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Critical Review of the Cybernetic Theory of Meta-Traits
To understand psychological science, it is necessary to understand how academia works. Research at universities is organized around paradigms (Kuhn, 1962). A paradigm is a set of shared ideas, assumptions, methods, and practices—something like a scientific culture.
The overarching paradigm in academic psychology is that scientific claims should be supported by empirical evidence, often obtained through quantitative analysis. In some areas of psychology, data are relatively inexpensive to collect, making it easy to conduct studies and produce results.
The harder part is getting the results published in a peer-reviewed journal. This is where belonging to an established research paradigm becomes advantageous. Researchers working within a paradigm share assumptions about which questions are important, which methods are appropriate, and which findings constitute progress. They also serve as reviewers and editors for journals in their field.
Consequently, research that conforms to an established paradigm has a natural audience and an established route to publication. Research that challenges its fundamental assumptions faces greater obstacles. Reviewers may be less receptive to findings that undermine the theoretical foundations of their own research programs.
These dynamics can allow paradigms to survive long after their scientific usefulness has diminished. In psychology, several competing research programs may coexist for decades, investigating similar phenomena with different theoretical interpretations while paying remarkably little attention to contradictory findings.
The resulting publications generate citations, support academic careers, and demonstrate research productivity. However, the production of publications is not the same as the production of knowledge. A research program can remain productive in terms of publications without making much progress toward answering its central scientific questions.
Kuhn (1962) described how established paradigms resist challenges and how scientific revolutions can involve generational changes in the scientific community. However, the conditions under which science operates are changing.
The internet has made it possible to distribute scientific findings and criticism without relying exclusively on peer-reviewed journals. Artificial intelligence has further accelerated access to information, allowing researchers to examine publications, compare competing claims, and identify inconsistencies more efficiently.
In my scientific utopia, weak paradigms would lose their influence more quickly because their shortcomings could be exposed and discussed almost immediately after publication. Researchers would have less opportunity to sustain an unproductive research program by repeatedly publishing variations on the same empirical finding.
This brings me to the subject of this blog post: The General Factor of Personality (GFP) paradigm.
A phenomenon discovered over 100 years ago
The General Factor of Personality is a relatively recent name for a phenomenon recognized more than a century ago.
Thorndike (1920) observed that when people evaluate other individuals, their ratings of different desirable characteristics tend to correlate more strongly than expected. People judged favorably on one characteristic also tend to receive favorable ratings on others.
For example, individuals rated as intelligent may also be rated as attractive, conscientious, and likable. Conversely, individuals who receive unfavorable ratings on one characteristic tend to receive unfavorable ratings on others.
Thorndike interpreted these correlations as evidence of a halo effect: a general impression of a person influences judgments of specific attributes. The phenomenon is robust and highly replicable. Nobody seriously needs another demonstration that favorable personality ratings tend to correlate. The scientific question is what produces these correlations.
Two competing explanations
The first explanation is evaluative bias. When we like someone, we tend to exaggerate their positive characteristics and minimize their negative ones. When we dislike someone, we tend to do the opposite. This produces correlations between ratings of attributes that may be only weakly related—or even unrelated—in reality.
The second explanation is that desirable attributes genuinely cluster together in individuals. According to proponents of the GFP, some people tend to possess more desirable characteristics across multiple personality domains. This tendency may reflect a broad psychological disposition, sometimes interpreted as social effectiveness. Some researchers have also proposed evolutionary or genetic explanations for the GFP.
These hypotheses are fundamentally different. The halo hypothesis explains correlations among ratings. The substantive GFP hypothesis proposes a common source of variation in people’s actual characteristics.
What have we learned in 100 years?
After a century of research, one might reasonably expect scientists to have made substantial progress in distinguishing these explanations.
One way to separate those explanations is to measure desirable attributes objectively. For example, cognitive ability and physical attractiveness can be measured with tests and ratings by others, respectively. These objective measures are not related to each other or to subjective ratings of happiness, but self-ratings of all three attributes show a positive correlation. This pattern is more consistent with the bias hypothesis (Anusic et al., 2009).
Another, particularly informative approach is to compare ratings obtained from different raters, self-ratings or ratings by friends and family members. Biesanz and West (2004) found that Big Five ratings were correlated when obtained from the same informant, whereas the structure was much closer to orthogonal when different informants were used. The main patter has been replicated many times. Sometimes bias does not explain all of the variance in the general factor, but it is a large part of it.
A new study using artificial intelligence
The new and more limited question is whether at least some of the variance reflects some real desirable disposition. However, the GFP paradigm continues to produce studies demonstrating the factor rather than demonstrating that it not just bias.
In 2025, Dimitri van der Linden and colleagues published a paper titled The General Factor of Personality (GFP) in Natural Language: A Deep Learning Approach in the Journal of Research in Personality. The authors used a large language model to examine associations among personality-descriptive adjectives in natural language.
They found that the first principal component distinguished desirable from undesirable personality attributes. Moreover, loadings on this component correlated highly ((r = .86)) with loadings from an earlier study based on human personality ratings.
The authors interpreted this finding as further evidence for the generality of the GFP.
However, an alternative explanation of this finding is that an LMM will learn the same biases that humans have when it learned from human descriptions of people’s personality. Natural language is intrinsically evaluative and linguists distinguish the descriptive meaning from the connotative meaning.
The descriptive, or denotative, meaning concerns what a word says about a person’s characteristics or behavior. The evaluative, or connotative, meaning concerns whether the attribute is considered good or bad.
Consider the difference between describing someone as relaxed and describing the same person as lazy. Both terms may refer to a tendency toward low activity, but they convey very different evaluations. Similarly, intelligent and kind describe different attributes, but both have positive evaluative meanings.
A language model trained on human language can learn these evaluative associations. Finding a general evaluative dimension in its representation of personality adjectives therefore tells us something about how personality descriptions are organized in language.
It is somewhat interesting to see whether an LLM produces an evaluative factor, but the finding does not provide any evidence that this factor is an actual personality disposition.
What did the new study teach us?
The authors’ discussion acknowledges the unresolved question of what the general factor represents. The paper acknowledge halo bias as a possible explanation, but it does not mention evidence that supports this hypothesis. This is the central problem. An article about the GFP was published and the new publication keeps the paradigm alive.
And in the discussion section, authors are able to make claims that go well beyond the results of a study and selectively cite evidence to support their paradigm.
“Although a substantive explanation of the GFP appears plausible, it is important to acknowledge that the general personality factor identified through natural language processing may still partly reflect bias” (p. 5).
This claim reverses the existing evidence and the burden of proof. There is ample evidence that the factor emerges in ratings by a single rater. It is much harder to find it with objective measures or in studies with multiple raters. The results of this article are entirely consistent with the bias hypothesis.
For example, the loading (relationship) of LLM rated intelligence (+.77) and politeness (.75), imples a correlation of r = .58. Actual studies of correlations between cognitive ability tests (intelligence) and politeness and broader measures of an agreeable personality show correlations close to zero or even small negative ones. So, there is no evidence that these two desirable traits really are related. This makes the bias interpretation of this GFP factor even more likely than the existing evidence with a narrower range of traits.
Even if a small substantive GFP exists, it would not follow that it is more useful than the specific attributes it is supposed to explain.A person’s intelligence, honesty, conscientiousness, sociability, and emotional stability can have important consequences. A weak general tendency toward desirable characteristics would not eliminate the need to measure these attributes separately.
Thus, even if a portion of the variance in this factor were valid, it would not alter the importance of possessing the actual desirable characteristics. Thus, research on the general factor of personality lacks importance. A more important question would be whether LMM are going to introduce biases when they are used to evaluate people (e.g., job applications). However, this question cannot be investigated by researchers who fundamentally assume that the factor is a real trait, not a bias. This is the paradigm trap. Researchers are forced to ask irrelevant questions that can be examined within the paradigm and are unable to ask important questions that challenge fundamental questions.
References
Anusic, I., Schimmack, U., Pinkus, R. T., & Lockwood, P. (2009). The nature and structure of correlations among Big Five ratings: The halo–alpha–beta model. Journal of Personality and Social Psychology, 97, 1142–1156. https://doi.org/10.1037/a0017159
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.
Thorndike, E. L. (1920). A constant error in psychological ratings. Journal of Applied Psychology, 4, 25–29. https://doi.org/10.1037/h0071663
van der Linden, D., Cutler, A., van der Linden, P. A., & Dunkel, C. S. (2025). The general factor of personality (GFP) in natural language: A deep learning approach. Journal of Research in Personality, 117, 104635. https://doi.org/10.1016/j.jrp.2025.104635