The evidence accumulation-based framework to explain human cognitive processes for considering quiz questions: A pilot study in Japan

People make judgments under uncertainty, but they can sometimes understand what is asked. This experience is common to competitive (buzzer) quizzes, where players are required to make judgments even in the middle of a question prompt to get the right to answer the question before the others. Although quizzes have become popular entertainment, there are few evidence-based behavioral findings on cognitive processes in quizzes because no framework to describe and explain them has been developed. This pilot study aimed to provide a framework to describe people’s cognitive processes, based on the evidence accumulation model: It was assumed that people accumulated evidence over time (i.e., by hearing words) and grasped what was asked when the evidence reached a threshold. Study 1 showed that cognitive processes in considering a question could be described within the evidence-accumulation framework, through computational modeling. Specifically, three possible models were proposed: Linear (i.e., evidence was linearly accumulated over time); fixing point (i.e., reaching a threshold when one critical word was heard) and drift (i.e., gradually reaching a threshold at times when important words were heard) models. Study 2 examined to what extent the proposed models could explain actual human judgments (comparing novices and experts), through behavioral experiments. As a result, (i) experts tended to grasp faster what was asked than novices, and (ii) novices’ judgments were better explained by the fixing point model while experts’ judgments were by the drift model. This suggests that while novices tend to identify the answer at a certain critical word, experts gradually identify the answer using various information contained in the question prompt. These findings will provide scaffolding for advancing behavioral studies on buzzer quizzes and may further clarify how people make judgments instantaneously and accurately under uncertainty.