Opposition logic and neural network models in artificial grammar learning

Consciousness and Cognition 13 (3):565-578 (2004)
  Copy   BIBTEX

Abstract

Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard , Tunney and Shanks argued that the opposition logic advocated by Higham et al. was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 89,764

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Prototypes and portability in artificial neural network models.Thomas R. Shultz - 2000 - Behavioral and Brain Sciences 23 (4):493-494.
Neural models of development and learning.Stephen Grossberg - 1997 - Behavioral and Brain Sciences 20 (4):566-566.
A moveable feast.Peter F. Dominey - 2000 - Behavioral and Brain Sciences 23 (4):537-538.
Analogy making in legal reasoning with neural networks and fuzzy logic.Jürgen Hollatz - 1999 - Artificial Intelligence and Law 7 (2-3):289-301.

Analytics

Added to PP
2010-08-24

Downloads
30 (#454,264)

6 months
1 (#1,018,607)

Historical graph of downloads
How can I increase my downloads?

References found in this work

Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.
Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
{Finding structure in time}.J. Elman - 1993 - {Cognitive Science} 48:71-99.
Implicit learning of artificial grammars.Arthur S. Reber - 1967 - Journal of Verbal Learning and Verbal Behavior 6:855-863.

View all 13 references / Add more references