The analysis of effect of visual cues in a binary decision making environment

Abstract: In this study, we plan to answer the fundamental question of what factors affect the human utility function and decision-making strategy. Utility function is an internally assigned value to each state to reflect the satisfaction of moving to that state. Decision Time (i.e., Reaction Time) is the time required for a user to make a decision after observing the current state. The assessment of human decision-making and Decision time has been frequently discussed in the fields such as psychology, neuroscience, and ergonomics.One of the most commonly used experiments to analyze the decision-making process is the choice task, where a set of choices are presented to users, and they need to select one of these choices. For the purpose of this study, we consider only two choices and assign a probabilistic reward to each choice. The task is named “Bias Coin Flip Game”, a web-based coin flip game where one side of the coin is more likely to appear. In another word, the coin is biased. Users are not aware of this bias and are asked to win as much as they can in the course of 250 tries. Probability Learning studies have indicated that after a sufficient number of tries, people are capable of learning the bias. However, the number of tries needed to learn the bias, the time spent between each try (e.g., Decision Time), and the strategy (e.g., matching and maximizing) users would choose to follow are highly susceptible to the visual cues represented to users. We consider multiple factors such as (a) the hidden/unhidden Win rate, (b) showing four last recent coin results, and (c) the order of visual cues. We analyze the effect of these cues on decision-making strategy and decision-making time on different genders and age groups using Factorial ANOVA (i.e., a statistical experimental design to analyze the significance of each cue). Results indicate how each visual cue affects the decision-making strategy chosen by users to design an environment that optimizes the chance of the optimality of the decisions made by the user, avoids convergence to suboptimal strategies, and controls reflection on the utility function. Finally, we suggest the relationship between the complexity of the utility function and the decision time for each environment with different sets of visual cues.

Keywords: Decision making, Reaction time, Choice Task, Probability Learning

DOI: 10.54941/ahfe1003874

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