Categorical frequency judgments as effective ensemble judgments for object features.

The present study explored the potential of categorical frequency judgments as effective ensemble judgments, motivated by the observation that most studies on ensemble judgments have focused on univariate statistics, such as mean and variance. However, these univariate statistics may not fully capture the complexity of real-world tasks that require judgments on complex object features. In such cases, categorical statistics like mode (the most frequent instance in a set) and diversity (the number of different instances in a set) may provide more relevant information. For instance, when a speaker enters an auditorium and scans her audience, relative frequencies of different emotional expressions could be more useful than the representation of the average face with a potentially faint expression. Study 1 examined the relationship between mode judgment and diversity comparison in facial identities, while Study 2 extended the examination of mode judgments across different object categories (faces and blobs). The results indicate that categorical frequency judgments share behavioral variability across tasks and object categories, supporting their potential as effective ensemble judgments. Future research may explore how these categorical frequency judgments interact with univariate statistical judgments to enhance our understanding of ensemble judgments.