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  1. The Algorithmic Level Is the Bridge Between Computation and Brain.Bradley C. Love - 2015 - Topics in Cognitive Science 7 (2):230-242.
    Every scientist chooses a preferred level of analysis and this choice shapes the research program, even determining what counts as evidence. This contribution revisits Marr's three levels of analysis and evaluates the prospect of making progress at each individual level. After reviewing limitations of theorizing within a level, two strategies for integration across levels are considered. One is top–down in that it attempts to build a bridge from the computational to algorithmic level. Limitations of this approach include insufficient theoretical constraint (...)
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  • The Repetition‐Break Plot Structure: A Cognitive Influence on Selection in the Marketplace of Ideas.Jeffrey Loewenstein & Chip Heath - 2009 - Cognitive Science 33 (1):1-19.
    Using research into learning from sequences of examples, we generate predictions about what cultural products become widely distributed in the social marketplace of ideas. We investigate what we term the Repetition‐Break plot structure: the use of repetition among obviously similar items to establish a pattern, and then a final contrasting item that breaks with the pattern to generate surprise. Two corpus studies show that this structure arises in about a third of folktales and story jokes. An experiment shows that jokes (...)
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  • Events, narratives and memory.Nazim Keven - 2016 - Synthese 193 (8).
    Whether non-human animals can have episodic memories remains the subject of extensive debate. A number of prominent memory researchers defend the view that animals do not have the same kind of episodic memory as humans do, whereas others argue that some animals have episodic-like memory—i.e., they can remember what, where and when an event happened. Defining what constitutes episodic memory has proven to be difficult. In this paper, I propose a dual systems account and provide evidence for a distinction between (...)
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  • Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
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  • Exploring the conceptual universe.Charles Kemp - 2012 - Psychological Review 119 (4):685-722.
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  • Anticipatory emotions in decision tasks: Covert markers of value or attentional processes?Tyler Davis, Bradley C. Love & W. Todd Maddox - 2009 - Cognition 112 (1):195-200.
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  • Schematic information influences memory and generalisation behaviour for schema-relevant and -irrelevant information.Jamie P. Cockcroft, Sam C. Berens, M. Gareth Gaskell & Aidan J. Horner - 2022 - Cognition 227 (C):105203.
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  • Effects of interleaved and blocked study on delayed test of category learning generalization.Paulo F. Carvalho & Robert L. Goldstone - 2014 - Frontiers in Psychology 5.
  • A neurobiological theory of automaticity in perceptual categorization.F. Gregory Ashby, John M. Ennis & Brian J. Spiering - 2007 - Psychological Review 114 (3):632-656.
  • You only had to ask me once: Long-term retention requires direct queries during learning.Yasuaki Sakamoto & Bradley C. Love - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  • The neurobiology of categorization.F. Gregory Ashby & Matthew J. Crossley - 2010 - In Denis Mareschal, Paul Quinn & Stephen E. G. Lea (eds.), The Making of Human Concepts. Oxford University Press. pp. 75--98.