Psychology Dictionary of Arguments

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Theory of neural networks as an explanation for mind states.:

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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 Item Summary Meta data
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.


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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.
The note [Author1]Vs[Author2] or [Author]Vs[term] 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


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