scholarly journals Structural priming in artificial languages and the regularisation of unpredictable variation

2016 ◽  
Vol 91 ◽  
pp. 158-180 ◽  
Author(s):  
Olga Fehér ◽  
Elizabeth Wonnacott ◽  
Kenny Smith
Keyword(s):  
Structural Priming ◽  
Artificial Languages ◽  
Unpredictable Variation

scholarly journals When regularization gets it wrong: children over-simplify language input only in production

2018 ◽  
Vol 45 (5) ◽  
pp. 1054-1072 ◽  
Author(s):  
Jessica F. SCHWAB ◽  
Casey LEW-WILLIAMS ◽  
Adele E. GOLDBERG
Keyword(s):  
Complex System ◽  
High Accuracy ◽  
Forced Choice ◽  
Choice Task ◽  
The Other ◽  
Natural Languages ◽  
Multiple Factors ◽  
Artificial Languages ◽  
Forced Choice Task ◽  
Unpredictable Variation

AbstractChildren tend to regularize their productions when exposed to artificial languages, an advantageous response to unpredictable variation. But generalizations in natural languages are typically conditioned by factors that children ultimately learn. In two experiments, adult and six-year-old learners witnessed two novel classifiers, probabilistically conditioned by semantics. Whereas adults displayed high accuracy in their productions – applying the semantic criteria to familiar and novel items – children were oblivious to the semantic conditioning. Instead, children regularized their productions, over-relying on only one classifier. However, in a two-alternative forced-choice task, children's performance revealed greater respect for the system's complexity: they selected both classifiers equally, without bias toward one or the other, and displayed better accuracy on familiar items. Given that natural languages are conditioned by multiple factors that children successfully learn, we suggest that their tendency to simplify in production stems from retrieval difficulty when a complex system has not yet been fully learned.

Setting boundaries: Tests of structural priming in word production

2009 ◽  
Author(s):  
Padraig G. O'Seaghdha ◽  
Julio Santiago ◽  
Antonio Roman ◽  
Jordan L. Knicely
Keyword(s):  
Word Production ◽  
Structural Priming

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.

Are there segmental and tonal effects on syntactic encoding? Evidence from structural priming in Mandarin

2021 ◽  
Vol 119 ◽  
pp. 104220
Author(s):  
Chi Zhang ◽  
Sarah Bernolet ◽  
Robert J. Hartsuiker
Keyword(s):  
Structural Priming
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