Data on human decision, feedback, and confidence during an artificial intelligence-assisted decision-making task.

The data are collected from a human subjects study in which 100 participants solve chess puzzle problems with artificial intelligence (AI) assistance. The participants are assigned to one of the two experimental conditions determined by the direction of the change in AI performance at problem 20: 1) high- to low-performing and 2) low- to high-performing. The dataset contains information about the participants' move before an AI suggestion, the goodness evaluation score of these moves, AI suggestion, feedback, and the participants' confidence in AI and self-confidence during three initial practice problems and 30 experimental problems.

Attention affordances: Applying attention theory to the design of complex visual interfaces.

The design of visual interfaces plays a crucial role in ensuring swift and accurate information search for operators, who use procedures and information tables to cope with problems arising during emergencies. The primary cognitive mechanism involved in information search is visual attention. However, design of interfaces is seldom done through applying predictions of theories of attention.