Philosophy Dictionary of Arguments
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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
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
> Pinker, >
similar inputs lead to similar outputs, no additional training is
Steven
Connectionism 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.
Meta
data
Pi I
St.
Pinker
How the
Mind
Works,
New
York
1997
German
Edition:
Wie das
Denken
im Kopf
entsteht
München
1998
Philosophy Dictionary of Arguments
Search
_____________
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
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.
_____________
Meta
data
Philosophy Dictionary of Arguments
Search
_____________
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
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.
> Counter arguments against Pinker
> Counter arguments in relation to Connectionism
Authors A B C D E F G H I J K L M N O P Q R S T U V W Z
Concepts A B C D E F G H I J K L M N O P Q R S T U V W Z
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Ed. Martin Schulz, access date 2019-07-02
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