How much spatial information is lost in the sensory substitution process? Comparing visual, tactile, and auditory approaches

Sensory substitution devices (SSDs) can convey visuospatial information through spatialised auditory or tactile stimulation using wearable technology. However, the level of information loss associated with this transformation is unknown. In this study novice users discriminated the location of two objects at 1.2m using devices that transformed a 16x 8 depth map into spatially distributed patterns of light, sound, or touch on the abdomen. Results showed that through active sensing, participants could discriminate the vertical position of objects to a visual angle of 1°, 14°, and 21°, and their distance to 2cm, 8cm, and 29cm using these visual, auditory, and haptic SSDs respectively. Visual SSDs significantly outperformed auditory and tactile SSDs on vertical localisation, whereas for depth perception, all devices significantly differed from one another (visual > auditory > haptic). Our findings highlight the high level of acuity possible for SSDs even with low spatial resolutions (e.g. 16 8) and quantify the level of information loss attributable to this transformation for the SSD user. Finally, we discuss ways of closing this ‘modality gap’ found in SSDs and conclude that this process is best benchmarked against performance with SSDs that return to their primary modality (e.g. visuospatial into visual)

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Regression modelling using priors depending on Fisher information covariance kernels (I-priors)

Regression analysis is undoubtedly an important tool to understand the relationship between one or more explanatory and independent variables of interest. In this thesis, we explore a novel methodology for fitting a wide range of parametric and nonparametric regression models, called the I-prior methodology (Bergsma, 2018). We assume that the regression function belongs to a reproducing kernel Hilbert or Kreĭn space of functions, and by doing so, allows us to utilise the convenient topologies of these vector spaces. This is important for the derivation of the Fisher information of the regression function, which might be infinite dimensional. Based on the principle of maximum entropy, an I-prior is an objective Gaussian process prior for the regression function with covariance function proportional to its Fisher information. Our work focusses on the statistical methodology and computational aspects of fitting I-priors models. We examine a likelihood-based approach (direct optimisation and EM algorithm) for fitting I-prior models with normally distributed errors. The culmination of this work is the R package iprior (Jamil, 2017) which has been made publicly available on CRAN. The normal I-prior methodology is subsequently extended to fit categorical response models, achieved by “squashing” the regression functions through a probit sigmoid function. Estimation of I-probit models, as we call it, proves challenging due to the intractable integral involved in computing the likelihood. We overcome this difficulty by way of variational approximations. Finally, we turn to a fully Bayesian approach of variable selection using I-priors for linear models to tackle multicollinearity. We illustrate the use of I-priors in various simulated and real-data examples. Our study advocates the I-prior methodology as being a simple, intuitive, and comparable alternative to similar leading state-of-the-art models