Research foundations in decision-making, learning, perception, and computational methods across the SOS+CD publication archive.
The archive includes earlier work by lab members on cognition, learning, and computational methods. These studies provide context for questions about how people and models learn, evaluate evidence, and act under uncertainty.
Decision-making requires more than choosing the best action under a known model. Our structure-learning work asks how people infer the environment that produces rewards and how this changes the interpretation of their choices.
A model makes assumptions about information, uncertainty, and objectives. Studies of Bayesian decision theory and reinforcement learning make those assumptions inspectable and explore the behavior they imply.
Related publications cover perception, motor learning, statistical models, and scientific text and image representations. This is a historical research thread; the archive preserves the dates and contexts of those contributions.
Which assumptions about a learner's knowledge change our judgment of whether a choice or model behavior is appropriate?
Explore information about joining the lab or contact Daniel Acuña about collaboration.
