PSYCHOACTIVE TRIGGERS AS A STIMULUS BATTERY FOR MEASURING LARGE LANGUAGE MODELS (LLMS): A BRIDGE BETWEEN PSYCHOMETRICS, CLINICAL PSYCHOLOGY, AND LLM ENGINEERING

Evaluating large language models (LLMs) in psychologically sensitive and human-centered domains faces two persistent challenges. First, conventional benchmarks capture instrumental capabilities but often fail to represent model behavior in open-ended dialogue where emotional context, conflict, ambiguity, and user safety define quality. Second, LLM outputs can be unstable across re-runs and highly sensitive to prompt phrasing, undermining reproducibility and cross-model comparisons [1]. This article introduces an applied framework of psychoactive triggers: standardized textual stimuli designed to evoke systematic shifts in response style, narrative coherence, explanatory stance, empathy calibration, and risk regulation. Psychoactive triggers are treated as an analogue of psychometric items adapted to LLMs: each trigger carries a controlled psychological load (e.g., threat, shame, guilt, control, intimacy, autonomy), allowing measurement of stable behavioral patterns rather than binary correctness. The framework is illustrated using the PersonaMatrix ecosystem, where trigger batteries are applied in multiple measurement waves. A four-class metric taxonomy is proposed, with this paper focusing on Class I metrics—reproducibility and stability (RSI/IDS/RCS)—using a single PersonaMatrix test, “What Is My Character Type?” (TestPersona). Written at the intersection of LLM research and clinical psychology, the article provides clinical rationale and ethical constraints for safe deployment of psychologically loaded evaluations.