Understanding the Cognitive Complexity in Language Elicited by Product Images
<|reference_start|>Understanding the Cognitive Complexity in Language Elicited by Product Images: 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.<|reference_end|>
@article{chen2024understanding,
    title={Understanding the Cognitive Complexity in Language Elicited by Product
  Images},
    author={Yan-Ying Chen, Shabnam Hakimi, Monica Van, Francine Chen, Matthew
  Hong, Matt Klenk, Charlene Wu},
    journal={Published by ICML 2024 Workshop on LLMs and Cognition},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16521},
    primaryClass={cs.CL}
}