A Novel Integrated Information Processing Model of Presence

The objective of this paper is to conduct a narrative literature review on multisensory integration and propose a novel information processing model of presence in virtual reality (VR). The first half of the paper introduces basic multisensory integration (implicit information processing) and the integration of coherent stimuli (explicit information processing) in the physical environment, offering an explanation for people's reactions during VR immersions and is an important component of our model. To help clarify these concepts, examples are provided. The second half of the paper addresses multisensory integration in VR. Three models in the literature examine the role that multisensory integration plays in inducing various perceptual illusions and the relationship between embodiment and presence in VR. However, they do not relate specifically to presence and multisensory integration. We propose a novel model of presence using elements of these models and suggest that implicit and explicit information processing lead to presence. We refer to presence as a perceptual illusion that includes a plausibility illusion (the feeling that the scenario in the virtual environment is actually occurring) and a place illusion (the feeling of being in the place depicted in the virtual environment), based on efficient and congruent multisensory integration.

The Role of Predictability of Perturbation in Control of Posture: A Scoping Review

Efficient maintenance of posture depends on the ability of humans to predict consequences of a perturbation applied to their body. The purpose of this scoping review was to map the literature on the role of predictability of a body perturbation in control of posture. A comprehensive search of MEDLINE, EMBASE, and CINAHL databases was conducted. Inclusion criteria were studies of adults participating in experiments involving body perturbations, reported outcomes of posture and balance control, and studies published in English. Sixty-three studies were selected. The reviewed information resources included the availability of sensory information and the exposure to perturbations in different sequences of perturbation magnitudes or directions. This review revealed that people use explicit and implicit information resources for the prediction of perturbations. Explicit information consists of sensory information related to perturbation properties and timing, whereas implicit information involves learning from repetitive exposures to perturbations of the same properties.

Why Do Actions in Slow Motion Appear to Last Longer? On the Effect of Video Speed Information

When displayed in slow motion, actions are often perceived longer compared with original speed. However, it remains to be determined why this bias exists. Is it possible that the bias emerges because participants underestimate the factor by which a video was slowed down and hence arrive at erroneous conclusions about the original duration? If true, providing explicit information about the respective video speed should eliminate this slow motion effect. To scrutinize the nature of this bias, participants rated the original duration of sports actions displayed at original speed or slow motion. Results revealed the expected overestimation bias consisting in longer ratings with increasing slow motion. However, the bias disappeared when information about the current video speed was provided. The observations suggest an influence of knowledge about video playback speed on cognitive-evaluative processes which may hold important implications for future research and practice.

Effects of message framing and evidence type on health information behavior: the case of promoting HPV vaccination

PurposeBased on the examination of the roles of message framing and evidence type, this study made an analysis of the promotion methods of intention and information need towards HPV vaccination.Design/methodology/approachThe study conducted a 2 (gain-framed messages vs loss-framed messages) × 2 (statistical evidence vs narrative evidence) quasi-experimental design built upon theories of message framing and evidence type. This experiment recruited college students who were not vaccinated against HPV as participants. The analysis of variance (ANOVA), the analysis of covariance (ANCOVA), and the independent sample T-test were used to test the hypotheses.FindingsThe results (N = 300) indicate that (1) Loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages. (2) Statistical evidence will lead to a more explicit information need than narrative evidence. (3) Message framing and evidence type will interact and (a) for statistical evidence, loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages and (b) for narrative evidence, gain-framed messages will lead to a more favorable intention towards HPV vaccination than loss-framed messages. (4) Message framing and evidence type will interact and (a) for loss-framed messages, statistical evidence will stimulate more explicit information need of HPV vaccination than narrative evidence and (b) for gain-framed messages, narrative evidence will stimulate more explicit information need of HPV vaccination than statistical evidence.Originality/valueThis paper can help to further understand the important roles of message framing and evidence type in health behavior promotion. The study contributes to the literature on how health information can be well organized to serve the public health communication and further enhance the health information service.

How origin stories shape children's social reasoning

How do we explain the behavior of the many people we meet throughout our lives? Children and adults sometimes consider other people in terms of their social category memberships (e.g., assuming that a girl likes pink because she is a girl), but people view some categories as more informative than others, and which people think of as informative varies across cultural contexts. One type of culturally-embedded knowledge that appears to shape whether people view particular categories as providing explanations for behavior are beliefs about how the category came to be. In the current studies with 4- to 5-year-old children (N = 206), we ask how learning about quasi-scientific or supernatural causal origins of a category shapes young children’s use of categories to predict and explain what category members are like. In Study 1, children more often used a category to explain behavior when they heard the category described as intentionally created by a powerful being than when they heard no explicit information about its origins. In Studies 2 and 3, learning about both quasi-scientific and supernatural causal origins shaped children’s social category beliefs via a common mechanism: by signaling that the category marked a non-arbitrary way of dividing up the social world.

Case Studies

Classic RT states that only purely verbal information can cue the derivation of explicatures, that is, completely explicit information, since explicatures find their source in coded information. This first case-study chapter argues that at least some visuals are completely coded and therefore allow for explicatures. Visual genres that enable explicatures include pictograms in public space; Michelin Guide pictograms; logos of brands and organizations; and traffic signs. The central claim is that such visuals, combined with the location where they appear, enable explicatures. Examples of these various types of coded visuals are discussed, and a tentative connection is proposed with art historical symbols in Vanitas paintings.

Unsupervised Learning Predicts Human Perception and Misperception of Gloss

Reflectance, lighting, and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, like the glossiness of a surface? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgments. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about them. Intriguingly, the resulting representations predict the specific patterns of ‘successes’ and ‘errors’ in human perception. Linearly decoding specular reflectance from the model’s internal code predicts human gloss perception better than ground truth, supervised networks, or control models, and predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape, and lighting. Unsupervised learning may underlie many perceptual dimensions in vision, and beyond.