Data for: Successfully Learning Non-Adjacent Dependencies in a Continuous Artificial Language Stream

Published: 15 Jun 2019 | Version 1 | DOI: 10.17632/xxm4p6vmt4.1
Contributor(s):

Description of this data

This zip file contains data from the 4 experiments reported in Wang, Zevin & Mintz (under review).

Experiment data files

Download all files (1)

This data is associated with the following publication:

Successfully learning non-adjacent dependencies in a continuous artificial language stream

Published in: Cognitive Psychology

Latest version

  • Version 1

    2019-06-15

    Published: 2019-06-15

    DOI: 10.17632/xxm4p6vmt4.1

    Cite this dataset

    Wang, Felix; Mintz, Toben; Zevin, Jason (2019), “Data for: Successfully Learning Non-Adjacent Dependencies in a Continuous Artificial Language Stream”, Mendeley Data, v1 http://dx.doi.org/10.17632/xxm4p6vmt4.1

Statistics

Views: 17
Downloads: 4

Categories

Psychology, Psycholinguistics, Language Acquisition, Artificial Language

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?

This dataset is licensed under a Creative Commons Attribution 4.0 International licence. What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

Read more

Report