Context-Calibrated Interpersonal Predictive Integration Theory (CCIPIT)

People often notice that another person's communication has changed before they can articulate which observable features changed or what those changes mean. Such experiences may involve learned expectations, contextual interpretation, uncertainty, affect, and later verification.
This manuscript proposes the Context-Calibrated Interpersonal Predictive Integration Theory (CCIPIT), a conceptual and computational framework for separating these operations rather than treating a felt intuition as direct evidence of another person's internal state.
Its default mechanism, the Relational Prediction Error Integration Loop (R-PEIL), comprises individualized baseline learning, context-aware observation, feature operationalization, predictive-distribution estimation, discrepancy and surprise measurement, competing-hypothesis inference, person-specific calibration, affective reporting, decision selection, and feedback-based updating.
A 13-layer architecture specifies the functions and interfaces of these operations. A probabilistic state model distinguishes ordinary variability, temporary contextual change, recurring interpersonal pattern change, measurement or channel artifacts, and unresolved cases.
The manuscript defines candidate variables, mathematical forms, measurement rules, boundary conditions, testable hypotheses, falsification criteria, simulation requirements, and a staged empirical program.
A worked primary case, DCPS-01, uses illustrative communication-latency and message-style values to demonstrate the inferential sequence; those values are not empirical observations.
The framework explicitly distinguishes observed behavioral change, inferred relational change, subjective intuition, and independently verified state. It does not claim that a specific neural circuit implements the proposed algorithm, that intuition is necessarily accurate, or that digital cues reveal another person's intentions.
CCIPIT should be evaluated against simpler baseline and context-only models using preregistered, out-of-sample tests and calibrated probabilistic metrics. Its contribution, if supported, would be a common operational architecture for investigating how interpersonal behavioral expectations are learned, how deviations are interpreted, and when felt impressions do or do not track independently assessed change.