Cognitive Biases in Intelligence Analysis: Amplification Mechanisms and Intervention Strategies in the Digital-Intelligent Era

Background and Aim The digital-intelligent era, characterized by information explosion, algorithmic penetration, and multi-tasking pressure, has reshaped the information processing environment for intelligence analysis. In high-stakes military scenarios, interactions between cognitive biases and technical factors drive intelligence judgment errors, with profound impacts on battlefield outcomes and national security. Existing studies lack systematic analysis of how digital-intelligent technical characteristics amplify these biases and in-depth discussion of cognitive psychology-based targeted intervention strategies. This study aims to explore typical cognitive bias manifestations across the cognitive process of intelligence analysis, reveal technical amplification mechanisms, construct an “individual-technical-organizational” three-dimensional model, and propose targeted intervention strategies to enrich relevant research and improve intelligence judgment accuracy. Methods A mixed-method research design was adopted, including systematic literature review, case coding analysis, and theoretical deduction. A systematic review of cognitive psychology, intelligence studies, and human-computer interaction literature was conducted. Ten typical intelligence failure cases were selected for qualitative coding analysis. Cognitive psychology theories guided the construction of the “technical environment - cognitive process - bias amplification - judgment error” framework, verified via case cross-validation. Coding reliability was evaluated using the Kappa coefficient. Results Cognitive biases permeate the entire cognitive process from perception to decision-making, forming chain reactions. Digital-intelligent factors amplify biases through mechanisms such as cognitive resource scarcity, cognitive anchor solidification, and reduced cognitive processing depth. Bias formation and amplification result from the interaction of individual, technical, and organizational factors. The “cognitive training - technical optimization - organizational adjustment” comprehensive strategy effectively mitigates bias impacts. Conclusions This study enriches the digital-intelligent era cognitive psychology theoretical system by revealing the interaction mechanism between technical environments and cognitive biases. The three-dimensional model and intervention strategies provide a new perspective for military cognitive bias research and universal reference for high-stakes military decision-making. Future research will verify strategy effectiveness through longitudinal tracking and controlled experiments.