AI & ML
Quantum-Like Spatial Decision Dynamics: A Falsifiable Model of Cue-Order Effects in Immersive Navigation with Implications for Human-Quantum Computer Interaction
arXiv cs.HC0 views
arXiv:2610.07336v1 Announce Type: new Abstract: The sequence in which a person encounters spatial evidence can change a later choice, yet an order effect alone does not identify a quantum-like cognitive structure. We introduce Quantum-Like Spatial Decision Dynamics (QSDD), a falsifiable state-space account of embodied decision making in which cue exposures are completely positive trace-preserving maps, intermediate judgments are quantum instruments, and route commitment is an operationally defined measurement. We distinguish this formal claim from any assertion that cognition is microscopically quantum. We then specify a minimal binary model with a fixed decision basis, noncommuting cue rotations, a fixed symmetry-breaking initial azimuth, dephasing, and lapse; its five fitted parameters must generalize across environments rather than being refit to individual conditions. A reproducible simulation study evaluates recovery and model discrimination under both QSDD and seven-parameter classical logistic ground truths. Across 40 replicates per design cell, QSDD was preferred on held-out environments in 85% of QSDD-generated datasets at 240 independent observations per environment-order cell and 95% at 480, but in 0% of classically generated datasets at every tested sample size. The simulation also exposes weak identification of the lapse parameter and is explicitly a design analysis, not human evidence. We provide a preregistrable virtual-reality experiment, telemetry schema, classical comparison set, and failure criteria. Finally, we show how the same process-measurement logic can be transferred to human inspection and debugging of quantum circuits. QSDD is therefore offered as a constrained model to be defeated or supported by data, and as a methodologically continuous route from immersive interaction research to human-quantum computer interaction.
Source:https://arxiv.org/abs/2610.07336PreviousMapping E-textiles Design Pain Points and Generative AI Opportunities: Insights from Workshops in Shanghai and WinchesterNextRedistributing Harm: Document Transition and the Limits of Trans Inclusion in India's Identity Systems
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