Any research on relationship between the dimensions of a (word2Vec) space and how the human mind constructs meaning (or reality) through language?
Some initial steps where taken here:
Connecting concepts in the brain by mapping cortical representations of semantic relations

To represent words as vectors, we used a pretrained word2vec model21.
Briefly, this model was a shallow neural network trained to predict
the neighboring words of every word in the Google News dataset,
including about 100 billion words
(https://code.google.com/archive/p/word2vec/). After training, the
model was able to convert any English word to a vector embedded in a
300-dimensional semantic space (extracted through software package
Gensim56 in python). Note that the basis functions learned with
word2vec should not be interpreted individually, but collectively as a
space.
Applying the encoding model to the differential vector of a word pair
could effectively generate the cortical representation of the
corresponding word relation. With this notion, we used the encoding
model to predict the cortical representations of semantic relations.
For each class of semantic relation, we calculated the relation vector
of every word pair in that class, projected the relation vector onto
the cortex using the encoding model, and averaged the projected
patterns across word-pair samples in the class.