– CogPlausAI

Language makes it possible to communicate complex thoughts by composing together simpler elements. What are the mechanisms that underpin language
comprehension in the brain? AI language models based on transformers, such as OpenAI's GPT-4, appear to provide a potential mechanism to understand this
process: like the brain, their behavior emerges from a complex interaction of a massive number of simple units, and they can learn from experience to process
language surprisingly well. Yet they also differ from the brain in crucial ways; in particular, their \textbf{working memory} capacity is vastly greater than that of
humans. Because they operate under very different constraints than humans, it is unlikely that their representations will match the ones used by humans, and as such
they are poor candidates for models of the brain. In fact, we hypothesize that these working memory constraints are an essential catalyst for the emergence of
hierarchical structure in language and thought.
This project will address this gap, following two main aims. (1) We will develop language models with working memory constraints that better approximate those of
humans, and compare their behavior to that of humans in memory retrieval and language processing experiments. We will also test the hypothesis that memory
constraints facilitate the emergence of structure by comparing the models' language acquisition trajectory and efficiency to children's. (2) We will conduct
neuroimaging and electrophysiological experiments on humans (using fMRI and intracranial recordings) as they listen to or hear sentences that require maintaining
dependencies between words across multiple other words, which taxes working memory. The sentences will either be embedded in a story, or presented in isolation
(enabling greater experimental control). We will determine to what extent the models we developed in Aim~1 are able to explain these neural data, as well as existing
data from EEG, MEG and fMRI experiments