Gittinger PAS

I did a random Google search for “Gittinger personality assessment system” (as one does), hoping to find a “mind control” related result. Instead, I found a “paranormal” related declassified result.

Approved For Release 2000/08/08 : CIA-RDP96-00789R002200230001-2
Final Report–Objective C, Tasks 2 and 3 December 1987
Review of the Personality Assessment System
By: Nevin D. Lantz
Prepared for: Peter J. McNelis, DSW
Contracting Officer’s Technical Representative
SRI International
333 Ravenswood Ave.
Menlo Park, CA 94025 U.S.A.


“During FY 1987, we conducted a thorough review of the Personality Assessment System (PAS) to gauge its continued usefulness as a screening and selection instrument and a personality descriptor for subjects in the psychoenergetics project. Data for this review came from published articles where the PAS was reviewed or used as a research tool, attendance at the annual PAS conference, and extensive interviews with several of the principal developers.

The PAS is a multifactored personality assessment instrument that has been evolving over the past 30 years using behavioral measures as raw data for making inferences and predictions about personality and behavior. The early development work was conducted by John Gittinger and his associates in a private firm that served clients in business and government. During the last 20 years, the test has begun to make small inroads into the academic environment, but it remains obscure and controversial.

This report traces the development of the PAS, gives an overview of the theory and methods of the test, and examines some of the problems connected with its use in the psychoenergetics project. It concludes that use of the PAS as a descriptive tool has continuing merit but that using the test for mass screening and mass selection of candidates for psychoenergetic training is not feasible at this time.”

Jake’s Conclusions

The PAS is apparently far more useful for trauma-based mind control than for mass screening and mass selection of candidates for (voluntary) training in how to be psychic (or “psychoenergetic”, to sound scientific and not “New Age”), although trauma-based mind control is not something I want to get into here.

The Big-Five factor personality model seems to me to be roughly similar to Gittinger’s MKULTRA PAS model, and what’s interesting is that there are papers published within the past 5 years describing how to probe an unwitting or perhaps even preverbal human experimental subject to determine their personality through EEG responses and machine learning techniques, exactly as “conspiracy theorists” Fritz Springmeier and Cisco Wheeler speak of the “Illuminati” doing in their 1990’s-era self-published works. That technology was available since the 1980’s, and now we see talk in the non-classified NIH pubs.

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing

Manousos A. Klados,1,2,*Panagiota Konstantinidi,2,3Rosalia Dacosta-Aguayo,4Vasiliki-Despoina Kostaridou,2Alessandro Vinciarelli,5 and Michalis Zervakis3


Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.

Keywords: Big-Five factor model, brain functional connectivity, electroencephalogram signal processing, emotional processing, neuroscience, personality detection




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