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  1. Cleotilde Gonzalez & Christian Lebiere (2013). Cognitive Architectures Combine Formal and Heuristic Approaches. Behavioral and Brain Sciences 36 (3):285 - 286.
    Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.
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  2. Christian Lebiere & John R. Anderson (2011). Cognitive Constraints on Decision Making Under Uncertainty. Frontiers in Psychology 2.
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  3. Andrea Stocco, Christian Lebiere & Alexei V. Samsonovich (2010). The B-I-C-a of Biologically Inspired Cognitive Architectures. International Journal of Machine Consciousness 2 (02):171-192.
  4. Christian Lebiere & Bradley J. Best (2009). Balancing Long-Term Reinforcement and Short-Term Inhibition. In. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  5. John R. Anderson & Christian Lebiere (2003). Optimism for the Future of Unified Theories. Behavioral and Brain Sciences 26 (5):628-633.
    The commentaries on our article encourage us to believe that researchers are beginning to take seriously the goal of achieving the broad adequacy that Newell aspired to. The commentators offer useful elaborations to the criteria we suggested for the Newell Test. We agree with many of the commentators that classical connectionism is too restrictive to achieve this broad adequacy, and that other connectionist approaches are not so limited and can deal with the symbolic components of thought. All these approaches, including (...)
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  6. John R. Anderson & Christian Lebiere (2003). The Newell Test for a Theory of Cognition. Behavioral and Brain Sciences 26 (5):587-601.
    Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories (...)
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  7. Cleotilde Gonzalez, Javier F. Lerch & Christian Lebiere (2003). Instance‐Based Learning in Dynamic Decision Making. Cognitive Science 27 (4):591-635.
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  8. Christian Lebiere & Dieter Wallach (1999). Implicit and Explicit Learning in a Hybrid Architecture of Cognition. Behavioral and Brain Sciences 22 (5):772-773.
    We present a theoretical account of implicit and explicit learning in terms of ACT-R, an integrated architecture of human cognition as a computational supplement to Dienes & Perner's conceptual analysis of knowledge. Explicit learning is explained in ACT-R by the acquisition of new symbolic knowledge, whereas implicit learning amounts to statistically adjusting subsymbolic quantities associated with that knowledge. We discuss the common foundation of a set of models that are able to explain data gathered in several signature paradigms of implicit (...)
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  9. John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder (1998). ACT-R: A Higher-Level Account of Processing Capacity. Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  10. Christian Lebière, John R. Anderson & Lynne M. Reder (1994). Error Modeling in the ACT-R Production System. In. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. 555--559.
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