Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which
computational analyses align best with the targeted neurocognitive/psychological functions that we want to
assess. In this paper we reflect on two decades of experience with the application of language-based assessment
to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it
should be measured and why we are measuring the phenomena. We address the questions by advocating for a
principled framework for aligning computational models to the constructs being assessed and the tasks being
used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the
accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled
approach can further the goal of transitioning language-based computational assessments to part of clinical
practice while gaining the trust of critical stakeholders
