Undergraduate Course: Online Experiments for Language Scientists (LASC10115)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This is a practical course which will provide a rapid tour of online experimental methods in the language sciences. Each week we will cover a paper detailing a study using online methods, and work with code to implement a similar experiment. We will 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 recruiting participants through those platforms.
This is a practical course which 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. audio or video recording, real-time interaction through text and/or streaming audio, iterated learning).
Each week will be structured around one 1-hour lecture, and one 2-hour lab where students work on practical content with support from teaching staff.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 9,
Supervised Practical/Workshop/Studio Hours 18,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
||Lab classes provide a regular opportunity for extremely rich one-on-one formative feedback as students attempt to work the weekly programming tasks with lab tutor support.
|No Exam Information
On completion of this course, the student will be able to:
- Demonstrate knowledge of major advantages, challenges and pitfalls of online data collection.
- Critically evaluate papers from across the language sciences which use online methods for data collection, with a particular focus on methodological strengths and weaknesses.
- Apply their technical knowledge of how to build experiments for online data collection.
|Intro to online data collection:|
Monroe, R. et al. (2010). Crowdsourcing and language studies: the new generation of linguistic data. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, pages 122-130.
Pavlick, E. et al. (2014). The Language Demographics of Amazon Mechanical Turk. Transactions of the Association for Computational Linguistics, 2, 79-92.
Sprouse, J. (2011). A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory. Behavior Research Methods, 43, 155-167.
Enochson, K., & Culbertson, J. (2015). Collecting Psycholinguistic Response Time Data Using Amazon Mechanical Turk. PLoS ONE, 10, e0116946.
Word learning (visual stimuli):
Ferdinand, V., Kirby, S., & Smith, K. (2019). The cognitive roots of regularization in language.Cognition, 184, 53-68.
Phonetic adaptation (audio stimuli):
Lev-Ari, S. (2017). Talking to fewer people leads to having more malleable linguistic representations. PLoS ONE, 12, e0183593.
Confederate priming (recording participant audio responses):
Joy, J. E., & Smith, K. (2020). Syntactic adaptation depends on perceived linguistic knowledge: Native English speakers differentially adapt to native and non-native confederates in dialogue. https://doi.org/10.31234/osf.io/pu2qa.
Dyadic interaction (peer-to-peer communication):
Kanwal, J., Smith, K., Culbertson, J., & Kirby, S. (2017). Zipf's Law of Abbreviation and the Principle of Least Effort: Language users optimise a miniature lexicon for efficient communication. Cognition, 165, 45-52.
Beckner, C., Pierrehumbert, J., & Hay, J. (2017). The emergence of linguistic structure in an online iterated learning task. Journal of Language Evolution, 2, 160-176.
|Graduate Attributes and Skills
||Graduate attributes and skills provided by the course include: a capacity for problem solving and analytical thinking, a capacity to evaluate information thoroughly, and a capacity to identify assumptions and appraise critically the methods and reasoning of researchers in the field.
|Course organiser||Prof Kenny Smith
Tel: (0131 6)50 3956
|Course secretary||Mr Liam Hedley
Tel: (0131 6)50 9870