Uncertainty in perception and the Hierarchical Gaussian Filter
By Christoph Mathys, Ekaterina I. Lomakina, Jean Daunizeau, Sandra Iglesias, Kay Henning Brodersen, Karl J. Friston, Klaas E. Stephan. Frontiers in Human Neuroscience, 2014
418 citations recorded by the source corpus.
In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder–Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient—but at the same time intuitive—framework for the resolution of perceptual uncertainty in behaving agents.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/da4a2fb0653d1024aa3844cebdcb1a57b0a57bd4
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Citations in the corpus
- How Do Expectations Shape Perception? (2018, methodology): “For example, using a model of hierarchical Bayesian inference [139], researchers could link autism to a propensity to overestimate the volatility (i.”
- Affective Value in the Predictive Mind (2017, background): “In fact, PP already includes second order expectations about precisions, which can be considered as (inverse squared) expected prediction errors (Mathys et al. 2014).”