Evaluating the feeling of control in virtual object translation on 2D interfaces

Abstract

Computer-aided design (CAD) plays an essential role in creative idea generation on 2D screens during the design process. In most CAD scenarios, virtual object translation is an essential operation, and it is commonly used when designers simulate their innovative solutions. The degrees of freedom (DoF) of virtual object translation modes have been found to directly impact users’ task performance and psychological aspects in simulated environments. Little is known in the existing literature about the sense of agency (SoA), which is a critical psychological aspect emphasizing the feeling of control, in translation modes on 2D screens during the design process. Hence, this study aims to assess users’ SoA in virtual object translation modes on mouse-based, touch-based, and handheld augmented reality (AR) interfaces through subjective and objective measures, such as self-report, task performance, and electroencephalogram (EEG) data. Based on our findings in this study, users perceived a greater feeling of control in 1DoF translation mode, which may help them come up with more creative ideas, than in 3DoF translation mode in the design process; additionally, the handheld AR interface offers less control feel, which may have a negative impact on design quality and creativity, as compared with mouse- and touch-based interfaces. This research contributes to the current literature by analyzing the association between virtual object translation modes and SoA, as well as the relationship between different 2D interfaces and SoA in CAD. As a result of these findings, we propose several design considerations for virtual object translation on 2D screens, which may enable designers to perceive a desirable feeling of control during the design process.

Applications of artificial intelligence and cognitive science in design

“…This study has provided practical guidance in virtual hand design for VR applications. Sun et al (2023b) evaluated the feeling of control in virtual objective on two-dimensional (2D) interfaces by measuring the users' sense of agency. The different degrees of freedom (DoF) of the virtual object translation modes and 2D interface types would impact users' performance.…”

“…This thematic collection encompasses a wide range of studies exploring various aspects of applying AI techniques and cognitive science in design. Some studies have focused on how AI can be used to augment and support humans in design (Sun et al, 2023a;Wang et al, 2023), some explored new user cognitive models for design (Tehranchi et al, 2023), while others probed human design cognition using neurophysiological and biometric measures (Hu et al, 2023;Sun et al, 2023b;Yin et al, 2023;Zhang et al, 2023). Additional studies have exploited AI for analyzing design cognitive data (Chiu et al, 2023;Hu et al, 2023;Sun et al, 2023b;Zhang et al, 2023).…”

“…Some studies have focused on how AI can be used to augment and support humans in design (Sun et al, 2023a;Wang et al, 2023), some explored new user cognitive models for design (Tehranchi et al, 2023), while others probed human design cognition using neurophysiological and biometric measures (Hu et al, 2023;Sun et al, 2023b;Yin et al, 2023;Zhang et al, 2023). Additional studies have exploited AI for analyzing design cognitive data (Chiu et al, 2023;Hu et al, 2023;Sun et al, 2023b;Zhang et al, 2023). These studies have collectively contributed to a deeper understanding of design and indicated the potential of bridging AI and cognitive science to understand and support humans in varying design activities.…”

“…However, in design research, most studies have focused on using one neurophysiological/biometric measure instrument only, which might have limited the findings. In this thematic collection, Yin et al (2023) employed EEG, Hu et al (2023 used fMRI, while Sun et al (2023b) used EEG and self-report and task performance, and Zhang et al (2023) utilized ET and self-report questionnaire. To adopt the latest cognitive science research multimodal approaches and incorporate conventional design research approaches, it leads to our second proposition for future research.…”

“…This leads to a new and effective way of studying design cognition and enables human-machine interactions for design. In this thematic collection, Hu et al (2023) employed HMM to study patterns in fMRI data, Sun et al (2023b) applied k-means and principal component analysis in EEG microstate maps, Zhang et al (2023) used k-means to cluster ET data, and Chiu et al (2023) used NLP to analyze written documentation containing design cognitive processes in a design project for visualizing the design progress. These studies have shown the potential of utilizing AI to analyze and interpret design cognitive data to unlock the inner workings of the human brain in design, and lead to the third proposition for future research.…”