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What are the functional units in           reading?               Evidence for statistical               variation influen...
The Debate   What information is used to map orthography    onto phonology?    The Debate   Theoretical Models   Contrast...
Competing Models   Dual-Route Models     –   Dual-Route Cascade Model (Coltheart et al, 1993)     –   Connectionist Dual ...
Dual-Route Models    Lexical Route    Sub-lexical Route    Serial Processing    Explicit level of     representation f...
Single Route Models                                                             The ‘triangle’ model.    Parallel Process...
Psycholinguistic Grain Size Theory(Ziegler & Goswami, 2005)   Type of processing that occurs in reading system    determi...
Graphemes   Written representations of phonemes   Can be composed of multiple letters:    – Digraphs    – TrigraphsFrom ...
Whammies and Double Whammies(Rastle & Coltheart, 1998)   Participants read non-words containing 3 phonemes (e.g. fooce)  ...
Grain-Size Effects in Reading(Pagliuca, Monaghan & McIntosh, 2008)   Findings seem to contradict those of Rastle & Colthe...
Modelling:Pagliuca, Monaghan & McIntosh, 2008   Single route model based on Harm & Seidenberg, 1999   Orthographic input...
Behavioural Study:Pagliuca, Monaghan & McIntosh, 2008   Same sets of words used in the behavioural study as used in the s...
Conclusions:Pagliuca, Monaghan & McIntosh, 2008   Modelling:      –   For digraphs two letter positions contribute to the...
Research Aims:1. Using a computational model of reading based   on Harm & Seidenberg, 1999.   Can we extend the digraph ef...
Modelling Study: Design (Model)   Computational Model:     –   Based on Harm & Seidenberg 1999     –   Orthographic Input...
Modelling Study: Design (Training)  –   Training corpus:       •   6229 monosyllabic words,       •   Words 1 to 8 letters...
Modelling Study: Design (Stimuli)–    Stimuli sets each containing 64 items:       •   Words with digraph in onset       •...
Modelling Study: Results (Words)Model performance on wordsets:  –   Both sets read with 100%      accuracy before noise   ...
Modelling Study: Results (Non-words)Model performance on non-wordsets:  –   Accuracy based on comparing      output to tar...
Model Predictions: Both words and non-words containing digraphs in the initial position will be identified with greater ac...
Behavioural Study: Design (Stimuli)  –   4 stimuli sets taken from simulation:            Control Non-words            C...
Behavioural Study: Design (Procedure)   Participants:     –   15 university students     –   All native English speakers...
Behavioural Study: Results (Accuracy)                                                                Accuracy of Response:...
Behavioural Study: Results(Response Times) Response Times:    Similar trends were found in participants reaction times alt...
Summary   Modelling:     –   Greater accuracy reading both words and non-words containing         digraphs in the initial...
Discussion (1)   Task differences:     –   Word naming task:          •   Pagliuca, Monaghan & McIntosh, 2008          • ...
Discussion (2)   Simulation and Behavioural data showed an advantage    for words containing digraphs:     –   Replicatio...
Discussion (3)   Combined findings:     –   Single Route (Parallel Processing) Model:          •   Provides explanation f...
Discussion (4)   Combined findings:     –   Dual Route (Serial Processing) Model:          •   Provides explanation for r...
Direction of Future Study   Non-word Naming Task   Digraphs in final position     –   If non-lexical route serial this s...
Special Thanks & Acknowledgements Experimental Psychology Society Lancaster University
Questions:
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What are the functional units in reading? Evidence for statistical variation influencing word processing

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What are the functional units in reading? Evidence for statistical variation influencing word processing

  1. 1. What are the functional units in reading? Evidence for statistical variation influencing reading Alastair Smith Padraic Monaghan
  2. 2. The Debate What information is used to map orthography onto phonology? The Debate Theoretical Models Contrasting Evidence Study Discussion
  3. 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. 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. 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. 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. 7. Graphemes Written representations of phonemes Can be composed of multiple letters: – Digraphs – TrigraphsFrom Exploring Grain-Size Effects in Reading.By Pagliuca, G., Monaghan, P., 2008Proc 30th Ann Conf Cog Sci Soc. Mahwah, NJ:Lawrence Erlbaum. The Debate Theoretical Models Contrasting Evidence Study Discussion
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Modelling Study: Results (Words)Model performance on wordsets: – 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. 18. Modelling Study: Results (Non-words)Model performance on non-wordsets: – 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30. Special Thanks & Acknowledgements Experimental Psychology Society Lancaster University
  31. 31. Questions:

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