1 research outputs found
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity
We evaluate 8 different word embedding models on their usefulness for
predicting the neural activation patterns associated with concrete nouns. The
models we consider include an experiential model, based on crowd-sourced
association data, several popular neural and distributional models, and a model
that reflects the syntactic context of words (based on dependency parses). Our
goal is to assess the cognitive plausibility of these various embedding models,
and understand how we can further improve our methods for interpreting brain
imaging data.
We show that neural word embedding models exhibit superior performance on the
tasks we consider, beating experiential word representation model. The
syntactically informed model gives the overall best performance when predicting
brain activation patterns from word embeddings; whereas the GloVe
distributional method gives the overall best performance when predicting in the
reverse direction (words vectors from brain images). Interestingly, however,
the error patterns of these different models are markedly different. This may
support the idea that the brain uses different systems for processing different
kinds of words. Moreover, we suggest that taking the relative strengths of
different embedding models into account will lead to better models of the brain
activity associated with words.Comment: accepted at Cognitive Modeling and Computational Linguistics 201