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  1. Analogy as relational priming: The challenge of self-reflection.Andrea Cheshire, Linden J. Ball & Charlie N. Lewis - 2008 - Behavioral and Brain Sciences 31 (4):381-382.
    Despite its strengths, Leech et al.'s model fails to address the important benefits that derive from self-explanation and task feedback in analogical reasoning development. These components encourage explicit, self-reflective processes that do not necessarily link to knowledge accretion. We wonder, therefore, what mechanisms can be included within a connectionist framework to model self-reflective involvement and its beneficial consequences.
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  • Varieties of consciousness.Paolo Bartolomeo & Gianfranco Dalla Barba - 2002 - Behavioral and Brain Sciences 25 (3):331-332.
    In agreement with some of the ideas expressed by Perruchet & Vinter (P&V), we believe that some phenomena hitherto attributed to processing may in fact reflect a fundamental distinction between direct and reflexive forms of consciousness. This dichotomy, developed by the phenomenological tradition, is substantiated by examples coming from experimental psychology and lesion neuropsychology.
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  • Abstract analogies not primed by relations learned as object transformations.Steven Phillips - 2008 - Behavioral and Brain Sciences 31 (4):393-394.
    Analogy by priming learned transformations of (causally) related objects fails to explain an important class of inference involving abstract source-target relations. This class of analogical inference extends to ad hoc relationships, precluding the possibility of having learned them as object transformations. Rather, objects may be placed into momentarily corresponding, symbolic, source-target relationships just to complete an analogy.
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  • 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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