Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition - Fazekas, Judit; Jessop, Andrew; Pine, Julian; Rowland, Caroline (Dryad, 2020)
Error-based theories of language acquisition suggest that children, like
adults, continuously make and evaluate predictions in order to reach an
adult-like state of language use. However, while these theories have
become extremely influential, their central claim - that unpredictable
input leads to higher rates of lasting change in linguistic
representations – has scarcely been tested. We designed a prime
surprisal-based intervention study to assess this claim. As predicted,
both 5- to 6-year-old children (n=72) and adults (n=72) showed a pre- to
post-test shift towards producing the dative syntactic structure they were
exposed to in surprising sentences. The effect was significant in both age
groups together, and in the child group separately when participants with
ceiling performance in the pre-test were excluded.  Secondary
predictions were not upheld: there were no verb-based learning effects and
there was only reliable evidence for immediate prime surprisal effects in
the adult, but not in the child group. To our knowledge this is the first
published study demonstrating enhanced learning rates for the same
syntactic structure when it appeared in surprising as opposed to
predictable contexts, thus providing crucial support for error-based
theories of language acquisition.
This dataset is associated with a psycholoinguistcics study targeting
error-based learning theories. The dataset contains video
descriptions from 72 adult and 72 5-6 year old
child participants. The study was carried out
in the form of a bingo game. The data collection took place in either the
participants' school or in the departmental laboratories.
The data was originally audiotaped then the relevant parts of the audio
recordings were transcribed and the responses were coded (as double object
dative, prepositional dative or other response). The
transcription and coding procedure is discussed in the associated
manuscript. The data uploaded here is an excel file
(Prediction_learning_dataset.csv) featuring the transcribed
responses from the anonymised participants and the
response codes associated with them. The responses were
not processed in any additional way other than being transcribed from the
audio recordings.
The dataset (Prediction_learning_dataset.csv) can be used in combination
with the ReadMe file (ReadMe for prediction_learning_dataset.docx)
describing the predictors featured in the different coloumns of the excel
file. There are missing values and excluded participants in this dataset.
The exlusion criteria is detailed in the correspondint manuscript (2.1.
Participants, 2.7. Coding and 3. Statistics and data analyses).
In addition to the main dataset, the repository contains the scripts
containing the analyses described in the manuscript, the original datafile
these analyses ran on (All_data_JF.csv, this file is identical to
Prediction_learning_dataset.csv, however all columns are named in the
latter file while the former contains unlabelled columns), the sentence
lists and the testing logs.
Keywords: error-based learning; Language acquisition