Undergraduate Course: Online Experiments for Language Scientists (LASC10115)
Course Outline
| 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 |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| 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. |
| Course description |
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.
Lectures will summarise and discuss a paper (plus related literature) detailing a study using online methods, then in lab classes we will work with code (written in javascript using jspsych) 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. No prior experience of programming is required.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 9,
Supervised Practical/Workshop/Studio Hours 18,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
169 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Feedback |
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 |
Learning Outcomes
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.
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Reading List
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.
Grammaticality judgments:
Sprouse, J. (2011). A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory. Behavior Research Methods, 43, 155-167.
Self-paced reading:
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.
Iterated learning:
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. |
Additional Information
| 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. |
| Keywords | Not entered |
Contacts
| Course organiser | Prof Kenny Smith
Tel: (0131 6)50 3956
Email: |
Course secretary | Mr Liam Hedley
Tel: (0131 6)50 9870
Email: |
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