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This book examines the neural underpinnings of decision-making using “bias” as its core concept, rather than the more common but noncommittal terms “selection” and “attention.” It offers an integrative, interdisciplinary account of the structure and function of bias, which it defines as a basic brain mechanism that attaches different weights to different information sources, prioritizing some cognitive representations at the expense of others. The author introduces the concepts of bias and sensitivity based on notions from Bayesian probability, which he translates into easily recognizable neural signatures, introduced by concrete examples from the experimental literature. He examines, among other topics, positive and negative motivations for giving priority to different sensory inputs, and looks for the neural underpinnings of racism, sexism, and other forms of “familiarity bias.” The author—a poet and essayist as well as a scientist—connects findings and ideas in neuroscience to analogous concepts in such diverse fields as post-Lacanian psychoanalysis, literary theory, philosophy of mind, evolutionary psychology, and experimental economics.

The field of neuroimaging has reached a watershed. Brain imaging research has been the source of many advances in cognitive neuroscience and cognitive science over the last decade, but recent critiques and emerging trends have been raising foundational issues of methodology, measurement, and theory. Concerns over interpretation of brain maps have created serious controversies in social neuroscience, and, more importantly, point to a larger set of issues that lie at the heart of the entire brain mapping enterprise. In this book, neuroimagers and philosophers of the mind reexamine these central issues and explore current controversies that have arisen in cognitive science, cognitive neuroscience, computer science, and signal processing. The contributors address both the statistical and dynamic analysis and modeling of neuroimaging data and interpretation, discussing localization, modularity, and the neuroimagers’ tacit assumptions about how these two phenomena are related; controversies over the correlation of functional magnetic resonance imaging (fMRI) data and social attributions (characterized for good or bad as “voodoo correlations”); and the standard inferential design approach in neuroimaging. Finally, they take a more philosophical approach, considering the nature of measurement in brain imaging, and offer a framework for novel neuroimaging data structures (effective and functional connectivity—“graphs”).