A decision is a deliberative process that results in the commitment to a categorical proposition. An apt analogy is a judge or jury that must take time to weigh evidence for alternative interpretations and/or possible ramifications before settling on a verdict. Here we evaluate progress in understanding how this process is implemented in the brain. Our scope is somewhat narrow: We consider primarily studies that relate behavior on simple sensory-motor tasks to activity measured in the brain because of the ability to precisely control sensory input, quantify motor output, and target relevant brain regions for measurement and analysis. Nevertheless, our intent is broad: We hope to identify principles that seem likely to contribute to the kinds of flexible and nuanced decisions that are a hallmark of higher cognition. SDT: signal detection theory SA: sequential analysis

The organization of this review is as follows. We first describe the computational elements that comprise the decision process. We then briefly review signal detection theory (SDT) and sequential analysis (SA), two related branches of statistical decision theory that represent formal, mathematical prescriptions for how to form a decision using these computational elements. We then dissect several experimental results in the context of this theoretical framework to identify neural substrates of decision making. We conclude with a discussion of the strengths and limitations of this approach for inferring principles of higher brain function.

Elements of a Decision
The decisions required for many sensory-motor tasks can be thought of as a form of statistical inference (Kersten et al. 2004, Rao 1999, Tenenbaum & Griffiths 2001, von Helmholtz 1925): What is the (unknown) state of the world, given the noisy data provided by the sensory systems? These decisions select among competing hypotheses h1 … hn (often n = 2) that each represent a state of the world (e.g., a stimulus is present or ab-sent). The elements of this decision process (see Figure 1) are described in terms of probability theory, as follows.

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