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  1.  18
    A Theory of the Discovery and Predication of Relational Concepts.Leonidas A. A. Doumas, John E. Hummel & Catherine M. Sandhofer - 2008 - Psychological Review 115 (1):1-43.
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  2.  3
    Technological Competence Is a Pre-Condition for Effective Implementation of Virtual Reality Head Mounted Displays in Human Neuroscience: A Technological Review and Meta-Analysis.Panagiotis Kourtesis, Simona Collina, Leonidas A. A. Doumas & Sarah E. MacPherson - 2019 - Frontiers in Human Neuroscience 13.
  3.  8
    Individual Differences in Relational Learning and Analogical Reasoning: A Computational Model of Longitudinal Change.Leonidas A. A. Doumas, Robert G. Morrison & Lindsey E. Richland - 2018 - Frontiers in Psychology 9.
  4.  29
    A Computational Account of the Development of the Generalization of Shape Information.Leonidas A. A. Doumas & John E. Hummel - 2010 - Cognitive Science 34 (4):698-712.
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  5.  43
    Recognising Relations: What Can Be Learned From Considering Complexity.Katherine A. Livins & Leonidas A. A. Doumas - 2015 - Thinking and Reasoning 21 (3):251-264.
    Analogy is an important cognitive process that has been researched extensively. Functional accounts of it typically involve at least four stages of processing ; however, these accounts take the way in which the base analogue is understood, along with its relational structure, for granted. The goal of this paper is to open up a discussion about how this process may occur. To this end, this paper describes two experiments that vary the level of relational complexity across exemplars. It was found (...)
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  6. A Theory of Relation Learning and Cross-Domain Generalization.Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin & John E. Hummel - forthcoming - Psychological Review.
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  7.  45
    Analogy and Conceptual Change in Childhood.John E. Opfer & Leonidas A. A. Doumas - 2008 - Behavioral and Brain Sciences 31 (6):723-723.
    Analogical inferences are an important consequence of the way semantic knowledge is represented, that is, with relations as explicit structures that can take arguments. We review evidence that this feature of semantic cognition successfully predicts how quickly and broadly children's concepts change with experience and show that Rogers & McClelland's (R&M's) parallel distributed processing (PDP) model fails to simulate these cognitive changes due to its handling of relational information.
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  8.  5
    Validation of the Virtual Reality Neuroscience Questionnaire: Maximum Duration of Immersive Virtual Reality Sessions Without the Presence of Pertinent Adverse Symptomatology.Panagiotis Kourtesis, Simona Collina, Leonidas A. A. Doumas & Sarah E. MacPherson - 2019 - Frontiers in Human Neuroscience 13.
  9.  10
    "A Theory of the Discovery and Predication of Relational Concepts": Correction to Doumas, Hummel, and Sandhofer.Leonidas A. A. Doumas, John E. Hummel & Catherine M. Sandhofer - 2013 - Psychological Review 120 (3):543-543.
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  10.  27
    The Problem with Using Associations to Carry Binding Information.Leonidas A. A. Doumas, Keith J. Holyoak & John E. Hummel - 2006 - Behavioral and Brain Sciences 29 (1):74-75.
    van der Velde & de Kamps argue for the importance of considering the binding problem in accounts of human mental representation. However, their proposed solution fails as a complete account because it represents the bindings between roles and their fillers through associations (or connections). In addition, many criticisms leveled by the authors towards synchrony-based bindings models do not hold.
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  11.  15
    Developing Structured Representations.Leonidas A. A. Doumas & Lindsey E. Richland - 2008 - Behavioral and Brain Sciences 31 (4):384-385.
    Leech et al.'s model proposes representing relations as primed transformations rather than as structured representations (explicit representations of relations and their roles dynamically bound to fillers). However, this renders the model unable to explain several developmental trends (including relational integration and all changes not attributable to growth in relational knowledge). We suggest looking to an alternative computational model that learns structured representations from examples.
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