How feelings of unpleasantness develop during the progression of motion sickness symptoms

To mitigate motion sickness in self-driving cars and virtual reality, one should be able to quantify its progression unambiguously. Self-report rating scales either focus on general feelings of unpleasantness or specific symptomatology. Although one generally feels worse as symptoms progress, there is anecdotal evidence suggesting a non-monotonic relationship between unpleasantness and symptomatology. This implies that individuals could (temporarily) feel better as symptoms progress, which could trouble an unambiguous measurement of motion sickness progression. Here we explicitly investigated the temporal development of both unpleasantness and symptomatology using subjective reports, as well as their mutual dependence using psychophysical scaling techniques. We found symptoms to manifest in a fixed order, while unpleasantness increased non-monotonically. Later manifesting symptoms were generally judged as more unpleasant, except for a reduction at the onset of nausea, which corresponded to feeling better. Although we cannot explicate the origin of this reduction, its existence is of importance to the quantification of motion sickness. Specifically, the reduction at nausea onset implies that rating how bad someone feels does not give you an answer to the question of how close someone is to the point of vomiting. We conclude that unpleasantness can unambiguously be inferred from symptomatology, but an ambiguity exists when inferring symptomatology from unpleasantness. These results speak in favor of rating symptomatology when prioritizing an unambiguous quantification of motion sickness progression.

Food and Drink: Thoughts on Base Size in Sensory and Consumer Research

In the applied world of product testing the appropriate number of panelists (base size) involves technical and business considerations. Base sizes range from very low (around six; used in expert panelist profiling) to high (hundreds; used in product tests by marketing researchers). Often base sizes are dictated by the requirement that the project identify statistical differences between or among samples. The probabilistic analysis of differences (significance vs. insignificance) derives from statistical theory, with base size used as a method to influence the sampling error (variability). This paper looks at base sizes another way-from the viewpoint of psychophysical scaling. The issue then can be re-stated as ‘what is the necessary base size at which the average rating stabilizes?’ Empirical data suggest that base sizes of 40-50 panelists generate stable averages and that beyond the 80 panelists the average is not particularly affected by the base size. These results hold for actual data for a variety of products, and for different types of attributes, specifically sensory (amount of a characteristic), and hedonic (liking of a characteristic).

Correspondence between population coding and psychophysical scaling models of working memory

A mathematical idealization of the way neural populations encode sensory information has been found to provide a parsimonious account of errors made by human observers on perceptual and short-term memory tasks. This includes the effects of set size and flexible prioritization of items within a set (Bays, 2014), the frequency and identity of “swap” or misbinding errors (Schneegans & Bays, 2017), subjective judgments of confidence (Bays, 2016; van den Berg et al., 2017), and biases and variation in precision due to serial dependent and stimulus-specific effects (Bliss et al., 2017; Taylor & Bays, 2018). A superficially quite different account of short-term recall has recently been proposed in work by Schurgin et al. (2018), who argue that taking into account the differences between physical and perceptual distance in a feature space reduces recall to a classical signal detection problem. Here I document a remarkable similarity between the two models, demonstrating a favourable convergence of neural- and cognitive-level accounts of working memory.

Evaluating the Magnitude Estimation Approach for Designing Sonification Mapping Topologies

A challenge in sonification design is mapping data param-eters onto acoustic parameters in a way that aligns with a listener’s mental model of how a given data parameter should sound. Studies have used the psychophysical scaling method of magnitude estimation to systematically evaluate how participants per-ceive mappings between data and sound parameters - giving data on perceived polarity and scale of the relationship between the data and sound parameters. As of yet, there has been little re-search investigating whether data-to-sound mappings that are de-signed based on results from these magnitude estimation experiments have any effect on users’ performance in an applied audi-tory display task. This paper presents an experiment that com-pares data-to-sound mappings in which the mapping’s polarity is based on results from a previous magnitude estimation experiment against mappings whose polarities are inverted. The experiment is based around a simple task in which participants need to rank WiFi networks based on how secure they are, where security is represented using an auditory display. Results suggest that for a simple auditory display like the one used here, whether or not the polarities of the data-to-sound mappings are based on magnitude estimation does not have a substantial effect on any objective per-formance measures gathered during the experiment. Finally, potential areas for future work are discussed that may continue to investigate the problems addressed by this paper.