Abstract
Research was performed in order to improve the efficiency of a user’s access to information and the interactive experience of task selection in a virtual reality (VR) system, reduce the level of a user’s cognitive load, and improve the efficiency of designers in building a VR system. On the basis of user behavior cognition-system resource mapping, a task scenario resource optimization method for VR system based on quality function deployment-convolution neural network (QFD-CNN) was proposed. Firstly, under the guidance of user behavior cognition, the characteristics of multi-channel information resources in a VR system were analyzed, and the correlation matrix of the VR system scenario resource characteristics was constructed based on the design criteria of human–computer interaction, cognition, and low-load demand. Secondly, analytic hierarchy process (AHP)-QFD combined with evaluation matrix is used to output the priority ranking of VR system resource characteristics. Then, the VR system task scenario cognitive load experiment is carried out on users, and the CNN input set and output set data are collected through the experiment, in order to build a CNN system and predict the user cognitive load and satisfaction in the human–computer interaction in the VR system. Finally, combined with the task information interface of a VR system in a smart city, the application research of the system resource feature optimization method under multi-channel cognition is carried out. The results show that the test coefficient CR value of the AHP-QFD model based on cognitive load is less than 0.1, and the MSE of CNN prediction model network is 0.004247, which proves the effectiveness of this model. According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality.
Highlights
The research on the user experience and cognitive load of human–computer interaction in virtual reality system has attracted attention
The results show that the test coefficient CR value of the analytic hierarchy process (AHP)-quality function deployment (QFD) model based on cognitive load is less than 0.1, and the mean square error (MSE) of CNN prediction model network is 0.004247, which proves the effectiveness of this model
According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality
Summary
The research on the user experience and cognitive load of human–computer interaction in virtual reality system has attracted attention. In the virtual reality (VR) system task scenario, the mapping relationship analysis between the visual expression of multi-channel information resources and the user’s hidden cognitive needs is an important part of studying the user’s cognitive load and user experience. In the research of user cognition theory, Lu Lu [5] and other scholars proposed a multi-channel information cognition processing model. In order to coordinate information capacity and user cognition in human–computer interaction, Based on the research results and theories of previous scholars combined with the existing problems, this paper proposes a cognitive load forecasting model based on the mapping of user cognitive behavior and system design resource elements under VR system multi-perception channels.
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