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Receptive Multilingualism Across the Lifespan: Cognitive and Linguistic Factors in Cognate Guessing

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Receptive Multilingualism Across the Lifespan: Cognitive and Linguistic Factors in Cognate Guessing Languages and Literatures \ Multilingualism research Receptive multilingualism across the lifespan Cognitive and linguistic factors in cognate guessing Jan Vanhove Dissertation zur Erlangung der Doktorwürde an der Philosophischen Fakultät der Universität Freiburg in der Schweiz Genehmigt von der philosophischen Fakultät auf Antrag der Professoren Raphael Berthele (1. Gutachter) und Charlotte Gooskens (2. Gutach- terin). Freiburg, den 17. Januar 2014. Prof. Marc-Henry Soulet, Dekan. 2014 Receptive multilingualism across the lifespan Cognitive and linguistic factors in cognate guessing Jan Vanhove Cite as: Vanhove, Jan (2014). Receptive multilingualism across the lifespan. Cognitive and linguistic factors in cognate guessing. PhD thesis. University of Fribourg (Switzerland). Data and computer code available from: http://dx.doi.org/10.6084/m9.figshare.795286. Contents Tables xi Figures xiii Preface xv I Introduction 1 1 Context and aims 3 1.1 Cross-linguistic similarities in language learning . 4 1.2 Receptive multilingualism . 5 1.3 Multilingualism and the age factor . 8 1.4 The present project . 9 1.4.1 The overarching project ‘Multilingualism through the lifespan’ . 9 1.4.2 Aim, scope and terminology . 10 1.5 Overview . 13 II The lifespan development of cognate guess- ing skills 15 2 Inter-individual differences in cognate guessing skills 17 2.1 Linguistic repertoire . 18 2.1.1 Typological relation between the Lx and the L1 . 18 2.1.2 The impact of multilingualism . 19 2.2 Previous exposure . 26 vi Contents 2.3 Attitudes . 28 2.4 Age . 29 3 The lifespan development of cognition 33 3.1 Intelligence . 34 3.1.1 Fluid and crystallised intelligence . 34 3.1.2 Lifespan trajectories . 36 3.1.3 Implications . 38 3.2 Working memory . 39 3.2.1 The Baddeley–Hitch multi-component model . 40 3.2.2 Measuring working memory capacity . 43 3.2.3 Lifespan trajectories . 44 3.2.4 Implications . 45 3.3 Cognitive control . 46 4 Method 49 4.1 Participants . 49 4.2 Tasks and procedure . 50 4.2.1 Language background questionnaire . 51 4.2.2 German vocabulary test . 52 4.2.3 English language proficiency test . 53 4.2.4 Backward digit span task . 53 4.2.5 Raven’s advanced progressive matrices . 54 4.2.6 Cognate guessing task . 54 4.2.7 Measures not used in the analyses . 58 4.3 Method of analysis: Mixed-effects modelling . 59 4.3.1 A gentle introduction to the generalised linear mixed model . 59 4.3.2 Generalised additive models . 63 5 Data inspection 67 5.1 Cognate guessing data . 67 5.2 Inspection of the linguistic and cognitive variables . 70 5.2.1 Self-assessed language skills and number of foreign languages . 70 5.2.2 English language proficiency test . 71 5.2.3 German vocabulary test . 73 5.2.4 Raven’s advanced progressive matrices . 73 5.2.5 Backward digit span task . 73 Contents vii 5.3 Missing data imputation . 74 5.4 Multicollinearity assessment . 75 6 Age trends in cognate guessing success 77 7 The impact of language skills and cognitive character- istics 85 7.1 Written items . 85 7.2 Spoken items . 90 7.3 Variable-by-modality interactions . 91 8 Discussion 95 8.1 Age trends . 95 8.2 Linguistic and cognitive predictors . 96 8.3 Residual age trends . 100 8.4 Outlook . 101 III Integrating item-related characteristics 103 9 Item-related determinants of cognate guessing 105 9.1 Previous findings . 106 9.1.1 Formal distance between cognates . 106 9.1.2 The importance of consonants . 112 9.1.3 The importance of word beginnings . 114 9.1.4 ‘Exotic’ phones, suprasegmentals and graphemes 115 9.1.5 Lexical stress differences . 116 9.1.6 Word frequency, neighbourhood density and word length . 117 9.1.7 Cross-modality influences . 119 9.1.8 Summary and implications . 120 9.2 Quantification of predictors . 122 9.2.1 Formal distance . 122 9.2.2 Cognate frequencies . 129 9.2.3 Stimulus length . 129 9.3 Variable selection . 129 9.3.1 Bivariate relationships . 130 9.3.2 Random forest-based conditional permutation im- portance . 131 viii Contents 9.3.3 Results and discussion . 135 9.4 Regression modelling . 139 9.4.1 Written items . 139 9.4.2 Spoken items . 144 9.5 Discussion . 145 9.5.1 Variables considered . 147 9.5.2 The use of multiple supplier languages . 150 9.5.3 Postscript . 152 10 Participant–item interactions 153 10.1 Possible interactions . 153 10.1.1 Formal distance and fluid intelligence . 154 10.1.2 Formal distance and crystallised intelligence . 154 10.1.3 Cognate frequency and crystallised intelligence . 154 10.1.4 Interactions with age . 156 10.2 Method of analysis . 157 10.3 Interactions between age and item-related predictors . 158 10.3.1 Written items . 159 10.3.2 Spoken items . 163 10.4 Interactions between cognitive and item-related predictors 165 10.4.1 Written items . 165 10.4.2 Spoken items . 169 10.5 Discussion . 174 10.5.1 Written items . 174 10.5.2 Spoken items . 176 IV Conclusions 179 11 Synthesis and new directions 181 11.1 Synthesis . 181 11.2 Avenues for further research . 183 Appendices 187 A Items for the cognate guessing task 189 A.1 Written items . 189 A.2 Spoken items . 192 Contents ix B Description and results of the Simon task 195 References 199 Tables 4.1 Main demographic characteristics of the participant sample 50 4.2 Sequence of tasks in the task battery . 52 5.1 Summary data for the number of correctly translated stimuli per participant in the cognate guessing task . 68 5.2 Summary data for the participant-related linguistic and cognitive measures . 71 6.1 GAMM modelling translation accuracy on written target words in function of age . 80 6.2 GAMM modelling translation accuracy on spoken target words in function of age . 81 7.1 GLMM modelling translation accuracy on written target words in function of participant-related predictors . 87 7.2 GLMM modelling translation accuracy on spoken target words in function of participant-related predictors . 92 7.3 Variable-by-modality interactions . 94 9.1 Ad-hoc vowel phone inventory . 124 9.2 Ad-hoc consonant phone inventory . 125 9.3 Correlations between the item-related predictor variables and the by-item random intercepts . 131 9.4 Descriptive statistics of the item-related variables affecting written cognate guessing accuracy . 141 9.5 GLMM modelling translation accuracy on written target words in function of item- and participant-related predictors142 9.6 Descriptive statistics of the item-related variable affecting spoken cognate guessing accuracy . 144 xii Tables 9.7 GLMM modelling translation accuracy on spoken target words in function of item- and participant-related predictors146 10.1 GAMM modelling translation accuracy on written target words in function of interactions between age and item- related predictors . 160 10.2 GAMM modelling translation accuracy on spoken target words in function of interactions between age and item- related predictors . 164 10.3 GAMM modelling translation accuracy on written target words in function of interactions between participant- and item-related predictors . 167 10.4 GAMM modelling translation accuracy on spoken target words in function of interactions between participant- and item-related predictors . 171 A.1 Written stimuli used in the Swedish cognate guessing task 189 A.2 Spoken stimuli used in the Swedish cognate translation task192 B.1 Summary data for the Simon task . 197 Figures 3.1 Lifespan trajectories of fluid and crystallised intelligence 37 3.2 The multi-component working memory model . 41 5.1 Target word performance in function of profile word per- formance . 69 5.2 Sample age trends in the participant-related predictors . 72 5.3 Intercorrelations between the participant-related predictors 76 6.1 Number of correctly translated target words per partici- pant as a function of age . 78 6.2 Modelled age trends in translation accuracy . 82 7.1 Partial fixed effects of the participant-related predictors on translation accuracy (written items) . 88 7.2 Partial fixed effects of the participant-related predictors on translation accuracy (spoken items) . 93 8.1 Residual age effects in the random intercepts . 100 9.1 Example of a Levenshtein distance computation . 107 9.2 A 7- and an 8-slot Levenshtein alignment for the same cognate pair . 108 9.3 Ad-hoc vowel phone inventory . 126 9.4 Illustration of a small random forest . 133 9.5 Conditional permutation importances (written items) . 136 9.6 Conditional permutation importances (spoken items) . 137 9.7 Conditional permutation importances with Germanic pre- dictors (written items) . 140 xiv Figures 9.8 Histograms of the item-related variables affecting cognate guessing accuracy for written items . 141 9.9 Partial fixed effects of the item-related predictors on trans- lation accuracy (written items) . 143 9.10 Histogram of the item-related variable affecting cognate guessing accuracy for spoken items . 145 9.11 Partial fixed effects of the item-related predictors on trans- lation accuracy (spoken items) . 147 10.1 Interaction between age and Germanic Levenshtein dis- tance (written items) . 161 10.2 Interaction between age and Germanic cognate frequency (written items) . 161 10.3 Interaction between age and German Levenshtein distance (spoken items) . 165 10.4 Interaction between crystallised intelligence and Germanic Levenshtein distance (written items) . 168 10.5 Interaction between fluid intelligence and Germanic Lev- enshtein distance (written items) . 169 10.6 Interaction between crystallised intelligence and Germanic cognate frequency (written items) . 170 10.7 Interaction between crystallised intelligence and German Levenshtein distance (spoken items) . 172 10.8 Interaction between fluid intelligence and German Leven- shtein distance (spoken items) . 173 B.1 Lifespan development of the Simon effect .
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