Author: Juan E. Sandoval CiteCite this article as: Sandoval, J (2017) Vultology as a Predictor of Career Choice - https://cognitivetype.com/vultology-predictor-career-choice/

First Published: December 28, 2017, Latest Revision: November 6, 2023

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Abstract: Preliminary research into facial micro-expressions and mannerisms (vultology) demonstrates a statistical leaning toward specific career paths among individuals who share the same expressive profile. Facial analysis using the CTVC (Cognitive Type Vultology Code 1.1) was used on 537 public and/or celebrity figures, grouping the subjects together into eight categories based on similarities in their expressions. The careers of the subjects were documented and narrowed to forty-four career categories and compared against the groupings by facial expressions. Eleven of the forty-four career categories measured were statistically tilted (at between 36%-61.5%) to one of these eight visual categories of expression, suggesting a significant connection between facial expressions/mannerisms and occupation.

Careers Research Statistics Vultology

1. Introduction

In recent decades, the connection between facial expressions, mannerisms and psychology has been explored most acutely through their ties to emotional states. Significant advances have been made by P. Ekman through his development of the Facial Action Coding System (FACS) to identify universal emotional expressions in people, as well as hundreds of compounding expressions generated by those universal expressions. The use of facial expressions as an objective window into subjective experiences (such as emotions) presents a promising alternative to psychometric instruments for ascertaining elements of psychology. In this study, the efficacy of a new system of facial codes - which postulate a correlation between a different set of micro-expressions and elements of cognitive processing and personality - was tested across 537 subjects to see whether it produced notable patterns in career path among those sharing the same set of micro-expressions.

1.1 Using the Instrument

The Cognitive Type Vultology Code 1.1 (CTVC) instrument ,under development by J.E. Sandoval and L. Renee Bayard, consists of 110 signals which catalog four main aspects of a person’s expression: facial muscle contractions, voice intonation, body posture/movements and elements of speech. The instrument quantifies a person’s expressions using eight main signal groupings (each consisting of ~10 signals). If the instrument identifies a predominance of one of these signal categories in a person’s facial/body expressions, that individual is grouped together with others who also share the same predominance in their bodily expression.The 110 signals used to categorize the subjects are publicly available in the form of static images, animaged images, audio files and video media at www.cognitivetype.com/ctvc.

The official study, when complete, will contain a second-by-second breakdown of the visible signals displayed by each subject during a specified timeframe, as well as a statistical aggregation of those signals to determine which of the eight signal categories ranks highest for them. However, for this preliminary study, the 537 subjects were ascertained and categorized by the leading researchers through an initial visual examination of 10-15 minutes of footage per subject. The subjects of this pilot study were chosen via a random survey of public and/or celebrity figures with ample media online for visual evaluation.

1.2 Careers

As many of the celebrity personalities used in this study hold multiple careers simultaneously, only the two most prominent careers (and the reasons for their notoriety/fame) were noted for each subject. A complete list of the subjects and their careers is available online at this link. Granularity was employed when initially ascertaining the subject’s career choices - resulting in 120 different careers represented among the 537 subjects. These 120 careers were then simplified together into 44 general career categories according to shared commonalities (for example, combining harpist, guitarist and instrumentalist under the same category of “music”). Figure 1 shows these 120 careers and the corresponding categories they were surmised into.

1.3 Background in Carl Jung

As it is beyond the scope of this paper to describe all the details of the CTVC - this pilot study assumes a familiarity with Cognitive Type theory and its theoretical framework. However, no special knowledge beyond the basics is necessary to understand the material herein. It is sufficient to know that the CTVC was designed as a means to ascertain the legitimacy of Jung’s functions through an objective metric; bodily expression. The eight categories of expression aim to quantify/measure the eight Jungian types, and via the addition of a supportive process, a total of sixteen types come together. The eight Jungian types (and their branches into 16) are as follows: Fe (FeNi & FeSi), Te (TeNi & TeSi), Ne (NeFi & NeTi), Se (SeFi & SeTi), Ti (TiSe & TiNe), Fi (FiSe & FiNe), Si (SiFe & SiTe), Ni (NiFe & NiTe)

For this pilot study, however, we simply tested whether a certain criteria for measuring bodily expressions produces significant statistical results in career path between individuals who share those same expressions.

2. Statistics

Figure 2 demonstrates how these sixteen visual categories (and types) were distributed among the 44 careers. The distribution of careers was noted to be uneven, with significant clustering of careers among certain types. The numbers here represent the number of careers, not the number of subjects. A total of 860 careers were measured, as many - but not all - subjects held two careers.

The results were then examined to identify what were the most highly represented careers among each of the sixteen types. In Figure 3 we see sixteen seperate tables, one for each type, and that type’s career distribution. The percentage ratios are calculated by taking a given career’s number count among a type, divided by the total number of careers held by subjects of that type (shown in the top left cell of each table).

Significant career path clusterings were shown among several type categories, with nine of the sixteen categories having over 20% representation in a single career. But given the circumstance of allowing two careers to be noted for each individual, it’s important to mention that Figure 3 specifically represents the ratio of careers among the types, and not the percentage of individuals who hold a given career. The percentage of real individuals of a type who hold this career would be greater - owing to the dilution that occurs by a two career tally. To illustrate, if we supposed that there were 50 individuals within a given type, and all subjects had dual careers in Acting and Modeling, the results of Figure 3 would measure the career count against 100 careers. Thus, although 100% of the individuals hold a career in Acting, the results would only show Acting to represent 50% of the demographic’s career path. This shouldn’t be taken to suggest only 50% of the samples have careers in acting. To demonstrate the alternative method of calculation, Figure 4 counts the number of individuals of a type holding a certain career, divided by the number of individuals represented in a given type. As with Figure 3, the top 10 careers are shown.

The connection between career and type is more clearly represented here, with ten of the sixteen type categories showing over 30% of individuals in the same career path. The strongest correlations can be summarized as:

Of 38 FeSi subjects, 39.5% held a career in acting.
Of 31 FiNe subjects, 32.3% held a career in music.
Of 24 FiSe subjects, 45.8% held a career in music.
Of 41 NeFi subjects, 34.1% held a career in comedy.
Of 27 NeTi subjects, 33.3% held a career in acting.
Of 40 SeFi subjects, 35.0% held a career in acting.
Of 17 SeTi subjects, 47.1% held a career in music.
Of 26 SiFe subjects, 30.8% held a career in acting.
Of 21 TiSe subjects, 42.9% held a career in acting.
Of 23 TiNe subjects, 30.4% held a career in psychology.

2.1 Accounting for Demographic Bias

Limitations exist in this method of evaluating career prevalence, owing to the selection of subjects from the celebrity sphere which holds a higher representation of actors and actresses. The high rates of actors/actresses among FeSi, NeTi, SeFi, SiFe and TiSe actors places into question whether the anomalous percentages are the result of an uneven sample pool. A formal study conducted among the civilian population, rather than the celebrity population, may show no significant statistical power between the two things.

To account for any potential monopolization of a few careers, statistics were next calculated (in Figure 5) by career category, to demonstrate what percentage of each type is visible in each career category. This approach highlights who is in what career, rather than what careers are most highly represented. Similar to Figure 3, this diagram represents the ratio of types per career. Here we find that despite the higher presence of acting as a career choice in the selected subjects, acting as a career category is evenly distributed among several types, with no type having more than 20% representation. The most significant correlations can be summraized as:

Of 43 subjects in Comedy, 39.5% were Ne types.
Of 36 subjects in News, 50% were Te types.
Of 36 subjects in Activism, 38.9% were Te types.
Of 31 subjects as Authors, 35.5% were Ni types.
Of 27 subjects in Politics, 44.4% were Si types.
Of 27 subjects in Life Coaching, 55.6% were Fe types.
Of 25 subjects in Modeling, 36% were Se types.
Of 18 subjects in Mysticism, 50% were Ni types.
Of 13 subjects in Trivia, 61.5% were Te types.
Of 9 subjects in Modeling, 44.4% were Se types.
Of 8 subjects in Philosophy, 50% were Ni types.

I’ve omitted mention of high percentages from career careers below a sample size of 8 subjects, as the sample size would be too small to consider the associations meaningful. Figure 6 below shows the same representation - a ratio of types per career - but as evident across the sixteen groupings. We note here that:

Of the 39.5% Ne types in Comedy:
- 32.6% are NeFi
- 7% are NeTi
Of the 50% Te types in News:
- 25% are TeSi
- 25% are TeNi
Of the 38.9% Te types in Activism:
- 25% are TeNi
- 13.9% are TeSi
Of the 35.5% Ni types in Authoring:
- 29% are NiFe
- 6.5% are NiTe
Of the 44.4% Si types in Politics:
- 40.7% are SiTe
- 3.7% are SiFe
Of the 55.6% Fe types in Life Coaching:
- 33.3% are FeSi
- 22.2% are FeNi
Of the 36% Se types in Modeling:
- 36% are SeFi
- 0% are SeTi
Of 50% Ni types in Mysticism:
- 33.3% are NiFe
- 16.7% are NiTe
Of the 61.5% Te types in Trivia:
- 61.5% are TeSi
- 0% are TeNi
Of the 44.4% Se types in Fashion:
- 44.4% are SeFi
- 0% are SeTi
Of the 50% Ni types in Philosophy:
- 25% are NiFe
- 25% are NiTe

3. Conclusions

While this initial pilot study contains many methodological problems such as with the demographic pooled for its selection of subjects and its dependence on the core researchers for the classification of the subjects, the information gathered thus far suggests a strong correlation between certain types and career paths.

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Vultology as a Predictor of Career Choice: A Pilot Study

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Vultology as a Predictor of Career Choice

1. Introduction

1.1 Using the Instrument

1.2 Careers

1.3 Background in Carl Jung

2. Statistics

2.1 Accounting for Demographic Bias

3. Conclusions

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