Part front matter for SECTION I Introduction to Section I: Theory and Fundamentals

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The chapters in Section I formalize the computational problems that need to be solved for successful cue combination. They focus on the issue of uncertainty and the Bayesian ways of solving such problems. Through the sequence of chapters in Section I, the reader will get a thorough overview and introduction into the current Bayesian formalization of sensory cue combination.

This section highlights the fundamental similarity between seemingly distinct problems of cue combination. The computational objectives and the algorithms that can be used to find the optimal solution do not depend on the modality or kind of cue that is considered. The solutions to progressively more complicated problems are largely derived using essentially the same statistical techniques.

The first five chapters of Section I are concerned with Bayesian theories of optimal cue combination. The book starts with an introductory chapter by Landy, Banks, and Knill that gives an overview of the basic philosophy and the mathematics that is used to calculate how an ideal observer would combine cues. These models form the backbone of much of the cue-combination research presented in this book. The following computational chapters provide more detailed insights into the basic techniques, computational tools, and behavioral evidence necessary to test the predictions arising from these models. Wei and Körding focus on ideal-observer models for cases where it is not a priori certain that the cues belong together. In this particular case, the nervous system needs to determine whether the cues belong together or, in other words, to determine their causal relationship. Battaglia, Kersten, and Schrater focus on object perception. They ask how the kinds of causal knowledge we have about the way objects are made can be used to constrain estimates of attributes such as shape and size. Vijayakumar, Hospedales, and Haith extend these ideas to model cue combination for sensorimotor integration. This is complicated because sensorimotor tasks depend on a set of variables that must be estimated simultaneously. Lastly, Sahani and Whiteley extend these concepts to cue integration in cluttered environments. They point out that complicated visual scenes are a special case of situations in which we are uncertain about the causal relationship between cues. These chapters use a coherent probabilistic language to develop methods appropriate for a wide range of problems.

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Part front matter for SECTION I Introduction to Section I: Theory and Fundamentals

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