Bramley Lab Research
The goal of our research is to better understand the algorithms, processes and representations that underpin human intelligence. We generally approach these issues by developing computational models and comparing them to behavioural data. This involves designing interactive tasks that distil elements of the challenges faced by natural cognition (see Demos) and having people and our models attempt to solve them. By comparing the behaviour of our models to that of people, we can gain insight into the mechanisms that people use to adapt their behaviour. As well as helping us understand human intelligence, insights from our research can inform the development of artificial systems capable of learning and behaving in more flexible and human-like ways.
We are particularly interested in how people succeed at learning in the face of the world’s radical complexity. We explore how people build up complex hypotheses, ideas and causal theories in settings where there are too many, or even an infinite number, of possibilities. A novelty of our research is a focus on active learning, i.e. how people act on the world in service of their learning and control goals. We investigate this in settings involving a variety of statistical cues from contingencies to continuous temporal and spatial dynamics. Going a step further, we are exploring model based control–i.e. situations where one balance learning a model of the environment with exploiting that model to achieve some goal.
To give you a better sense of some of the basic scientific questions we are interested in, we highlighted below some of our main lines of current research. The lab paper archive has a full list of papers with abstracts, pdfs, and links to the experiments, data and other resources.
▼ Causal cognition ▼
Representative publications
How do people generalize causal relations over objects? A non-parametric Bayesian account. Zhao, B., Lucas, C. G. & Bramley N. R. Computational Brain & Behavior, 5, 22-44 (2021).
Bramley, N. R., Lagnado, D. A., & Speekenbrink, M. (2015). Conservative forgetful scholars: How people learn causal structure through sequences of interventions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 708.
Bramley, N. R., Dayan, P., Griffiths, T. L. & Lagnado, D. A. (2017). Formalizing Neurath’s ship: Approximate algorithms for online causal learning. Psychological Review, 124 (3), 301-338.
▼ Concept learning ▼
Representative publications
Zhao, B. Lucas, C. G. & Bramley, N. R. (2023). How cognition bootstraps its way to complex concepts. Nature Human Behavior
Bramley, N. R., Xu, F. (2023). Active inductive inference in children and adults: A constructivist perspective. Cognition, 238, 105471.
Fränken, J.-P., Theodoropoulos, N. C., Bramley, N. R. (2022). Algorithms of Adaptation in Inductive Inference. Cognitive Psychology, 137, 101506. (2022)
▼ Active learning ▼
Representative publications
Gong, T., Gerstenberg, T., Mayrhofer, R., Bramley, N. R. (2023). Active Causal Structure Learning in Continuous Time. Cognitive Psychology, 140, 101542.
Bramley, N.R., Jones, A., Gureckis, T.M. & Ruggeri, A. (2022). Children’s failure to control variables may reflect adaptive decision-making. Psychonomic Bulletin & Review 29, 2314–2324.
Cheyette, S. J., Callaway, F., Bramley, N. R., Nelson, J. D., Tenenbaum, J. B. (2023). People seek easily interpretable information. Proceedings of the 45th Annual Meeting of the Cognitive Science Society.
▼ Developmental ▼
Representative Publications
Bramley, N. R., & Ruggeri, A. (2022). Children’s active physical learning is as effective and goal-targeted as adults’. Developmental Psychology, 58(12), 2310–2321.
Bramley, N.R., Jones, A., Gureckis, T.M. & Ruggeri, A. (2022). Children’s failure to control variables may reflect adaptive decision-making. Psychonomic Bulletin & Review 29, 2314–2324.
McCormack, T., Bramley, N. R., Frosch, C., Patrick, F. & Lagnado, D. A. (2016). Children’s Use of Interventions to Learn Causal Structure. Journal of Experimental Child Psychology. 141, 1-22.
▼ Time and causality ▼
Representative publications
Gong, T., Gerstenberg, T., Mayrhofer, R., Bramley, N. R. (2023). Active Causal Structure Learning in Continuous Time. Cognitive Psychology, 140, 101542.
Bramley, N. R., Gerstenberg, T., Mayrhofer, R. & Lagnado, D. A. (2018). Time in causal structure learning. Journal of Experimental Psychology: Learning, Memory & Cognition.
Rehder, R. E., Davis, Z. & Bramley, N. R. (2022). The paradox of time in dynamic causal systems. Entropy, , 24, 863.
▼ Control ▼
Representative publications
Davis, Z., Bramley, N. R., Rehder, R. E. & Gureckis, T. M (2018). A causal model approach to dynamic control. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 281-286). Austin, TX: Cognitive Science Society.
Schulz, E., Klenske, E. D., Bramley, N. R. & Speekenbrink, M. (2017). Strategic exploration in human adaptive control. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
▼ Physical causality ▼
Representative publications
Bramley, N. R., Gerstenberg, T., Tenenbaum, J. B. & Gueckis, T. M. (2018). Intuitive experimentation in the physical world. Cognitive Psychology, 195, 9-38.
Bramley, N. R., & Ruggeri, A. (2022). Children’s active physical learning is as effective and goal-targeted as adults’). Developmental Psychology, 58(12), 2310–2321.
Ethan J. Ludwin-Peery, Neil R. Bramley, Ernest Davis, Todd M. Gureckis (2020) Broken physics: A conjunction fallacy effect in intuitive physical reasoning. Psychological Science, 31 (12), 1602-1611.
