Token Anxiety as a Sociotechnical Constraint on Human–Transformer Interaction: Formal Definition, Mechanistic Pathway, Algorithmic Contrast, and a Complete Program for Cumulative Validation

The commercial metering of large language model (LLM) inference has created a new boundary condition in human-AI interaction. We define Token Anxiety as a situational scarcity response in which users experience inference capacity—remaining token quota, rate-limit headroom, or model-tier access—as simultaneously finite, uncertain, salient, and goal-consequential. This state captures working-memory resources through continuous metacognitive monitoring, imposes extraneous cognitive load, and induces attentional tunneling onto cost minimization. Under Token Anxiety, users systematically engage in lossy human prompt hyper-compression: the removal of syntactic scaffolding, negative constraints, few-shot exemplars, and referential anchors. This human process is mechanistically and teleologically distinct from algorithmic prompt compression (e.g., the LLMLingua-2 distillation pipeline). In the Transformer architecture, human under-specification flattens query-key similarity structure, elevating both Shannon and Rényi attention entropy and increasing the probability of fluent but unanchored outputs. These errors consume additional tokens, further depleting remaining quota and intensifying anxiety, thereby closing a self-reinforcing sociotechnical feedback loop. This paper makes seven primary contributions: (1) a precise multi-component operational definition of Token Anxiety; (2) a constrained-optimization formalization of the user's real-time decision problem; (3) an extended analysis of cognitive load theory with special emphasis on the working-memory demands of software engineering and coding; (4) a detailed technical examination of the LLMLingua-2 distillation methodology and its contrast with human compression; (5) a causal pathway linking human linguistic rationing to measurable elevation of attention entropy; (6) interface design principles that reduce metacognitive taxation; and (7) a fully specified, staged empirical program including a concrete attention-entropy measurement procedure.