Learning nonadjacent dependencies embedded in sentences of an artificial language: When learning breaks down.

2018 ◽  
Vol 44 (4) ◽  
pp. 604-614 ◽  
Author(s):  
Felix Hao Wang ◽  
Toben H. Mintz
Keyword(s):  
Artificial Language ◽  
Nonadjacent Dependencies

scholarly journals Top-down structure influences learning of nonadjacent dependencies in an artificial language.

2017 ◽  
Vol 146 (12) ◽  
pp. 1738-1748 ◽  
Author(s):  
Felix Hao Wang ◽  
Jason D. Zevin ◽  
Toben H. Mintz
Keyword(s):  
Artificial Language ◽  
Top Down ◽  
Nonadjacent Dependencies

scholarly journals Auditory statistical learning in children: Novel insights from an online measure

2018 ◽  
Vol 40 (2) ◽  
pp. 279-302 ◽  
Author(s):  
IMME LAMMERTINK ◽  
MEREL VAN WITTELOOSTUIJN ◽  
PAUL BOERSMA ◽  
FRANK WIJNEN ◽  
JUDITH RISPENS
Keyword(s):  
Response Times ◽  
Learning Trajectory ◽  
Online Measurement ◽  
Artificial Language ◽  
Future Studies ◽  
Grammaticality Judgment ◽  
Grammaticality Judgments ◽  
Syntax Acquisition ◽  
Nonadjacent Dependencies ◽  
Child Friendly

AbstractNonadjacent dependency learning is thought to be a fundamental skill for syntax acquisition and often assessed via an offline grammaticality judgment measure. Asking judgments of children is problematic, and an offline task is suboptimal as it reflects only the outcome of the learning process, disregarding information on the learning trajectory. Therefore, and following up on recent methodological advancements in the online measurement of nonadjacent dependency learning in adults, the current study investigates if the recording of response times can be used to establish nonadjacent dependency learning in children. Forty-six children (mean age: 7.3 years) participated in a child-friendly adaptation of a nonadjacent dependency learning experiment (López-Barroso, Cucurell, Rodríguez-Fornells, & de Diego-Balaguer, 2016). They were exposed to an artificial language containing items with and without nonadjacent dependencies while their response times (online measure) were measured. After exposure, grammaticality judgments (offline measure) were collected. The results show that children are sensitive to nonadjacent dependencies, when using the online measure (the results of our offline measure did not provide evidence of learning). We therefore conclude that future studies can use online response time measures (perhaps in addition to the offline grammaticality judgments) to further investigate nonadjacent dependency learning in children.

scholarly journals Twelve-Month-Old Infants Benefit From Prior Experience in Statistical Learning

2008 ◽  
Vol 19 (12) ◽  
pp. 1247-1252 ◽  
Author(s):  
Jill Lany ◽  
Rebecca L. Gómez
Keyword(s):  
Statistical Learning ◽  
Verbal Memory ◽  
Prior Experience ◽  
Memory Capacity ◽  
Artificial Language ◽  
Learning Mechanisms ◽  
Language Structure ◽  
Statistical Regularities ◽  
Nonadjacent Dependencies ◽  
Enhanced Learning

A decade of research suggests that infants readily detect patterns in their environment, but it is unclear how such learning changes with experience. We tested how prior experience influences sensitivity to statistical regularities in an artificial language. Although 12-month-old infants learn adjacent relationships between word categories, they do not track nonadjacent relationships until 15 months. We asked whether 12-month-old infants could generalize experience with adjacent dependencies to nonadjacent ones. Infants were familiarized to an artificial language either containing or lacking adjacent dependencies between word categories and were subsequently habituated to novel nonadjacent dependencies. Prior experience with adjacent dependencies resulted in enhanced learning of the nonadjacent dependencies. Female infants showed better discrimination than males did, which is consistent with earlier reported sex differences in verbal memory capacity. The findings suggest that prior experience can bootstrap infants' learning of difficult language structure and that learning mechanisms are powerfully affected by experience.

An Alternative to Self-Reports of Trait and State Affect

2014 ◽  
Vol 30 (3) ◽  
pp. 231-237 ◽  
Author(s):  
Markus Quirin ◽  
Regina C. Bode
Keyword(s):  
Self Report ◽  
Factorial Validity ◽  
Artificial Language ◽  
Cognitive Representation ◽  
Affective States ◽  
Novel Variants ◽  
Self Reports ◽  
State Affect ◽  
Affective Experiences ◽  
Automatic Activation

Self-report measures for the assessment of trait or state affect are typically biased by social desirability or self-delusion. The present work provides an overview of research using a recently developed measure of automatic activation of cognitive representation of affective experiences, the Implicit Positive and Negative Affect Test (IPANAT). In the IPANAT, participants judge the extent to which nonsense words from an alleged artificial language express a number of affective states or traits. The test demonstrates appropriate factorial validity and reliabilities. We review findings that support criterion validity and, additionally, present novel variants of this procedure for the assessment of the discrete emotions such as happiness, anger, sadness, and fear.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

scholarly journals In search of resilient and fragile properties of language

2014 ◽  
Vol 41 (S1) ◽  
pp. 64-77 ◽  
Author(s):  
SUSAN GOLDIN-MEADOW
Keyword(s):  
Young Children ◽  
Language Learning ◽  
Language Learners ◽  
Great Promise ◽  
Artificial Language ◽  
Fundamental Properties ◽  
Linguistic Input ◽  
Language Input

ABSTRACTYoung children are skilled language learners. They apply their skills to the language input they receive from their parents and, in this way, derive patterns that are statistically related to their input. But being an excellent statistical learner does not explain why children who are not exposed to usable linguistic input nevertheless communicate using systems containing the fundamental properties of language. Nor does it explain why learners sometimes alter the linguistic input to which they are exposed (input from either a natural or an artificial language). These observations suggest that children are prepared to learn language. Our task now, as it was in 1974, is to figure out what they are prepared with – to identify properties of language that are relatively easy to learn, the resilient properties, as well as properties of language that are more difficult to learn, the fragile properties. The new tools and paradigms for describing and explaining language learning that have been introduced into the field since 1974 offer great promise for accomplishing this task.

Principles of compiling artificial languages

2020 ◽  
Vol 56 (07) ◽  
pp. 40-46
Author(s):  
Khayala Mugamat Mursaliyeva ◽  
Keyword(s):  
Natural Language ◽  
Modern World ◽  
Linguistic Landscape ◽  
Artificial Language ◽  
Natural Consequence ◽  
Modern Language ◽  
Artificial Languages ◽  
The World ◽  
The Creation

The explosion of information and the ever-increasing number of international languages make the modern language situation very difficult. The interaction of languages ultimately leads to the creation of international artificial languages that operate in parallel with the world`s languages. The expansion of interlinguistic issues is a natural consequence of the aggravation of the linguistic landscape of the modern world. The modern interlinguistic dialect, which is defined as a field of linguistics that studies international languages and international languages as a means of communication, deals with the importance of overcoming the barrier.The problem of international artificial languages is widely covered in the writings of I.A.Baudouin de Courtenay, V.P.Qrigorev, N.L.Gudskov, E.K.Drezen, A.D.Dulchenko, M.I.Isayev, S.N.Kuznechov, A.D.Melnikov and many other scientists. Key words:the concept of natural language, the concept of artificial language, the degree of artificiality of language, the authenticity of language

Morphological Knowledge as an Aid to Word Learning: A Cross-Situational Word Learning Study

2021 ◽  
Author(s):  
Sara Finley
Keyword(s):  
Word Learning ◽  
Word Meaning ◽  
Learning Task ◽  
Artificial Language ◽  
Initial Training ◽  
Experimental Conditions ◽  
Morphological Knowledge ◽  
Semantic Categories ◽  
English Speaking ◽  
Morphological Cues

The present study explores morphological bootstrapping in cross-situational word learning. Adult, English-speaking participants were exposed to novel words from an artificial language from three different semantic categories: fruit, animals, and vehicles. In the Experimental conditions, the final CV syllable was consistent across categories (e.g., /-ke/ for fruits), while in the Control condition, the endings were the same, but were assigned to words randomly. After initial training on the morphology under various degrees of referential uncertainty, participants were given a cross-situational word learning task with high referential uncertainty. With poor statistical cues to learn the words across trials, participants were forced to rely on the morphological cues to word meaning. In Experiments 1-3, participants in the Experimental conditions repeatedly outperformed participants in the Control conditions. In Experiment 4, when referential uncertainty was high in both parts of the experiment, there was no evidence of learning or making use of the morphological cues. These results suggest that learners apply morphological cues to word meaning only once they are reliably available.

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