Academic

Response time is readily available in computer-based discrete choice experiments, and the emergence of webcam-based eye-tracking will soon enable the collection of visual attention data from large samples. Existing literature shows that both response time and visual attention offer valuable insights into choice outcomes and processes. However, their integration into choice models has been limited, likely due to the prevalence of process-agnostic utility-based models. Integrating these process datasets into sequential sampling models (SSMs) from cognitive psychology offers a promising way to explain consumer preferences, as these models focus on the decision-making process. Two key aspects will be discussed. First, the asymptotic theory for SSMs will be contextualized, demonstrating that the incorporation of response time can lead to a reduction in standard error and sample size requirements in choice experiments. Second, an extension of SSM will be presented that integrates visual attention data, with a discussion on its potential to improve choice prediction accuracy.