  Can deep language models be explanatory models of human cognition? If so,
what are their limits? In order to explore this question, we propose an
approach called hyperparameter hypothesization that uses predictive
hyperparameter tuning in order to find individuating descriptors of
cognitive-behavioral profiles. We take the first step in this approach by
predicting human performance in the semantic fluency task (SFT), a well-studied
task in cognitive science that has never before been modeled using
transformer-based language models (TLMs). In our task setup, we compare several
approaches to predicting which word an individual performing SFT will utter
next. We report preliminary evidence suggesting that, despite obvious
implementational differences in how people and TLMs learn and use language,
TLMs can be used to identify individual differences in human fluency task
behaviors better than existing computational models, and may offer insights
into human memory retrieval strategies -- cognitive process not typically
considered to be the kinds of things TLMs can model. Finally, we discuss the
implications of this work for cognitive modeling of knowledge representations.
