  Human thinking requires the brain to understand the meaning of language
expression and to properly organize the thoughts flow using the language.
However, current natural language processing models are primarily limited in
the word probability estimation. Here, we proposed a Language guided
imagination (LGI) network to incrementally learn the meaning and usage of
numerous words and syntaxes, aiming to form a human-like machine thinking
process. LGI contains three subsystems: (1) vision system that contains an
encoder to disentangle the input or imagined scenarios into abstract population
representations, and an imagination decoder to reconstruct imagined scenario
from higher level representations; (2) Language system, that contains a
binarizer to transfer symbol texts into binary vectors, an IPS (mimicking the
human IntraParietal Sulcus, implemented by an LSTM) to extract the quantity
information from the input texts, and a textizer to convert binary vectors into
text symbols; (3) a PFC (mimicking the human PreFrontal Cortex, implemented by
an LSTM) to combine inputs of both language and vision representations, and
predict text symbols and manipulated images accordingly. LGI has incrementally
learned eight different syntaxes (or tasks), with which a machine thinking loop
has been formed and validated by the proper interaction between language and
vision system. The paper provides a new architecture to let the machine learn,
understand and use language in a human-like way that could ultimately enable a
machine to construct fictitious 'mental' scenario and possess intelligence.
