Intelligent agents(IA) are increasingly integrated into daily life, performing tasks ranging from household assistance to supporting various industries. However, their inevitable errors present significant challenges in human-IA interactions, often leading to reduced user trust and satisfaction. Consequently, effective recovery strategies are crucial for mitigating the negative impacts of such errors.This study investigates how different recovery strategies and error types influence human perceptions across two experimental studies. The results show that recovery strategies significantly improve human perceptions of IAs following errors. While perceptions of warmth can recover to levels comparable to no-error conditions, perceptions of competence do not fully recover. Furthermore, the effectiveness of recovery strategies varies depending on the type of error. Specifically, for execution errors, strategies such as apologies, providing alternative options, and self-repair are effective. However, the impact of apology strategies demonstrates inconsistency across different contexts. For planning errors, only self-repair strategies consistently yield positive effects, particularly improving perceptions of warmth.These findings offer valuable insights for the design and development of intelligent agents, emphasizing the importance of tailoring recovery strategies to specific error types. By adopting approaches focus on different error types, designers can optimize human perceptions and foster more effective collaboration in human-IA interactions.

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift transitions on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to quantitatively track the flow state variations from synchronous motor control performance. We select eight performance metrics from fingertip force sequence and reveal their significant differences under distinct self-reported flow states. Further, we built a machine learning-based decoder that aims to predict the continuous flow intensity during the user experiment through the performance metrics, taking the self-reported flow as the label. Cross-validation shows that the predicted flow intensity reaches significant correlation with the self-reported flow intensity (r= 0.81). Based on the decoding results, we can capture the flow fluctuations during the intervals between sparse self-reporting probes. This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures and may serve as foundation for future work aiming to take advantage of flow's dynamics to enhance performance and positive emotions.

Zeming Liu, Ping Nie, Jie Cai, Haifeng Wang, Zheng-Yu Niu, Peng Zhang, Mrinmaya Sachan, Kaiping Peng. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Since chronic loneliness is both a painful individual experience and an increasingly serious social problem, robot companions have emerged as a result of robotization of social work to confront this issue. We foresee that social robots will become pervasive in the near future. Thus, it is crucial to pinpoint the relationship between chronic experiences of loneliness (i.e., trait loneliness) and both anthropomorphism and acceptance of such artificial intelligent agents. Previous research demonstrated that experimentally induced state loneliness increases anthropomorphic inferences about nonhuman agents such as pets. However, in the present research we found that trait (vs. state) loneliness - a permanent personality disposition that is not easily relieved (vs. transitory experiences caused by circumstance, and easily relieved) - reduced participants' anthropomorphic tendencies and acceptance of a social robot (regardless of the form: a picture of the robot, an on-site robot, or direct interaction with the robot). In particular, believing that the robot lacks good "unique humanness" traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) is one reason why dispositionally lonely participants are less likely to anthropomorphize a robot, which further prompts reduced acceptance of it. This finding suggests that unique humanness, exemplifying secondary emotions, is vital, not only in interpersonal contexts, but in establishing connections with social robots.

Previous research showed that different cultures play a significant role in influencing people's perception and cognition: Eastern people attend more to the background and context, whereas western people focus more on objects. However, the culture influence on people's aesthetic perception and experience has not been systematically explored. In the first part of our study, we measured the eye movements of Chinese and western participants while they viewed Chinese traditional Ink-wash paintings, and we found that the Chinese participants had longer fixation time in the white space areas than did the western participants. In the second part of the study, we measured their eye movements while they viewed different paintings composed by the linear perspective or scattered perspective techniques, and the results suggest that the western participants are more sensitive to the perspective difference between paintings. The findings contribute to the understanding of culture influence on people's aesthetic perception of visual art and visual communication.

We developed a Chinese questionnaire for assessing the user experience of the design of interactive products. The initial Chinese UX Questionnaire was based on a widely used English questionnaire, AttracDiff, proposed and refined by Hassenzahl and his colleagues, but extended with an additional dimension and several new items received from the interviews of Chinese users. We also conducted a study of applying the initial questionnaire for accessing the user experience of interacting three different mobile phones involved with thirty-six Chinese participants. We applied both exploratory factor analysis and confirmatory factor analysis on the items with good item-total correlations. The result showed that three factors or dimensions were identified: Hedonic Quality (Stimulation), Pragmatic Quality, and Conformity, as compared to the four dimensions in the AttracDiff. This confirmed our initial hypothesis that cultures not only influence people's cognition, but also affect their experience of interacting with products. We also discuss the future steps for refining the Chinese UX Questionnaire.