As robotic technology advances, robots are increasingly being designed and utilized for applications involving children across various fields. Since the acceptance of robots is profoundly affected by their appearance, understanding children’s preferences for the looks of robots becomes imperative. The uncanny valley effect (UVE) posits that the perceived likeability of robots increases as their appearance becomes more humanlike but then sharply drops when they approach near-perfect human similarity before rising once an extremely realistic likeness is achieved. Due to inconsistent results in the literature regarding when humans develop a sense of uncanniness toward humanlike robots, the present study aims to examine the emergence of the UVE in children. 67 1st-graders (aged six to seven years) and 95 4th-graders (aged nine to ten years) evaluated 12 robot faces on the scales of likeability, disgust, humanness, and intention to play with the robots (i.e., acceptance). The results demonstrated that the UVE was not observed in 1st-graders or 4th-grade boys. Nevertheless, a UVE-like pattern emerged among 4th-grade girls, particularly in their ratings of likeability and acceptance as a function of robots’ degree of humanness. A positive correlation between likeability and acceptance was consistently found across all age and gender groups. Additionally, negative correlations were found between likeability and disgust, as well as disgust and acceptance, specifically in the fourth graders but not in the first graders. These results suggest that the UVE may not be fully developed in 10-year-old children, with girls potentially exhibiting earlier development than boys. We conclude that age and gender differences should be considered when evaluating children’s preferences for robot appearance and their willingness to interact with robots.

Cognitive offloading refers to using physical actions to reduce cognitive demand by altering a task’s information processing requirements. It enables problem-solving beyond the brain’s capacity. This study introduces subjective trust toward tools as a key factor in cognitive offloading, reflecting the connection between users and the tools they rely on. Participants rated their trust toward three tools before using them in a short-term memory task with four difficulty levels. Task performance and cognitive offloading frequency were recorded. Results showed that cognitive offloading significantly improved performance, with stronger effects when trust toward the tool was high. When faced with tasks of medium difficulty, participants were more inclined to adopt cognitive offloading strategies with tools they trusted than those they trusted less. These findings highlight the importance of trust toward tool selection for cognitive offloading, offering insights into designing and choosing effective tools for enhancing cognitive performance.

Light field AR glasses can provide better visual comfort than conventional AR glasses; however, studies on user performance comparison between them are notably scarce. In this article, we present a systematic method employing a serial visual search task without confounding factors to quantify and compare the user performance and experience between these two types of AR glasses at two different viewing distances, 30 cm and 60 cm, and in two modes, purely virtual VR mode and virtual-real integration AR mode. The results show that the light field AR glasses led to a significantly faster reaction speed and higher accuracy than the conventional AR glasses at 30 cm in the AR mode. The participant feedback also shows that the former led to better virtual-real integration. User performance and experience of the light field AR glasses remained consistent across different viewing distances. Although the conventional AR glasses had a better search efficiency than the light field AR glasses at 60 cm in both AR and VR modes, it had more negative feedback from the participants. Overall, the design of this experiment successfully allows us to quantify the effect of VAC and underscores the strength of the evaluation method.

Mind-wandering (MW), which is usually defined as a lapse of attention has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW. In this work, we first collected a multi-modal Sustained Attention to Response Task (MM-SART) database for MW detection. Eighty-two participants' data were collected in our dataset. For each participant, we collected measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of the EEG signals, we utilize entropy-based features. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we propose correlation importance feature elimination (CIFE) along with AUC-based channel selection. By using two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By applying CIFE on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE). Finally, we can apply the current work to educational scenarios nowadays, especially in remote learning systems.

Human social interactions are laden with behavioral preferences that stem from hidden social network representations. In this study, we applied an artificial neural network with machine theory of mind (ToMnet+) to learn and predict social preferences based on implicit information from the way agents and social targets interact behaviorally. Our findings have implications for machine applications that seek to infer hidden information structures solely from third-person observation of behaviors. We consider that social machines with such an ability would have an enhanced potential for more naturalistic human-machine interactions.

In this work, we created an end-to-end autonomous robotic platform to give emotional support to children in long-term, multi-session interactions. Using a mood estimation algorithm based on visual cues of the user's behaviors through their facial expressions and body posture, a multidimensional model predicts a qualitative measure of the subject's affective state. Using a novel Interactive Reinforcement Learning algorithm, the robot is able to learn over several sessions the social profile of the user, adjusting its behavior to match their preferences. Although the robot is completely autonomous, a third party can optionally provide feedback to the robot through an additional UI to accelerate its learning of the user's preferences. To validate the proposed methodology, we evaluated the impact of the robot on elementary school aged children in a long-term, multi-session interaction setting. Our findings show that using this methodology, the robot is able to learn the social profile of the users over a number of sessions, either with or without external feedback as well as maintain the user in a positive mood, as shown by the consistently positive rewards received by the robot using our proposed learning algorithm.

Understanding older adults’ attitudes toward robots has become increasingly important as robots have been introduced in various settings, such as retirement homes. We investigated whether there are age differences in both implicit and explicit attitudes toward robots after interacting with an assistive robot. Twenty-four younger and 24 older adults were recruited. Explicit attitudes were measured by self-reported questionnaires both before and after interacting with the robot. State curiosity toward robots was also measured as a momentary form of explicit attitude. Implicit attitude was measured via an implicit association test. Our results showed that (1) both older and younger adults had more positive explicit attitudes toward robots after interaction; (2) older adults had lower state curiosity than younger adults, however, their state curiosity would be up to the same level as younger adults when they perceived the robot with higher levels of personal association; and (3) the implicit association between robots and negative words was stronger for older adults than younger adults, suggesting that older adults had more implicit negative attitude toward robots. The results suggest that, despite older adults’ relatively more negative implicit attitude toward robots, personally relevant positive experiences could help improve their explicit attitudes toward robots.

Recent study in vision science has shown that blue light in a certain frequency band affects human circadian rhythm and impairs our health. Although applying a light blocker to an image display can block the harmful blue light, it inevitably makes an image look like an aged photo. In this paper, we show that it is possible to reduce harmful blue light while preserving the blue appearance of an image. Moreover, we optimize the spectral transmittance profile of blue light blocker based on psychophysical data and develop a color compensation algorithm to minimize color distortion. A prototype using notch filters is built as a proof of concept.

The effective end-to-end transport of delay-sensitive voice data has long been a problem in multimedia networking. One of the major issues is determining the sending rate of real-time VoIP streams such that the user experience is maximized per unit network resource consumed. A particularly interesting complication that remains to be addressed is that the available bandwidth is often dynamic. Thus, it is unclear whether a marginal increase warrants better user experience. If a user naively tunes the sending rate to the optimum at any given opportunity, the user experience could fluctuate. To investigate the effects of magnitude and frequency of rate changes on user experience, we recruited 127 human participants to systematically score emulated Skype calls with different combinations of rate changes, including varying magnitude and frequency of rate changes. Results show that: 1) the rate change frequency affects the user experience on a logarithmic scale, echoing Weber-Fechner's Law; 2) the effect of rate change magnitude depends on how users perceive the quality difference; and 3) this study derives a closed-form model of user perception for rate changes for Skype calls.

As Skype has become popular and a profitable business, the long-standing problem of how to deliver Skype calls deserves a serious revisit from an economic viewpoint. This study proposes a rate control mechanism for Skype calls that satisfies more users and satisfies users more than the greedy-naïve mechanism, as well as the mechanism implemented in Skype.

Switching the liquid crystal display (LCD) backlight of a portable multimedia device to a low power level saves energy but results in poor image quality especially for the low-luminance image areas. In this paper, we propose an image enhancement algorithm that overcomes such effects of dim LCD backlight by taking the human visual property into consideration. It boosts the luminance of image areas below the perceptual threshold while preserving the contrast of the other image areas. We apply the just noticeable difference theory and decompose an image into an HVS response layer and a background luminance layer. The boosting and compression processes, which enhance the visibility of the low-luminance image areas, are carried out in the background luminance layer to avoid luminance gradient reversal and over-compensation. The contrast of the processed image is further enhanced by exploiting the Craik-O'Brein-Cornsweet visual illusion. Experimental results are provided to show the performance of the proposed algorithm.

Unconscious attention shift has been proven to be automatic, effortless, and unresisting; however, whether it is applicable to images of complex scene remains to be verified. We propose a novel method for directing human visual attention by flashing a short-duration visual stimulus before presenting the image. This method directs viewer's visual attention to an arbitrary location in the image without engaging the viewer's awareness. Unlike previous studies using simple stimuli (e.g. simple geometric figures, image of uniform color, or artificial white-noise), we use complex color images for guiding unconscious visual attention in real-world. To our best knowledge, this is the first study demonstrating that short-duration cues presented at arbitrary locations can attract human visual attention in a complex scene. The proposed method is useful for applications that require efficient and unresisting attention shift to the target image area.

The effective end-to-end transport of delay-sensitive voice data has long been a problem in multimedia networking. One of the major issues is determining the sending rate of real-time VoIP streams such that the user experience is maximized per unit network resource consumed. A particularly interesting complication that remains to be addressed is that the available bandwidth is often dynamic. Thus, it is unclear whether a marginal increase warrants better user experience. If a user naively tunes the sending rate to the optimum at any given opportunity, the user experience could fluctuate.

Dimming the LCD backlight saves the battery power of a portable multimedia device but causes image quality degradation. In this paper, we propose an algorithm to overcome the effect of dim backlight on image quality. This algorithm enhances the backlight-scaled image by exploiting the human visual property. It performs a two-layer image decomposition based on the just noticeable difference theory and enhances the backlight-scaled image by luminance boosting and compression. Experimental results are provided to demonstrate the performance of the proposed algorithm.

Local dimming of the LED backlight is a popular technique for saving the power of a liquid crystal display. This paper presents a novel approach that improves the performance of conventional local dimming algorithms by incorporating a visual attention prediction model in the backlight dimming process. The approach saves the energy of the liquid crystal display and, in the mean time, maintains the perceptual image quality. This is achieved by preserving the backlight luminance of the image areas that attract human attention while reducing that of the other image areas. Experimental results are provided to show the effectiveness of the proposed approach.

A new computational scheme for visual attention modeling is proposed. It adopts both low-level and high-level features to predict visual attention from a video signal and fuses the features by using machine learning. We show that such a scheme is more robust than those using purely single level features. Unlike conventional techniques, our scheme is able to avoid perceptual mismatch between the estimated saliency and the actual human fixation. We show that selecting the representative training samples according to the fixation distribution improves the efficacy of regressive training. Experimental results are shown to demonstrate the advantages of the proposed scheme.

Visual attention, which is an important characteristic of human visual system, is a useful clue for image processing and compression applications in the real world. This paper proposes a computational scheme that adopts both low-level and high-level features to predict visual attention from video signal by machine learning. The adoption of low-level features (color, orientation, and motion) is based on the study of visual cells, and the adoption of the human face as a high-level feature is based on the study of media communications. We show that such a scheme is more robust than those using purely single low- or high-level features. Unlike conventional techniques, our scheme is able to learn the relationship between features and visual attention to avoid perceptual mismatch between the estimated salience and the actual human fixation. We also show that selecting the representative training samples according to the fixation distribution improves the efficacy of regressive training. Experimental results are shown to demonstrate the advantages of the proposed scheme.

Maintaining image quality under various lighting conditions is critical to portable multimedia devices. In this paper, an image enhancement algorithm is proposed to deal with extremely dim LCD backlight (or strong ambient light) under which the image often becomes imperceptible. The crux of our idea is to keep the detail content of the image in the visible luminance range. To do that, a two-layer image decomposition architecture based on the just noticeable difference (JND) theory is proposed for extracting the details, and a boosting scheme is applied to the details that are in the low intensity regions of the image. Experimental results are provided to show the superiority of the proposed algorithm.