The observer effect in quantum physics states that observation inevitably influences the system being observed. This work introduces an epistemic framework that treats the observer as an integral part of sensory information processing within entangled quantum systems, leading to subjective and probabilistic observation and inference. We propose fuzzy instance classification by encoding sensory input to align with the observer’s pre-existing beliefs in a feature–attribute–truth value hierarchical model as ‘bells’ of quantum oscillators whose states represent the degree of activation, associated with quantum probability. We demonstrate that within this framework, sensory data evolve via interaction with quantum-based observer states during the pre-decision phase, as described by the Lindblad master equation, and are then classified adaptively using positive operator-valued measures (POVM). The POVM enables the customisable parametrisation of measures of concurrent similarity and dissimilarity, facilitating subjectivity of perceptual associations and asymmetric cognition. Additionally, the observer’s position on a sceptic-believer spectrum determines their robustness to noisy perceptions in ambiguous matching, balancing precision and flexibility. We show that sensory information becomes intricately entangled with observer states, yielding a wide array of probabilistic classification results. This framework lays the groundwork for a quantum probability–based understanding of the observer effect in cognitive processes, providing a formal basis for subjective interpretation not as a flaw in observation, but as a fundamental consequence of the observer-system interaction with quantum properties and correlations.

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A research paper introducing a quantum epistemic framework that models the observer effect in cognitive classification. The framework encodes sensory input as quantum oscillator states in a feature-attribute-truth value hierarchy, uses the Lindblad master equation to describe how sensory data evolve through interaction with observer states, and applies positive operator-valued measures (POVM) for adaptive probabilistic classification. The approach formalizes subjective perception and asymmetric cognition, treating observer bias not as a flaw but as a fundamental consequence of observer-system quantum entanglement. A sceptic-believer spectrum parameter controls robustness to noisy or ambiguous inputs.

Observer effect modulates classification in a quantum epistemic framework