Search

We investigated age-related decline in adult learning of Dutch as an additional language (Ln) in speaking, writing, listening, and reading proficiency test scores for 56,024 adult immigrants with 50 L1s who came to the Netherlands for study or work. Performance for all four language skills turned out to decline monotonically after an age of arrival of about 25 years, similar to developmental trajectories observed in earlier aging research on additional language learning and in aging research on cognitive abilities. Also, linguistic dissimilarity increased age-related decline across all four language skills, but speaking in particular. We measured linguistic dissimilarity between first languages (L1s = 50) and Dutch (Ln) for morphology, vocabulary, and phonology. Our conclusion is that the L1 language background influences the effects of age-related decline in adult language learning, and that the constraints involved reflect both biological (language learning ability) and experience-based (acquired L1 proficiency) cognitive resources.

Older adults (OA) need to make many important and difficultdecisions. Often, there are too many options available to ex-plore exhaustively, creating the ubiquitous tradeoff betweenexploration and exploitation. How do OA make these com-plex tradeoffs? We investigated age-related shifts in solvingexploration-exploitation tradeoffs depending on the complex-ity of the choice environment. Participants played four andeight option bandit problems with numbers of gambles and av-erage rewards available on the screen. OA reliably performedworse in a more complex choice environment and were alsomore deviant from an optimality model (Thompson sampling),which keeps track of uncertainty beyond just the mean or lastreward. OA seem to process important information in morecomplex choice environments sub-optimally, suggesting lim-ited representations of future rewards. This interpretation fitsto multiple contexts in the complex cognitive aging literature,in particular to the context of challenges in the maintenance ofgoal-directed learning.

Applied linguistics may benefit from a morphological complexity measure to get a better grip on language learning problems and to better understand what kind of typological differences between languages are more important than others in facilitating or impeding adult learning of an additional language. Using speaking proficiency scores of 9,000 adult learners of Dutch as an additional language, we reproduced the findings of the Schepens et al. (2013a) study, using a reduced morphological complexity measure. We wanted to define a reduced measure to reveal which morphological features constitute the really important learning problems. Adult language learners whose first language (L1) has a less complex morphological feature configuration than Dutch turned out to have more learning difficulties in acquiring Dutch the less complex their L1 is in relation to Dutch. The reduced measure contains eight features only. In addition, we found cognitive aging effects that corroborate the construct validity of the morphological measure we used. Generally, adult language learners' speaking skills in Dutch improve when residing longer in the host country. However, this conclusion is only warranted when their L1 morphological complexity is at least comparable to Dutch morphological complexity. If the morphological complexity of their L1 is lower as compared to Dutch, the effect of length of residence may even reverse and have a negative impact on speaking skills in Dutch. It was observed that the negative effect of age of arrival is mitigated when adult language learners have a command of a second language (L2) with higher morphological complexity. We give morphological information for five additional target languages: Afrikaans, Chinese, English, German, and Spanish.

This study investigates head nods in natural dyadic German Sign Language (DGS) interaction, with the aim of finding whether head nods serving different functions vary in their phonetic characteristics. Earlier research on spoken and sign language interaction has revealed that head nods vary in the form of the movement. However, most claims about the phonetic properties of head nods have been based on manual annotation without reference to naturalistic text types and the head nods produced by the addressee have been largely ignored. There is a lack of detailed information about the phonetic properties of the addressee's head nods and their interaction with manual cues in DGS as well as in other sign languages, and the existence of a form-function relationship of head nods remains uncertain. We hypothesize that head nods functioning in the context of affirmation differ from those signaling feedback in their form and the co-occurrence with manual items. To test the hypothesis, we apply OpenPose, a computer vision toolkit, to extract head nod measurements from video recordings and examine head nods in terms of their duration, amplitude and velocity. We describe the basic phonetic properties of head nods in DGS and their interaction with manual items in naturalistic corpus data. Our results show that phonetic properties of affirmative nods differ from those of feedback nods. Feedback nods appear to be on average slower in production and smaller in amplitude than affirmation nods, and they are commonly produced without a co-occurring manual element. We attribute the variations in phonetic properties to the distinct roles these cues fulfill in turn-taking system. This research underlines the importance of non-manual cues in shaping the turn-taking system of sign languages, establishing the links between such research fields as sign language linguistics, conversational analysis, quantitative linguistics and computer vision. [ABSTRACT FROM AUTHOR]

Many people speak more than two languages. How do languages acquired earlier affect the learnability of additional languages? We show that linguistic distances between speakers' first (L1) and second (L2) languages and their third (L3) language play a role. Larger distances from the L1 to the L3 and from the L2 to the L3 correlate with lower degrees of L3 learnability. The evidence comes from L3 Dutch speaking proficiency test scores obtained by candidates who speak a diverse set of L1s and L2s. Lexical and morphological distances between the L1s of the learners and Dutch explained 47.7% of the variation in proficiency scores. Lexical and morphological distances between the L2s of the learners and Dutch explained 32.4% of the variation in proficiency scores in multilingual learners. Cross-linguistic differences require language learners to bridge varying linguistic gaps between their L1 and L2 competences and the target language.

Researchers on bilingual processing can benefit from computational tools developed in artificial intelligence. We show that a normalized Levenshtein distance function can efficiently and reliably simulate bilingual orthographic similarity ratings. Orthographic similarity distributions of cognates and non-cognates were identified across pairs of six European languages: English, German, French, Spanish, Italian, and Dutch. Semantic equivalence was determined using the conceptual structure of a translation database. By using a similarity threshold, large numbers of cognates could be selected that nearly completely included the stimulus materials of experimental studies. The identified numbers of form-similar and identical cognates correlated highly with branch lengths of phylogenetic language family trees, supporting the usefulness of the new measure for cross-language comparison. The normalized Levenshtein distance function can be considered as a new formal model of cross-language orthographic similarity.

We investigated age-related decline in adult learning of Dutch as an additional language (L n) in speaking, writing, listening, and reading proficiency test scores for 56,024 adult immigrants with 50 L1s who came to the Netherlands for study or work. Performance for all four language skills turned out to decline monotonically after an age of arrival of about 25 years, similar to developmental trajectories observed in earlier aging research on additional language learning and in aging research on cognitive abilities. Also, linguistic dissimilarity increased age-related decline across all four language skills, but speaking in particular. We measured linguistic dissimilarity between first languages (L1s = 50) and Dutch (L n) for morphology, vocabulary, and phonology. Our conclusion is that the L1 language background influences the effects of age-related decline in adult language learning, and that the constraints involved reflect both biological (language learning ability) and experience-based (acquired L1 proficiency) cognitive resources. [ABSTRACT FROM AUTHOR]

A reanalysis of data drawn by Hartshorne, Tenenbaum, and Pinker (2018) from two‐thirds of a million English speakers showed that their overall conclusion of one sharply defined critical age at 17.4 for all language learners is based on artificial results. We show that instead of a discontinuous exponential learning with sigmoidal decay (ELSD) model, a continuous ELSD model had a better fit when applied separately to monolinguals, bilinguals, and early immersion learners. Only for nonimmersion learners and later immersion learners did a discontinuous ELSD model have a better fit, with a critical age of 18.6 and 19.0 years of age, respectively. These age effects can be interpreted as schooling effects. We suggest that personal and societal factors, including differences in living circumstances and socialization, may bring about age‐specific discontinuity patterns in language learning and in language learning rate. The implication is that they are not the outcome of cognition‐driven developmental factors leading to one or more critical periods. [ABSTRACT FROM AUTHOR]

Many people speak more than two languages. How do languages acquired earlier affect the learnability of additional languages? We show that linguistic distances between speakers' first (L1) and second (L2) languages and their third (L3) language play a role. Larger distances from the L1 to the L3 and from the L2 to the L3 correlate with lower degrees of L3 learnability. The evidence comes from L3 Dutch speaking proficiency test scores obtained by candidates who speak a diverse set of L1s and L2s. Lexical and morphological distances between the L1s of the learners and Dutch explained 47.7% of the variation in proficiency scores. Lexical and morphological distances between the L2s of the learners and Dutch explained 32.4% of the variation in proficiency scores in multilingual learners. Cross-linguistic differences require language learners to bridge varying linguistic gaps between their L1 and L2 competences and the target language. [ABSTRACT FROM AUTHOR]

Researchers on bilingual processing can benefit from computational tools developed in artificial intelligence. We show that a normalized Levenshtein distance function can efficiently and reliably simulate bilingual orthographic similarity ratings. Orthographic similarity distributions of cognates and non-cognates were identified across pairs of six European languages: English, German, French, Spanish, Italian, and Dutch. Semantic equivalence was determined using the conceptual structure of a translation database. By using a similarity threshold, large numbers of cognates could be selected that nearly completely included the stimulus materials of experimental studies. The identified numbers of form-similar and identical cognates correlated highly with branch lengths of phylogenetic language family trees, supporting the usefulness of the new measure for cross-language comparison. The normalized Levenshtein distance function can be considered as a new formal model of cross-language orthographic similarity. [ABSTRACT FROM PUBLISHER]