Room UF36, Psychology Building
- 7 George Square, Edinburgh
- Post code
- EH8 9JZ
My regular CONSULTATION (OFFICE) HOURS are Tuesday from 1500-1700 (you can contact me on Teams (Leonidas Doumas)). Feel free to drop me an email to schedule an appointment outwith those times.
I received my PhD in Cognitive and Developmental Psychology at UCLA working with John Hummel, Keith Holyoak, and Cathy Sandhofer. I worked as a post-doc in Linda Smith's lab at Indiana University before working as an Assistant Professor at the University of Hawaii. I came to Edinburgh as a Lecturer in 2013. I used to read a lot of comic books. I still try to read the occasional sci-fi novel. My partner, Brandy, and I have a daughter called Violet, and a cat called Bernoulli (I was only allowed to name one of those two).
I regularly teach on topics in developmental science, cognitive science, and statistics. At the moment I am teaching on the Year 1 and Year 2 statistics courses, and the Year 1 Cognitive Science course.
My CONSULTATION (OFFICE) HOURS are on are published at the top of the page. If you would like to meet outside those times, feel free to drop me an email or knock on my door.
Open to PhD supervision enquiries?
Areas of interest for supervision
I am currently accepting PhD. and MSc. students. If you'd like to pursue a Ph.D. and might be interested in working with me, please send me an email.
I'm happy to answer questions, and may be able to meet if you're near Edinburgh, or we’re attending the same conference.
It's wise to contact prospective supervisors sooner rather than later. First, there are more funding opportunities available the earlier one starts. Second, it’s a good idea to make sure you’re clear about whom you are proposing to work with and what you are proposing to work on in your application.
Current PhD students supervised
Analogy, relational reasoning, mental representation, cognitive development, computational modelling and neural networks.
I am interested in how distributed systems (like brains and artifical neural networks) come to represent and reason about relational concepts (like 'above', 'next-to', or 'likes'). Specifically, I am interested in how children and adults learn to think about, represent, and use relations for solving problems. People are remarkably good at generalising across situations or domains (often referred to as cross-domain transfer). We routinely use representations used in one domain to characterise another (e.g., a child might learn about a concept like above in the context of their toys and then deploy that representation in the context of a video game). We actively use strategies learned about one situation to behave in another (e.g., a child might learn that unsupported things fall in the context of eating at a table and then generalise that strategy to knock a toy off a high shelf). My colleagues and I have argued that humans make these kinds of inferences so readily because we represent the world in terms of it's underlying relational structure, and our representations are structured (i.e., symbolic).
My students, my collaborators, and I explore these issues in domains like analogy making, mathematical reasoning, and learning. We employ empirical methods with children and adults, computional models, and techniques from neuroscience (e.g., eeg, tms) to understand how we think and learn.
(all publications are available from the University of Edinburgh Research Explorer; link below.)
Doumas, L. A. A., Puebla, G., Martin, A. E., & Hummel, J. E. (2022). A theory of relation learning and cross-domain generalization. Psychological review.
Puebla, G., Martin, A. E., & Doumas, L. A. A. (2021). The relational processing limits of classic and contemporary neural network models of language processing. Language cognition and neuroscience, 36(2), 240-254.
Doumas, L. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current opinion in behavioral sciences, 37, 158-166.
Martin, A. E., & Doumas, L. A. (2020). Tensors and compositionality in neural systems. Philosophical Transactions of the Royal Society B, 375(1791), 20190306.
Martin, A. E., & Doumas, L. A. (2017). A mechanism for the cortical computation of hierarchical linguistic structure. PLoS biology, 15(3), e2000663.
Doumas, L. A. A., Hummel, J. E., & Sandhofer, C. M. (2008). A theory of the discovery and predication of relational concepts. Psychological review, 115, 1-43.