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Abstract

Understanding perception and the underlying cognitive processes on a behavioral level requires a solution to the feature identification problem: Which are the features on which sensory systems base their computations and what techniques can we use to extract them? Thus one of the central challenges in psychophysics is to try and infer the critical features, or cues, human observers make use of when they see or hear: for real-world, complex stimuli, what aspect of the visual or auditory stimulus actually influences behaviour? Over the last years in my laboratory we have developed exploratory, data-driven non-linear system identification techniques based on modern machine learning methods to infer the critical features from human behavioural judgments. I will present these methods and show what their benefits are over the traditional “classification image” and “bubbles technique” approaches.