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* A standard model captures a community consensus over a coherent region of science, serving as a cumulative reference point for the field that can provide guidance for both research and applications, while also focusing efforts to extend or revise it. Here we propose developing such a model for humanlike minds, computational entities whose structures and processes are substantially similar to those found in human cognition. Our hypothesis is that cognitive architectures provide the appropriate computational abstraction for defining a standard model, although the standard model is not itself such an architecture. The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition, but is extended here through a synthesis across three existing cognitive architectures: ACT-R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing, memory and content, learning, and perception and motor, and highlights loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. The hope is that this work will provide an important step toward engaging the broader community in further development of the standard model of the mind.
A mind is a functional entity that can think, and thus support intelligent behavior. Humans possess minds, as do many other animals. In natural systems such as these, minds are implemented through brains, one particular class of physical device. However, a key foundational hypothesis in artificial intelligence is that minds are computational entities of a special sort - that is, cognitive systems - that can be implemented through a diversity of physical devices (a concept lately reframed as substrate independence [Bostrom 2003]), whether natural brains, traditional generalpurpose computers, or other sufficiently functional forms of hardware or wetware.
Artificial intelligence, cognitive science, neuroscience, and robotics all contribute to our understanding of minds, although each draws from a different perspective in directing its research. Artificial intelligence concerns building artificial minds, and thus cares most for how systems can be built that exhibit intelligent behavior. Cognitive science concerns modeling natural minds, and thus cares most for understanding cognitive processes that generate human thought. Neuroscience concerns the structure and function of brains, and thus cares most for how minds arise from brains. Robotics concerns building and controlling...