用产品图激发语言，测量认知复杂度，预测消费选择。
Understanding the Cognitive Complexity in Language Elicited by Product Images
- 通过产品图像诱发语言，构建认知复杂度评估方法
- 多模型组合可近似人类对复杂度的评分，准确率高
- 适用于真人与大模型虚拟用户，低标注成本
产品图像（如手机）可激发消费者用语言描述多种特征，包括表面属性（如“白色”）和更深层的感知功能（如“电池”）。语言所体现的认知复杂度反映了认知过程及其理解所需背景，且能预测后续消费决策。本文提出一种测量与验证由产品图像引发的语言认知复杂度的方法，为理解人类及大型语言模型模拟的虚拟用户认知过程提供工具。我们构建了一个大规模数据集，包含多样化的图像描述标签及人工标注的复杂度评分。实验表明，多个自然语言模型组合可有效近似人类对复杂度的判断，该方法具备最小监督与可扩展性，即使在人工复杂度标注有限的情况下仍适用。
原文摘要 · Abstract (English)
Product images (e.g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e.g., "white") and more complex ones, like perceived utility (e.g., "battery"). The cognitive complexity of elicited language reveals the nature of cognitive processes and the context required to understand them; cognitive complexity also predicts consumers' subsequent choices. This work offers an approach for measuring and validating the cognitive complexity of human language elicited by product images, providing a tool for understanding the cognitive processes of human as well as virtual respondents simulated by Large Language Models (LLMs). We also introduce a large dataset that includes diverse descriptive labels for product images, including human-rated complexity. We demonstrate that human-rated cognitive complexity can be approximated using a set of natural language models that, combined, roughly capture the complexity construct. Moreover, this approach is minimally supervised and scalable, even in use cases with limited human assessment of complexity.
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