Cognitive Alignment Deciphered: A Self-Developed Scenario-Based Prompt Scale Coupled with Representational Similarity Analysis and Social Network Analysis for Unraveling Bias Mechanisms Across Humans and LLMs

Abstract

Traditional cognitive bias measurement tools are limited by narrow bias coverage, low ecological validity, and reliance on abstract self reports, constraining scenario based and human AI comparisons. We introduce the context based Cognitive Bias Assessment Scale CBAS, a scenario driven prompt template covering 58 cognitive biases across five hot cold dual system dimensions: Calculation, Belief, Information, Social, and Memory. Psychometric testing with 330 participants shows satisfactory reliability Cronbachs alpha 0.714 and good model fit chi squared df 1.83, RMSEA 0.057, CFI 0.908, TLI 0.903. We then combine Representational Similarity Analysis RSA and Social Network Analysis SNA to compare human age groups and three large language models Baidu ERNIE 3.5 8K, DeepSeek V3, DeepSeek R1. Humans show coherent hot cold integration with high inter individual variability, whereas LLMs display fragmented, inflexible response patterns and lower variability. Human cognitive networks exhibit strong inter module connectivity, while LLMs show fixed core biases and isolated information processing components. Prompt interventions integrating role playing and bias mitigation instructions effectively improve LLM response accuracy, reaching 84.86 percent for DeepSeek R1 and 78.24 percent for DeepSeek V3, and partially reshape their internal representations. Our work establishes a replicable assessment and analysis pipeline for cognitive alignment research, bridging empirical psychological evaluation and interpretable artificial intelligence.