English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We evaluate 8 different word embedding models on their usefulness forpredicting the neural activation patterns associated with concrete nouns. Themodels we consider include an experiential model, based on crowd-sourcedassociation data, several popular neural and distributional models, and a modelthat reflects the syntactic context of words (based on dependency parses). Ourgoal is to assess the cognitive plausibility of these various embedding models,and understand how we can further improve our methods for interpreting brainimaging data.  We show that neural word embedding models exhibit superior performance on thetasks we consider, beating experiential word representation model. Thesyntactically informed model gives the overall best performance when predictingbrain activation patterns from word embeddings; whereas the GloVedistributional method gives the overall best performance when predicting in thereverse direction (words vectors from brain images). Interestingly, however,the error patterns of these different models are markedly different. This maysupport the idea that the brain uses different systems for processing differentkinds of words. Moreover, we suggest that taking the relative strengths ofdifferent embedding models into account will lead to better models of the brainactivity associated with words.

Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity