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Where Do Features Come From?

Cognitive Science 38 (6):1078-1101 (2014)

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  1. Are Generative Models Structural Representations?Marco Facchin - 2021 - Minds and Machines 31 (2):277-303.
    Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...)
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  • Computational Functionalism for the Deep Learning Era.Ezequiel López-Rubio - 2018 - Minds and Machines 28 (4):667-688.
    Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...)
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  • Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2017 - Synthese:1-22.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  • Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2018 - Synthese 195 (11):4817-4838.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition.James L. McClelland, Daniel Mirman, Donald J. Bolger & Pranav Khaitan - 2014 - Cognitive Science 38 (6):1139-1189.
    In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis—the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation (...)
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  • Incorporating Rapid Neocortical Learning of New Schema-Consistent Information Into Complementary Learning Systems Theory.James L. McClelland - 2013 - Journal of Experimental Psychology: General 142 (4):1190-1210.