How Interactive Data Visualization and Users' BMI (Body Mass Index) Influence Obesity Prevention Intentions: The Mediating Effect of Cognitive Absorption.

The current study examines how interactive data visualization can augment the persuasive potential of health information. In an experiment using an obesity-awareness website (N = 248), we manipulated the level of interactivity in a data visualization tool that featured obesity prevalence in the U.S. and measured participants' absorption into the website, perceived issue severity, content perceptions, and intentions to prevent/treat obesity. Our data demonstrated that lower BMI participants reported greater cognitive absorption when highly interactive data visualization was available on the website. Subsequently, greater cognitive absorption into the website led to higher perceived issue severity, greater content perceptions, and higher obesity prevention intentions among lower BMI participants but not among higher BMI participants.

Person-identifying brainprints are stably embedded in EEG mindprints.

Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.