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  1. Perhaps Unidimensional Is Not Unidimensional.Pennie Dodds, Babette Rae & Scott Brown - 2012 - Cognitive Science 36 (8):1542-1555.
    Miller (1956) identified his famous limit of 7 ± 2 items based in part on absolute identification—the ability to identify stimuli that differ on a single physical dimension, such as lines of different length. An important aspect of this limit is its independence from perceptual effects and its application across all stimulus types. Recent research, however, has identified several exceptions. We investigate an explanation for these results that reconciles them with Miller’s work. We find support for the hypothesis that the (...)
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  • RACE/A: An Architectural Account of the Interactions Between Learning, Task Control, and Retrieval Dynamics.Leendert van Maanen, Hedderik van Rijn & Niels Taatgen - 2012 - Cognitive Science 36 (1):62-101.
    This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture–word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term (...)
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  • A dynamic stimulus-driven model of signal detection.Brandon M. Turner, Trisha Van Zandt & Scott Brown - 2011 - Psychological Review 118 (4):583-613.
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  • Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability.Nathan J. Evans, Mark Steyvers & Scott D. Brown - 2018 - Cognitive Science 42 (6):1925-1944.
    Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. (...)
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  • The multiattribute linear ballistic accumulator model of context effects in multialternative choice.Jennifer S. Trueblood, Scott D. Brown & Andrew Heathcote - 2014 - Psychological Review 121 (2):179-205.
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  • Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences.Guy E. Hawkins, A. A. J. Marley, Andrew Heathcote, Terry N. Flynn, Jordan J. Louviere & Scott D. Brown - 2014 - Cognitive Science 38 (4):701-735.
    Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The (...)
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  • Sequential effects in response time reveal learning mechanisms and event representations.Matt Jones, Tim Curran, Michael C. Mozer & Matthew H. Wilder - 2013 - Psychological Review 120 (3):628-666.
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