Quantum Fluctuations in Intuitive Judgement and Their Predictive Power in Complex Cognitive Tasks

Abstract

Intuitive judgement plays a central role in human decision-making, particularly in environments characterized by uncertainty, time pressure, and high cognitive load. Recent advances in quantum cognition propose that intuitive processing may not follow classical probabilistic structures, but instead exhibits fluctuation patterns analogous to quantum uncertainty, superposition, and interference. The present study investigates whether measurable quantum-like fluctuations in intuitive responses can predict performance accuracy in complex cognitive tasks. To examine this question, behavioural, neurophysiological, and temporal-response data were collected from participants performing multi-stage reasoning and rapid-decision paradigms designed to evoke intuition-driven choices. High-density EEG and physiological recordings were used to quantify moment-to-moment variability in intuitive judgements, which were then compared to task accuracy and cognitive load measures. The results demonstrate that intuitive decisions exhibit statistically identifiable fluctuation signatures that align with quantum probability models rather than classical linear models. Participants displaying higher amplitude fluctuation patterns in key temporal windows showed significantly greater predictive accuracy in complex reasoning tasks. These findings suggest that intuition may operate through a non-classical cognitive mechanism sensitive to contextual constraints, uncertainty, and dynamic information integration. Additional analyses revealed that the interplay of emotional arousal and cognitive complexity further modulates the fluctuation–accuracy relationship, indicating a multi-layered interaction between affective states and intuitive judgement. This study contributes to emerging theoretical frameworks by empirically linking quantum-inspired cognitive variability to real-time intuitive performance. The findings highlight the potential value of quantum probabilistic modelling for predicting human behaviour in complex decision environments, advancing both theoretical understanding and applied assessment tools. Such models may offer more precise predictions of intuitive reliability across contexts such as clinical decision-making, strategic planning, and high-stakes environments. The study underscores the importance of integrating behavioural data, neurophysiological markers, and advanced mathematical modelling to better characterize the foundations of intuitive cognition.