Human Perception of Surprise: A User Study

TL;DR

This study explores how humans perceive surprise compared to algorithms, demonstrating that computational models can effectively induce surprise in users, which is valuable for engagement and learning applications.

Contribution

It provides empirical evidence on the alignment between human and algorithmic surprise rankings and shows the potential of computational models to evoke surprise in users.

Findings

Computational models can predict human surprise rankings.

Humans and algorithms show some alignment in surprise perception.

Surprise induction can enhance user engagement and learning.

Abstract

Understanding how to engage users is a critical question in many applications. Previous research has shown that unexpected or astonishing events can attract user attention, leading to positive outcomes such as engagement and learning. In this work, we investigate the similarity and differences in how people and algorithms rank the surprisingness of facts. Our crowdsourcing study, involving 106 participants, shows that computational models of surprise can be used to artificially induce surprise in humans.