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  1. Cognitive penetration and informational encapsulation: Have we been failing the module?Sam Clarke - 2021 - Philosophical Studies 178 (8):2599-2620.
    Jerry Fodor deemed informational encapsulation ‘the essence’ of a system’s modularity and argued that human perceptual processing comprises modular systems, thus construed. Nowadays, his conclusion is widely challenged. Often, this is because experimental work is seen to somehow demonstrate the cognitive penetrability of perceptual processing, where this is assumed to conflict with the informational encapsulation of perceptual systems. Here, I deny the conflict, proposing that cognitive penetration need not have any straightforward bearing on the conjecture that perceptual processing is composed (...)
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  2. Equivalence of the Frame and Halting Problems.Eric Dietrich & Chris Fields - 2020 - Algorithms 13 (175):1-9.
    The open-domain Frame Problem is the problem of determining what features of an open task environment need to be updated following an action. Here we prove that the open-domain Frame Problem is equivalent to the Halting Problem and is therefore undecidable. We discuss two other open-domain problems closely related to the Frame Problem, the system identification problem and the symbol-grounding problem, and show that they are similarly undecidable. We then reformulate the Frame Problem as a quantum decision problem, and show (...)
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  3. Updating the Frame Problem for Artificial Intelligence Research.Lisa Miracchi - 2020 - Journal of Artificial Intelligence and Consciousness 7 (2):217-230.
    The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...)
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  4. Why Emotions Do Not Solve the Frame Problem.Madeleine Ransom - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 353-365.
    Attempts to engineer a generally intelligent artificial agent have yet to meet with success, largely due to the (intercontext) frame problem. Given that humans are able to solve this problem on a daily basis, one strategy for making progress in AI is to look for disanalogies between humans and computers that might account for the difference. It has become popular to appeal to the emotions as the means by which the frame problem is solved in human agents. The purpose of (...)
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  5. Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language.Theodore Bach - 2012 - Philosophy Compass 7 (5):348-360.
    Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...)
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  6. Context-Switching and Responsiveness to Real Relevance.Erik Rietveld - 2012 - In Julian Kiverstein & Michael Wheeler (eds.), Heidegger and Cognitive Science. Palgrave-Macmillan.
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