Psychology Magazine

Using Big Data to Track Major Shifts in Human Cognition

By Deric Bownds @DericBownds
I want to pass on the first few paragraphs of a fascinating commentary by Simon DeDao on an article by Scheffer et al. that was the subject of MindBlog's 12/31/21 post. Motivated readers can obtain a copy of the whole article by emailing me.:
Scheffer et al.’s (1) exciting new work reports an historic rearrangement, occurring in the late 20th century, of the balance between reason and emotion. Its approach is part of a new trend in the psychological sciences that uses extremely large volumes of text to study basic patterns of human cognition. Recent work in this vein has included studies of the universal properties of gender representations (2), the rise of causal thinking (3), and a cognitive bias towards positivity in language itself (4). The goal of going “from text to thought” (5) is an attractive one, and the promise of the machine learning era is that we will only get better at extracting the imprints left, in text, by the mechanisms of the mind.
To establish their claims, Scheffer et al. (1) use principal component analysis to identify two major polarities of correlated vocabulary words in the Google Books corpus (6). The first polarity (PC1) tracks a shift from archaic to modern, in both material life (“iron” is archaic, “computer” is modern) and culture (“liberty” is archaic, “privacy” is modern). The second polarity (PC2) that emerges is the intriguing one, and forms the basis of their paper: Its two poles, the authors argue, correspond to the distinction between “rational” and “intuitive” language.
Their main finding then has two pieces: a shift from the intuitive pole to the rational pole (the “rise” of rationality) and then back (the “fall”) (1). The rise has begun by the start of their data in 1850, and unfolds over the course of a century or more. They attribute it to a society increasingly concerned with quantifying, and justifying, the world through scientific and impersonal language—a gradual tightening of Max Weber’s famous “iron cage” of collectivized, rationalized bureaucracy in service of the capitalist profit motive (7). The fall, meaning a shift from the rational back to the intuitive, begins in 1980, and is more rapid than the rise: By 2020, the balance is similar to that seen in the early 1900s. The fall appears to accelerate in the early 2000s, which leads the authors to associate it with social media use and a “post-truth era” where “feelings trump facts.” Both these interpretations are supported by accompanying shifts toward “collective” pronouns (we, our, and they) in the Weberian period, and then toward the “individualistic” ones (I, my, he, and she) after.
The raw effect sizes the authors report are extraordinarily large (1). At the peak in 1980, rationality words outnumbered intuition words, on average, three to one. Forty years later (and 100 y earlier), however, the balance was roughly one to one. If these represent changes in actual language use, let alone the time devoted to the underlying cognitive processes, they are enormous shifts in the nature of human experience.
1. M. Scheffer, I. van de Leemput, E. Weinans, J. Bollen, The rise and fall of rationality in language. Proc. Natl. Acad. Sci. U.S.A. 118, e2107848118 (2021).
2. T. E. S. Charlesworth, V. Yang, T. C. Mann, B. Kurdi, M. R. Banaji, Gender stereotypes in natural language: Word embeddings show robust consistency across child and adult language corpora of more than 65 million words. Psychol. Sci. 32, 218–240 (2021).
3. R. Iliev, R. Axelrod, Does causality matter more now? Increase in the proportion of causal language in English texts. Psychol. Sci. 27, 635–643 (2016).
4. P. S. Dodds et al, Human language reveals a universal positivity bias. Proc. Natl. Acad. Sci. U.S.A. 112, 2389–2394 (2015).
5. J. C. Jackson et al, From text to thought: How analyzing language can advance psychological science. Perspect. Psychol. Sci., 10.117/17456916211004899 (2021).
6. J. B. Michel et al.; Google Books Team, Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011).

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