Zettersten, M., Potter, C., & Saffran, J. (2020). Tuning in to non-adjacencies: Exposure to learnable patterns supports discovering otherwise difficult structures. Cognition, 104283. doi: [10.1016/j.cognition.2020.104283](https://doi.org/10.1016/j.cognition.2020.104283) Abstract: Non-adjacent dependencies are ubiquitous in language, but difficult to learn in artificial language experiments in the lab. Previous research suggests that non-adjacent dependencies are more learnable given structural support in the input – for instance, in the presence of high variability between dependent items. However, not all non-adjacent dependencies occur in supportive contexts. How are such regularities learned? One possibility is that learning one set of non-adjacent dependencies can highlight similar structures in subsequent input, facilitating the acquisition of new non-adjacent dependencies that are otherwise difficult to learn. In three experiments, we show that prior exposure to learnable non-adjacent dependencies - i.e., dependencies presented in a learning context that has been shown to facilitate discovery - improves learning of novel non-adjacent regularities that are typically not detected. These findings demonstrate how the discovery of complex linguistic structures can build on past learning in supportive contexts. Link to github page: https://github.com/mzettersten/apg-non-adjacent Link to analysis walkthrough (R markdown script): https://mzettersten.github.io/apg-non-adjacent/data_analysis/APG_analysis.html Link to AsPredicted pre-registration form for Experiment 2: https://osf.io/7ewmc Link to AsPredicted pre-registration form for Experiment 3: https://osf.io/va657 Link to AsPredicted pre-registration form for Pre-Exposure Experiment S1: https://osf.io/upa6g Notes on folders & files: **ANALYSIS** The folder analysis contains the following documents: - **APG_data.txt**: The complete data set - **APG_analysis.R**: An R script documenting all analyses included in the manuscript - **APG_analysis.Rmd**: An R markdown file documenting the central models reported in the manuscript - **APG_analysis.html**: The R markdown file exported as an html (if you only want to inspect the models and their summary outputs rather than reproduce the analyses in R) **DATA** The folder data contains the following documents: - **APG_data.txt**: The complete data set - **APG_codebook.txt**: A codebook for the dataset **STIMULI** The folder stimuli contains all pre-exposure, exposure and test stimuli across Experiments 1-3 (exposure and test stimuli are identical across all experiments) and Experiment S1