<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In their work McCulloch and Pitts describe an idea of representing all of nervous activity in terms of propositional logic. This idea was quickly challenged. One of reasons for this challenge was rising believe that logic is unable to describe most of human cognitive processes. In this paper we will analyse premises of original McCulloch and Pitts proposition. Following that, we will ask about ability of symbolic (logical) systems to represent human cognition. We will finish by analysing relation between symbolic and subsymbolic computing, in hope of bridging the gap between the two.</p></abstract>

In their work McCulloch and Pitts describe an idea of representing all of nervous activity in terms of propositional logic. This idea was quickly challenged. One of reasons for this challenge was rising believe that logic is unable to describe most of human cognitive processes. In this paper we will analyse premises of original McCulloch and Pitts proposition. Following that, we will ask about ability of symbolic (logical) systems to represent human cognition. We will finish by analysing relation between symbolic and subsymbolic computing, in hope of bridging the gap between the two.

Role of Logic in Cognitive Science

Studia Humana

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Role of Logic in Cognitive Science"}

In their work McCulloch and Pitts describe an idea of representing all of nervous activity in terms of propositional logic. This idea was quickly challenged....

Role of Logic in Cognitive Science

Online veröffentlicht: 04. Juni 2018

Seitenbereich: 21 - 30

DOI: https://doi.org/10.2478/sh-2018-0002

Schlüsselwörter
nonmonotonic logic, neural networks, human reasoning

© 2018 Paweł Balcerak, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Role of Logic in Cognitive Science

Online veröffentlicht: 04. Juni 2018

Seitenbereich: 21 - 30

DOI: https://doi.org/10.2478/sh-2018-0002

Schlüsselwörternonmonotonic logic, neural networks, human reasoning

© 2018 Paweł Balcerak, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
nonmonotonic logic, neural networks, human reasoning