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

Despite being designed for performance rather than cognitive plausibility,transformer language models have been found to be better at predicting metricsused to assess human language comprehension than language models with otherarchitectures, such as recurrent neural networks. Based on how well theypredict the N400, a neural signal associated with processing difficulty, wepropose and provide evidence for one possible explanation - their predictionsare affected by the preceding context in a way analogous to the effect ofsemantic facilitation in humans.

Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?