To enable real walking in a virtual environment (VE) that is larger than the available physical space, redirection techniques that introduce multisensory conflicts between visual and nonvisual cues to manipulate different aspects of a user's trajectory could be applied. When applied within certain thresholds, these manipulations could go unnoticed and immersion remains intact. Research effort has been spent on identifying these thresholds and a wide range of thresholds was reported in different studies. These differences in thresholds could be explained by many factors such as individual differences, walking speed, or context settings such as environment design, cognitive load, distractors, etc. In this paper, we present a study to investigate the role of gender on curvature redirection thresholds (RDTs) using the maximum likelihood procedure with the classical two-alternative force choice task. Results show high variability in individuals' RDTs, and that on average women have higher curvature RDTs than men. Furthermore, results also confirm existing findings about the negative correlation between walking speed and curvature RDTs.
Redirected walking allows users of virtual reality applications to explore virtual environments larger than the available physical space. This is achieved by manipulating users’ walking trajectories through visual rotation of the virtual surroundings, without users noticing this manipulation. Apart from its applied relevance, redirected walking is an attractive paradigm to investigate human perception and locomotion. An important yet unsolved question concerns individual differences in the ability to detect redirection. Addressing this question, we administered several perceptual-cognitive tasks to healthy participants, whose thresholds of detecting redirection in a virtual environment were also determined. We report relations between individual thresholds and measures of multisensory weighting (visually-assisted postural stability (Romberg quotient), subjective visual vertical (rod-and-frame test) and illusory self-motion (vection)). The performance in the rod-and-frame test, a classical measure of visual dependency regarding postural information, showed the strongest relation to redirection detection thresholds: The higher the visual dependency, the higher the detection threshold. This supports the interpretation of users’ neglect of redirection manipulations as a “visual capture of gait”. We discuss how future interdisciplinary studies, merging the fields of virtual reality and psychology, may help improving virtual reality applications and simultaneously deepen our understanding of how humans process multisensory conflicts during locomotion.
Background: Neuropsychological screening becomes increasingly important for the evaluation of subarachnoid hemorrhage (SAH) and stroke patients. It is often performed during the surveillance period on the intensive (ICU), while it remains unknown, whether the distraction in this environment influences the results. We aimed to study the reliability of the Montreal Cognitive Assessment (MoCA) in the ICU environment. Methods: Consecutive stable patients with recent brain injury (tumor, trauma, stroke, etc.) were evaluated twice within 36 h using official parallel versions of the MoCA (ΔMoCA). The sequence of assessment was randomized into (a) busy ICU first or (b) quiet office first with subsequent crossover. For repeated MoCA, we determined sequence, period, location effects, and the intraclass correlation coefficient (ICC). Results: N = 50 patients were studied [ n = 30 (60%) male], with a mean age of 57 years. The assessment's sequence [“ICU first” mean ΔMoCA −1.14 (SD 2.34) vs. “Office first” −0.73 (SD 1.52)] did not influence the MoCA ( p = 0.47). On the 2nd period, participants scored 0.96 points worse (SD 2.01; p = 0.001), indicating no MoCA learning effect but a possible difference in parallel versions. There was no location effect ( p = 0.31) with ΔMoCA between locations (Office minus ICU) of −0.32 (SD 2.21). The ICC for repeated MoCA was 0.87 (95% CI 0.79–0.92). Conclusions: The reliability of the MoCA was excellent, independent from the testing environment being ICU or office. This finding is helpful for patient care and studies investigating the effect of a therapeutic intervention on the neuropsychological outcome after SAH, stroke or traumatic brain injury.
In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships.
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