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Viewing images of food triggers the desire to eat and this effect increases when images represent food in a more vivid way. Cinemagraphs are a new medium that is intermediate between photographs and videos: most of the frame is static, while some details are animated in a seamless loop, resulting in a vivid viewing experience. On social media cinemagraphs are increasingly used for food-related communication. Given their vivid appearance we hypothesized that food cinemagraphs may evoke stronger appetitive responses than their static counterparts (stills). This would make them a promising medium for food advertisements on the Internet or on digital menu boards. In this study we measured the ‘wanting’ (appetitive) and ‘liking’ (affective) responses to both cinemagraph and stills representing a wide range of different food products. Our results show that food cinemagraphs slightly increase ‘wanting’ scores while not affecting ‘liking’ scores, compared to similar stills. Although we found no overall main effect of image dynamics on ‘liking’, we did observe a significant effect for some individual food items. The effects of image dynamics on ‘wanting’ and ‘liking’ appear to be product specific: while dynamic images were scored higher on ‘wanting’ or ‘liking’ for some products, static images were scored higher on these factors for other products. Observer responses to a free association task indicate that image dynamics can affect the appeal of a food product in two ways: by emphasizing its hedonic qualities (lusciousness, freshness) and by enhancing the observers’ awareness of their own core affect (‘liking’) for the product. We conclude that the effective use of cinemagraphs in food advertisements therefore requires a careful consideration of the characteristics (hedonic aspects) of the food product that are to be highlighted through image motion and the inherent preferences (core liking) of the target group.

Multiday weather forecasts often include graphical representations of uncertainty. However, visual representations of probabilistic events are often misinterpreted by the general public. Although various uncertainty visualizations are now in use, the parameters that determine their successful deployment are still unknown. At the same time, a correct understanding of possible weather forecast outcomes will enable the public to make better decisions and will increase their trust in these predictions. We investigated the effects of the visual form and width of temperature forecast visualizations with uncertainty on estimates of the probability that the temperature could exceed a given value. The results suggest that people apply an internal model of the uncertainty distribution that closely resembles a normal distribution, which confirms previous findings. Also, the visualization type appears to affect the applied internal model, in particular the probability estimates of values outside the depicted uncertainty range. Furthermore, we find that perceived uncertainty does not necessarily map linearly to visual features, as identical relative positions to the range are being judged differently depending on the width of the uncertainty range. Finally, the internal model of the uncertainty distribution is related to participants’ numeracy. We include some implications for makers or designers of uncertainty visualizations.

Modern immersive multisensory communication systems can provide compelling mediated social communication experiences that approach face-to-facecommunication. Existing methods to assess the quality of mediated social communication experiences are typically targeted at specific tasks or communication technologies. As a result, they do not address all relevant aspects of social presence (i.e., the feeling of being in the presence of, and having an affective and intellectual connection with, other persons). Also, they are typically unsuitable for application to social communication in virtual (VR), augmented (AR), or mixed (MR) reality. We propose a comprehensive, general, and holistic multi-scale (questionnaire-based) approach, based on an established conceptual framework for multisensory perception, to measure the quality of mediated social communication experiences. Our holistic approach to mediated social communication (H-MSC) assessment comprises both the experience of Spatial Presence (i.e., the perceived fidelity, internal and external plausibility, and cognitive, reasoning, and behavioral affordances of an environment) and the experience of Social Presence (i.e., perceived mutual proximity, intimacy, credibility, reasoning, and behavior of the communication partners). Since social presence is inherently bidirectional (involving a sense of mutual awareness) the multiscale approach measures both the internal (‘own’) and external (‘the other’) assessment perspectives. We also suggest how an associated multiscale questionnaire (the Holistic Mediated Social Communication Questionnaire or H-MSC-Q) could be formulated in an efficient and parsimonious way, using only a single item to tap into each of the relevant processing levels in the human brain: sensory, emotional, cognitive, reasoning, and behavioral. The H-MSC-Q can be sufficiently general to measure social presence experienced with any (including VR, AR, and MR) multi-sensory (visual, auditory, haptic, and olfactory) mediated communication system. Preliminary validation studies confirm the content and face validity of the H-MSC-Q. In this paper, we focus on the underlying concepts of the H-MSC-Q. We make the initial draft questionnaire available to the community for further review, development, and validation. We hope it may contribute to the unification of quality measures for mediated social communication.

The structural image similarity index (SSIM), introduced by Wang and Bovik (IEEE Signal Processing Letters 9-3, pp. 81-84, 2002) measures the similarity between images in terms of luminance, contrast en structure. It has successfully been deployed to model human visual perception of image distortions and modifications in a wide range of different imaging applications. Chang and Zhang (Infrared Physics & Technology 51-2, pp. 83-90, 2007) recently introduced the target structural similarity (TSSIM) clutter metric, which deploys the SSIM to quantify the similarity of a target to its background in terms of luminance, contrast en structure. They showed that the TSSIM correlates significantly with mean search time and detection probability. However, it is not immediately obvious to what extent each of the three TSSIM components contributes to this correlation. Here we evaluate the TSSIM by deploying it to a set of natural images for which human visual search data are available: the Search_2 dataset. By analyzing the predictive performance of each of the three TSSIM components, we find that it is predominantly the structural similarity component which determines human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with human performance. Since the structural similarity component of the TSSIM is equivalent to a matched filter, it appears that matched filtering predicts human visual performance when searching for a known target.

Abstract: Previous studies showed that orange odor reduces the anticipatory anxiety and improves the mood of patients waiting for scheduled appointments in small dental practices. We replicated these previous studies in the setting of three large dental clinics. In addition, we investigated whether another pleasant fruity smell (apple odor) is similarly associated with reduced anxiety. We included 219 patients (117 males, 102 females) between the ages of 18 and 81 in this study. While they were waiting for dental treatment, the participants were either exposed to the ambient odor of orange (N = 81) or apple (N = 69), or they received no stimulation. State anxiety, mood, and perceived level of pain of the participants were assessed using questionnaires. Statistical analysis showed no significant difference between the responses of patients in each of the three experimental groups. We therefore conclude that orange and apple odors have no effect on the anticipatory anxiety or mood of patients waiting for scheduled appointments in large dental clinics.

We investigated if cybersickness has an effect on the affective appraisal of a virtual environment (VE). For many applications it is essential that users experience the simulated environment in a similar way as the corresponding real one. Navigation through VEs is known to negatively influence the physical well-being of observers by inducing cybersickness. Since people tend to misattribute their feelings to the environment they perceive, cybersicknesss may influence their affective appraisal of a VE. Participants passively watched a simulated walk through a VE, while the visual scene continuously performed a quasi-sinusoidal frontal roll oscillation. Immediately after the exposure, they reported their experienced level of cybersickness and assessed the environment on a semantic differential scale. People experiencing cybersickness rated the environment as less pleasant and more arousing, as compared to people with no symptoms. Thus, users suffering from cybersickness misattributed their unpleasant feelings to the affective qualities of the VE. Applications that rely on VEs to evoke the same emotional and affective user responses as their real equivalent should therefore minimise or account for the incidence of cybersickness.

Restrictions of field-of-view are known to impair human performance for a range of different tasks. However, such effects on human locomotion through a complex environment are still not clear. Effects of both horizontal (30°, 75°, 112°, 120°, 140°, 160°, and 180°) and vertical (18° and 48°) field-of-view restrictions on the walking speed and head movements of participants maneuvering through an obstacle course were investigated. All field-of-view restrictions tested significantly increased time to complete the entire course, compared to the unrestricted condition. The time to traverse the course was significantly longer for a vertical field-of-view of 18° than for a vertical field-of-view of 48°. For a fixed vertical field-of-view size, the traversal time was constant for horizontal field-of-view sizes ranging between 75° and 180° and increased significantly for the 30° horizontal field-of-view condition. In the restricted viewing conditions, the angular velocity of head movements made while stepping over an obstacle increased significantly over that for the unrestricted field-of-view condition, but no difference was found between the different field-of-view sizes. Implications of the current findings for the development of devices with field-of-view restrictions are discussed.

Effects of field-of-view restrictions on the speed and accuracy of participants performing a real-world manoeuvring task through an obstacled environment were investigated. Although field-of-view restrictions are known to affect human behaviour and to degrade performance for a range of different tasks, the relationship between human manoeuvring performance and field-of-view size is not known. This knowledge is essential to evaluate a trade-off between human performance, cost, and ergonomic aspects of field-of-view limiting devises like head-mounted displays and night vision goggles which are frequently deployed for tasks involving human motion through environments with obstacles. In this study the speed and accuracy of movement were measured in 15 participants (8 men, 7 women, 22.9 ± 2.8 yr. of age) traversing a course formed by three wall segments for different field-of-view restrictions. Analysis showed speed decreased linearly with decreasing field-of-view extent, while accuracy was consistently reduced for all restricted field-of-view conditions. Present results may be used to evaluate cost and performance trade-offs for field-of-view restricting devices deployed to perform time-limited human-locomotion tasks in complex structured environments, such as night-vision goggles and head-mounted displays.

This study investigates observer performance in visual search and target detection tasks for two different conditions of image viewing: binocular viewing of single line of sight images versus stereoscopic display of wide baseline stereo images. A multiple baseline imagery database was obtained during a field test. The targets were personnel wearing forest camouflage uniforms. They were arrayed at two rural terrain sites. The weather condition at the first site was clear to partly cloudy and at the second site overcast with light rain. The preliminary analysis of the first site database indicated that wide baseline stereo can improve search and target acquisition, but that the baseline used was too wide for many of the closer targets at that site. The database from the second site was then analyzed through observer testing, with single line of sight and wide baseline stereo displays. The results indicate that with training, stereo vision effectively reduces false alarm detection by a factor of 2. Additionally, guidelines are obtained for optimum stereo display that can be used to improve positive target detection by 20. © 2001 Society of Photo-Optical Instrumentation Engineers.

The TNO Human Factors Search_2 image dataset consists of: a set of 44 high-resolution digital color images of different complex natural scenes, the ground truth corresponding to each of these scenes, and the results of psychophysical experiments on each of these images. The images in the Search_2 dataset are a subset of a larger set that has been used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. The image dataset, an Excel file with the ground truth and observer data, and an extensive report describing the dataset are available on CD-ROM (requests by email to the first author). The dataset can be used to develop and validate digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and models of human visual search and detection. The dataset has already been used in more than ten different studies in the literature, ranging from studies evaluating target detectability metrics to eye movement studies and attempts to model the human visual system. In addition to this work, eight other articles in this special section address the Search_2 dataset. © 2001 Society of Photo-Optical Instrumentation Engineers.

Two methods for estimating the visual effects of light scattered from a laser glare source were compared: (1) a veiling luminance (VL) model that convolves a radiometric scan of the corneal light distribution with a point-spread function to calculate the retinal distribution and (2) psychophysically determined equivalent background luminance (EBL). For six subjects, detection thresholds for a 12-arc-min-diameter test spot were measured at 24 points in the glare field (4 quadrants × 6 eccentricities between 0.25 and 8 deg). Measured Weber fractions were used to calculate EBL’s for each test point. Output of the VL model matched the EBL data well, but underestimated the EBL at the smallest (0.25-deg) eccentricity and overestimated it at eccentricities from 1 to 4 deg. This model can be a useful predictor of visual decrements in a variety of glare situations.

Advance knowledge of the time required by an observer to detect a target visually is of interest, e.g., in preparing flight scenarios, in modeling mission performance, in evaluating camouflage effectiveness, and in visual-scene generator calibration. A wide range of computational models has therefore been developed to predict human visual search and detection performance. This study is performed to test the quality of the predictions of three of these models: ORACLE, Visdet, and a formula by Travnikova. The three different models are used to predict the results of an experiment in which observers searched for military vehicles in complex rural scenes. The models predict either the mean time required to find the target, or the probability of finding the target after a given amount of time, from a few physical parameters describing the scene (the mean scene luminance, the angular dimensions of the field of view and the target, the intrinsic target contrast, etc.). None of the models reliably predicts observer performance for most of the scenes used in this study. ORACLE and Visdet both overestimate the detection probability for most situations. The formula by Travnikova does not apply to the scenes used here. © 2000 Society of Photo-Optical Instrumentation Engineers.

This paper presents three computational visual distinctness measures, computed from image representational models based on selective filtering, statistical features, and visual patterns, respectively. They are applied to quantify the visual distinctness of targets in complex natural scenes. The measure that applies a simple decision rule to the distances between segregated visual patterns is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, and (2) to correlate strongly with visual target distinctness estimated by human observers. © 2000 Society of Photo-Optical Instrumentation Engineers.

A new computational visual distinctness metric based on principles of the early human visual system is presented. The metric is applied to quantify (1) the visual distinctness of targets in complex natural scenes and (2) the perceptual differences between compressed and uncompressed images. The new metric is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, (2) to correlate with visual target distinctness estimated by human observers, and (3) to correlate with the quality factor of Joint Photographic Experts Group (JPEG) compressed imagery. © 1998 Society of Photo-Optical Instrumentation Engineers.

A simple and efficient psychophysical procedure is presented to quantify the visual conspicuity of a target in a complex (natural) scene. Measurements can easily and quickly be performed in the field or in complex environments. Only a few observers (typically two to three) are needed to achieve sufficient accuracy. We show that this conspicuity measure predicts human visual search performance in realistic and military relevant complex scenarios. Also, conspicuity measured on photographic slides agrees with conspicuity measured in the field, which implies that the new conspicuity measure can be used in combination with photosimulation studies to optimize and evaluate the visual distinctness of displayed information. © 1998 Society of Photo-Optical Instrumentation Engineers.

A hierarchical image fusion scheme is presented that preserves those details from the input images that are most relevant to visual perception. Results show that fused images present a more detailed representation of the scene and provide information that cannot be obtained by viewing the input images separately. Detection, recognition, and search tasks may therefore benefit from this fused image representation.