The Power of Perception in Human-AI Interaction: Investigating Psychological Factors and Cognitive Biases that Shape User Belief and Behavior

TL;DR

This research explores how psychological factors and cognitive biases influence user belief and perception of AI predictions, revealing a 'rational superstition' phenomenon and emphasizing the importance of managing user expectations in human-AI interactions.

Contribution

It provides empirical evidence on the psychological underpinnings of belief in AI, comparing it to astrology and personality predictions, and highlights the impact of prediction valence on user perception.

Findings

Belief in AI correlates with astrology and personality-based predictions.

Positive predictions are perceived as more valid and reliable than negative ones.

Cognitive style does not significantly influence belief in predictions.

Abstract

This thesis investigates the psychological factors that influence belief in AI predictions, comparing them to belief in astrology- and personality-based predictions, and examines the "personal validation effect" in the context of AI, particularly with Large Language Models (LLMs). Through two interconnected studies involving 238 participants, the first study explores how cognitive style, paranormal beliefs, AI attitudes, and personality traits impact perceptions of the validity, reliability, usefulness, and personalization of predictions from different sources. The study finds a positive correlation between belief in AI predictions and belief in astrology- and personality-based predictions, highlighting a "rational superstition" phenomenon where belief is more influenced by mental heuristics and intuition than by critical evaluation. Interestingly, cognitive style did not significantly affect belief in predictions, while paranormal beliefs, positive AI attitudes, and conscientiousness played significant roles. The second study reveals that positive predictions are perceived as significantly more valid, personalized, reliable, and useful than negative ones, emphasizing the strong influence of prediction valence on user perceptions. This underscores the need for AI systems to manage user expectations and foster balanced trust. The thesis concludes with a proposal for future research on how belief in AI predictions influences actual user behavior, exploring it through the lens of self-fulfilling prophecy. Overall, this thesis enhances understanding of human-AI interaction and provides insights for developing AI systems across various applications.