Title: Modeling Reading Development From First Grade Text
1Modeling Reading Development From First Grade Text
- Michael W. Harm, CMU
- Mark S. Seidenberg, Wisconsin/Madison
2Why Construct Computational Models?
- Can complement empirical studies
- Allow manipulation of multiple factors that would
be prohibitively difficult in a classroom setting - We can look under the hood and see what kinds
of internal representations are formed during
learning
3Computational Models of Reading
- Have a long history
- Rumelhart McClelland 1981, Seidenberg
McClelland 1989, Plaut Hinton, 1991, Coltheart
et. al, 1993, Plaut et. al 1996, Zorzi 1999, etc. - Historically, have been aimed at explaining
adult performance - Architecture of the reading system
- Acquired dyslexia (due to brain damage)
- Have traditionally not examined issues of reading
development
4Modeling Development Was Difficult With Earlier
Models
- Earlier models did not learn about phonology
- But phonology is important in learning to read!
- Sampled large corpus of (adult) text in toto
- Did not allow for manipulation of actual sequence
or nature of word exposures - Prohibited examination of effects of different
basals, interventions
5The Harm Seidenberg 1999 Model of Reading
Begin by modeling pre- literate phonological
knowledge that children have
Can vary the strength and consistency of this
knowledge
and simulate the different degrees of
phonological ability children bring to bear
learning to read
6Reading Uses this Phonological Knowledge
The nature of the phono representations
influences what is learned during reading
Core result the phonologically impaired model
learns differently
7Two New Applications
We have applied the Harm Seidenberg 1999 model
to two novel applications
- Simulation of effects of different instructional
basals - Simulation of an reading intervention scheme
8Simulating Effect of Different Reading Basals
- Some early reading texts are more tuned to
overlap in spelling/sound - Others emphasize variety in text exposure to
wider range of words
Q How does this interact with reading
impairments?
9Results Nonword Reading
- Basal 1 More tuned to spelling/sound
correspondences - Basal 2 Less systematic text
- For both normal and impaired model, Basal 1
better than Basal 2.
10Results Word Reading
- For normal models, large effect of basal
- For impaired models, floor effect smaller
difference
11Summary Simulating Effects of Basals
- We can explore the impact of reading materials,
and differential effects on normal and impaired
reading - Holds promise for more sophisticated
explorations/manipulations - and direct ties to more fine grained properties
of basals (see other talks in this session)
12Simulating Reading Interventions
- There is extensive evidence that one cause of
poor reading development is a phonological
impairment - However, interventions targeted at auditory
phonology generally are not very effective - Interventions aimed at spelling/sound
representations have greater success
Why?
13Analysis of the Model
Phonology
Spelling
So effective interventions must target the
relationship between spelling and sound
14The McCandliss et al. Intervention
- Use lessons based on the Beck word-building
scheme - Break words apart when errors are made
- Emphasizes componential structure of words
- and its relation to components of sound
15Performed Simulation of this Intervention
- Used phonologically impaired simulation from Harm
Seidenberg 1999 - Simulated intervention using actual items from
lessons - Brought about improvements in nonword reading
- Analyzed internal representations of words
16Normal and Impaired Simulation
17Representations Cluster Better Due to Intervention
Remediated Simulation
18Conclusions
- Computational simulations are now poised to
explore more detailed aspects of childrens
experience - With reading basals
- And with interventions
- Opens up a promising new line of research linking
behavioral experimentation with computational
analysis
19With thanks to...
Mark S. Seidenberg Bruce McCandliss
20fin