Models of Early Spatial Vision: Bayesian Statistics and Population Decoding

Models of Early Spatial Vision: Bayesian Statistics and Population Decoding F Wichmann
University of Tübingen, Tübingen, Germany
In psychophysical models of human pattern detection it is assumed that the retinal image is analyzed through (nearly) independent and linear pathways (“channels”) tuned to different spatial frequencies and orientations followed by a simple maximum-output decoding rule. This hypothesis originates from a series of very carefully conducted and frequently replicated psychophysical pattern detection, summation, adaptation, and uncertainty experiments, whose data are all consistent with the simple model described above. However, spatial-frequency tuned neurons in primary visual cortex are neither linear nor independent, and ample evidence suggests that perceptual decisions are mediated by pooling responses of multiple neurons. Here I will present recent work by Goris, Putzeys, Wagemans & Wichmann (Psychological Review, in press), proposing an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically-inspired model of population activity in primary visual cortex. We demonstrate that this model predicts a broad range of classic detection results. Using a single set of parameters, our model can account for several summation, adaptation and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection. One key component of this model is a task-specific, normative decoding mechanisms instead of a task-independent maximum-output---or any Minkowski-norm---typically employed in early vision models. This opens the possibility that perceptual learning may at least sometimes be understood in terms of learning the weights of the decoder: Why and when can we successfully learn it, as in the examples presented by Goris et al. (in press)? Why do we fail to learn it in other cases, e.g. Putzeys, Bethge, Wichmann, Wagemans & Goris (PLoS Computational Biology, 2012)? Furthermore, the success of the Goris et al. (2013) model highlights the importance of moving away from ad-hoc models designed to account for data of a single experiment, and instead moving towards more systematic and principled modeling efforts accounting for many different datasets using a single model. Finally, I will briefly show how statistical modeling can complement the mechanistic modeling approach by Goris et al. (2013). Using a Bayesian graphical model approach to contrast discrimination, I show how Bayesian inference allows to estimate the posterior distribution of the parameters of such a model. The posterior distribution provides diagnostics of the model that help drawing meaningful conclusions from a model and its parameters.

Up Home