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Super-intelligence or Superstition? Exploring Psychological Factors Influencing Belief in AI Predictions about Personal Behavior

Eunhae Lee, Pat Pataranutaporn, Judith Amores, Pattie Maes

TL;DR

This study investigates the phenomenon of rational superstition in human-AI interaction by comparing fictitious predictions from AI, astrology, and personality psychology in a financial decision task. Using 238 participants and four believability subscales (validity, reliability, usefulness, personalization), the authors apply regression and mixed-effects models to identify predictors of AI believability. Key findings show that belief in AI is positively linked to belief in astrology and personality predictions, with paranormal beliefs and favorable attitudes toward AI driving higher believability; cognitive style shows little predictive power, while conscientiousness reduces believability and topic interest increases it. The work highlights the role of mental models and cognitive biases in shaping trust in AI, underscoring the need for AI literacy and design strategies that promote appropriate skepticism and transparent communication for responsible AI deployment.

Abstract

Could belief in AI predictions be just another form of superstition? This study investigates psychological factors that influence belief in AI predictions about personal behavior, comparing it to belief in astrology- and personality-based predictions. Through an experiment with 238 participants, we examined how cognitive style, paranormal beliefs, AI attitudes, personality traits, and other factors affect perceived validity, reliability, usefulness, and personalization of predictions from different sources. Our findings reveal that belief in AI predictions is positively correlated with belief in predictions based on astrology and personality psychology. Notably, paranormal beliefs and positive attitudes about AI significantly increased perceived validity, reliability, usefulness, and personalization of AI predictions. Conscientiousness was negatively correlated with belief in predictions across all sources, and interest in the prediction topic increased believability across predictions. Surprisingly, we found no evidence that cognitive style has an impact on belief in fictitious AI-generated predictions. These results highlight the "rational superstition" phenomenon in AI, where belief is driven more by mental heuristics and intuition than critical evaluation. This research advances our understanding of the psychology of human-AI interaction, offering insights into designing and promoting AI systems that foster appropriate trust and skepticism, critical for responsible integration in an increasingly AI-driven world.

Super-intelligence or Superstition? Exploring Psychological Factors Influencing Belief in AI Predictions about Personal Behavior

TL;DR

This study investigates the phenomenon of rational superstition in human-AI interaction by comparing fictitious predictions from AI, astrology, and personality psychology in a financial decision task. Using 238 participants and four believability subscales (validity, reliability, usefulness, personalization), the authors apply regression and mixed-effects models to identify predictors of AI believability. Key findings show that belief in AI is positively linked to belief in astrology and personality predictions, with paranormal beliefs and favorable attitudes toward AI driving higher believability; cognitive style shows little predictive power, while conscientiousness reduces believability and topic interest increases it. The work highlights the role of mental models and cognitive biases in shaping trust in AI, underscoring the need for AI literacy and design strategies that promote appropriate skepticism and transparent communication for responsible AI deployment.

Abstract

Could belief in AI predictions be just another form of superstition? This study investigates psychological factors that influence belief in AI predictions about personal behavior, comparing it to belief in astrology- and personality-based predictions. Through an experiment with 238 participants, we examined how cognitive style, paranormal beliefs, AI attitudes, personality traits, and other factors affect perceived validity, reliability, usefulness, and personalization of predictions from different sources. Our findings reveal that belief in AI predictions is positively correlated with belief in predictions based on astrology and personality psychology. Notably, paranormal beliefs and positive attitudes about AI significantly increased perceived validity, reliability, usefulness, and personalization of AI predictions. Conscientiousness was negatively correlated with belief in predictions across all sources, and interest in the prediction topic increased believability across predictions. Surprisingly, we found no evidence that cognitive style has an impact on belief in fictitious AI-generated predictions. These results highlight the "rational superstition" phenomenon in AI, where belief is driven more by mental heuristics and intuition than critical evaluation. This research advances our understanding of the psychology of human-AI interaction, offering insights into designing and promoting AI systems that foster appropriate trust and skepticism, critical for responsible integration in an increasingly AI-driven world.
Paper Structure (38 sections, 4 equations, 7 figures, 4 tables)

This paper contains 38 sections, 4 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Results
  3. People who are more likely to believe in astrology and personality-based predictions are more likely to believe in AI predictions.
  4. People generally find fictitious AI predictions about their personal behavior convincing.
  5. There is no evidence of correlation between cognitive style and belief in predictions.
  6. Higher paranormal beliefs increase perceived validity, reliability, usefulness, and personalization of AI predictions.
  7. Positive attitudes toward AI increase belief in AI predictions, especially perceived reliability.
  8. People with high conscientiousness are less likely to believe in predictions about personal behavior.
  9. Level of interest in the prediction topic increases perceived validity, reliability, usefulness, and personalization.
  10. There is no evidence that the level of familiarity with the prediction sources influences belief in predictions.
  11. Other results
  12. Discussion
  13. General Discussion
  14. Limitations and Future Work
  15. Conclusion
  16. ...and 23 more sections

Figures (7)

  • Figure 1: Relationship between belief in AI predictions and astrology predictions (left) and belief in AI predictions and personality predictions (right), measured on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). Scatterplots display individual scores with linear regression lines indicating positive correlations, and the shaded area represents the 95% confidence interval for the regression lines.
  • Figure 2: Boxplot illustrating the distribution of subscale scores (7-point Likert scale: 1 = Strongly disagree, 7 = Strongly agree) across prophecy sources and subscales. The boxes represent the interquartile range, with green triangles indicating the means and notches marking the 95% confidence intervals for the medians.
  • Figure 3: Contrast plots showing the relationship between cognitive style (Range: -4.29 to 2.61; higher values indicate a more analytic style) and centered subscale scores (7-point Likert scale). The top panel illustrates interactions across subscales (validity, personalization, reliability, usefulness), with "Validity" serving as the reference level. The bottom panel shows interactions across prediction sources (AI, astrology, personality), with "AI" as the reference level. Blue lines represent predicted contrasts with 95% confidence intervals, and gray points show individual observations. (*p < 0.05, **p < 0.01, ***p < 0.001). Contrast plots are presented for predictors central to the study’s hypotheses. Results for other predictors are summarized in writing within the Results section.
  • Figure 4: Contrast plots showing the relationship between paranormal beliefs (higher values indicate stronger paranormal beliefs) and centered subscale scores (7-point Likert scale). Top: interactions across subscales (validity [reference level], personalization, reliability, usefulness). Bottom: interactions across prediction sources (AI [reference level], astrology, personality). Blue lines represent predicted contrasts with 95% confidence intervals, and gray points show individual observations. The plot ratio was adjusted to align with other contrast plots for visual comparability. (*p < 0.05, **p < 0.01, ***p < 0.001)
  • Figure 5: Contrast plots showing the relationship between attitude toward AI (higher values indicate more positive attitudes) and centered subscale scores (7-point Likert scale). Top: interactions across subscales (validity [reference level], personalization, reliability, usefulness). Bottom: interactions across prediction sources (AI [reference level], astrology, personality). Blue lines represent predicted contrasts with 95% confidence intervals, and gray points show individual observations. The plot ratio was adjusted to align with other contrast plots for visual comparability. (*p < 0.05, **p < 0.01, ***p < 0.001)
  • ...and 2 more figures