The Science Behind the Technology
At HRMNY, we are driven by a single idea — to unite cutting-edge technology with deep-rooted scientific principles. Our goal is to help people understand themselves and others through precision, not assumption.
What began as a study of human psychology evolved into a breakthrough: a system that reads cognitive processes objectively, with no tests, no bias, and no interpretation errors.
“Any sufficiently advanced technology is indistinguishable from magic.”
— Sir Arthur C. Clarke, science fiction writer and futurist
How it all began
Analytical Psychology
and Personality Traits: Unearthing the Essence
It all began with the exploration of analytical psychology, rooted in the work of Carl Gustav Jung.
Jung revealed that every person has innate preferences — psychological traits that define how we perceive, process, and respond to information. These are not learned behaviors; they are part of our nature.
Yet their expression depends on life circumstances. A supportive environment strengthens these natural tendencies, while a limiting one suppresses them. Over time, this imbalance creates distortions in personality and behavior — something that HRMNY’s approach helps to restore.
Modeling Personality and Self-Assessment
Throughout the 20th century, researchers built multiple frameworks to describe human individuality: the Myers-Briggs Type Indicator, Eysenck’s PEN model, DISC, the Big Five, and Socionics.
Each of them sought to capture stable psychological patterns that define a person’s consistent way of thinking and acting. Traditional tools — questionnaires, self-assessment tests, and projective methods like TAT, MBTI, and DISC — helped classify people but had inherent weaknesses.
Results often depended on mood, self-awareness, and context. Respondents could easily distort answers, consciously or not. These approaches were slow, subjective, and lacked universal standards of validity.
Facial Features and Personality Links
A new generation of researchers began exploring more objective methods — those based on observation rather than self-reporting.
Facial expressions, tone of voice, body language, and even writing style became measurable sources of psychological data. In recent years, AI-assisted research — such as the work of Dr. Michal Kosinski at Stanford University — has confirmed that artificial intelligence can detect patterns in faces and speech more accurately than humans, linking them to personality and cognitive traits with remarkable precision.
This shift from what a person says to how they behave laid the foundation for HRMNY AI.
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1. Borkenau P., Brecke S., Möttig C., Paelecke M. (2009). Extraversion is accurately perceived after a 50-ms exposure to a face; J. Res. Pers. 43, 703–706
2. Briggs Myers I.; Kirby L.K.; Myers K.D. (2015). Introduction to Myers-Briggs type: a guide to understanding your results on the MBTI assessment. Palo Alto, Calif. : Consulting Psychologists Press
3. Cristani M., Vinciarelli A., Segalin C., Perina A. (2013). Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis; Proceedings of the 21st ACM international conference on Multimedia, pp. 213–222. ACMGeyer,
4. Jung, C. G., and Antony Storr. (1983). The Essential Jung. Princeton, NJ: Princeton University Press.
5. Kamenskaya E., Kukharev G. (2008). Recognition of psychological characteristics from face; Metody Informatyki Stosowanej 1(1), 59–73
6. Kosinski, M. (2021). Facial recognition technology can expose political orientation from naturalistic facial images. Sci Rep 11, 100. https://doi.org/10.1038/s41598-020-79310-1
7. Kramer R. S. S., King J. E., Ward R. (2011). Identifying personality from the static, non expressive face in humans and chimpanzees: Evidence of a shared system for signaling personality; Evol. Hum. Behav. https://doi.org/10.1016/j.evolhumbehav.2010.10.005
8. Kulkarni, V., Kern, M.L., Stillwell, D., Kosinski, M., Matz, S., Ungar, L., Skiena, S., & Schwartz, H.A. (2018). Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach. PLOS ONE.
9. Little A. C., Perrett D. I. (2007). Using composite images to assess accuracy in personality attribution to faces; Br. J. Psychol. 98, 111–126
10. Naumann L. P., Vazire S., Rentfrow P. J., Gosling S. D. (2009). Personality judgments based on physical appearance; Personal. Soc. Psychol. Bull. 35, 1661–1671
11. Penton-Voak I. S., Pound N., Little A. C., Perrett D. I. (2006). Personality judgments from natural and composite facial images: more evidence for a “kernel of truth” in social perception; Soc. Cogn. 24, 607–640
12. Pervin L.A., John O.P. (1999). Handbook of personality: Theory and research, Elsevier
13. Peter. (2012). Extraversion – Introversion: what C.G. Jung meant and how contemporaries responded. AusAPT National Conference
14. Segalin, C., Celli, F., Polonio, L., Kosinski, M., Stillwell, D., Sebe, N., … Lepri, B. (2017). What your Facebook profile picture reveals about your personality. Proceedings of the 2017 ACM on Multimedia Conference.
15. Shevlin M., Walker S., Davies M. N. O., Banyard P., Lewis C. A. (2003). Can you judge a book by its cover? Evidence of self-stranger agreement on personality at zero acquaintance; Pers. Individ. Dif. https://doi.org/10.1016/S0191-8869(02)00356-2
16. Strube MJ, Lott CL, Lê-Xuân-Hy GM, Oxenberg J, Deichmann AK. (1986). Self-evaluation of abilities: accurate self-assessment versus biased self-enhancement. J Pers Soc Psychol. 1986 Jul;51(1):16-25. doi: 10.1037/0022-3514.51.1.16. PMID: 3735065.
17. Walker M., Vetter T. (2016). Changing the personality of a face: Perceived big two and big five personality factors modeled in real photographs; J. Pers. Soc. Psychol. 110, 609–624
18. Wang & Kosinski (2018) Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology (JPSP)
We’re here to help you understand yourself better and grow professionally. Contact us at hello@hrmny.ai or on Telegram .
Decoding Human Behavior Through Generations
The journey of understanding human behavior has evolved through seven generations of discovery.
HRMNY AI: A 7th-Generation
Breakthrough
Throughout the 20th century, researchers built multiple frameworks to describe human individuality: the Myers-Briggs Type Indicator, Eysenck’s PEN model, DISC, the Big Five, and Socionics.
Each of them sought to capture stable psychological patterns that define a person’s consistent way of thinking and acting. Traditional tools — questionnaires, self-assessment tests, and projective methods like TAT, MBTI, and DISC — helped classify people but had inherent weaknesses.
Results often depended on mood, self-awareness, and context. Respondents could easily distort answers, consciously or not. These approaches were slow, subjective, and lacked universal standards of validity.
