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Psychology Dictionary of ArgumentsHome | |||
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Connectionism: Connectionism is the theory of neural networks as an explanation for mind states and learning. See also Neural networks, Networks, Learning, Artificial Intelligence, Artificial Neural Networks._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
Author | Concept | Summary/Quotes | Sources |
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Steven Pinker on Connectionism - Dictionary of Arguments
I 128 ff - 145 Neural Networks/Pinker: Learning/Problem: there are incorrect reinforcements with "XOR" (exclusive or; Sheffer stroke). Solution: we have to interpose internal >representation. I 142 Neural nets/Rumelhart: neural nets return all errors. "Hidden levels": several statements that can be true or wrong can be assembled into a complex logical function, the values then vary continuously. The system can place the correct emphasis itself if input and output are given - as long as similar inputs lead to similar outputs, no additional training is required. >Homunculi. I 144f Connectionism/Rumelhart: the mind is a large neural network. - Rats have only fewer nets. PinkerVsConnectionism: networks alone are not sufficient for handling symbols - the networks have to be structured in programs. - Even past tense overstretches a network. Precursors: "association of ideas": Locke/Hume/Berkeley/Hartley/Mill >Association/Hume. 1) contiguity (context): frequently experienced ideas are associated in the mind 2) Similarity: similar ideas activate each other. >Similarity/Locke. I 146 Computer variant: is a statistical calculation with multiple levels. I 147 VsConnectionism: units with the same representations are indistinguishable. - The individual should not be construed as the smallest subclass. I 151 Connectionism cannot explain compositionality of representation. >Compositionality. I 158ff Recursion/Recursive/Neural Networks/Memory/Pinker: recursion solution for the problem of an infinite number of possible thoughts: Separation of short/long-term memory. The whole sentence is not comprehended at once, but words are processed individually in loops. >Recursion/Pinker. I 159 Networks themselves have to been as recursive processor: for thoughts to be well-formed. I 166 Neural Networks/Pinker: the networks do not reach down to the rules - they only interpolate between examples that have been put in. >VsConnectionism._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Pi I St. Pinker How the Mind Works, New York 1997 German Edition: Wie das Denken im Kopf entsteht München 1998 |