Abstract
Psychology is a complicated science. It has no general axioms or mathematical proofs, is rarely directly observable, and is the only discipline in which the subject matter (i.e., human psychological phenomena) is also the tool of investigation. Like the Flatlanders in Edwin Abbot's famous short story (1884), we may be led to believe that the parsimony offered by our low‐dimensional theories reflects the reality of a much higher‐dimensional problem. Here we contend that this “Flatland fallacy” leads us to seek out simplified explanations of complex phenomena, limiting our capacity as scientists to build and communicate useful models of human psychology. We suggest that this fallacy can be overcome through (a) the use of quantitative models, which force researchers to formalize their theories to overcome this fallacy, and (b) improved quantitative training, which can build new norms for conducting psychological research.
Highlights
Few works consider the nature of perception and dimensionality as elegantly as Edwin Abbott’s (1884) novella Flatland: A Romance of Many Dimensions
Human psychology is rife with complexity, the product of an immensely high-dimensional space characterized by interactions between trillions of neural connections, billions of unique individuals, and dynamic changing contexts spanning thousands of years of history
How could something as complex as the human mind be consistently described in two dimensions, irrespective of the mental faculty under consideration? these theories have provided a bedrock for empirical investigation, we argue that rather than reflecting a rich characterization of the complexity of human psychology, they instead reflect a simplistic view of our scientific understanding (Flatland fallacy)—a product of the limits of our cognition
Summary
Few works consider the nature of perception and dimensionality as elegantly as Edwin Abbott’s (1884) novella Flatland: A Romance of Many Dimensions. Human psychology is rife with complexity, the product of an immensely high-dimensional space characterized by interactions between trillions of neural connections, billions of unique individuals, and dynamic changing contexts spanning thousands of years of history Despite this complexity, the majority of theoretical developments in psychological research have consistently converged on producing a number of low (typically two) dimensional/factor theories/process models of human mental life. We outline several reasons why we believe psychologists consistently converge on two-factor solutions to characterize our understanding of human psychology We argue that these conclusions arise from our limited cognitive capacities, social norms ubiquitous in the field of psychology, and our reliance on low-bandwidth channels to communicate research findings (e.g., natural language and simple visualizations). We suggest that moving beyond low-dimensional thinking requires formalizing psychological theories as quantitative computational models capable of making precise predictions about cognition and/or behavior, and we advocate for improving training in technical skills and quantitative reasoning in psychology
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