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Letting structure emerge: connectionist and dynamical systems approaches to cognition.
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Complementary learning systems in the brain. A connectionist approach to explicit and implicit cognition and memory.
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Computational approaches to cognition: top-down approaches.
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Fractal analysis illuminates the form of connectionist structural gradualness.
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Probabilistic models of cognition: exploring representations and inductive biases.
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The detection of algebraic auditory structures emerges with self-supervised learning.
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How to grow a self: development of self-representation in the Bayesian brain.
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Computational modeling approaches to emotional development.
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Abstract representations emerge in human hippocampal neurons during inference.
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Language models, like humans, show content effects on reasoning tasks.
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A mathematical theory of relational generalization in transitive inference.
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Shared functional specialization in transformer-based language models and the human brain.
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本文引用的文献

2
Probabilistic models of cognition: exploring representations and inductive biases.
Trends Cogn Sci. 2010 Aug;14(8):357-64. doi: 10.1016/j.tics.2010.05.004. Epub 2010 Jun 22.
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A connectionist model of a continuous developmental transition in the balance scale task.
Cognition. 2009 Mar;110(3):395-411. doi: 10.1016/j.cognition.2008.11.017. Epub 2009 Jan 25.
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Structured statistical models of inductive reasoning.
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Cue salience and infant perseverative reaching: tests of the dynamic field theory.
Dev Sci. 2009 Jan;12(1):26-40. doi: 10.1111/j.1467-7687.2008.00769.x.
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Postural change effects on infants' AB task performance: visual, postural, or spatial?
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Young infants reach correctly in A-not-B tasks: on the development of stability and perseveration.
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