A computational model of the self-teaching hypothesis based on the dual-route cascaded model of reading

Stephen C. Pritchard, Max Coltheart, Eva Marinus, Anne Castles

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present a new computational implementation of self-teaching within the dual-route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self-teaching under a variety of circumstances. The new model (ST-DRC) uses DRC's sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight-word reading. ST-DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular-word learning. Beyond modeling orthographic learning and self-teaching, ST-DRC's performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.

LanguageEnglish
Pages722-770
Number of pages49
JournalCognitive Science
Volume42
Issue number3
DOIs
Publication statusPublished - Apr 2018

Fingerprint

Reading
Teaching
Learning
Cues
Decoding
Empirical Research

Keywords

  • Self-teaching
  • DRC
  • computational modeling
  • reading
  • reading development

Cite this

@article{fe245f9174ff4b43b81669a80e3b7326,
title = "A computational model of the self-teaching hypothesis based on the dual-route cascaded model of reading",
abstract = "The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present a new computational implementation of self-teaching within the dual-route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self-teaching under a variety of circumstances. The new model (ST-DRC) uses DRC's sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight-word reading. ST-DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular-word learning. Beyond modeling orthographic learning and self-teaching, ST-DRC's performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.",
keywords = "Self-teaching, DRC, computational modeling, reading, reading development",
author = "Pritchard, {Stephen C.} and Max Coltheart and Eva Marinus and Anne Castles",
year = "2018",
month = "4",
doi = "10.1111/cogs.12571",
language = "English",
volume = "42",
pages = "722--770",
journal = "Cognitive Science",
issn = "0364-0213",
publisher = "Wiley-Blackwell, Wiley",
number = "3",

}

A computational model of the self-teaching hypothesis based on the dual-route cascaded model of reading. / Pritchard, Stephen C.; Coltheart, Max; Marinus, Eva; Castles, Anne.

In: Cognitive Science, Vol. 42, No. 3, 04.2018, p. 722-770.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A computational model of the self-teaching hypothesis based on the dual-route cascaded model of reading

AU - Pritchard, Stephen C.

AU - Coltheart, Max

AU - Marinus, Eva

AU - Castles, Anne

PY - 2018/4

Y1 - 2018/4

N2 - The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present a new computational implementation of self-teaching within the dual-route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self-teaching under a variety of circumstances. The new model (ST-DRC) uses DRC's sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight-word reading. ST-DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular-word learning. Beyond modeling orthographic learning and self-teaching, ST-DRC's performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.

AB - The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present a new computational implementation of self-teaching within the dual-route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self-teaching under a variety of circumstances. The new model (ST-DRC) uses DRC's sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight-word reading. ST-DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular-word learning. Beyond modeling orthographic learning and self-teaching, ST-DRC's performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.

KW - Self-teaching

KW - DRC

KW - computational modeling

KW - reading

KW - reading development

UR - http://www.scopus.com/inward/record.url?scp=85044217898&partnerID=8YFLogxK

UR - http://purl.org/au-research/grants/arc/CE110001021

U2 - 10.1111/cogs.12571

DO - 10.1111/cogs.12571

M3 - Article

VL - 42

SP - 722

EP - 770

JO - Cognitive Science

T2 - Cognitive Science

JF - Cognitive Science

SN - 0364-0213

IS - 3

ER -