Personal website – any views expressed herein are my own unless otherwise obvious
Learn how to use Lean, an interactive theorem prover, to prove that \(2 + 2 = 4\) and \(x + y = y + x\): the Natural Number Game. Spotted in AI for Maths Resources, which is linked in Tao, T. (2025). Machine-Assisted Proof. Notices of the American Mathematical Society, 72(1), 6–13.
I completed my MSc and PhD through the Neuroinformatics Doctoral Training Centre at the University of Edinburgh, hosted by the Institute for Adaptive and Neural Computation in the School of Informatics. I was also part of the School’s Institute for Communicating and Collaborative Systems, now named the Institute for Language, Cognition and Computation.
My office was in the Psychology department (different School) and I was part of the Human Cognitive Neuroscience group there. My computer was supplied by Informatics – hand-delivered in a bin bag (cf. Dijkstra, “Computer science is no more about computers than astronomy is about telescopes”).
My thesis was in cognitive science and officially my degree was in Informatics, which makes sense according to how Edinburgh divides up the disciplines:
Informatics has many aspects, and encompasses a number of existing academic disciplines – Artificial Intelligence, Cognitive Science and Computer Science. Each takes part of Informatics as its natural domain. In broad terms:
The psychologists would have located my research somewhere between individual differences psychology and cognitive psychology. A Psychology department administrator once declared in the staff room, “I thought you were a psychologist, but you’re not.”
This is what interdisciplinary means.
“The problem is that politics and evaluation are both intrinsically connected and intrinsically separated. We need independence if we’re to be competent, objective, and credible. However, independence can’t always be preserved without a fight; this doesn’t make us a lot of friends; and yet we need friends, partners, and teamwork to be effective in a political universe.”
—Eleanor Chelimsky (2008, p. 414). A Clash of Cultures: Improving the “Fit” Between Evaluative Independence and the Political Requirements of a Democratic Society. American Journal of Evaluation, 29(4), 400–415.
Zimmer, S. A., Powell, R. J., & Velásquez, I. C. (2024). Exploring Complex Survey Data Analysis Using R: A Tidy Introduction with {srvyr} and {survey}. Chapman & Hall: CRC Press.
Part 1: ATT(g, t) estimates and pooling across groups. Standard errors coming later.
“For our purposes, the same physical object or person at a different time is a different unit.” (Guido Imbens and Donald Rubin, 2015, p. 4)
“I guess I wont send that note now, for the mind is such a new place, last night feels obsolete.” (Emily Dickinson, 1870)
Dickinson, E. (1870). Letter to Elizabeth Holland.
Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
graph G {
node [shape=box];
Logic -- "Psychology of reasoning";
"Formal methods" -- Logic;
"Formal methods" -- "Computer science";
"Programming language design" -- Logic;
"Programming language design" -- "Computer science";
"Computer science" -- Logic;
"Psychology of reasoning" -- Psychology;
Logic -- Philosophy;
Statistics -- Evaluation;
"Is-ought problem" -- Philosophy;
"Is-ought problem" -- Evaluation;
Logic -- Counterfactuals;
Counterfactuals -- Evaluation;
Psychology -- Evaluation;
"Mental health research" -- Psychology;
"Mental health research" -- Sociology;
"Mental health research" -- Evaluation;
Counterfactuals -- Statistics;
Logic -- Statistics;
"Causal inference" -- Philosophy;
"Causal inference" -- Logic;
"Causal inference" -- Evaluation;
"Causal inference" -- Statistics;#
"Causal inference" -- "Computer science";
Sociology -- Evaluation;
"Psychosocial studies" -- Sociology;
"Psychosocial studies" -- Psychology;
"Psychosocial studies" -- Psychoanalysis;
"Mental health research" -- Psychoanalysis;
"Group relations conference" -- Psychoanalysis;
}
Draw here
I happened upon an interesting question on Philosophy Stack Exchange: “What are the main views among academics on the question of the relation between logic and human cognition?” Here follows (a lightly edited version of) my answer.
Van Benthem (2008) offers examples of how cognition and social practices influence the development of logics, citing, e.g., Prior’s work on temporal structures and Lewis and Stalnaker on comparative orderings of possible worlds. He concedes that much of the work in this direction is informal (p. 74):
Logicians analyzing natural language, or computer scientists modeling “common sense”, tend to go by their own intuitions, anecdotal evidence from colleagues, email surveys of sometimes surprising naiveness, and other easy procedures that avoid the laboratories and statistical packages of the world of careful experimental design. But even so, experimental evidence is relevant, in that these theories can be, and sometimes are, modified under pressure of evidence from actual usage, even when it comes through these home-grown sources.
He also points to work in the other direction, where cognitive scientists take non-classical logics and use them to model social and cognitive phenomena. An example I know about (it was one of the reasons I chose my PhD topic and supervisor) is Stenning and van Lambalgen’s (2005) analysis of the “suppression task” in psychology, using nonmonotonic logic. Probability theory may be viewed as a kind of logic and it is used in cognitive science too; try e.g., Pfeifer (2013) and references that follow.
One key element of cognitive science studies that use logics is how people interpret tasks, and often have different interpretations, which can be modelled as choosing a logic and setting its parameters. Geis and Zwicky (1971) observe that logic students sometimes map the natural language indicative “if A, then B” to a biconditional in classical logic, so even in logic class interpretations aren’t naive.
Cognitive scientists don’t expect non-specialists to be aware of the logics that model their interpretations and inferences, e.g., here’s Da Silva Neves, Bonnefon, and Raufaste (2002, p. 110) discussing the relationship between potential properties of people’s reasoning systems and how people feel that they reason:
… even if we expect human inference to corroborate these properties, we know of no sufficient reason to think that lay reasoners would recognize any rationality postulate as valid, neither that they would conscientiously use them to guide their reasoning. […W]e assume that human inference is constrained by knowledge organisation in memory and that its formal properties emerge from a spreading activation process operating directly on knowledge structures.
van Benthem, J. (2008). Logic and reasoning: Do the facts matter? Studia logica, 88, 67-84.
Da Silva Neves, R., Bonnefon, J. F., & Raufaste, E. (2002). An empirical test of patterns for nonmonotonic inference. Annals of Mathematics and Artificial Intelligence, 34, 107-130.
Geis, M. L., & Zwicky, A. M. (1971). On invited inferences. Linguistic inquiry, 2(4), 561-566.
Pfeifer, N. (2013). The new psychology of reasoning: A mental probability logical perspective. Thinking & Reasoning, 19(3-4), 329-345.
Stenning, K., & van Lambalgen, M. (2005). Semantic interpretation as computation in nonmonotonic logic: The real meaning of the suppression task. Cognitive Science, 29(6), 919-960.
Increased consumption of dark, but not milk, chocolate was “associated with” lower risk of type 2 diabetes (Lin, et al., 2024). Refresh your teaching examples for causal inference using non-experimental data 🤓
Liu, B., Zong, G., Zhu, L., Hu, Y., Manson, J. E., Wang, M., Rimm, E. B., Hu, F. B., & Sun, Q. (2024). Chocolate intake and risk of type 2 diabetes: prospective cohort studies. BMJ, e078386.
I was interested to see a recent review of process tracing in health research (Johnson, Beach, & Al-Janabi, 2024). How are its users getting on…?
Of the 19 evaluations satisfying review criteria, only 2 got full marks:
These were praised for how they justified using process tracing, their complete and detailed description of methods, their use of multiple types of evidence, and how they considered and attempted to rule out alternative or counterfactual explanations of outcomes. Finally, they linked their findings back to the theory. I have added them to my reading stack.
The review’s conclusions are damning:
“… a majority of studies reported do not describe in-depth how they conduct their process trace, and fewer still support their claims consistently and comprehensively with verifiable and well organised evidence. This is somewhat counterintuitive given that one of the features of process tracing is to support the more robust depth of analysis to explore hypotheses and confirm or counter with supportive evidence. However, this is not just a feature of studies reporting process tracing in health. Applications of process tracing in policy studies and political science more generally have similar trends, with many studies providing little detail of their methodology or a mechanistic depiction of the theory.”
As ever, to be continued…
Alvarado, M., Penney, T. L., Unwin, N., Murphy, M. M., & Adams, J. (2021). Evidence of a health risk ‘signalling effect’ following the introduction of a sugar-sweetened beverage tax. Food Policy, 102.
Johnson, R., Beach, D., & Al-Janabi, H. (2024). How is process tracing applied in health research? A systematic scoping review. Social Science & Medicine, in press, journal pre-proof.
Ryu, S.-K., & Chung, S.-G. (2021). Korea’s Early COVID-19 Response: Findings and Implications. International Journal of Environmental Research and Public Health, 18(16), 8316.