Selected and recent publications

2023

1. Psych Rev

   Counterfactuals and the logic of causal selection

   Quillien, Tadeg, and Lucas, Christopher G

   2023
2. Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making

   Gyevnar, Balint, Wang, Cheng, Lucas, Christopher G, Cohen, Shay B, and Albrecht, Stefano V

   arXiv preprint arXiv:2302.10809 2023
3. CogSci Proc

   Charting children’s fruit categories with Markov-Chain Monte Carlo with People

   Leon-Villagra, Pablo, Ehrlich, Isaac, Lucas, Christopher G, and Buchsbaum, Daphna

   2023
4. Designing Optimal Behavioral Experiments Using Machine Learning

   Valentin, Simon, Kleinegesse, Steven, Bramley, Neil R, Seriès, Peggy, Gutmann, Michael U, and Lucas, Christopher G

   arXiv preprint arXiv:2305.07721 2023
5. CogSci Proc

   Selective imitation on the basis of reward function similarity

   Taylor-Davies, Max, Droop, Stephanie, and Lucas, Christopher G

   arXiv preprint arXiv:2305.07421 2023
6. CogSci Proc

   Characterizing Shifts in Strategy in Active Function Learning

   Gelpi, Rebekah, Zhou, Caiqin, Lucas, Christopher G, and Buchsbaum, Daphna

   2023
7. Naı̈ve information aggregation in human social learning

   Fränken, Jan-Philipp, Valentin, Simon, Lucas, Christopher G, and Bramley, Neil

   2023

2022

1. AAAI

   Bayesian Optimisation for Active Monitoring of Air Pollution

   Hellan, Sigrid Passano, Lucas, Christopher G, and Goddard, Nigel H

   In Proceedings of the AAAI Conference on Artificial Intelligence 2022
2. Comp Brain & Beh

   Discovering Common Hidden Causes in Sequences of Events

   Valentin, Simon, Bramley, Neil R, and Lucas, Christopher G

   Computational Brain & Behavior 2022
3. CogSci Proc

   Actively learning to learn causal relationships

   Jiang, Chentian, and Lucas, Christopher G

   arXiv preprint arXiv:2206.09777 2022
4. Comp Brain & Beh

   How do people generalize causal relations over objects? A non-parametric Bayesian account

   Zhao, Bonan, Lucas, Christopher G, and Bramley, Neil R

   Computational Brain & Behavior 2022
5. CogSci Proc

   Uncovering variability in children’s concepts and conceptual change

   León-Villagrá, Pablo, Lucas, Christopher G, Buchsbaum, Daphna, and Ehrlich, Isaac

   2022
6. AAAI

   Bayesian Optimisation for Active Monitoring of Air Pollution

   Hellan, Sigrid Passano, Lucas, Christopher G, and Goddard, Nigel H

   arXiv preprint arXiv:2202.07595 2022
7. CogSci Proc

   Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars

   Zhao, Bonan, Bramley, Neil R, and Lucas, Chris

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2022
8. CogSci Proc

   The logic of guesses: how people communicate probabilistic information

   Quillien, Tadeg, and Lucas, Chris

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2022
9. ACL

   Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation

   Dankers, Verna, Lucas, Christopher G, and Titov, Ivan

   arXiv preprint arXiv:2205.15301 2022
10. CogSci Proc

    Uncovering Childrens’ Category Representations with MCMCP

    Leon Villagra, Pablo, Ehrlich, Isaac, Lucas, Chris, and Buchsbaum, Daphna

    In Proceedings of the Annual Meeting of the Cognitive Science Society 2022
11. CogSci Proc

    Dissecting causal asymmetries in inductive generalization

    Xia, Zeyu, Zhao, Bonan, Quillien, Tadeg, and Lucas, Chris

    In Proceedings of the Annual Meeting of the Cognitive Science Society 2022

2021

1. CogSci Proc

   Sampling heuristics for active function learning

   Gelpi, Rebekah, Saxena, Nayan, Lifchits, George, Buchsbaum, Daphna, and Lucas, Christopher G

   2021
2. CogSci Proc

   Modelling Recognition in Human Puzzle Solving

   Prystawski, Ben, Gelpi, Rebekah, Lucas, Chris, and Buchsbaum, Daphna

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2021
3. CogSci Proc

   Recovering human category structure across development using sparse judgments

   León-Villagrá, Pablo, Ehrlich, Isaac, Lucas, Christopher G, and Buchsbaum, Daphna

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2021
4. NeurIPS AI4Sci

   Bayesian Optimal Experimental Design for Simulator Models of Cognition

   Valentin, Simon, Kleinegesse, Steven, Bramley, Neil R, Gutmann, Michael U, and Lucas, Christopher G

   arXiv preprint arXiv:2110.15632 2021
5. CogSci Proc

   Bayesian Experimental Design for Intractable Models of Cognition

   Valentin, Simon, Kleinegesse, Steven, Bramley, Neil R, Gutmann, Michael, and Lucas, Chris

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2021
6. CogSci Proc

   Exploring Causal Overhypotheses in Active Learning

   Jiang, Chentian, and Lucas, Chris

   In Proceedings of the Annual Meeting of the Cognitive Science Society 2021
7. Cognition

   What’s new? A comprehension bias in favor of informativity

   Rohde, Hannah, Futrell, Richard, and Lucas, Christopher G

   Cognition 2021

2020

1. CogSci Proc

   Incremental hypothesis revision in causal reasoning across development

   Gelpi, Rebekah, Prystawski, Ben, Lucas, Christopher G, and Buchsbaum, Daphna

   2020

2019

1. CogSci Proc

   Generalizing functions in sparse domains

   León-Villagrá, Pablo, and Lucas, Christopher G

   2019
2. Child Dev

   Causal learning across culture and socioeconomic status

   Wente, Adrienne O, Kimura, Katherine, Walker, Caren M, Banerjee, Nirajana, Fernández Flecha, Marı́a, MacDonald, Bridget, Lucas, Christopher, and Gopnik, Alison

   Child development 2019

2017

1. Cognition

   People learn other people’s preferences through inverse decision-making

   Jern, Alan, Lucas, Christopher G, and Kemp, Charles

   Cognition 2017
2. PNAS

   Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

   Gopnik, Alison, O’Grady, Shaun, Lucas, Christopher G, Griffiths, Thomas L, Wente, Adrienne, Bridgers, Sophie, Aboody, Rosie, Fung, Hoki, and Dahl, Ronald E

   Proceedings of the National Academy of Sciences 2017

2015

1. Psych Rev

   An improved probabilistic account of counterfactual reasoning.

   Lucas, Christopher G, and Kemp, Charles

   Psychological review 2015
2. NeurIPS

   The human kernel

   Wilson, Andrew G, Dann, Christoph, Lucas, Chris, and Xing, Eric P

   Advances in neural information processing systems 2015
3. Cog Dev

   Preschoolers’ understanding of graded preferences

   Hu, Jane, Lucas, Christopher G, Griffiths, Thomas L, and Xu, Fei

   Cognitive Development 2015
4. Psychon Bull Rev

   A Rational Model of Function Learning

   Lucas, Christopher G., Griffiths, Thomas L., Williams, Joseph J., and Kalish, Michael L.

   Psychon Bull Rev 2015

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   Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

2014

1. PLOS ONE

   The Child as Econometrician: A Rational Model of Preference Understanding in Children

   Lucas, Christopher G., Griffiths, Thomas L., Xu, Fei, Fawcett, Christine, Gopnik, Alison, Kushnir, Tamar, Markson, Lori, and Hu, Jane

   PLOS ONE 2014

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   Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children’s ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.

2. CSJ

   When Children Are Better (or at Least More Open-Minded) Learners than Adults: Developmental Differences in Learning the Forms of Causal Relationships

   Lucas, Christopher G., Bridgers, Sophie, Griffiths, Thomas L., and Gopnik, Alison

   Cognition 2014

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   Children learn causal relationships quickly and make far-reaching causal inferences from what they observe. Acquiring abstract causal principles that allow generalization across different causal relationships could support these abilities. We examine children’s ability to acquire abstract knowledge about the forms of causal relationships and show that in some cases they learn better than adults. Adults and 4- and 5-year-old children saw events suggesting that a causal relationship took one of two different forms, and their generalization to a new set of objects was then tested. One form was a more typical disjunctive relationship; the other was a more unusual conjunctive relationship. Participants were asked to both judge the causal efficacy of the objects and to design actions to generate or prevent an effect. Our results show that children can learn the abstract properties of causal relationships using only a handful of events. Moreover, children were more likely than adults to generalize the unusual conjunctive relationship, suggesting that they are less biased by prior assumptions and pay more attention to current evidence. These results are consistent with the predictions of a hierarchical Bayesian model.

2010

1. CSJ

   Inferring Hidden Causal Structure

   Kushnir, Tamar, Gopnik, Alison, Lucas, Chris, and Schulz, Laura

   Cognitive Science 2010

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   We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.

2. CSJ

   Learning the Form of Causal Relationships Using Hierarchical Bayesian Models

   Lucas, Christopher G., and Griffiths, Thomas L.

   Cognitive Science 2010

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   People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.