Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-29T08:18:59.195Z Has data issue: false hasContentIssue false
  • English
  • Français

The Law of Practice and localist neural network models

Published online by Cambridge University Press:  30 August 2019

Andrew Heathcote  and
Scott Brown
Andrew Heathcote
Affiliation:
Department of Psychology, The University of Newcastle, Callaghan, 2308, NSW, Australia{heathcote; sbrown}@psychology.newcastle.edu.aupsychology.newcastle.edu.au/
Scott Brown
Affiliation:
Department of Psychology, The University of Newcastle, Callaghan, 2308, NSW, Australia{heathcote; sbrown}@psychology.newcastle.edu.aupsychology.newcastle.edu.au/
Get access Rights & Permissions [Opens in a new window]

Abstract

An extensive survey by Heathcote et al. (in press) found that the Law of Practice is closer to an exponential than a power form. We show that this result is hard to obtain for models using leaky competitive units when practice affects only the input, but that it can be accommodated when practice affects shunting self-excitation.

Type
Brief Report
Information
Behavioral and Brain Sciences , Volume 23 , Issue 4 , August 2000 , pp. 479 - 480
Copyright
2000 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)