Learning the methods of psychophysics is an essential part of training for perceptual experimentation, and hands-on experience is vital, but gaining this experience is difficult because good tools for learning are not available. The FechDeck is an ordinary deck of playing cards that has been modified to support learning the methods of psychophysics. Card backs are printed with noise patterns that span a range of densities. Faces are augmented with line segments arranged in “L” patterns. Jokers are printed with ruled faces and with backs that serve as standards. Instructions provided with the FechDeck allow users to perform threshold experiments using Fechner's methods of adjustment, limits, and constant stimuli; scaling experiments using Thurstone's ranking, paired comparison, and successive categories methods; and Stevens's magnitude estimation method. Spreadsheets provided with the deck support easy data entry and meaningful data analysis. An online repository supporting the FechDeck has been established to facilitate dissemination and to encourage open source development of the deck.

Vision in real environments stabilizes balance compared to an eyes-closed condition. For virtual reality to be safe and fully effective in applications such as physical rehabilitation, vision in virtual reality should stabilize balance as much as vision in the real world. Older virtual reality technology was previously found to stabilize balance but by less than half as much as real-world vision. Recent advancements in display technology might allow for vision in virtual reality to be as stabilizing as vision in the real world. This study evaluated whether viewing a virtual environment through the HTC Vive—a new consumer-grade head-mounted display—stabilizes balance, and whether visual stabilization is similar to that provided by real-world vision. Participants viewed the real laboratory or a virtual replica of the laboratory and attempted to maintain an unstable stance with eyes open or closed while standing at one of two viewing distances. Vision was significantly stabilizing in all conditions, but the virtual environment provided less visual stabilization than did the real environment. Regardless of the environment, near viewing led to greater visual stabilization than did far viewing. The smaller stabilizing influence of viewing a virtual compared to real environment might lead to greater risk of falls in virtual reality and smaller gains in physical rehabilitation using virtual reality.

Eye motions constitute an important part of our daily face-to-face interactions. Even subtle details in the eyes’ motions give us clues about a person’s thoughts and emotions. Believable and natural animation of the eyes is therefore crucial when creating appealing virtual characters. In this article, we investigate the perceived naturalness of detailed eye motions, more specifically of jitter of the eyeball rotation and pupil diameter on three virtual characters with differing levels of realism. Participants watched stimuli with six scaling factors from 0 to 1 in increments of 0.2, varying eye rotation and pupil size jitter individually, and they had to indicate if they would like to increase or decrease the level of jitter to make the animation look more natural. Based on participants’ responses, we determine the scaling factors for noise attenuation perceived as most natural for each character when using motion-captured eye motions. We compute the corresponding average jitter amplitudes for the eyeball rotation and pupil size to serve as guidelines for other characters. We find that the amplitudes perceived as most natural depend on the character, with our character with a medium level of realism requiring the largest scaling factors.

Measuring cognitive load and stress is crucial for ubiquitous human--computer interaction applications to dynamically understand and respond to the mental status of users, such as in smart healthcare, smart driving, and robotics. Various quantitative methods have been employed for this purpose, such as physiological and behavioral methods. However, the sensitivity, reliability, and usability are not satisfactory in many of the current methods, so they are not ideal for ubiquitous applications. In this study, we employed a reflected photoplethysmogram-based stress-induced vascular response index, i.e., the reflected sVRI (sVRI-r), to non-invasively measure the cognitive load and stress. This method has high usability as well as good sensitivity and reliability compared with the previously proposed transmitted sVRI (sVRI-t). We developed the basic methodology and detailed algorithm framework to validate the sVRI-r measurements, and it was implemented by employing two light sources, i.e., infrared light and green light. Compared with the simultaneously recorded blood pressure, heart rate variation, and sVRI-t, our findings demonstrated the greater potential of the sVRI-r for use as a sensitive, reliable, and usable parameter, as well as suggesting its potential integration with ubiquitous touch interactions for dynamic cognition and stress-sensing scenarios.

Underestimation of egocentric distances in immersive virtual environments using various head-mounted displays (HMDs) has been a puzzling topic of research interest for several years. As more commodity-level systems become available to developers, it is important to test the variation of underestimation in each system since reasons for underestimation remain elusive. In this article, we examine several different systems in two experiments and comparatively evaluate how much users underestimate distances in each one. To observe distance estimation behavior, a standard indirect blind walking task was used. An Oculus Rift DK1, weighted Oculus Rift DK1, Oculus Rift DK1 with an artificially restricted field of view, Nvis SX60, Nvis SX111, Oculus Rift DK2, Oculus Rift consumer version (CV1), and HTC Vive were tested. The weighted and restricted field of view HMDs were evaluated to determine the effect of these factors on distance underestimation; the other systems were evaluated because they are popular systems that are widely available. We found that weight and field of view restrictions heightened underestimation in the Rift DK1. Results from these conditions were comparable to the Nvis SX60 and SX111. The Oculus Rift DK1 and CV1 possessed the least amount of distance underestimation, but in general, commodity-level HMDs provided more accurate estimates of distance than the prior generation of HMDs.

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications.

Motivated by the need to support those self-managing chronic pain, we report on the development and evaluation of a novel pressure-based tangible user interface (TUI) for the self-report of scalar values representing pain intensity. Our TUI consists of a conductive foam-based, force-sensitive resistor (FSR) covered in a soft rubber with embedded signal conditioning, an ARM Cortex-M0 microprocessor, and Bluetooth Low Energy (BLE). In-lab usability and feasibility studies with 28 participants found that individuals were able to use the device to make reliable reports with four degrees of freedom as well map squeeze pressure to pain level and visual feedback. Building on insights from these studies, we further redesigned the FSR into a wearable device with multiple form factors, including a necklace, bracelet, and keychain. A usability study with an additional 7 participants from our target population, elderly individuals with chronic pain, found high receptivity to the wearable design, which offered a number of participant-valued characteristics (e.g., discreetness) along with other design implications that serve to inform the continued refinement of tangible devices that support pain self-assessment.

The objective of this set of experiments was to evaluate thermal pattern recognition on the hand and arm and to determine which features of thermal stimuli are encoded by cutaneous thermoreceptors and perceived by the user of a thermal display. Thermal icons were created by varying the direction, rate, and magnitude of change in temperature. It was found that thermal icons were identified more accurately when presented on the thenar eminence or the wrist, as compared to the fingertips and that thermal patterns as brief as 8s could be reliably identified. In these experiments, there was no difference in performance when identifying warm or cool stimuli. A dynamic model of the change in skin temperature as a function of the thermal input was developed based on linear system identification techniques. This model was able to predict the change in skin temperature from an unrelated experiment involving thermal icons. This opens the possibility of using a model-based approach to the development of thermal icons.

This study compares three popular modalities for analyzing perceived video quality; user ratings, eye tracking, and EEG. We contrast these three modalities for a given video sequence to determine if there is a gap between what humans consciously see and ...

This study compares three popular modalities for analyzing perceived video quality; user ratings, eye tracking, and EEG. We contrast these three modalities for a given video sequence to determine if there is a gap between what humans consciously see and what we implicitly perceive . Participants are shown a video sequence with different artifacts appearing at specific distances in their field of vision; near foveal, middle peripheral, and far peripheral. Our results show distinct differences between what we saccade to (eye tracking), how we consciously rate video quality, and our neural responses (EEG data). Our findings indicate that the measurement of perceived quality depends on the specific modality used.