Edinburgh Virtual Workshop on
Artificial Language Learning
4-6 October, 2021

In this practical tutorial, we will discuss various factors to consider when designing stimuli for artificial language learning experiments. The discussion will focus on phonology-oriented studies, but some principles will be broadly applicable. Topics will include issues such as choosing the phonemic/phonetic inventory for the language, choosing word shape, designing critical and filler items, considering the L1 of participants, recording/synthesising stimuli, using visual stimuli (or not), making linguistic patterns of interest more or less apparent, and so forth.

The majority of artificial language learning experiments conducted each year use adult participants, while many fewer are conducted with children. One reason for this imbalance is that children are less willing and able to participate in these lengthy (often boring) experiments that can require hundreds of learning and test trials. In this tutorial, I’ll cover some basic design principles that will allow you to convert your adult artificial language learning experiment into a version suitable for use with children. I’ll include some tips for both live in-person experiments and online unsupervised experiments. Then, you’ll work together in small groups to apply these principles to a real artificial language learning experiment, converting an experiment originally designed for adults into a child-friendly version.

While data collection traditionally takes place face-to-face, recent years have seen an explosion in the use of online data collection: participants take part remotely, providing responses through a survey tool or custom experimental software running in their web browser, with surveys or experiments often being advertised on crowdsourcing websites like Amazon Mechanical Turk (MTurk) or Prolific. Online methods potentially allow rapid and low-effort collection of large samples, and are particularly useful in situations where face-to-face data collection is not possible (e.g. during a pandemic); however, building and running these experiments poses challenges that differ from lab-based methods. In this tutorial we’ll give you a brief overview of how to build online artificial language learning experiments that run in a web browser (using jsPsych and javascript, which we think provide a nice balance of flexibility and accessibility), and how to crowdsource participants; we’ll focus on summarising the main technical challenges of this work and sharing some tips and tricks we’ve picked up in the course of doing online data collection in our own research, and we’ll provide pointers to materials that will allow you to work on building your experiments in your own time.

Had to be there :)