Enhancing realism in virtual environments (VEs) requires effective weight rendering, which is influenced not only by visual or haptic cues but also by material-weight illusions and contextual factors. This study investigates how multisensory feedback affects weight perception in context-rich scenarios through a material-informed audio-vibrational approach. An experiment using an equivalent weight substitution method examined the effects of feedback modality and material-informed conditions. Results show that audio-vibrational cues informed by material properties significantly enhance perceived realism and lead to higher weight perception compared to vibration-only feedback. Furthermore, material-informed conditions produced significant differences in perceived weight, suggesting that contextual relevance is crucial in shaping weight perception. Importantly, the approach enables the perception of heavier weights without stronger vibrations, reducing actuator demands and balancing sensory load by relying on non-visual modalities. These findings highlight the potential of material-informed feedback to enhance realism and efficiency in virtual weight rendering.

Olfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.

Due to the limited internal space and economic costs, virtual keyboards(VKs) of mobile devices commonly use monotonous global sound and vibration feedback, resulting in unsatisfactory realism and negative experience. To mitigate the monotonous feedback of high-frequency keystrokes on VKs, we propose an interactive sound-vibrotactile feedback design. This system dynamically adjusts sound and vibrotactile re-sponses based on the user's typing speed. We conducted a user study (N=30) and found that the collaborative varied auditory-vibrotactile feedback provided a better experience, created a local feedback illusion, and improved global feedback. Additionally, users preferred subtle variations over distinct variations. Our method can improve global VK feedback and provide a better experience without increasing additional costs and burdens. This study contributes to improving the VK experience on low-cost mobile devices and provides design suggestions for VKs.

Selecting appropriate fonts for sentence-level text, such as slide titles and poster headings, requires pleasing glyphs and semantic congruence with the text. This task is especially challenging for users without design backgrounds. Previous studies established associations between tags and fonts but overlooked the different design intents between sentence-level text and word-level text influencing font selections. Recently, large language models (LLMs) like ChatGPT with extensive knowledge bases have emerged as promising solutions for various tasks. This study evaluated the capabilities of GPT-3.5 in recommending fonts compared to professional designers to explore the application of LLMs in the design field. We conducted comprehensive evaluations, including both quantitative metrics to assess font diversity, accuracy, and consistency, and a user study involving designers and non-designers. The results indicate that while GPT-3.5 performs well in selecting common serif and sans-serif fonts suitable for general use that align with the preferences of users without design backgrounds, it falls short in stylized font selection. Our findings highlight the potential and limitations of using GPT-3.5 for font selection in collaborative design environments and provide insights into integrating such models into design tools to support multidisciplinary teams.

This study aimed to detect stressful hazard perception scenarios subjectively and objectively when using intelligent driving systems. We used electrooculography (EOG), electroencephalography (EEG), subjective ratings, and interviews to identify potential stressful hazard perceptions and record improvements in an intelligent navigation-guided pilot (NGP) system. Moreover, we analyzed electrophysiological data. Our study contributes to the use of engagement, concentration, and phase locking value connectivity based on EEG to support previous research methodologies using beta power, pupil size, fixation ratio, fixation duration, and subjective evaluations for investigating hazard perception. Our analyses showed that stressful hazard perception scenarios occurred mainly when encountering broken and solid lines, frequent lane changes, cars approaching suddenly, several cars driving in parallel, and the decision to change lanes but immediately pulling back upon using the NPG system. Our findings shed light on obtaining accurate results based on subjective and objective evaluations for developing intelligent driving systems.

In the digital era, the significance of design education is on the rise due to its ability to cultivate creativity. However, the disconnect between design practice and theory, coupled with the abundance of design knowledge, poses challenges to learning in this field. Despite the potential of large language models (LLMs) to integrate various data sources for facilitating design knowledge dissemination, they face obstacles such as the scarcity of design-related datasets and limited natural language representations. To overcome these challenges, we introduce DRELM, a design-centric language model that serves as an assistant providing UI design recommendations. We also offer corresponding resources to advance language modeling research in the design domain. Importantly, DesignInstruct stands out as a premier dataset for guiding user interface tasks, while DesignEvaluation significantly contributes to autonomous design evaluation and decision support. In our research, we utilize supervised data from DesignInstruct and DesignEvaluation to fine-tune pre-trained Qwen-7B models for design tasks. Experiments conducted on test data affirm the effectiveness of our dataset in enhancing knowledge comprehension, design execution, and evaluation. We commit to making all training data and DRELM models at https://github.com/sssala/DRELM-A-Language-Model-for-Design-Recommendation-and-Evaluation.

This study addresses the challenge of font retrieval in design by proposing a novel approach utilizing contrastive learning to establish a shared embedding space for texts and fonts. In contrast to previous methods limited to word-level queries, our method enables text-font retrieval at the sentence level. We collected text-font pair data from web pages and design templates on the Internet, finetuned the CLIP model on these pairs, and obtained text and font encoders for our application. The top-k fonts were then retrieved using the cosine distance between input text and font embeddings. Our approach offers three key advantages: (1) retrieving fonts with sentence-level text as input, which is intuitively consistent with design behaviors; (2) leveraging text-font pair data available on the Internet without manual annotation; and (3) scalability, the trained font encoder can encode new font candidates without retraining the model. We introduced an evaluation metric for font retrieval results. The results indicate that the retrieved fonts in the top 3 score better than those from baseline methods, and the top 1 retrieved font is competitive with the fonts selected by experienced graphic designers.

People are accustomed to using mobile phones to purchase products online. Previous studies identified brain activities associated with online buying decisions but did not assess mismatch negativity (MMN), P300 (P3a and P3b), and slow waves. We applied visual animation and sound effects to test visual–auditory multisensory concepts in product promotion strategies. Event-related potential (ERP) responses from 53 participants were analyzed to help design sound effects based on MMN, P300, and slow waves. We designed five short visual–auditory animation videos: visual animation sound effect (VASE), VA sound beep (VASB), visual standard sound beep (VSSB), visual animation–no sound (VANS), and visual standard–no sound (VSNS). These five apps were used to test how matched and unmatched multisensory user experience (UX) influences online shopping decisions. We also used Arrow’s impossibility theorem to design the purchasing experiment. VASE evoked MMN (cluster p = 0.0184) compared with VSSB. VASE evoked the strongest P300 (P3a and P3b) responses (cluster p = 1 × 10−5), and VASB evoked more negative slow wave amplitudes (cluster p = 1.7 × 10−4) compared with VASE. Participants buy products unconsciously via most VASE and VANS mobile apps compared with via VASB and VSNS. Our findings suggest that sensory memory from multisensory UX can promote product consumption in mobile phone shopping interfaces. This study’s testing of memory and attention from MMN, P300, and slow waves makes a novel contribution to the marketing discipline. The implications can motivate researchers to apply ERP to analyze the multisensory concept in future interdisciplinary research.

Nowadays, social robots have become human's important companions. The anthropomorphic features of robots, which are important in building natural user experience and trustable human-robot partnership, have attracted increasing attention. Among these features, eyes attract most audience's attention and are particularly important. This study aims to investigate the influence of robot eye design on users’ trustworthiness perception. Specifically, a simulation robot model was developed. Three sets of experiments involving sixty-six participants were conducted to investigate the effects of (i) visual complexity of eye design, (ii) blink rate, and (iii) gaze aversion of social robots on users’ perceived trustworthiness. Results indicate that high visual complexity and gaze aversion lead to higher perceived trustworthiness and reveal a positive correlation between the perceived anthropomorphic effect of eye design and users’ perceived trust, while a non-significant effect of blink rate has been found. Preliminary suggestions are provided for the design of social robots in future works.