Projects per year
Description
This dataset contains all experimental data generated within the first experiment of a larger PhD project ("Acquiring the social meaning of language variation: An experimental exploration").
Abstract
This dataset contains all experimental data generated within the first experiment of a larger PhD project ("Acquiring the social meaning of language variation: An experimental exploration").
Usage-based accounts of the acquisition of sociolinguistic variation assign a pivotal role to the frequency with which a linguistic variant and a social category co-occur in listeners’ input. However, few studies have so far compared the effects of varying frequency distributions on acquisition in a lab-based context. Based on the available literature, we designed a semi-artificial language learning experiment that manipulates the probabilistic association between a new linguistic variable and macro-social categories between participants. In a first training phase, participants were exposed to the variable – (de)voicing of the word medial stop in disyllabic pseudowords – produced by two male and two female sounding speakers (social categories of ‘gender’). Participants’ learning of the link between the linguistic variable and perceived speaker gender was subsequently tested in a perception task which also tested the generalisation of learning to words that participants did not hear during training. While results show relatively limited learning overall, the experiment points to key methodological insights that can serve as a pivot for the future of the artificial sociolinguistic language learning paradigm.
Usage-based accounts of the acquisition of sociolinguistic variation assign a pivotal role to the frequency with which a linguistic variant and a social category co-occur in listeners’ input. However, few studies have so far compared the effects of varying frequency distributions on acquisition in a lab-based context. Based on the available literature, we designed a semi-artificial language learning experiment that manipulates the probabilistic association between a new linguistic variable and macro-social categories between participants. In a first training phase, participants were exposed to the variable – (de)voicing of the word medial stop in disyllabic pseudowords – produced by two male and two female sounding speakers (social categories of ‘gender’). Participants’ learning of the link between the linguistic variable and perceived speaker gender was subsequently tested in a perception task which also tested the generalisation of learning to words that participants did not hear during training. While results show relatively limited learning overall, the experiment points to key methodological insights that can serve as a pivot for the future of the artificial sociolinguistic language learning paradigm.
| Date made available | 21 May 2025 |
|---|---|
| Publisher | OSF |
| Date of data production | 23 Oct 2023 - 19 Dec 2023 |
Keywords
- artificial language learning
- social meaning
- usage-based linguistics
- language variation
- sociolinguistic acquisition
- cognitive sociolinguistics
- developmental sociolinguistics
- learning
Format
- Format
- csv
- txt
- html
Projects
- 1 Finished
-
FWOAL1014: Acquiring social meaning of language variation: an experimental exploration
Rosseel, L. (Administrative Promotor)
1/01/21 → 31/12/24
Project: Fundamental
Research output
- 3 Unpublished abstract
-
The role of co-occurrence patterns in the acquisition of sociolinguistic variation: shaping a methodological framework
Van Puyvelde, M., Rosseel, L., Zenner, E. & Speelman, D., 2023.Research output: Unpublished contribution to conference › Unpublished abstract
File -
He says, she says: The acquisition and abstraction of social meaning
Van Puyvelde, M., Rosseel, L., Zenner, E. & Speelman, D., 18 Nov 2022.Research output: Unpublished contribution to conference › Unpublished abstract
Open AccessFile -
The more the merrier? The role of linguistic input in the acquisition of social meaning
Van Puyvelde, M., Rosseel, L., Zenner, E. & Speelman, D., 2022.Research output: Unpublished contribution to conference › Unpublished abstract
Open AccessFile
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