Literature DB >> 15641901

The influence of grammatical, local, and organizational redundancy on implicit learning: an analysis using information theory.

Randall K Jamieson1, D J K Mewhort.   

Abstract

People behave as if they know the structure of their environment. Because people rarely study that structure explicitly, several theorists have postulated an implicit learning system that abstracts that structure automatically. An alternative view is that people respond to local structure that derives from global structure. Measures are developed that quantify structure in a set of stimuli, in individual stimuli, and in encoded stimuli. The authors apply the measures to examine serial recall for sequences of colors generated using a stationary Markov grammar. They demonstrate that the 3 kinds of redundancy are confounded and show that the memorial advantage for grammatical stimuli reflects participants' use of local expressions of grammatical structure to aid learning. 2005 APA

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Year:  2005        PMID: 15641901     DOI: 10.1037/0278-7393.31.1.9

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  9 in total

1.  Implicit sequence learning in deaf children with cochlear implants.

Authors:  Christopher M Conway; David B Pisoni; Esperanza M Anaya; Jennifer Karpicke; Shirley C Henning
Journal:  Dev Sci       Date:  2011-01

2.  Stimulus set size and statistical coverage of the grammar in artificial grammar learning.

Authors:  Fenna H Poletiek; Tessa J P van Schijndel
Journal:  Psychon Bull Rev       Date:  2009-12

3.  Information theory and artificial grammar learning: inferring grammaticality from redundancy.

Authors:  Randall K Jamieson; Uliana Nevzorova; Graham Lee; D J K Mewhort
Journal:  Psychol Res       Date:  2015-04-01

4.  Implicit statistical learning in language processing: word predictability is the key.

Authors:  Christopher M Conway; Althea Bauernschmidt; Sean S Huang; David B Pisoni
Journal:  Cognition       Date:  2009-11-18

5.  An entropy model for artificial grammar learning.

Authors:  Emmanuel M Pothos
Journal:  Front Psychol       Date:  2010-06-17

6.  State Entropy and Differentiation Phenomenon.

Authors:  Masanari Asano; Irina Basieva; Emmanuel M Pothos; Andrei Khrennikov
Journal:  Entropy (Basel)       Date:  2018-05-23       Impact factor: 2.524

Review 7.  Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks.

Authors:  Rachel Schiff; Pesia Katan
Journal:  Front Psychol       Date:  2014-09-25

8.  Cross-Domain Statistical-Sequential Dependencies Are Difficult to Learn.

Authors:  Anne M Walk; Christopher M Conway
Journal:  Front Psychol       Date:  2016-02-25

9.  Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta-Analysis Approach.

Authors:  Antony S Trotter; Padraic Monaghan; Gabriël J L Beckers; Morten H Christiansen
Journal:  Top Cogn Sci       Date:  2019-09-08
  9 in total

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