This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Scientists spend their careers trying to understand how the world works. Yet even in their own specialties, complete understanding may remain elusive.
That idea lies at the center of a new article by Richard Shiffrin, distinguished and Luther Dana Waterman professor of Psychological and Brain Sciences in the College of Arts and Sciences at Indiana University Bloomington, Stephen Stigler of the University of Chicago, and Frank Keil of Yale University. Published in Computational Brain & Behavior, the article, "Illusions of Understanding in the Sciences," explores the claim that scientists often believe they understand more than they actually do. The authors posit that this gap between confidence and understanding is a fundamental feature of how science operates in a world of nearly limitless complexity.
"The universe we inhabit being infinitely complex, scientists never understand completely, even in their narrow specialties," Shiffrin said. "Often they have an illusion of understanding far exceeding their reality." While Shiffrin is best known for decades of influential research on cognition and memory, this project reflects a broader line of inquiry into how scientists themselves practice science. Stigler is a historian of statistics and emeritus professor in the University of Chicago's Department of Statistics, and Keil is a developmental psychologist and professor at Yale University whose work has examined how both children and adults explain the world around them.
Together, the researchers asked a deceptively simple question: How deep is scientific understanding? Their answer begins with an example many scientists encounter throughout their careers: linear regression, a statistical technique used to estimate how one factor changes as another changes, such as how crop yields respond to rainfall or how test scores relate to study time. Researchers across disciplines routinely use the method to identify relationships among variables and analyze data.
Yet the authors argue that even many experienced users do not fully understand all of the assumptions, limitations and causal implications involved. The example serves as a gateway to a larger argument. "Scientists seek to understand the causes of observed phenomena," the authors write in the study.
"Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete." According to the article, understanding exists on a continuum rather than as an all-or-nothing achievement. Scientists may possess enough knowledge to make accurate predictions, develop useful theories, design successful experiments or create technologies that benefit society without fully understanding every mechanism at work. The authors illustrate the concept with examples from psychology and other disciplines.
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