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Computational tools for developing and testing models of quantum cognition

Jennifer S. Trueblood, James M. Yearsley, Jerome R. Busemeyer, Zheng Wang, Timothy Joseph Pleskac, Emmanuel M. Pothos

Published Jul 19, 2017• 0 citations
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This study utilises an experiment famous in quantum physics, the Stern-Gerlach experiment, to inform the structure of an experimental protocol from which a quantum cognitive decision model can be developed. The 'quantumness' of this model is tested by computing a discrete quasi-probabilistic Wigner function. Based on theory from quantum physics, our hypothesis is that the Stern-Gerlach protocol will admit negative values in the Wigner function, thus signalling that the cognitive decision model is quantum. A crowdsourced experiment of two images was used to collect decisions around three questions related to image trustworthiness. The resultant data was used to instantiate the quantum model and compute the Wigner function. Negative values in the Wigner functions of both images were encountered, thus substantiating our hypothesis. Findings also revealed that the quantum cognitive model was a more accurate predictor of decisions when compared to predictions computed using Bayes' rule.
Understanding the structures of human semantic memory search has been a critical initiative in cognitive science.One testing method is the semantic memory task, which asks patients to recall as many words as possible within a specific category, often to test for neurological disorders.Recent studies have shown that classical computers can simulate brain mechanisms using random behaviors.But unlike classical models, which assume that semantic activation spreads along a single probabilistic pathway at a time, quantum random walks allow for simultaneous exploration of multiple conceptual associations, reflecting the parallel and context-dependent retrieval mechanisms observed in cognition through the use of superposition and interference.To address this, we introduce a quantum random walk computation approach for more detailed modeling to address this.By developing the first quantum computing procedure for classical INVITE random walks using quantum random walks, we provide the maximum probabilities of generating semantically related word categories pertinent to the semantic fluency task in assessing a patient's potential semantic memory recall for cognitive diseases.Thus, this method promises the potential for various quantum search algorithms and offers greater scalability in statistical analysis.
The integration of quantum computing and artificial intelligence (AI) has opened new frontiers for enhancing cognitive adaptability and advancing artificial general intelligence (AGI). This study explores the conceptual synergy between quantum–AI integration as an independent variable, cognitive adaptability as a mediating variable, and AGI cognitive performance as the dependent variable. By examining how quantum principles such as superposition and entanglement enhance AI’s capacity for non-linear reasoning, this research outlines a transformative model that aligns with human-like cognition. The study employs simulation-based analyses comparing classical and quantum–AI models across diverse cognitive tasks, revealing that quantum–AI systems outperform traditional AI in adaptability, problem generalization, and decision precision. The results indicate that quantum-enhanced learning enables faster adaptation and improved generalization, suggesting significant implications for cognitive modeling, human–machine collaboration, and AGI development. The theoretical contribution lies in expanding AGI’s cognitive architecture through quantum principles, while the practical implications include the development of adaptive, self-learning AI systems for complex, dynamic environments. Future research should explore empirical testing of quantum–AI frameworks, cross-disciplinary modeling between quantum cognition and neuroscience, and ethical considerations in autonomous decision-making systems.