Just out in American Psychologist, might be of interest to some ..

Gray, K., Anderson, S., Chen, E.E., Kelly, J.M., Christian, M.S., Patrick, J., Huang, L., Kenett, Y.N., & Lewis, K. (2019). "Forward flow": A new measure to quantify free thought and predict creativity. American Psychologist, 74, 5, 539-554.( http://dx.doi.org/10.1037/amp0000391 ) [paywall]

Abstract

When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people’s streams of free thought—free association—its utility for scientific research is undermined by two open questions: (a) How can streams of thought be quantified? (b) Do such streams predict psychological phenomena? We resolve the first issue—quantification—by presenting a new metric, “forward flow,” that uses latent semantic analysis to capture the semantic evolution of thoughts over time (i.e., how much present thoughts diverge from past thoughts). We resolve the second issue—prediction—by examining whether forward flow predicts creativity in the lab and the real world. Our studies reveal that forward flow predicts creativity in college students (Study 1) and a representative sample of Americans (Study 2), even when controlling for intelligence. Studies also reveal that membership in real-world creative groups—performance majors (Study 3), professional actors (Study 4) and entrepreneurs (Study 5)—is predicted by forward flow, even when controlling for performance on divergent thinking tasks. Study 6 reveals that forward flow in celebrities’ social media posts (i.e., on Twitter) predicts their creative achievement. In addition to creativity, forward flow may also help predict mental illness, emotional experience, leadership ability, adaptability, neural dynamics, group productivity, and cultural success. We present open-access online tools for assessing and visualizing forward flow for both illustrative and large-scale data analytic purposes.

Unfortunately, the authors show little understanding of what that word ‘predict’ means, and how predictive accuracy is required to be both quantified and described accurately/honestly via both computational/actuarial and qualitative approaches. They are also all over the place when it comes to understanding the meaning of the words “measure”, “quantification”, and “metric” as applied to “Forward Flow” assessment .. but that’s par for the course for so many psychologists.

However, the methodology of “forward flow” is interesting (semantic distance analysis – forget the “latent” cobblers), and there are small associations/effects with a variety of target groups .. although this may reflect particular groups’ use of language than “creativity” per se . But with such low correlations throughout, and with an  r-square average predictive accuracy of 4% (that’s the only measure of predictive accuracy employed by the authors which is not optimal), the end result is somewhat underwhelming.

“Given the variety of effect sizes across our previous studies, we now report results of a “superanalysis” that includes all participants from Studies 1–5 as well as the pilot study. Unlike a traditional meta-analysis, which combines effect sizes of individual studies, this analysis combines all forward flow and creativity ratings into one sample, allowing us to capitalize on the large diversity among samples. Across all participants (N = 1,397), forward flow was positively associated with creativity, r(1,395) = .19, p < .001, even when controlling for cognitive capacity in the studies that measured it.  ” p. 548, column 2, para 1).

And, as they themselves note on p. 549-550:

“We recognize creativity is multifaceted and its breadth cannot be reduced to a single metric. Indeed, the point of these studies is not to argue that forward flowing thought is the best predictor of creativity, but instead to provide a proof of concept for an important addition to researchers’ toolboxes.”

Regards .. Paul

Chief Research Scientist

Cognadev Ltd.

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