TITLE:
Stress-Integrated Learning and Decision Framework (SILDF): A Comprehensive Theoretical Model for High-Stress Learning Environments
AUTHORS:
Gregory Henderson, Christan Horton
KEYWORDS:
Discriminative Stress Cues (S* Cues), Schema Retrieval, Challenge-Threat Appraisal, Allostatic Load, High-Stress Learning Environments, Emotion Regulation, Simulation-Based Training
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.6,
June
30,
2026
ABSTRACT: This paper introduces the Stress-Integrated Learning and Decision Framework (SILDF), describing how discriminative stress cues (S* cues) modulate attention, learning, memory, and decision-making in high-stress environments. SILDF proposes that S* cues from time pressure, social-evaluative threat, physiological arousal, task complexity, and resource uncertainty act as critical signals reshaping human information processing during high-stakes performance. The framework provides a mechanistically specified account of how S* cues interact with appraisal, emotion regulation, working memory, and schema structures to produce outcomes from catastrophic failure to remarkable resilience. These interactions are governed by trainable mechanisms spanning milliseconds to months. SILDF specifies how structured training builds cue-to-schema associations accessible under acute and chronic stress. The framework organizes its contributions around five processing stages, eight falsifiable propositions, and a measurement/training architecture applicable across domains (e.g., military, aviation, surgery, athletics, law enforcement). SILDF relates to established predecessors—Attentional Control Theory, Processing Efficiency Theory, the Biopsychosocial Model of Challenge and Threat, and Catastrophe Theory—identifying theoretical inheritance and extensions. Neurobiological substrates include HPA axis cortisol dynamics, LC-NE phasic/tonic signaling, prefrontal-amygdala regulation, hippocampal-striatal competition, and dopaminergic reward systems. The model addresses chronic stress and allostatic load reshaping these substrates over time. Proposed methods include laboratory paradigms, VR simulations, wearable monitoring, ecological momentary assessment, and longitudinal field studies. Outcome measures span behavioral, cognitive, physiological, neural (EEG/fMRI), and well-being indicators. The paper concludes with implications for curriculum design, simulation standards, individual differences, equity, learner well-being, ethical governance, and translational pathways optimizing performance and long-term learner health.