Can exocentric direction be dissociated from its exocentric distance in virtual environments?

We conducted two psychophysical experiments to investigate the relationship between processing mechanisms for exocentric distance and direction. In the first experiment, the task was to discriminate exocentric distances. In the second one, the task was to discriminate exocentric directions. The individual effects of distance and direction on each task were dissociated by analyzing their corresponding psychophysical functions. Under stereoscopicviewing conditions, distancejudgments of excentric intervals were not affected by exocentric direction. However, directionjudgments were influenced by the distance between the pair of stimuli. Therefore, the mechanism processing exocentric direction is dependent on exocentric distance, but the mechanism processing exocentric distance does not require exocentric: direction measures. As a result, we suggest that exocentric distance and direction are hierarchically processed, with distance preceding direction. Alternatively, and more probably, a necessary condition for processing the exocentric direction between two stimuli may be to know the location of each of them.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.