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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2023/2024

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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Language Sciences

Postgraduate Course: Online Experiments for Language Scientists (LASC11167)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 27, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 71 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Project (build an experiment plus write short report on the rationale and connection to existing literature) of 1000 words (100%)
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.
Formative annotated bibliography.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. demonstrate extensive and detailed knowledge of major advantages, challenges and pitfalls of online data collection
  2. critically evaluate papers from across the language sciences which use online methods for data collection, with a particular focus on methodological strengths and weaknesses
  3. creatively apply their technical knowledge of how to building experiments for online data collection, including demonstrating ability to build original experiments
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 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.
Keywordsonline experiments,platforms,participants,online methods
Contacts
Course organiserProf Kenny Smith
Tel: (0131 6)50 3956
Email: kenny@ling.ed.ac.uk
Course secretaryMs Sasha Wood
Tel:
Email: swood310@ed.ac.uk
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