The way psychological scientists think on an individual level is tied to the theoretical camps they attend and the research tools they prefer. These individual intellectual habits help explain why deep disagreements persist in science, even when researchers examine the same data. The study was published in the journal natural human behavior.
The traditional view of science assumes that accumulating data will ultimately settle academic debates. According to this perspective, disagreements between researchers are usually caused by differences in what they know. As evidence mounts against an outdated idea, the scientific community theoretically discards it and creates a more accurate model.
However, deep divisions still exist in fields such as psychology. Researchers regularly debate whether to focus on biological mechanisms or the social environment when explaining human behavior. Some scholars suspect that these separate camps persist for reasons unrelated to the raw facts, since access to the same methods or data does not always guarantee consensus.
The researchers wanted to see whether these persistent academic divides actually reflected underlying mental habits. They designed a study to test whether scientists’ personal thinking oriented them toward particular theories and research tools. They assessed how these personal characteristics relate to the laboratory environment.
LMU Munich researcher Justin Sulik led the study. Sulik collaborated with Nakwon Lim and James Evans of the University of Chicago, Elizabeth Pontix of the University of California, Davis, and Gary Lupyan of the University of Wisconsin-Madison.
The team surveyed around 8,000 scientists working in psychology and related fields. They asked participants to explain their positions on the 16 topics being discussed. These topics include whether human behavior is best explained by the rules of rational egoism, whether brain biology is essential to understanding the mind, and whether cognition is highly dependent on the social environment.
The study then measured several well-established cognitive traits among the participants. One characteristic is ambiguity tolerance, which refers to how comfortable a person is with uncertainty and poorly structured problems. Another was the need for cognitive constructs that measure preferences for logical planning and predictable routines.
The study also tested the differences between visual and verbal thinking styles. Researchers divided visual imagination into two categories. Spatial imagery involves the ability to mentally rotate three-dimensional geometric shapes, whereas object imagery involves depicting scenes with great clarity and detail.
The results showed that researchers’ basic mental tendencies are related to their positions in broader scientific debates. Scientists with a high tolerance for ambiguity tended to reject the idea that humans always act with rational self-interest. They also supported a holistic explanation of behavior that valued the subject’s cultural background.
Conversely, scientists with a high need for cognitive structure leaned in the other direction. They were more likely to believe that theoretical concepts like working memory corresponded to real, physical things in the human brain. They also preferred to explain human behavior logically, based on rules.
The physical tools that scientists used in the laboratory were also related to their abstract beliefs. People who used brain imaging techniques were less likely to believe that the social environment is important for explaining human behavior. Researchers who reported better spatial imagination were more likely to use mathematical modeling in their daily work.
The scientists noted that these methodological correlations are very clear. Studying large interacting social groups while using brain scanners may be difficult in practice. But the users of those machines also held a widespread philosophical belief that social context is less important for understanding cognition.
To elaborate on these worldviews, researchers grouped controversial themes into five fundamental belief systems. These potential mathematical factors were categorized as intrinsic, biological, logical, contextual, and objective. Scientists who score high on essential elements generally believe that most human abilities are innate and that personality is stable throughout life.
Tolerance of ambiguity was a psychological trait associated with all five of these scientific belief systems. People with a high tolerance for ambiguity are less likely to see the human brain as a computer. They were also less likely to prioritize evolutionary explanations of behavior, preferring instead situational social explanations.
The research team also wanted to see if the survey responses translated into actual scientific results. They received permission from some participants to securely link their survey responses to their professional publication records.
The team used machine learning technology to analyze the text of abstracts and paper titles published by scientists. Computer algorithms measured how well words and expressions matched across different authors. They also built an algorithm to map who these scientists collaborated with and which older papers they cited as foundational literature.
The algorithm revealed that cognitive characteristics are associated with differences in real-world publishing activity. This is true even when controlling for a researcher’s specific subfield or preferred tools. Two psychologists studying the exact same topic using the same methods are even more likely to cite the same references if they happen to share similar internal thinking styles.
The authors note that these patterns reveal the difficulty of translating ideas between different scientific camps. This problem is not just abstract logic, but is deeply connected to individual human cognition. Researchers simply have different internal thresholds for what explanations they find satisfactory and closest to the truth.
There are some caveats to keep in mind when interpreting the results. The mathematical effect size in this study was relatively small. This means that while mathematical trends are consistent across thousands of people, a single scientist’s cognitive characteristics do not determine every research choice they make.
The survey also had a low response rate of 3%, which is standard for a mass email survey, but means that participants were biased toward scientists who publish frequently. Additionally, the researchers only surveyed psychologists. They hope to extend this framework to other scientific fields to see if similar patterns emerge in fields like physics and sociology.
Ultimately, the researchers suggest, science could benefit from actively managing diverse cognitive styles within research groups. Combining a wide range of natural problem-solving approaches can help bridge deep theoretical gaps that data alone cannot resolve. In the paper, the authors conclude that “science is a human enterprise, and understanding the development of scientific knowledge depends on an explanation of human thought processes.”
The study, “Differences in cognitive characteristics among psychologists are associated with scientific disparities,” was authored by Justin Sulik, Nakwonrim, Elizabeth Pontikes, James Evans, and Gary Lupyan.
