One of the most important functions of concepts is that of producing classifications; and since there are at least two different types of such things, we better give a preliminary short description of them both. The first kind of classification is based on the existence of a property common to all the things that fall under a concept. The second, instead, relies on similarities between the objects belonging to a certain class A and certain elements of a subclass AS of A, the so-called ‘stereotypes.’ In what follows, we are going to call ‘proto-concepts’ all those concepts whose power of classification depends on stereotypes, leaving the term ‘concepts’ for all the others. The main aim of this article is showing that, if a proto-concept is given simply in terms of the ability to make the appropriate distinctions, then there are stimulus-response cognitive systems — whose way of manipulating information is based on Neural Networks (NN) — able to make the appropriate distinctions typical of proto-concepts in the absence of high-level cognitive features such as consciousness, understanding, representation, and intentionality. This, of course, implies that either proto-concepts cannot be given simply in terms of the ability to make the appropriate distinctions, or that we need to modify our traditional conception of mind, because the induction-like procedure followed by a NN in producing its classifications, far from being the ultimate product of a ‘linguistic mind,’ is, rather, inscribed in the nuts and bolts of the system’s biology/electronics to which the NN belongs.

Augello A., Gaglio S., Oliveri G., Pilato G. (2019). Concepts, proto-concepts, and shades of reasoning in neural networks. In A. Chella, I. Infantino, A. Lieto (a cura di), CEUR Workshop Proceedings (pp. 111-124). Aachen : CEUR-WS.

Concepts, proto-concepts, and shades of reasoning in neural networks

Augello A.
Investigation
;
Gaglio S.
Investigation
;
Oliveri G.
Investigation
;
Pilato G.
Investigation
2019-01-01

Abstract

One of the most important functions of concepts is that of producing classifications; and since there are at least two different types of such things, we better give a preliminary short description of them both. The first kind of classification is based on the existence of a property common to all the things that fall under a concept. The second, instead, relies on similarities between the objects belonging to a certain class A and certain elements of a subclass AS of A, the so-called ‘stereotypes.’ In what follows, we are going to call ‘proto-concepts’ all those concepts whose power of classification depends on stereotypes, leaving the term ‘concepts’ for all the others. The main aim of this article is showing that, if a proto-concept is given simply in terms of the ability to make the appropriate distinctions, then there are stimulus-response cognitive systems — whose way of manipulating information is based on Neural Networks (NN) — able to make the appropriate distinctions typical of proto-concepts in the absence of high-level cognitive features such as consciousness, understanding, representation, and intentionality. This, of course, implies that either proto-concepts cannot be given simply in terms of the ability to make the appropriate distinctions, or that we need to modify our traditional conception of mind, because the induction-like procedure followed by a NN in producing its classifications, far from being the ultimate product of a ‘linguistic mind,’ is, rather, inscribed in the nuts and bolts of the system’s biology/electronics to which the NN belongs.
Settore M-FIL/02 - Logica E Filosofia Della Scienza
https://scholar.google.it/citations?view_op=view_citation&hl=it&user=fAPQPiUAAAAJ&pagesize=100&sortby=pubdate&citation_for_view=fAPQPiUAAAAJ:FAceZFleit8C
Augello A., Gaglio S., Oliveri G., Pilato G. (2019). Concepts, proto-concepts, and shades of reasoning in neural networks. In A. Chella, I. Infantino, A. Lieto (a cura di), CEUR Workshop Proceedings (pp. 111-124). Aachen : CEUR-WS.
File in questo prodotto:
File Dimensione Formato  
Neural_Networks_2018.pdf

accesso aperto

Tipologia: Versione Editoriale
Dimensione 1.07 MB
Formato Adobe PDF
1.07 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/414431
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact