Group projects
You can find a more detailed overview about each project here.
P1: Wisdom or madness of crowds? (based on Toyokawa, Whalen & Laland (2019))
Investigate human choice behaviour in a three-armed bandit task to see when social learning is helpful and when it isn’t.
- Beginner friendly
- Model code written in R
- Data and code are here
P2: Social learning in correlated environments (based on Witt, Toyokawa, Lala, Gaissmaier & Wu (2023))
Investigate how humans integrate social information when it is positively correlated in a multi-armed bandit task.
- Advanced
- Model code written in Python
- Data and code are here
P3: Learning other people’s values based on the decisions they make (based on Jern, Lucas & Kemp (2017))
Simulate inverse decision-making to understand an agent’s prefrences.
- Beginner friendly
- No previous code to be used - choose your own programming language!
- Data are here
P4: Tracking competence and preference inference from spatial navigation (based on Jara-Ettinger, Schulz & Tenenbaum (2020))
Simulate an artificial agent navigating an environment, and an observer inferring its preferences and competence. Compare how well this matches human ratings.
- Advanced
- Model code written in Python (or code your own MDP)
- Data are here in the Data folder and code (if desired) is here
P5: Learning to communicate about shared procedural abstractions (based on McCarthy, Hawkins, Wang, Holdaway & Fan (2021))
Replicate or expand on an analysis of how human participants effectively communicate in a physical assembly task.
- Beginner friendly or advanced, depending on whether you choose to replicate or expand
- Model code written in Python
- Data and code are here
P6: Modelling algorithm-mediated social learning (inspired by Acerbi (2019) and Brady et al. (2023))
Extend models of transmission biases including algorithmic-mediation (possibly using data from social media).
- Beginner friendly
- Model code written in R
- Code is here
P7: Interplay between direct learning and social learning under uncertainty (based on Zhang & Gläscher (2020))
Work with an existing model of direct and social learning to predict human choices in an experiment or simulate agents.
- Advanced
- Model code written in Matlab, Stan, and R
- Data and code are here)
P8: From pattern to process (based on Kandler, Fogarty & Karsdorp (2023))
Figure out what evolutionary process underlies the distribution of a population’s cultural traits in five datasets.
- Beginner friendly
- Model code written in R, Matlab, or Julia
- Data and code are here
P9: Quantifying leadership in animal groups (based on Sridhar et al. (2023))
Explore the effect of different pairwise predictors on leadership / social influence within groups of golden shiner fish and/or pigeons.