14.01.2014

Felix Wichmann is invited to give the opening lecture at the XX. LIPP Symposium 2014.

It will take place in February 2014 at the Ludwig-Maximilians-Universität in München, Germany.

<link https: www.lipp-lmu.de _blank external-link-new-window externen link in neuem>Link to LIPP (Graduate School Language & Literature Munich - Class of Language)

Abstract:

Machine learning methods for system identification in sensory psychology

Over the last decades research in sensory psychology has witnessed a transition from the phenomenological descriptions of perception to its quantitative analysis. Ultimately, we strive not only to re-describe perception in quantitative or statistical terms, but we aspire to quantitative process models of perception. This last step has thus far proven difficult, because as a prerequisite for psychophysical process models it is necessary to know to what extent decisions in behavioral tasks depend on specific stimulus features, the perceptual cues: Given the high-dimensional input---images, sequences of images or sound streams--- which are the stimulus features the sensory systems base their computations on? In engineering terms, we require tools for system identification in sensory psychology. Over the last years I was involved in the development of inverse machine learning methods for (potentially nonlinear) system identification in sensory psychology, and we applied our methods to identify regions of visual saliency (Kienzle et al., 2009), to gender discrimination of human faces (Wichmann et al., 2005; Macke & Wichmann, 2010), and to the identification of auditory tones in noise (Schönfelder & Wichmann, 2012; 2013). In my presentation I will concentrate on how stimulus-response data can be analyzed, and how to prevent both over-fitting to noisy data and how to enforce sparse solutions.