1. What are the functional units in
reading?
Evidence for statistical
variation influencing
reading
Alastair Smith
Padraic Monaghan

2. The Debate
 What information is used to map orthography
onto phonology?
The Debate Theoretical Models Contrasting Evidence Study Discussion

3. Competing Models
 Dual-Route Models
– Dual-Route Cascade Model (Coltheart et al, 1993)
– Connectionist Dual Process Model (Zorzi et al, 1998)
– CDP+ (Perry, Zorzi & Ziegler, 2007)
 Single Route Models
– Parallel Distributed Processing Model
– Seidenberg & McClelland, 1989
– Plaut, McClelland, Seidenberg & Patterson, 1996
– Harm & Seidenberg, 1999
The Debate Theoretical Models Contrasting Evidence Study Discussion

4. Dual-Route Models
 Lexical Route
 Sub-lexical Route
 Serial Processing
 Explicit level of
representation for
graphemes
The dual-route cascaded model.
From The CDP+ Model of Reading Aloud.
By Perry, C., Ziegler, J.C. & Zorzi, M., 2007
Psychological Review, 114, p.275.
The Debate Theoretical Models Contrasting Evidence Study Discussion

5. Single Route Models
The ‘triangle’ model.
 Parallel Processing From Computing the meanings of words in reading.
By Harm M.W., Seidenberg M.S., 2004
Psychological Review, 111, p.663.
 Encodes statistical
relations between
patterns of letters and
their pronunciation
 Single letters provide
input
The Debate Theoretical Models Contrasting Evidence Study Discussion

6. Psycholinguistic Grain Size Theory
(Ziegler & Goswami, 2005)
 Type of processing that occurs in reading system
determined by statistical relations between orthography
and phonology.
 Grain sizes:
– Language specific
– Allow for efficient mapping
 Learning to read is learning to find shared grain sizes in
orthography and phonology.
The Debate Theoretical Models Contrasting Evidence Study Discussion

7. Graphemes
 Written representations of phonemes
 Can be composed of multiple letters:
– Digraphs
– Trigraphs
From Exploring Grain-Size Effects in Reading.
By Pagliuca, G., Monaghan, P., 2008
Proc 30th Ann Conf Cog Sci Soc. Mahwah, NJ:
Lawrence Erlbaum.
The Debate Theoretical Models Contrasting Evidence Study Discussion

8. Whammies and Double Whammies
(Rastle & Coltheart, 1998)
 Participants read non-words containing 3 phonemes (e.g. fooce)
slower than control non-words containing 5 graphemes (e.g. fruls)
 Behavioural study supported by simulation data from dual route
model
– Non-lexical route processes non-word serially left to right, letter by
letter
 Conclusions:
– Reading system
is serial
– Functional unit is the
letter, not the digraph From Whammies and double whammies.
By Rastle, K., Coltheart, M., 1998
Psychon Bull Rev, 5, 277-282
The Debate Theoretical Models Contrasting Evidence Study Discussion

9. Grain-Size Effects in Reading
(Pagliuca, Monaghan & McIntosh, 2008)
 Findings seem to contradict those of Rastle & Coltheart, 1998
 Indicates grain-size adapts according to statistics in the
orthography - phonology mapping
 Hypothesis:
– If graphemes are functional units within the reading system, then a
word containing a multi-letter grapheme should be read more
accurately than a word without given the same kind of perceptual
noise to impair the orthographic input.
 Modelling data from single route model supported by behavioural
study
The Debate Theoretical Models Contrasting Evidence Study Discussion

10. Modelling:
Pagliuca, Monaghan & McIntosh, 2008
 Single route model based on Harm & Seidenberg, 1999
 Orthographic input represented by 8 letter slots
 Activation from input letter slots reduced along monotonic gradient from left to right
so that the lowest level of activation was in the left most slot
– two severities of impairment applied, severe and mild
 Model tested on two sets of 62 words, all 5 letters in length and monosyllabic
– Set 1: Digraphs in initial position
 ch, sh, th
– Set 2: Control set (no digraphs)
 cr, st, tr From Exploring Grain-
Size Effects in
Reading.
By Pagliuca, G.,
 Words beginning with digraphs Monaghan, P., 2008
Proc 30th Ann Conf
were read more accurately Cog Sci Soc. Mahwah,
NJ:
Lawrence Erlbaum.
The Debate Theoretical Models Contrasting Evidence Study Discussion

11. Behavioural Study:
Pagliuca, Monaghan & McIntosh, 2008
 Same sets of words used in the behavioural study as used in the simulation. 84
additional filler words selected, each five letters long with different initial bigrams and
initial letters to the experimental and control stimuli
 Visual noise applied to stimuli from left to right,
similar to noise applied in simulation study
 Participants completed naming task in which each word was presented for 250ms
 15 university students participated From Exploring Grain-Size
Effects in Reading.
all native English speakers By Pagliuca, G., Monaghan,
P., 2008
Proc 30th Ann Conf Cog Sci
Soc. Mahwah, NJ:
Lawrence Erlbaum.
 Words with digraphs were reported more
accurately than words without, confirming
predictions made by the model
The Debate Theoretical Models Contrasting Evidence Study Discussion

12. Conclusions:
Pagliuca, Monaghan & McIntosh, 2008
 Modelling:
– For digraphs two letter positions contribute to the activation of a single
phoneme, whereas for non-digraphs each letter only contributes to one
phoneme’s activation
– Graphemes emerge in the course of a system learning the regularities between
orthographic and phonological representations of words
 Behavioural study:
– Indicates computational properties have a profound affect on reading, at least
under conditions where visual input is impaired
 Different computational properties of the mapping between letters and
phonemes suggests psycholinguistic effects of words should vary
according to the compositionality of the mapping
The Debate Theoretical Models Contrasting Evidence Study Discussion

13. Research Aims:
1. Using a computational model of reading based
on Harm & Seidenberg, 1999.
Can we extend the digraph effects found in
Pagliuca, Monaghan & McIntosh to non-
words?
2. Test predictions raised by model in
experimental studies.
The Debate Theoretical Models Contrasting Evidence Study Discussion

14. Modelling Study: Design (Model)
 Computational Model:
– Based on Harm & Seidenberg 1999
– Orthographic Input Layer:
• 10 letter slots
• One of 26 units active in
each slot to represent letter
– Hidden Layer: 100 units From Phonology,
Reading Acquisition,
and Dyslexia.
By Harm, M. W.,
– Phonological Output Layer: Seidenberg, M.S.,
1999,
• 8 phoneme slots Psychol Rev, 106,
491-528
• Each phoneme represented in terms
of 25 phonological features
– 25 Clean-up units
The Debate Theoretical Models Contrasting Evidence Study Discussion

15. Modelling Study: Design (Training)
– Training corpus:
• 6229 monosyllabic words,
• Words 1 to 8 letters in length
– Training algorithm:
• backpropagation learning algorithm (Rumelhart, 1986)
– 5 million cycles of training, words submitted randomly
according to frequency
– 99.9% accuracy following training (tested on training corpus)
The Debate Theoretical Models Contrasting Evidence Study Discussion

16. Modelling Study: Design (Stimuli)
– Stimuli sets each containing 64 items:
• Words with digraph in onset
• Non-words with digraph in onset
• Control Words
• Control Non-words
– All Words and Non-words 5 letters in length and Monosyllabic
– Onset pairings matched for same initial letter and similar bigram frequency
– Controls applied:
• Word frequency
• Body Friends and Body Enemies
• Neighbours
• Unigram and Bigram frequency
• Partial View Predictability
– Non-words were formed by switching onsets and rimes within given word set
(Controls were performed on non-words following formation)
– Noise applied in three conditions:
• No Noise
• Uniform 50% reduction in input activation
• Decreasing noise condition (replication of Pagliuca, Monaghan & McIntosh, 2008)
The Debate Theoretical Models Contrasting Evidence Study Discussion

17. Modelling Study: Results (Words)
Model performance on word
sets:
– Both sets read with 100%
accuracy before noise
applied **
– Digraph set read with
greater accuracy when
input uniformly impaired
(t(126) = 2.453, p < 0.01)
**
– Digraph set read with
greater accuracy in
decreasing noise condition
(t(126) = 4.396, p < 0.01)
** p<0.01
* p<0.05
The Debate Theoretical Models Contrasting Evidence Study Discussion

18. Modelling Study: Results (Non-words)
Model performance on non-word
sets:
– Accuracy based on comparing
output to target. Target formed by
combining phonetic representation
of onset and rhyme extracted from
corpus
– Lower accuracy in reproduction of **
digraph set before noise applied
– Digraphs read more accurately in
non-words when input uniformly
impaired
++
(t(126) = 3.355, p < 0.01)
– Non-words containing digraphs
read more accurately in
decreasing noise condition
(t(126) = 2.495, p < 0.01)
** p<0.01, * p<0.05, ++ p<0.01 based on error in onset
The Debate Theoretical Models Contrasting Evidence Study Discussion

19. Model Predictions:
Both words and non-words containing digraphs
in the initial position will be identified with
greater accuracy than controls.
– For digraphs in both words and non-words two letter
positions contribute to the activation of a single
phoneme
The Debate Theoretical Models Contrasting Evidence Study Discussion

20. Behavioural Study: Design (Stimuli)
– 4 stimuli sets taken from simulation:
 Control Non-words
 Control Words
 Words with digraphs in onset
 Non-words with digraphs in onset
– 2-dimensional digital pixel noise applied across word
in decreasing gradient from left to right
Example of control word with visual noise applied Example of control non-word with visual noise applied
The Debate Theoretical Models Contrasting Evidence Study Discussion

21. Behavioural Study: Design (Procedure)
 Participants:
– 15 university students
– All native English speakers
 Lexical decision task:
– departs from Pagliuca, Monaghan
& McIntosh, 2008
 Procedure:
– Short practice period
– Fixation cross presented before stimuli
– Stimuli selected at random without replacement
– Stimuli presented for 250ms
– Response recorded by key press
– 256 trials completed by participant
The Debate Theoretical Models Contrasting Evidence Study Discussion

22. Behavioural Study: Results (Accuracy)
Accuracy of Response:
*
 Words containing
** digraphs were
responded to more
accurately than controls
(t(14) = 3.254, p<0.01)
 Non-words containing
digraphs were
responded to less
accurately than controls
(t(14) = 2.457, p<0.05)
** p<0.01, * p<0.05
The Debate Theoretical Models Contrasting Evidence Study Discussion

23. Behavioural Study: Results
(Response Times)
Response Times:
Similar trends were found in participants reaction times although significance levels were
not reached
The Debate Theoretical Models Contrasting Evidence Study Discussion

24. Summary
 Modelling:
– Greater accuracy reading both words and non-words containing
digraphs in the initial position in high level noise conditions.
– For digraphs two letter positions contributing to activation of single
phoneme
 Behavioural study:
– Words containing digraphs identified with greater accuracy than
controls when visual noise applied in a decreasing gradient across
word
– Non-words containing digraphs identified with less accuracy than
controls when visual noise applied in a decreasing gradient across
word
The Debate Theoretical Models Contrasting Evidence Study Discussion

25. Discussion (1)
 Task differences:
– Word naming task:
• Pagliuca, Monaghan & McIntosh, 2008
• Rastle and Coltheart, 1998
• Modelling study
– Lexical decision task:
• Behavioural study
The Debate Theoretical Models Contrasting Evidence Study Discussion

26. Discussion (2)
 Simulation and Behavioural data showed an advantage
for words containing digraphs:
– Replication of Pagliuca, Monaghan & McIntosh, 2008
– Indicates the grain size for reading in English is adaptable
according to statistics of the letter-sound mapping
– Challenges views on independence of letter recognition (Pelli,
Farrell and Moore, 2003) indicating word perception affected by
statistics in the language
The Debate Theoretical Models Contrasting Evidence Study Discussion

27. Discussion (3)
 Combined findings:
– Single Route (Parallel Processing) Model:
• Provides explanation for increased accuracy in identifying
digraph words displayed by simulation and behavioural
data
(Pagliuca, Monaghan & McIntosh, 2008)
• Model predicted advantage for reading digraph non-words,
however behavioural data showed lower accuracy of
response and slower reaction times
The Debate Theoretical Models Contrasting Evidence Study Discussion

28. Discussion (4)
 Combined findings:
– Dual Route (Serial Processing) Model:
• Provides explanation for reduced accuracy in digraph non-
word response
(Rastle & Coltheart, 1998)
• Digraph word advantage not predicted by models lexical
route
The Debate Theoretical Models Contrasting Evidence Study Discussion

29. Direction of Future Study
 Non-word Naming Task
 Digraphs in final position
– If non-lexical route serial this should lead to slower response
times
(Rastle & Coltheart, 1998)
 Use similar paradigm to investigate grain-size effects
in languages with differing grain-size
The Debate Theoretical Models Contrasting Evidence Study Discussion

30. Special Thanks & Acknowledgements
Experimental Psychology Society
Lancaster University

31. Questions: