Understanding the Cognitive Complexity in Language Elicited by Product Images
This provides a tool for understanding cognitive processes in consumer behavior and virtual respondents, but it is incremental as it builds on existing methods for language analysis.
The paper tackles the problem of measuring cognitive complexity in language elicited by product images, introducing a minimally supervised approach that approximates human-rated complexity using natural language models and a new large dataset with diverse labels.
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.