Online Experiments for Language Scientists

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Abstract

Many areas in the language sciences rely on collecting data from human participants, from grammaticality judgments to behavioural responses (key presses, mouse clicks, spoken responses). 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.

This course will provide a rapid tour of online experimental methods in the language sciences, covering a range of paradigms, from survey-like responses (e.g. as required for grammaticality judgments) through more standard psycholinguistic methods (button presses, mouse clicks) up to more ambitious and challenging techniques (e.g. voice recording, real-time interaction through text and/or streaming audio, iterated learning). Each week we will read a paper detailing a study using online methods, and look at code (written in javascript using jspsych) to implement a similar experiment - the examples will skew towards the topics I am interested in (language learning, communication, language evolution), but we’ll cover more standard paradigms too (grammaticality judgments, self-paced reading) and the techniques are fairly general anyway. We’ll also look at the main platforms for reaching paid participants, e.g. MTurk and Prolific, and discuss some of the challenges around data quality and the ethics of running on those platforms.

No prior experience in coding is assumed, but you have to be prepared to dive in and try things out; the assessment will involve elements of both literature review and coding.