Monica Van

Yan-Ying Chen, Shabnam Hakimi, Monica Van et al.

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.

Surabhi S Nath, Vindula Jayawardana, Monica Van et al.

The creative design process involves transforming abstract goals into concrete outcomes through a series of decisions made under constraints. While such processes are commonly shaped by feedback like rewards, their impact on design decision making remains unclear. To better understand the role of rewards in the design process, we modeled a 3D parametric, goal-based chair design task as a Markov Decision Process. We tracked participants' decisions as they iteratively developed designs for an abstract design goal, and presented either a goal-aligned or goal-agnostic reward at every step. We tested the effect of these rewards on task behaviour and self-reported experience. With rewards, participants more thoroughly explored the design space, and maximised goal-aligned over goal-agnostic rewards while preserving diversity across designs. The nature of the goal also mattered, influencing participants' perception of the reward's usefulness. Building on these insights, we propose guidelines for designing effective feedback for design decision making.

Nikos Arechiga, Francine Chen, Rumen Iliev et al.

Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, strengthening opposition to electric vehicles. In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs). One of the constraints in building models to suggest interventions for shifting preferences is that each intervention can influence the effectiveness of later interventions. This, in turn, requires many subjects to evaluate effectiveness of each possible intervention. To address this, we propose to identify personalized factors influencing BEV adoption, such as barriers and motivators. We present a method for predicting these factors and show that the performance is better than always predicting the most frequent factors. We then present a Reinforcement Learning (RL) model that learns the most effective interventions, and compare the number of subjects required for each approach.