Content revision 4e771eaf0390cc87b6c7c6fd8469f3508f6ceeec7119472e6c1ccf17e2e1e5d2

## The Resolving Power of Transfer Experiments: When Can Designs Distinguish Accounts of Generalization?

Research
Source ID research:transfer
Original https://www.henryw.me/#/research#transfer

We developed a framework that links models of human generalisation to the design of experiments. I construct models by varying both how a learner represents information and how the learner uses it to respond. The same representation can support rule application, comparison with a category prototype or retrieval of remembered examples. Varying these mechanisms independently lets me build matched accounts and simulate how each would respond in the same task. I use these predictions to shape an experiment before collecting participant data. Candidate separation measures where the models predict different responses, and candidate recovery estimates how reliably the planned observations can distinguish them. Together, they guide whether to change the test items or response measure, or collect more evidence where a useful contrast already exists.

Applications to categorisation and artificial orthography show how these choices change what we can learn about the underlying mechanisms. I developed this framework into the open-source TIDE (Transfer-design Identifiability and Diagnosticity Evaluation) toolkit and an interactive app, so researchers can construct model comparisons, inspect their predictions and explore revisions to a design.