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. The dataset contains 100 CSV files, one per participant. There is opportunity for this dataset to be utilized in various domains that research human-AI collaboration scenarios such as human-computer interaction, psychology, computer science, and team management in engineering/business. Not only can the dataset enable further cognitive and behavioral analysis in human-AI collaboration contexts but also provide an experimental platform to develop and test future confidence calibration methods.

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. Conversely, theories of attention are seldom tested in applied contexts. Combining application and attention research thus stands to benefit both fields. Therefore, this study tested three theories of visual attention that are especially relevant for information processing in emergencies-Load Theory, Feature Integration Theory, and Dilution Theory-as well as predictions about attentional guidance and capture of color in a complex visual interface. Evidence was found for several predictions from theory, especially from Feature Integration Theory. Implications for design practice and attention research are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).