Go above and beyond: Does input variability affect children’s ability to learn spatial adpositions in a novel language?use asterix (*) to get italics

Eva Viviani1, Michael Ramscar2, Elizabeth Wonnacott1 [1: University of Oxford, 2: University of Tübingen]Please use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2022

<p>Human language is characterized by productivity, that is, the ability to use words and structures in novel contexts. How do learners acquire these productive systems? Under a <em>discriminative learning approach,</em> language learning involves using cues to predict and discriminate linguistic outcomes and “generalization” involves dissociating idiosyncratic irrelevant cues in favor of informative, invariant cues. The current work tests the predictions of this account using the learning of spatial adpositions as a test case. Spatial adpositions describe the location of one object in relation to another (e.g. English prepositions “above” and “below”) and may occur in reversible sentences, such as <em>the picture is above the window</em>; generalization involves using these terms in novel contexts, such as with unattested nouns. Computational simulations implementing an error-driven, discriminative learning process, demonstrate that broadening the irrelevant cues associated with the stimuli may boost the discovery of invariant cues, i.e., the association between the adposition and the spatial relation. We explore the predictions of these models in human learners by adapting a training paradigm introduced by Hsu and Bishop (2014) to teach typically-developing 7 year olds spatial adpositions in an unfamiliar language (Japanese) using a computerized learning game. We manipulate the cue variability by comparing groups of children trained with more variable sentences (high variability) with a condition with repetition of the same sentences (low variability). A third condition (skew) tests whether learning and generalization are boosted when learning from a heavy tailed distribution that more closely resembles that of natural language. We will examine the following predictions: (1) for sentences with novel nouns, participants trained with variable sentences will show better performance (i.e., stronger generalization) than those trained with repeated sentences; (2) in contrast, those trained with repeated sentences will shows stronger performance in training itself (i.e, stronger item learning); (3) training with a heavy tailed distribution – more closely resembling the natural one – will lead to the strongest item learning and generalization.</p>

language, learning, adpositions, discriminative learning

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Social sciences

2021-11-15 15:04:42

Chris Chambers