and Chinese word reading .............................................................................. 59 Chapter 4: Context effects when naming Japanese (but not Chinese), and degraded Dutch nouns: evidence for processing costs? .............. 73 Experiment 1: Naming Japanese kanji with homophonic distractor pictures ...................................................................... 81 Experiment 2: Naming Chinese hànzì with homophonic pictures .................................................................................................... 86 Experiment 3: Dutch bare noun, det+N, and degraded-word naming........................................................................................ 89 Chapter 5: The functional unit of Japanese word naming: evidence from masked priming. ...................................................................... 105 Experiment 1: Segment versus mora priming .......................... 115 Experiment 2: The effects of kana and romaji scripts on masked onset priming............................................................................ 118 Experiment 3: Effect of hiragana primes on romaji targets .... 121 Experiment 4a: Distinguishing the CV from the syllable: MOPE group. ....................................................................................... 123 Experiment 4b: Distinguishing the CV from the syllable: MORA group. ....................................................................................... 125 Chapter 6: Summary and Conclusion............................................... 149 Samenvatting in het Nederlands....................................................... 163 Curriculum Vitae.............................................................................. 169 - 7 - Chapter 1: General Introduction - 8 - General introduction. This thesis is concerned with reading aloud words written in a logographic script such as Japanese kanji and Chinese hànzì which are amongst the most complex writing systems in the world. Imagine therefore, for a second, that you have to read aloud a word presented on a screen. How long would that take you? The answer is: not very long, generally around half a second. The ability and speed with which we convert visually presented material (such as alphabetic words, logographic words, symbols and pictures) to intelligible speech output is a quite impressive cognitive achievement. However, the exact amount of time it takes to name aloud visually presented words or pictures depends on a large number of factors, such as the language or script used, specific properties of a word or picture and the setting (or experimental condition) in which a target word or picture has to be named. Word properties that affect naming times are e.g. language, frequency and length, but also regularity. Glushko (1979) found, for instance, that irregular words (i.e. words that do not have a consistent spelling-to-sound correspondence) are named slower compared to regular words; however, others (e.g. Jared, McRae & Seidenberg, 1990) found this to hold only for low-frequency items (such as the first syllable /die/ in DIESEL and DIET which is pronounced /di/ or /daI/). Picture naming latencies are also found to be dependent on properties such as frequency. Oldfield and Wingfield (1964) for instance, found a linear correlation between picture name frequency and naming latency (i.e. higher frequency leads to faster RTs). For many decades now scientists have designed chronometric experiments and investigated speech errors to better understand which components of information processing are involved and which routes are taken when producing words. Models of language production Models of language (and word) production usually discriminate between different processing levels which may or may not (depending on the model) include: conceptualization, assignment of syntactic features, phonological word-form encoding, and ultimately articulation (for overviews see e.g., Caramazza, 1997; Dell, 1986; Levelt, Roelofs, & Meyer, 1999; Coltheart, Rastle, Perry, Langdon & Ziegler, 2001). In this thesis, I do refer to other models to account for my findings, but my primary focus lies on the most - 9 - detailed model of language production called “Word Encoding by Activation and VERification” or WEAVER++ (which is largely based on evidence from chronometric experiments) developed by Roelofs (1992) and Levelt et al. (1999). According to this model, speech production involves a specific number of levels (see Figure 1). Figure 1. The route a to-be-named picture follows in naming in the WEAVER++ model of language production (Roelofs, 1992; Levelt et al., 1999; sg = singular) First of all, at the conceptual level, contents of the utterance related to the communicative situation or intention become activated. For instance, if one is instructed to name a picture (e.g., of a “house”), the presented object is visually processed and activates the appropriate concept. It is commonly assumed that not only the target concept “house” but also semantically related concepts (such as “building” or “farm”) receive activation (Collins & Loftus, 1975; Levelt et al., 1999; Glaser & Düngelhoff, 1984; Starreveld & La Heij, 1995). Subsequently, the activated conceptual nodes will spread activation to the level of lexical representations containing the word’s lexical-syntactic (or lemma) representation. This involves accessing parameters concerning morpho-syntactic make-up of the words. For - 10 - instance, if one has been presented with multiple houses (“housepl”), then at this level the parameter for plural (+s) for this specific word will be set. Obviously, syntactic information for irregular words, such as “fish” having an irregular plural form (e.g. “fish”), is also stored and incorporated at this level. The time it takes to select the target lexical-syntactic representation depends on the number of co-activated representations (and their respective activation strengths) that are assumed to compete for selection (but see Mahon, Costa, Peterson, Vargas, & Caramazza, 2007 for an alternative proposal). Competition at the lexical-syntactic level results from cascading activation between the conceptual and lexical-syntactic levels. According to Levelt et al. (1999; see also Levelt et al., 1991), one lexical-syntactic representation is ultimately selected and only this representation