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Increased preference for immediate over delayed rewards and for risky over certain rewards has been associated with unhealthy behavioral choices. Motivated by evidence that enhanced cognitive control can shift choice behavior away from immediate and risky rewards, we tested whether training executive cognitive function could influence choice behavior and brain responses. In this randomized controlled trial, 128 young adults (71 male, 57 female) participated in 10 weeks of training with either a commercial web-based cognitive training program or web-based video games that do not specifically target executive function or adapt the level of difficulty throughout training. Pretraining and post-training, participants completed cognitive assessments and functional magnetic resonance imaging during performance of the following validated decision-making tasks: delay discounting (choices between smaller rewards now vs larger rewards in the future) and risk sensitivity (choices between larger riskier rewards vs smaller certain rewards). Contrary to our hypothesis, we found no evidence that cognitive training influences neural activity during decision-making; nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity. Participants in the commercial training condition improved with practice on the specific tasks they performed during training, but participants in both conditions showed similar improvement on standardized cognitive measures over time. Moreover, the degree of improvement was comparable to that observed in individuals who were reassessed without any training whatsoever. Commercial adaptive cognitive training appears to have no benefits in healthy young adults above those of standard video games for measures of brain activity, choice behavior, or cognitive performance.

INTRODUCTIONIn such diverse environments as air traffic control and nuclear power plant operation, researchers in human factors have accumulated extensive empirical knowledge of human performance in complex and dynamic tasks. However, the development of detailed computational models that can explain how people are able to perform and learn such tasks has lagged behind. One reason for this disparity is that theoretical investigations of skill acquisition and empirical investigations of complex and dynamic task performance have existed largely as separate and independent areas of research, with theoretical development of skill acquisition primarily focusing on models of learning simple tasks. Although valuable insights have been gained from studying simple tasks, in order to move toward a more complete theory of skill acquisition, researchers need to develop and test their models against complex and dynamic tasks that are more typical of human learning in the real world.The goal of this article is to show how the theoretical gap between learning simple tasks and performing complex tasks can be bridged. Our approach offers a new way to conceptualize and validate task analyses and provides insights into the nature of human skill acquisition. We believe our approach can help human factors researchers in developing applications in which learning is an integral aspect of the task. Our approach is to use the ACT-Rational (ACT-R) cognitive architecture, which is grounded in psychological theory, to model learning and performance in complex tasks. The key aspect of the architecture is production compilation, a computational account of skill acquisition that combines aspects of theories proposed by Anderson (1982Anderson ( , 1987 and Newell and Rosenbloom (1981). We use production compilation to develop a detailed model of learning in a simulated air traffic control task. SPECIAL SECTIONProduction Compilation: A Simple Mechanism to Model Complex Skill Acquisition Niels A. Taatgen, University of Groningen, Groningen, Netherlands, and Frank J. Lee, Rensselaer Polytechnic Institute, Troy, New YorkIn this article we describe production compilation, a mechanism for modeling skill acquisition. Production compilation has been developed within the ACTRational (ACT-R; J. R. Anderson, D. Bothell, M. D. Byrne, & C. Lebiere, 2002) cognitive architecture and consists of combining and specializing task-independent procedures into task-specific procedures. The benefit of production compilation for researchers in human factors is that it enables them to test the strengths and weaknesses of their task analyses and user models by allowing them to model the learning trajectory from the main task level and the unit task level down to the keystroke level. We provide an example of this process by developing and describing a model learning a simulated air traffic controller task. Actual or potential applications of this research include the evaluation of user interfaces, the design of systems that support learning, and the building of user models.

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Cognitive modeling has evolved into a powerful tool for understanding and predicting user behavior. Higher-level modeling frameworks such as GOMS and its variants facilitate fast and easy model development but are sometimes limited in their ability to model detailed user behavior. Lower-level cognitive architectures such as EPIC, ACT-R, and Soar allow for greater precision and direct interaction with real-world systems but require significant modeling training and expertise. In this paper we present a modeling framework, ACT-Simple, that aims to combine the advantages of both approaches to cognitive modeling. ACT-Simple embodies a "compilation" approach in which a simple description language is compiled down to a core lower-level architecture (namely ACT-R). We present theoretical justification and empirical validation of the usefulness of the approach and framework.