  Word embeddings can reflect the semantic representations, and the embedding
qualities can be comprehensively evaluated with human natural reading-related
cognitive data sources. In this paper, we proposed the CogniFNN framework,
which is the first attempt at using fuzzy neural networks to extract non-linear
and non-stationary characteristics for evaluations of English word embeddings
against the corresponding cognitive datasets. In our experiment, we used 15
human cognitive datasets across three modalities: EEG, fMRI, and eye-tracking,
and selected the mean square error and multiple hypotheses testing as metrics
to evaluate our proposed CogniFNN framework. Compared to the recent pioneer
framework, our proposed CogniFNN showed smaller prediction errors of both
context-independent (GloVe) and context-sensitive (BERT) word embeddings, and
achieved higher significant ratios with randomly generated word embeddings. Our
findings suggested that the CogniFNN framework could provide a more accurate
and comprehensive evaluation of cognitive word embeddings. It will potentially
be beneficial to the further word embeddings evaluation on extrinsic natural
language processing tasks.
