Modern authorship attribution methods are often comprised of powerful yet opaque machine learning algorithms. While much of this work lends itself to concrete outcomes in the form of probability scores, advanced approaches typically preclude deeper insights in the form of psychological interpretation. Additionally, few attribution methods exist for single-candidate authorship problems, most of which require large amounts of supplemental data to perform and none of which rely upon explicitly psychological measures. The current study introduces Mental Profile Mapping, a new authorship attribution technique for single-candidate authorship questions that is founded on previous scientific research pertaining to the nature of language and psychology. In the current study, baseline expectations for results and performance are set using an advanced technique known as “unmasking” on the test case of Aphra Behn, a 17^th century English playwright. Following this, Mental Profile Mapping is introduced and tested for its psychometric properties, tested using a “bogus insertion” method, and then applied to canonical Aphra Behn plays. Results from both attribution methods suggest that 2 of 5 questioned plays are likely to have been authored by Behn, with the remaining 3 plays exhibiting a poor fit for Behn’s psychological fingerprint. Mental Profile Mapping results are then decomposed into deeper psychological interpretation, a quality unique to this new method.