Mate choice is one of the most important choices for all individuals. It is a typical multi-attribute choice, in which people will use various kinds of choice strategies. These different multi-attribute strategies describe the decision process with different hypotheses.
Eye-movement data can reveal how attention are distributed and how information is processed during decision making process. On one hand, decision makers give different attention distribution to different attributes according to their attribute importance. And gaze distribution just reflects attention distribution.Therefore, we can use the gaze distribution data to measure the importance of different attribute, that is attribute weight. On the other hand, different decision strategies have different hypothese on how information are searched and processed.And some eye-movement data, like transitions, the order of fixation, or scanpath just reflect how decision maker search and process information directly. Therefore, we can use eye-movement data to classify what strategies decision makers are using.
In Study 1，we developed a new weighting method一the eye-movement weighting method. Three experiments were used to examine the validity of the eye-movement weighting method and found that: (1) the eye-movement weighting methods has a high congruent validity. The attribute weights measured by eye-movement weighting method are highly correlated with the attribute weights measured by traditional subjective weighting method; (2) the eye-movement weighting methods has a high predictive validity. When using the eye-movement weighting methods to predict mate choice, the prediction accuracy is higher than or equal to the prediction accuracy using traditional weighting methods. (3) the biggest advantage of eye-movement weighting method is that it can measure real-time decision weight. Therefore, we can using the real-time weighting methods to realize the real-time prediction.
Study 2 aim to using eye-movement data to classify what strategies decision maker are using. In this study, participants firstly finished a free mate choice task.And then they finished two imposed rule tasks: one imposed rule task asked participants to follow a classic "rational" strategies (weighted additive rule) to make mate choice, and the other one imposed rule task asked them to follow a heuristic strategies (Equate-to-differentiate rule) to make mate choice. We explored two different kinds of methods to classify decision strategy: (1) based on knowledge of Machine Learning, we firstly trained the eye-movement data recorded from the two imposed tasks, and then used the trained classifier to classify what strategies were used in the free choice task. Results showed that a Mixed Logistic Regression model with SM index and mean fixation duration as features inputs performed the best, the classifier reached 89.95% (Study 2a) and 98.4% (Study 2b) accuracy in distinguishing whether people are using the heuristic strategies or the classic "rational" strategies to make mate choice. Finally, we used this Mixed Logistic Regression model to classify trails in the free mate choice task, and found that most of the time, decision maker were using a non-compensatory, attribute-based heuristic strategies, similar to equate-to-differentiate rule; (2) we classify decision strategy based on the similarity scores of scanpath between trials in different task and found this classification methods can reach 88% (Study 2a) and 91.53% (Study 2b) accuracy in distinguish whether people are using the heuristic strategies or the classic "rational" strategies to make mate choice.
Study 3 explored why people make choice reversed in repeated mate choice questions. We found that if decision makers use different strategies in repeated choice questions, they are more likely to choose differently; if decision makers change the decision attribute weight, they are more likely to choose differently; and if decision makers judge two options are similar or two attributes' differences are equal, they are more likely to make choice randomly and reversed.
This study is innovative in methods in three aspects: Firstly, we developed a new weighting method which can measure real-time decision weight. This weighting method will be useful in human-computer interaction, artificial intelligence, and virtual reality areas. Secondly, the methods which use eye-movement data to classify decision strategies are helpful in future studies on decision making. Most importantly,this research provides a process-based prediction method to predict mate choice. This process-based predition may help researchers achieve the "mind reading" goal eventually.