On the way to understanding complex perception tasks based on psychophysical data alone, we propose a general framework using multivariate analysis methods to derive a low-dimensional mapping of the underlying perception space. Psychophysical data can be interpreted in two fundamentally different ways: That is, the characterization of stimuli (e.g. colors) using several verbal descriptions (e.g. bright) - a stimuli-as-points view - and conversely, the characterization of the given verbal descriptions by several stimuli - a descriptors-as-points view. We argue that only the latter view enables us to reach an objective mapping of the perception space. For color perception, we show how it is possible to derive objective maps of the perception space, just based on non-comparative verbal descriptions of color stimuli. We also give an example where we analyze odor perception in the same way to derive a quantitative map of odor perception, a perceptual space that is still fairly unknown in its detailed structure.
|Title of host publication
|2017 International Joint Conference on Neural Networks (IJCNN)
|Number of pages
|Published - 30.06.2017
|International Joint Conference on Neural Networks (IJCNN 2017) - William A. Egan Civic and Convention Center , Anchorage, Alaska, United States
Duration: 14.05.2017 → 19.05.2017