  Stylistic analysis of text is a key task in research areas ranging from
authorship attribution to forensic analysis and personality profiling. The
existing approaches for stylistic analysis are plagued by issues like topic
influence, lack of discriminability for large number of authors and the
requirement for large amounts of diverse data. In this paper, the source of
these issues are identified along with the necessity for a cognitive
perspective on authorial style in addressing them. A novel feature
representation, called Trajectory-based Style Estimation (TraSE), is introduced
to support this purpose. Authorship attribution experiments with over 27,000
authors and 1.4 million samples in a cross-domain scenario resulted in 90%
attribution accuracy suggesting that the feature representation is immune to
such negative influences and an excellent candidate for stylistic analysis.
Finally, a qualitative analysis is performed on TraSE using physical human
characteristics, like age, to validate its claim on capturing cognitive traits.
