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  1. The causal and explanatory role of information stored in connectionist networks.Daniel M. Haybron - 2000 - Minds and Machines 10 (3):361-380.
    In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine information-based explanation. Finally, (...)
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  • (1 other version)Networks with Attitudes.Paul Skokowski - 2007 - Artificial Intelligence and Society 22 (3):461-470.
    Does connectionism spell doom for folk psychology? I examine the proposal that cognitive representational states such as beliefs can play no role if connectionist models - - interpreted as radical new cognitive theories -- take hold and replace other cognitive theories. Though I accept that connectionist theories are radical theories that shed light on cognition, I reject the conclusion that neural networks do not represent. Indeed, I argue that neural networks may actually give us a better working notion of cognitive (...)
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  • (1 other version)Networks with attitudes.Paul Skokowski - 2009 - AI and Society 23 (4):461-470.
    Does connectionism spell doom for folk psychology? I examine the proposal that cognitive representational states such as beliefs can play no role if connectionist models—interpreted as radical new cognitive theories—take hold and replace other cognitive theories. Though I accept that connectionist theories are radical theories that shed light on cognition, I reject the conclusion that neural networks do not represent. Indeed, I argue that neural networks may actually give us a better working notion of cognitive representational states such as beliefs, (...)
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