  Cognitive processing signals can be used to improve natural language
processing (NLP) tasks. However, it is not clear how these signals correlate
with linguistic information. Bridging between human language processing and
linguistic features has been widely studied in neurolinguistics, usually via
single-variable controlled experiments with highly-controlled stimuli. Such
methods not only compromises the authenticity of natural reading, but also are
time-consuming and expensive. In this paper, we propose a data-driven method to
investigate the relationship between cognitive processing signals and
linguistic features. Specifically, we present a unified attentional framework
that is composed of embedding, attention, encoding and predicting layers to
selectively map cognitive processing signals to linguistic features. We define
the mapping procedure as a bridging task and develop 12 bridging tasks for
lexical, syntactic and semantic features. The proposed framework only requires
cognitive processing signals recorded under natural reading as inputs, and can
be used to detect a wide range of linguistic features with a single cognitive
dataset. Observations from experiment results resonate with previous
neuroscience findings. In addition to this, our experiments also reveal a
number of interesting findings, such as the correlation between contextual
eye-tracking features and tense of sentence.
