Cognitive Modeling of Semantic Fluency Using Transformers
This work addresses the challenge of bridging deep learning and cognitive science for researchers in both fields, though it is incremental as it builds on existing transformer methods applied to a new cognitive task.
The paper tackled the problem of using transformer language models as explanatory models of human cognition by predicting human performance in the semantic fluency task, finding that these models can identify individual differences better than existing computational models and offer insights into memory retrieval strategies.
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.