The University of Southampton
University of Southampton Institutional Repository

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

Implicit learning of conjunctive rule sets: An alternative to artificial grammars
Implicit learning of conjunctive rule sets: An alternative to artificial grammars
A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.
implicit learning, subjective measures, artificial grammar, classification, chunks
1053-8100
1393-1400
Neil, G. J.
85453750-0611-48d9-a83e-da95cd4e80b3
Higham, P. A.
4093b28f-7d58-4d18-89d4-021792e418e7
Neil, G. J.
85453750-0611-48d9-a83e-da95cd4e80b3
Higham, P. A.
4093b28f-7d58-4d18-89d4-021792e418e7

Neil, G. J. and Higham, P. A. (2012) Implicit learning of conjunctive rule sets: An alternative to artificial grammars. Consciousness and Cognition, 21 (3), 1393-1400. (doi:10.1016/j.concog.2012.07.005). (PMID:22871460)

Record type: Article

Abstract

A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

Text
Neil&Higham_C&C_2012.pdf - Author's Original
Download (343kB)

More information

e-pub ahead of print date: 5 August 2012
Published date: September 2012
Keywords: implicit learning, subjective measures, artificial grammar, classification, chunks

Identifiers

Local EPrints ID: 340942
URI: http://eprints.soton.ac.uk/id/eprint/340942
ISSN: 1053-8100
PURE UUID: 845044e7-2f79-441d-870f-3a0522d9ba16
ORCID for P. A. Higham: ORCID iD orcid.org/0000-0001-6087-7224

Catalogue record

Date deposited: 11 Jul 2012 10:56
Last modified: 15 Mar 2024 03:08

Export record

Altmetrics

Contributors

Author: G. J. Neil
Author: P. A. Higham ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×