Multisensory models for human spatial orientation including threshold effects

Abstract

E-Observer, a stand-alone executable version of the Observer model developed by Newman and Oman (2009), was developed. The complicated structure of the Observer model and its parameters made this conversion challenging. The resulting Windows PC executable uses a publically available library (MATLAB component runtime v7.1 0). E-Observer parameters are limited to the preset choices in Observer. A hypothetical example of the use of E-Observer to analyze an aircraft accident radar trajectory data is discussed. Like many other dynamic models for human spatial orientation, Observer does not incorporate perception thresholds, which limits its use to relatively large stimuli and hence cannot be used for investigation of certain accidents and flight simulator design, which involve sub-threshold motions. The literature on motion thresholds is reviewed which suggests that vestibular perception thresholds are not mechanical thresholds, but are due to signal-in-noise phenomenon. As a fIrst step towards incorporating thresholds in Observer, modeling yaw perception thresholds was attempted and two detection models are proposed - a Matched Filter model and a Two-Threshold model. The Matched Filter detector model matches the noisy perception with a noise-free stimulus template and evaluates how much they correlate. Based on the correlation, the model fInally decides if the signal is present or not. However, this model applies only in cases where the subject is in an experiment, and knows the expected stimulus waveform. Grabherr et al (2008) proposed a high pass filter model for direction recognition thresholds based on their recognition data. This thesis explores an alternative modeling approach assuming that the CNS samples the angular velocity estimate and its derivative, and applies thresholds to both. Whether the motion stimulus is detected or not depends on how many of these samples cross the threshold level. The performance of both models was compared against the Grabherr et. al. data It was found that both models are able to approximate the 79.4% detection criterion for thresholds determined in Grabherr's study. However, the two threshold model does not assume that the subject knows the stimulus waveform. Supported by Project SA1302 by the National Space Biomedical Research Institute through NASA NCC 9-58.