Resources | AICDL

Cognitive Neuroscience & fMRI

Ward, J. (2020). The student’s guide to cognitive neuroscience (Fourth edition). Routledge, Taylor & Francis Group.

Eysenck, M. W., & Keane, M. T. (2020). Cognitive psychology: A student’s handbook (Eighth edition). Routledge.

Psycholinguistics

Traxler, M. J. (2023). Introduction to psycholinguistics: Understanding language science (Second Edition). John Wiley & Sons Ltd.

Rüschemeyer, S.-A., & Gaskell, M. G. (2018). The Oxford handbook of psycholinguistics (2nd edition). Oxford university press.

Godfroid, A., & Hopp, H. (Eds.). (2023). The Routledge handbook of second language acquisition and psycholinguistics. Routledge, Taylor & Francis Group.

Irony

Kreuz, R. J. (2020). Irony and sarcasm. The MIT Press.

Skalicky, S. (2023). Verbal Irony Processing (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781009234566

Colston, H. (2017). Irony and Sarcasm. In S. Attardo (Ed.), The Routledge Handbook of Language and Humor-Routledge (pp. 234–249). Routledge.

Banasik-Jemielniak, N., & Kałowski, P. (2022). Socio-cultural and individual factors in verbal irony use and understanding: What we know, what we don’t know, what we want to know. Review of Communication Research, 10, 80–113. https://doi.org/10.12840/ISSN.2255-4165.036

Zhu, N., & Filik, R. (2023). Individual differences in sarcasm interpretation and use: Evidence from the UK and China. Journal of Experimental Psychology: Learning, Memory, and Cognition, 49(3), 445–463. https://doi.org/10.1037/xlm0001227

Filik, R., Ţurcan, A., Ralph-Nearman, C., & Pitiot, A. (2019). What is the difference between irony and sarcasm? An fMRI study. Cortex, 115, 112–122. https://doi.org/10.1016/j.cortex.2019.01.025

Filik, R., Leuthold, H., Wallington, K., & Page, J. (2014). Testing theories of irony processing using eye-tracking and ERPs. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(3), 811–828. https://doi.org/10.1037/a0035658

Autism

Tesink, C. (2013). Neurobiological insights into language comprehension in autism: Context matters [Radboud University]. https://hdl.handle.net/2066/102332

Kieckhäfer, C., Felsenheimer, A. K., & Rapp, A. M. (2019). A New Test for Irony Detection: The Influence of Schizotypal, Borderline, and Autistic Personality Traits. Frontiers in Psychiatry, 10, 28. https://doi.org/10.3389/fpsyt.2019.00028

Hull, L., Mandy, W., Lai, M.-C., Baron-Cohen, S., Allison, C., Smith, P., & Petrides, K. V. (2019). Development and Validation of the Camouflaging Autistic Traits Questionnaire (CAT-Q). Journal of Autism and Developmental Disorders, 49(3), 819–833. https://doi.org/10.1007/s10803-018-3792-6

English, M. C. W., Gignac, G. E., Visser, T. A. W., Whitehouse, A. J. O., Enns, J. T., & Maybery, M. T. (2021). The Comprehensive

Autistic Trait Inventory (CATI): Development and validation of a new measure of autistic traits in the general population. Molecular

Autism, 12(1), 37. https://doi.org/10.1186/s13229-021-00445-7

Barzy, M., Filik, R., Williams, D., & Ferguson, H. J. (2020). Emotional Processing of Ironic Versus Literal Criticism in Autistic and Nonautistic Adults: Evidence From Eye‐Tracking. Autism Research, 13(4), 563–578. https://doi.org/10.1002/aur.2272

Sasson, N. J., & Bottema-Beutel, K. (2022). Studies of autistic traits in the general population are not studies of autism. Autism, 26(4), 1007–1008. https://doi.org/10.1177/13623613211058515

Individual differences in computer assisted language learning

Pawlak, M., & Kruk, M. (2023). Individual differences in computer assisted language learning research. Routledge.

Tlili, A., Essalmi, F., Jemni, M., Kinshuk, & Chen, N.-S. (2016). Role of personality in computer based learning. Computers in Human Behavior, 64, 805–813. https://doi.org/10.1016/j.chb.2016.07.043

Data analysis

ChatGPT: https://chat.openai.com/

SPSS in 30 minutes: https://www.bilibili.com/video/BV1yf4y1V7x1/?spm_id_from=333.337.search-card.all.click

Harrison, V., Kemp, R., Brace, N., & Snelgar, R. (2022). SPSS for psychologists (Seventh edition). Bloomsbury Academic.

Clark-Carter, D. (2019). Quantitative psychological research: The complete student’s companion (4th Edition). Routledge.

Ward, M. K., & Meade, A. W. (2023). Dealing with Careless Responding in Survey Data: Prevention, Identification, and Recommended Best Practices. Annual Review of Psychology, 74(1), 577–596. https://doi.org/10.1146/annurev-psych-040422-045007

Brysbaert, M., & Stevens, M. (2018). Power Analysis and Effect Size in Mixed Effects Models: A Tutorial. Journal of Cognition, 1(1), 9. https://doi.org/10.5334/joc.10

Kumle, L., Võ, M. L.-H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53(6), 2528–2543. https://doi.org/10.3758/s13428-021-01546-0

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629

Experiment design

Frank, M., Braginsky, M., Cachia, J., Coles, N., Hardwicke, T., Hawkins, R., Mathur, M., & Williams, R. (2024). Experimentology: An Open Science Approach to Experimental Psychology Methods. MIT Press. https://experimentology.io/

Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108–1109. https://doi.org/10.1126/science.adi1778

Bishop, D. V. M., & Thompson, P. A. (2024). Evaluating what works: An intuitive guide to intervention research for practitioners (First edition). CRC Press. https://bookdown.org/dorothy_bishop/Evaluating_What_Works/

Master program suggestions for undergraduates in English/social science majors

Note: My recommendations stem from the conviction that acquiring quantitative skills—or at least one research methodology—is crucial for social science students seeking to enhance their competitive edge. Contrary to common perceptions, these skills are not as daunting as they might seem, especially in the current era where tools like ChatGPT are available to facilitate learning and application.

General

University of Chicago: Masters in Computational Social Sciences (MACSS) https://macss.uchicago.edu/

University of Chicago: Master of Arts Program in Social Sciences (MAPSS) https://mapss.uchicago.edu/

Columbia University: Master of Arts Degree in Quantitative Methods in the Social Sciences (QMSS) https://www.qmss.columbia.edu/the-program

UCLA: Master of Social Science (MaSS) https://mass.ss.ucla.edu/

LSE: MSc Applied Social Data Science https://www.lse.ac.uk/study-at-lse/Graduate/degree-programmes-2024/MSc-Applied-Social-Data-Science

LSE: MSc Social Research Methods https://www.lse.ac.uk/study-at-lse/Graduate/degree-programmes-2024/MSc-Social-Research-Methods

LSE: MSc Behavioural Science https://www.lse.ac.uk/study-at-lse/Graduate/degree-programmes-2024/MSc-Behavioural-Science

UCL: Social Research Methods MSc https://www.ucl.ac.uk/prospective-students/graduate/taught-degrees/social-research-methods-msc

The University of Manchester MSc Social Research Methods and Statistics: https://www.manchester.ac.uk/study/masters/courses/list/07982/msc-social-research-methods-and-statistics/#course-profile

Language

UCL: MSc Language Sciences (Neuroscience, Technology, Development, Principles, Sign Languages) https://www.ucl.ac.uk/pals/study/postgraduate-taught-degrees/language-sciences-and-linguistics

University of Edinburgh: MSc Psychology of language https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2024&id=1056

Application

UK Acceptance Rate: https://www.admissionreport.com

Online Dictionary

Cambridge Dictionary: https://dictionary.cambridge.org