domain knowledge to the simulated human. Transfer from one domain to the next is largely a function of the degree to which the knowledge in the two domains overlaps. The reason this is problematic for scientific progress is that the domains typically used to study human cognitive functioning in the laboratory are very different from the domains of application in the real world. Laboratory domains are mostly simple, abstract, and of short duration, whereas real-world application domains are complex, situated, and of long duration. Thus, in the field of cognitive science we must look for ways to build bridges between laboratory and applied contexts.

The second barrier I will emphasize here is a disciplinary barrier. Cognitive science is a field of study comprising seven subdisciplines: anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, and psychology. These subdisciplines involve very different methods, frameworks, and theories, and it is both challenging and exciting to make progress at disciplinary intersections. For instance, there is a powerful zeitgeist currently associated with neuroscience-based explanations of everything from attentional, perceptual, and related cognitive phenomena (leading to the creation of a field known as computational cognitive neuroscience—see Itti’s paper in this volume) to complex economic decision making (leading to the creation of a field known as neuroeconomics—see Glimcher, 2003). This has led people in some circles to speculate that there ought to be ways to improve the readiness of our military personnel by capitalizing on the tools, methods, empirical results, and theories of neuroscience. Simultaneously there is interest in bringing together the subdisciplines of anthropology, artificial intelligence, and psychology in order to better understand and prepare for multicultural interaction (see the paper by van Lent and colleagues in this volume). Making scientific progress across these disciplinary boundaries requires that we build bridges among the neural, cognitive, and social bands of human experience (Newell, 1990). Anderson and Gluck (2001) noted that the same challenge exists in connecting neuroscience and educational practice and proposed that cognitive architectures are an appropriate formalism for building such bridges. I propose that cognitive architectures also are an appropriate formalism for building bridges from neuroscience to the military’s cognitive readiness applications, using cognitive phenomena and models.

The purpose of all scientific disciplines is to identify invariant features and explanatory mechanisms for the purpose of understanding the phenomena of interest in the respective disciplines. Within the cognitive science community there is an approximately 50-year history of empirical research that involves using carefully constructed (usually simple and abstract) laboratory tests to isolate components of the human cognitive system in order to model and understand them. Sometimes optimistically referred to as “divide and conquer,” this approach has