Christopher Lucas
Reader in Computational Cognitive Science
Combines computational models and psychological experiments to understand human learning in changing situations, and builds systems inspired by human cognition.
Christopher Lucas's lab at Edinburgh works in Machine Learning. 25 papers since 2023, 7 at top venues, mostly AAMAS and ICML. 3 PhD students. 8 alumni.
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 28%
- PhD students
- 3
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 3
- 7 papers2024 · T1 3
- 9 papers2023 · T1 0
T1 venuesOtherVenues: AAMAS, ICML, CHI, Findings of EMNLP, ICLR
Research focuspaper titles that use each topic's words
- human1 paper
- experiments0 papers
- human learning0 papers
- human cognition0 papers
- inspired0 papers
Paper list
From the lab homepage and DBLP.
20261 paper
20258 papers
- CogSciA Normative Account of Specialization: How Task and Environment Shape Role Differentiation in Collaboration (opens in a new tab)
- LIONBayesian Optimisation Against Climate Change: Applications and Benchmarks (opens in a new tab)
- Nat. Mac. Intell.Embodied large language models enable robots to complete complex tasks in unpredictable environments (opens in a new tab)
- NeurIPS (T1)Partner Modelling Emerges in Recurrent Agents (But Only When It Matters) (opens in a new tab)
- CHI (T1)People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI (opens in a new tab)
- CogSciPreschool Children's Learning and Generalization of Continuous Causal Functions (opens in a new tab)
- CogSciResource-rational belief revision can mitigate as well as amplify polarization (opens in a new tab)
- ICLR (T1)Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning (opens in a new tab)
20247 papers
- ICML (T1)Bayesian Program Learning by Decompiling Amortized Knowledge (opens in a new tab)
- CogSciCan Children Learn Functional Relations Through Active Information Sampling? (opens in a new tab)
- AAMAS (T1)Causal Explanations for Sequential Decision-Making in Multi-Agent Systems (opens in a new tab)
- AAAI Spring SymposiaConstructing Deep Concepts through Shallow Search (opens in a new tab)
- CogSciDistinguishing Between Process Models of Causal Learning (opens in a new tab)
- ICML (T1)DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design (opens in a new tab)
- CogSciParadoxical parsimony: How latent complexity favors theory simplicity (opens in a new tab)
20239 papers
- CogSciA rational model of spatial neglect (opens in a new tab)
- CogSciCausal inference shapes counterfactual plausibility (opens in a new tab)
- CogSciCharacterizing Shifts in Strategy in Active Function Learning (opens in a new tab)
- CogSciCharting children’s fruit categories with Markov-Chain Monte Carlo with People (opens in a new tab)
- —Counterfactuals and the logic of causal selection
- CogSciHow do instructions, examples, and testing shape task representations? (opens in a new tab)
- —Naı̈ve information aggregation in human social learning
- Findings of EMNLPNon-Compositionality in Sentiment: New Data and Analyses (opens in a new tab)
- CogSciSelective imitation on the basis of reward function similarity (opens in a new tab)
Where alumni went8 listed
Apply to EdinburghFall 2027
Not announced yet
Informatics PhD
- Round
- Rolling (funding consideration)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Not yet confirmed for this intake.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage and DBLP
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.