Introspectionspan

Introspectionspan is a term used in cognitive science and artificial intelligence to describe the degree to which a system can examine and reflect upon its own internal states, processes, and decisions. It captures both temporal scope—the retrospective window over which internal states can be recalled—and content scope—the range of mental contents (sensations, beliefs, goals, plans) accessible to self-reflection. The concept is typically framed as a spectrum, from narrow, task-specific monitoring to broad, domain-general self-inspection, and as a property of a system rather than a single agent.

In humans, introspectionspan is thought to be limited by working memory, attention, and metacognitive control. Longer spans may increase the potential for insight but also raise costs and susceptibility to biases. In artificial agents, introspectionspan refers to the engineered capacity to inspect internal models, predictions, uncertainties, and decision processes to improve action selection, learning, and safety. A wider introspectionspan can support better error detection and adaptive behavior, but at the expense of computational resources and latency.

Measurement and evaluation of introspectionspan draw on multiple methods. In humans, think-aloud protocols, retrospective reports, and neural correlates are used, bearing in mind that introspection can alter the very processes it seeks to measure. In AI, metrics include self-monitoring accuracy, calibration of uncertainty estimates, and the reliability of self-generated explanations.

Applications and research directions include metacognition, explainable AI, education, and robotics. Challenges involve biases in self-report, stability of introspective judgments, and the trade-offs between depth of introspection and performance.