DOI: 10.5061/dryad.3n5tb2rdq
title: Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition

description: 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.
description: 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.
description: 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.