[This review is intended solely for my personal learning]

Paper Info

DOI: 10.1111/tops.12404
Title: The Flatland Fallacy: Moving Beyond Low–Dimensional Thinking
Authors: Eshin Jolly, Luke J. Chang

Prior Knowledge

Psychological and cognitive sciences have long relied on simplified, low-dimensional models to explain complex human behavior. While these models provide theoretical clarity and empirical tractability, they often fail to capture the full intricacy of psychological phenomena. The reliance on two-factor or low-dimensional frameworks—such as dual-process theories of cognition—raises concerns about scientific oversimplification.

Goal

The authors challenge the tendency of psychological research to reduce human cognition to low-dimensional constructs, a bias they term the Flatland Fallacy. They argue that such simplifications arise from cognitive limitations, social norms in scientific practice, and communication constraints. The paper proposes computational modeling and improved quantitative training as pathways to overcome this issue.

Method

The authors identify four key reasons why psychological research gravitates toward low-dimensional models:

  1. Feelings of Understanding Bias

    • People overestimate their grasp of complex systems due to cognitive biases.
    • Folk psychology and intuitive explanations contribute to the illusion of deep understanding.

  2. Cognitive Limitations

    • Humans struggle with reasoning in high-dimensional spaces, leading to heuristic-based simplifications.
    • Judgment, decision-making, and theory-of-mind tasks reveal a preference for low-dimensional approximations.

  3. Methodological and Cultural Norms

    • Psychological research heavily relies on factorial experimental designs (e.g., 2x2 ANOVA).
    • Traditional statistics training emphasizes significance testing over high-dimensional modeling.

  4. Communication Constraints

    • Psychology lacks a formal language like mathematics in physics, leading to vague theoretical constructs.
    • Data visualization techniques are limited in their ability to represent high-dimensional relationships.

The authors suggest that overcoming these limitations requires embracing computational modeling to formalize psychological theories and adopting rigorous quantitative training to encourage high-dimensional thinking.

Results

The paper presents theoretical and empirical arguments supporting the Flatland Fallacy:

  • Psychological research frequently adopts two-dimensional theories (e.g., dual-process models of reasoning, emotion theories, and social cognition frameworks).
  • Experimental evidence suggests that humans default to simpler models when processing complex information.
  • Statistical biases, such as the bias-variance tradeoff in machine learning, explain why low-dimensional approximations sometimes outperform high-dimensional models on small datasets.
  • Cognitive science has developed tools (e.g., deep learning, Bayesian inference) that allow researchers to analyze and interpret high-dimensional data, yet these methods are underutilized in psychology.

Conclusion

The Flatland Fallacy highlights an inherent bias in psychological research that prioritizes simple, intuitive models over more comprehensive, high-dimensional representations of human cognition. The authors advocate for a shift toward computational psychology, where theories are expressed in formal mathematical or algorithmic terms, facilitating reproducibility, extension, and falsifiability.

Proposed Solutions:

  1. Computational Modeling: Formalizing psychological theories through mathematical models forces explicit representation of complexity.
  2. Enhanced Training: Expanding quantitative skills in psychology curricula can help overcome cognitive biases toward low-dimensional thinking.
  3. Data-Driven Research: Leveraging big data and machine learning methods can reveal richer, more nuanced psychological phenomena.

Reference