Introduction

Pupillometric measures of cognitive and emotional processes

The objective of the study was to find the answer to the question: if an image is worth more than a thousand words, how much is the virtual reality (VR) valuable for emotion recognition among participants in technologically advanced environments, with a focus on the measurement and metrology fundamentals? In the presented study, mixed methods involve the integration of Virtual Reality (VR) with pupil tracking, Computer Vision Technologies (CVT), and Neural Network (NN) models to address critical substantive and technical challenges associated with interpreting human reactions during the perception of the surrounding space. It introduces a novel approach for measuring and assessing virtual reality's impact on emotion recognition, thereby contributing to the broader field of measurement science. The research advances the state-of-the-art in measurement science by applying these tools and confirms the use of unconditional reactions of humans enables the assessment of environment features significance.

Eye gaze is a prominent feature of human social lives, but little is known on whether fitting eyes on machines makes humans trust them more. In this study we compared subjective and objective markers of human trust when collaborating with eyed and non-eyed robots of the same type. We used virtual reality scenes in which we manipulated distance and the presence of eyes on a robot's display during simple collaboration scenes. We found that while collaboration with eyed cobots resulted in slightly higher subjective trust ratings, the objective markers such as pupil size and task completion time indicated it was in fact less comfortable to collaborate with eyed robots. These findings are in line with recent suggestions that anthropomorphism may be actually a detrimental feature of collaborative robots. These findings also show the complex relationship between human objective and subjective markers of trust when collaborating with artificial agents.

Multi-channel human-computer interaction offers a new perspective for optimization research in the digital museum, though there is no empirical evidence on how these factors affect cognition. This paper conducted a controlled experiment using the audio guide as a breakthrough point, aiming to determine the cognitive changes before and after the audio-visual dual-channel interaction. The results demonstrate that adding the auditory channel brought more cognitive load but not better cognitive outcomes. This paper evaluated the reasons behind it and noted that designing channels based solely on technical feasibility is undesirable, with suggestions proposed for museum practitioners and platform developers.