Abstract

In this work, we apply computational methods and machine learning to evaluate Carl Jung’s original personality theories using quantifiable data. We derive mathematical proxy equations for Jung’s eight cognitive functions and calculate them using a publicly available dataset of approximately one million responses to the statistically supported Big Five Inventory (BFI).   While contemporary personality psychology relies on trait-based models such as the Big Five, Jung’s cognitive-function framework remains influential despite limited empirical validation and its popular association with the Myers–Briggs Type Indicator (MBTI). This study returns to Jung’s Psychological Types to map the conceptual definitions of the eight cognitive functions onto measurable Big Five traits using theoretically informed weighting schemes. The resulting proxy functions are evaluated using a permutation-based null model to test whether their interrelationships reflect Jung’s predicted oppositions, and Principal Component Analysis (PCA) is used to examine their latent structure.   The validation analysis indicates that the derived proxy equations capture theoretically predicted relationships among cognitive functions at levels significantly above random expectation. Principal Component Analysis further reveals structured variance among the computed functions, suggesting that some aspects of Jung’s theoretical oppositions may be partially reflected in large-scale trait data, while other relationships collapse into broader behavioral dimensions consistent with the Big Five. These findings suggest that while Jung’s cognitive function framework does not emerge as a fully independent structure within trait data, elements of its conceptual organization may still be detectable through computational modeling of behavioral traits.

Advisor

Visa, Sofia

Department

Computer Science

Disciplines

Cognitive Psychology | Personality and Social Contexts

Keywords

Computational Personality Modeling, Jungian Cognitive Functions, Big Five Personality Traits, Principal Component Analysis (PCA), Monte Carlo Simulation, Latent Structure Analysis

Publication Date

2026

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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