Narrative-affect discrepancy as a regulated degree of freedom in 351,734 relationship narratives

Clinical Snapshot

Key Findings

• P Value: p < 0.0001 for permutation test of expressive space differences

• Effect Size: Human narratives occupied approximately 1.69 times larger expressive space than AI model

• Primary Outcome: Four distinct expressive regimes identified: coupled expression (91.3%), strategic understatement (5.7%), collapse (2.3%), and strategic overstatement (0.6%)

• Nnt Or Sensitivity: 91.3% of narratives showed coupled expression pattern

• Confidence Interval: Bootstrap 95% CI [1.68, 1.70] for hull area difference

Clinical Application

Computational approach could be adapted for clinical assessment tools, but requires validation against established psychological measures Limited direct relevance to Australian clinical practice without validation in local populations and integration with existing mental health assessment frameworks Potentially relevant for understanding emotional expression patterns in therapeutic contexts, though validation needed in clinical populations

Abstract

In naturalistic emotional narratives, the intensity of expressed affect does not scale proportionally with narrative structure. Using 351,734 English-language relationship narratives from online support communities (the ANEST Narrative-Affect Dataset, ANAD v1.1.0), we constructed a two-dimensional expressive space defined by narrative complexity (N) and linguistically inferred affective intensity (A), with their signed discrepancy ([Formula: see text]) treated as a derived coordinate. Rather than converging toward low discrepancy, human narratives occupied a broad but structured space consistent with trade-offs between relational exposure and cognitive effort. We identified four empirically separable regimes of expressive organization: coupled expression (non-extreme discrepancy; the complement of the extreme regimes), strategic understatement (high A, low N, D < 0), strategic overstatement (high N, low A, D > 0), and collapse (high A with limited narrative scaffolding). A data-anchored cost model (NCS) formalized these regimes as arising from the interaction of exposure risk and cognitive effort, not from discrepancy minimization per se. Coupled expression dominated the corpus (91.3%), while the remaining regimes formed smaller but non-negligible subpopulations (Understatement: n = 20,223; Collapse: n = 8,040; Overstatement: n = 2,223), indicating that extreme discrepancy configurations occur systematically rather than as isolated outliers. As a comparative probe, we projected an RLHF-aligned large language model into the same space using matched prompts and identical feature extraction. Because D is a deterministic function of N and A, expressive extent was quantified as convex hull area in the clipped [Formula: see text] plane. Under this procedure, the model occupied a markedly smaller region (approximately [Formula: see text] smaller hull area; bootstrap 95% CI [1.68, 1.70]; permutation p < 0.0001) and concentrated near low-discrepancy configurations, with sparse occupancy of extreme under- and overstatement regimes. Together, these findings suggest that narrative-affect discrepancy is a measurable and regulated dimension of emotional expression and provide a reproducible geometric basis for comparing expressive degrees of freedom across populations and systems.