The interaction between stroke patients and rehabilitation training robots is difficult because the motion intention cannot be exacted by limbs due to their weak motor ability. Although brain-computer interaction(BCI) is beneficial for perceiving the motion of patients, it cannot solve the problem independently without receiving enough data about the effectiveness of the robotic assistance. This study proposes a BCI method that integrates rehabilitation training and training effect evaluation in a mixed reality(MR) environment. Three EEG experimental paradigms were designed, using motor imagery to convert the four-classification problem of motor execution into three binary classification problems. An EEG classification model of a multi-scale convolution residual network based on a multi-head attention mechanism was built, consisting of a multi-head attention mechanism layer, four convolutional layers, and a pooling layer. The highest accuracy and best Kappa coefficient of 12 participants on the testing dataset were recorded. The average classification accuracy of MI, ME1, and ME2 achieves 80.35%, 87.52%, and 86.51%, respectively, proving the proposed classification method has sufficient decoding accuracy. Comparing the hemodynamic response curves of the participants’ upper limbs before and after robotic assistance during rehabilitation training, hemodynamic response curves were analyzed quantitatively. With the paired-sample t-test to compare the hemodynamics of a single participant, the significance of the overall mean difference was calculated to evaluate the influence of robotic assistance on enhancing local tissue blood oxygen, improving tissue function, and accelerating local tissue repair. The study verifies the effectiveness of robotic rehabilitation training and helps develop new types of BCI with better coordination between motion intention perception and motor ability.

Virtual health assistants, digital characters designed to guide patients through healthcare interventions, offer scalable solutions, but their effectiveness may depend on user perceptions of realism. This study investigated how the visual fidelity and voice modality of virtual health assistants influence perceived realism, perceived voice naturalness, and intentions to screen for colorectal cancer among Black American adults aged 45–75. In a 2 × 4 factorial between-participants experiment (N = 266), participants were randomized to virtual health assistants varying in visual fidelity and voice modality. Structural equation modeling revealed that voice naturalness had a strong positive effect on perceived realism, which in turn significantly predicted screening intentions. Visual fidelity also contributed directly to perceived realism, although to a lesser extent. These results suggest that enhancing voice naturalness in virtual health assistants may be a critical design priority for improving engagement and promoting preventive health behaviors in digital health interventions.

Negative psychological effects from losing in online competitive games, such as frustration and reduced self-evaluation, can lead to player disengagement. Previous research using puzzle tasks has shown that providing losers with positive feedback on their performance after the task can enhance their intrinsic motivation. However, such findings were demonstrated in analog settings where feedback was delivered verbally by a human experimenter, and it remains unclear whether similar effects hold in digital environments such as online competitive games. This study investigates a feedback intervention using a virtual agent on a computer, aimed at mitigating such negative impacts and promoting sustained intrinsic motivation. In a controlled experiment with 45 participants, we compared three types of feedback: No Feedback (NF), Feedback for the Participant (FP), which included praise for the player's good moves, and Feedback for Both (FB), which also praised the opponent and emphasized social connection. The results indicated that providing feedback, whether it was directed at the player alone or at both the player and the opponent, significantly increased perceived competence, autonomy, intrinsic motivation, and the intention to play again compared to not providing any feedback at all. We discuss the potential of personalized, praise-based feedback in sustaining healthy motivation during competitive gameplay.

Large-scale screening programs for vision impairments can incur substantial costs. Computer-based screening methods, which combine different measurements within a single system, can facilitate and reduce the costs of such programs. Here, we present a virtual reality (VR) software, which includes tests for the assessment of visual acuity, stereoacuity, eye misalignments, and interocular suppression as well as games targeting different visual functions that may serve as treatment methods. The software can be easily extended to incorporate new tests and games. We present a proof of concept demonstrating the functionality of the software and its applicability in individuals with impaired binocularity. We evaluate a stereoacuity test in VR based on disparity detection using contoured objects by comparing its results to those obtained by standard clinical tests, i.e., TNO, Randot, and Titmus, for 7 amblyopes and 6 healthy controls. We evaluate the applicability of a new VR-based suppression test in 10 amblyopes and 6 healthy individuals. For the latter, we exploit the effects of short-term monocular deprivation, which induce a change of ocular dominance. Finally, we outline technical limitations and discuss potential applications.

Analyzing infant visual fixations and gaze patterns from the earliest stages of life can provide valuable insights into early cognition and development. Eye-tracking methodologies provide a means to measure infant visual perception with high temporal and spatial accuracy, and in increasingly naturalistic contexts with the innovation of head-mounted eye tracking. The current review provides a comparison between screen-based and head-mounted eye-tracking methods for use with infant populations, reviewing the advantages and disadvantages of each approach. This review also aims to summarize recommendations for best practices in infant eye tracking for both screen-based and head-mounted technology, including suggestions for designing experiments, calibration techniques, and analysis approaches. Moreover, we discuss the need for standardized procedures for experimental design and reporting data quality for investigations using eye tracking to examine infant development.

Self-overlapping spaces, also known as impossible spaces, are a design mechanic in virtual reality (VR) that allows a user to naturally walk through an environment that is larger than the physical space available to them. Prior work has focused on generating these spaces and evaluating when their self-overlapping nature is detectable. Comparatively, little work has evaluated how the self-overlapping nature of these spaces impacts users’ spatial understanding and whether any misperceptions carry over into altered action judgments. We present a study evaluating how self-overlapping spaces influence action judgments related to relative distances within the virtual environment. Participants were presented with a variety of self-overlapping spaces and, after exploring them, were asked to judge which of the two locations was closer to their current position in the environment. Participants’ were more likely to make correct decisions as the relative difference in distance between the two locations increased; however, this effect was affected by both the amount of overlap present in a particular environment and by the relative position from which they made their decision.

The development of novel computer interfaces has led to the possibility of integrating inputs from multiple individuals into a single avatar, fostering collaboration by combining skills and sharing the cognitive load. However, the collaboration dynamic and its effectiveness may vary depending on the individuals involved. Particularly in scenarios where two individuals remotely control a robotic avatar without the possibility of direct communication, understanding each other’s characteristics can result in enhanced performance. To achieve this, it is essential to ascertain if individuals can discern their partner’s characteristics within the merged embodiment. This paper investigates the accuracy with which participants can distinguish between two different collaborating partners (one attempting to lead and one attempting to follow) when sharing control of a robot arm during a block pick-and-place task. The results suggested that participants who changed their roles according to the different roles of the two partners achieved the highest discrimination rates. Furthermore, participants changed their movements through the trials, adapting their actions to their preferred approach. This research provides insights into the factors determining individuals’ ability to understand partner characteristics during control of collaborative avatars.

Color composition (or color theme) is a key factor to determine how well a piece of art work or graphical design is perceived by humans. Despite a few color harmony models have been proposed, their results are often less satisfactory since they mostly neglect the variations of aesthetic cognition among individuals and treat the influence of all ratings equally as if they were all rated by the same anonymous user. To overcome this issue, in this article we propose a new color theme evaluation model by combining a back propagation neural network and a kernel probabilistic model to infer both the color theme rating and the user aesthetic preference. Our experiment results show that our model can predict more accurate and personalized color theme ratings than state of the art methods. Our work is also the first-of-its-kind effort to quantitatively evaluate the correlation between user aesthetic preferences and color harmonies of five-color themes, and study such a relation for users with different aesthetic cognition.

When two brief vibrotactile stimulations are sequentially applied to observers’ lower back, there is systematic mislocalization of the stimulation: if the second stimulation is of higher intensity than the first one, observers tend to respond that the second stimulation was above the first one, and vice versa when weak intensity stimulation follows a strong one. This haptic mislocalization effect has been called the intensity order illusion . In the original demonstration of the illusion, frequency and amplitude of the stimulation were inextricably linked so that changes in amplitude also resulted in changes in frequency. It is therefore unknown whether the illusion is caused by changes in frequency, amplitude or both. To test this, we performed a multifactorial experiment, where we used L5 actuators that allow independent manipulation of frequency and amplitude. This approach enabled us to investigate the effects of stimulus amplitude, frequency and location, and to assess any potential interactions among these factors. We report four main findings: (1) we were able to replicate the intensity order illusion with the L5 tactors; (2) the illusion mainly occurred in the upwards direction, or in other words, when strong stimulation following a weaker one occurred above or in the same location as the first stimulation; (3) the illusion did not occur when similar stimulation patterns were applied in the horizontal direction; and (4) the illusion was solely due to changes in amplitude, whereas changes in frequency (100 Hz vs 200 Hz) had no effect.

No abstract available.