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Psychophysics and Computational Models of Behaviour

Felix Wichmann used to be a project leader for Psychophysics in the Department of Empirical Inference. After taking up a position in Berlin, he was recently recruited to a professorship at the University of Tübingen, allowing us to formally link him to the department via a side appointment, and continuing our joint work aimed at exploring synergies between machine learning and psychophysics. We investigate perceptual processes from the level of simple artificial stimuli up to complex object and scene perception, combining psychophysical experiments and computational modeling driven by methods of machine learning and inference. Currently we have two main research foci: First, to uncover the critical features observers use in complex perceptual tasks, and, second, to develop a computationally efficient model of early spatial vision. Not knowing the critical, that is behaviourdetermining features constitutes one of the major obstacles preventing the development of successful computational models of perceptual processes: Given the high-dimensional input, which are the features the sensory systems base their computations on? We develop inverse machine learning methods for (nonlinear) system identification, and have applied them to identify regions of visual saliency [3], to gender discrimination of human faces [4], and to the identification of auditory tones in noise [6]. [6] demonstrate how stimulus-response data can be analyzed relying on L1-regularized multiple logistic regression. This method prevents both over-fitting to noisy data and enforces sparse solutions. In simulations, “behavioral” data from a classical auditory tone-in-noise detection task were generated, and L1-regularized logistic regression precisely identified observer cues from a large set of covarying, interdependent stimulus features— a setting where standard correlational and regression methods fail. In addition, the method succeeds for deterministic as well as probabilistic observers. The decision rules of the simulated observers could be reconstructed from the estimated model weights, thus allowing predictions of responses on the basis of individual stimuli. In an opinion article in Trends in Cognitive Sciences, one of the flagship journals of the field, we provided examples where kernel methods have proven to be powerful tools for analyzing behavioral data. We also discuss how kernel methods relate to perceptrons and exemplar models of categorization, and how theoretical results concerning their behavior are potentially relevant for human category learning. Furthermore, we argue that kernel methods provide explanations ranging from—in the terminology of David Marr—the implementational via the algorithmic to the computational level [2]. Our second research focus is to develop a computationally efficient implementation of a model of early spatial vision, c.f. our psychophysical work in [5] and [1]. Developing a successful model of the early visual encoding process is not only of great importance to neuroscience, but it should also provide us with realistic constraints for the machine learning methods developed for feature identification, and help us to incorporate domain knowledge into the pre-processing stage of our system identification algorithms.