Language 2

Question 1

Learning Outcomes from last year:

Answer 1

• Understand and describe the challenges of comprehension using appropriate terminology
• Explain how words are recognised in the mental lexicon
• Explain how we comprehend sentences
• Understand and describe models of word comprehension
• Explain how the evidence supports current assumptions

Question 2

Learning Outcomes from this year:

Answer 2

• Understand and describe the process of reading
• Explain how written words are recognised in the mental lexicon
• Describe the Dual Route Cascaded (DRC) Model of reading (Coltheart et al., 2001)
• Describe the self-teaching hypothesis of learning to read
• Evaluate the evidence for the self-teaching hypothesis

Question 3

The mental lexicon

Answer 3

Our mental lexicon contains different representation of words.

Today we are focusing on the orthographic representation – the letters and writing systems that make up a language, to a certain extent the phonological representation may also become involved when reading (sounds).

Question 4

What is reading?

Answer 4

When we are reading we are trying to get insight into what they have written, which is a representation of what is in their minds – when reading we are forming our own representations of what they are trying to say.

We do this using writing systems, orthography is the writing system of a language, graphemes are it’s constitutional units (visual representations of a phoneme) – there are about 44 phonemes/ graphemes in English.

Question 5

Writing systems

Answer 5

Different types of writing systems exist…

• Logographic systems (based on whole characters/ concepts/ structures)

• Japanese 愛 ai (love)
• Chinese 香港 heung gong (Hong Kong; fragrant harbour)

• Alphabetic Systems (A single letter represents a distinctive sound in spoken language)
- Korean 학교 hakkyo (‘school’) ㅎ ㅏ ㄱ ㄱ ㅛ

Question 6

Grapheme correspondence with phonemes

Answer 6

So, remember a grapheme is a unit in a writing system, and a phoneme is a unit of sound. A grapheme is a representation of a phoneme, a phoneme can be represented by one or more than one letter.

A grapheme can be made up of a number of letters e.g.,

2. that /ðæt/
3. Night /naɪt/
4. Through /θru/

The words are transcribed on the write as how we write the sounds of the words (phonetic transcript, IPA). In through, the 4 letter phoneme ough is represented by a single grapheme – lots of structures are possible.

1 grapheme can represent more than 1 phoneme

e. g., the ‘i’ in Mint /mɪnt/ Pint /paɪnt/
e. g., ‘th’ /ð/ (in that) or /θ/ (in through)

1 phoneme can be represented by more than 1 grapheme
e.g., /k/ can be represented by ‘c’ ‘k’ ‘ck’

Question 7

Types of Writing Systems –

Answer 7

Transparent
The spelling of each word maps directly on to its pronunciation (e.g., Finnish or Italian)

Opaque
The spelling of each word does not map directly on to its pronunciation (e.g., English)

English has some transparency
DOG, PRINT, COBWEB = regular words

But a lot of opaque spellings
YACHT, KNIGHT, COLONEL = irregular words

Regular and irregular words ‘mint’ and ‘pint’
Mint is the regular word here

Question 8

Models of Visual Word Recognition

Answer 8

Two competing models we are only going to look at one;

DRC: Dual Route Cascaded model of visual word recognition and reading aloud (Coltheart, Rastle, Perry, Langdon & Ziegler, 2001)

Question 9

DRC: Dual-Route Cascaded model of visual word recognition and reading aloud

Overview of the model structure and how it works

Answer 9

SEE IMAGE IN NOTES

The two routes are the lexical route and non-lexical route for reading (remember lexical means words, whole words in this context – so non-lexical means not whole words)

So, when we are trying to process the printed word we can activate a whole word orthographic representation of the word in the lexical route by activating a print representation that we have stored in our lexicon. Alternatively, we can activate a non-lexical route which will do a process called grapheme-phoneme correspondence (where it will convert the graphemes into phonemes (letters into sounds), so we can access a whole word phonological representation (whole word sound) that we have stored in our phonological lexicon). By doing that we can access the semantic meaning of the word via activation of phonological or orthographic representations of the word.

Lexical route –> Orthographic representation – representation of the whole word –> phonological lexicon –> semantics

Non-lexical route –> Grapheme-phoneme correspondence –> phonological lexicon –> semantics

Question 10

Lexical Route

Answer 10

Lexical route –> Orthographic representation – representation of the whole word –> phonological lexicon –> semantics

Question 11

Non-Lexical Route

Answer 11

Non-lexical route –> Grapheme-phoneme correspondence –> phonological lexicon –> semantics

Question 12

IRREGULAR WORDS CAN’T USE THE NON-LEXICAL ROUTE

Answer 12

There are certain words that we can access the semantics of via either route e.g. text/ mint because they are regular words, the word “pint” however would only be accessible via the lexical route as it is irregular – using the non-lexical route and breaking it down into sounds would convert the graphemes into phonemes that wouldn’t access the correct phonemes (would end up pronouncing pint like “mint”. So to access the correct phonological representation of pint the lexical route must be used.

Question 13

DRC: Dual-Route Cascaded model of visual word recognition and reading aloud

Theory behind it

Answer 13

• DRC is a cascaded model (like Dell’s model of speech production) and not a discrete model (like Levelt et al.) – generally info passes down through the model from one level to the next, but you don’t have to finish processing at one level to begin processing at the next
• Imaging evidence of different routes for reading supports the model (e.g., Seghier, Lee, Schofield, Ellis & Price, 2008)
• Provides a clear account of disordered reading e.g., different types of dyslexia (see Eysenck & Keane for a review)

Question 14

Lexical Access for written words (aka visual word recognition)

The system accesses lexical items based on

Answer 14

The system accesses lexical items based on
– Visual input (e.g. graphemes and their correspondence with phonemes)
– Lexical characteristics (e.g., frequency, familiarity)
– Context
– Prediction
– Less impacted by temporal information than speech (e.g., able to ‘read’ a whole word, do not have to wait for it to ‘unfold’)

Question 15

Lexical Access for written words (aka visual word recognition)

There are some specific lexical characteristics that can impact the speed or ease you can recognise written words…

(List)

Answer 15

• Familiarity
• Frequency
• Length
• Neighbourhood Density
• (Context)

Question 16

Lexical Access for written words (aka visual word recognition)

Lexical characteristics

Familiarity

Answer 16

Familiarity:

Eichelman presented participants with word pairs which we either words or non-words. Ppts made same/different decisions faster when the pairs were words compared to when they were nonwords. Words (e,g., SEAT) are read faster than pseudohomophones (e.g., SEET)

Question 17

Lexical Access for written words (aka visual word recognition)

Lexical characteristics

Frequency

Answer 17

Frequency:

Words more frequently accessed are read + processed more quickly
Naming speed was slower with increasing length for pseudowords and low frequency but not for high frequency words

Question 18

Lexical Access for written words (aka visual word recognition)

Lexical characteristics

Length

Answer 18

Length:

Larger impact of length on low compared to high frequency words
The less familiar the word, more decoding of graphemes, slows recognition of word

Question 19

Lexical Access for written words (aka visual word recognition)

Lexical characteristics

Neighbourhood density

Answer 19

Lexical access will be slower for words with lots of neighbours

e.g.
“REED”
Orthographic neighbours;
reek, rend, peed, heed, deed, reel, reef, weed, seed, feed, read, need
Phonological neighbours;
rowed, cede, wreath, ream, wreak, knead, keyed, heed, mead, bead, rode, raid, she’d, rude, we’d, he’d, ride, reach, lead, road

Question 20

Neighbourhood density effect in speech recognition

Answer 20

Lexical access will be slower for words with lots of neighbours e.g. yacht will be faster than peach (neighbours like pea, piece, peel, peace). In spoken word recognition, lots of neighbours is inhibitory.

Question 21

Neighbourhood density effect in visual word recognition

Answer 21

Where when we are listening to someone speak words with lots of neighbours might be slower to be recognised we don’t tend to get that with written words. Lexical access faster for low frequency words with lots of neighbours but no effect for high frequency words with lots of neighbours. In written word recognition, lots of neighbours is facilitative (if the word is low frequency, no difference for high frequency due to Hebbian learning - neuronal activation is high, threshold is low, and neurones are practiced).

Question 22

Context and Visual Word Recognition

Answer 22

Context:

Ambiguous words can have dominant and subordinate meanings attached to them. For example an ambiguous word such as bark could have a dominant meaning of dog attached to it and a subordinate meaning of tree. So in an exp we would present the word bark, then either dog or tree and test the reaction – respond more quickly to dominant meaning.

Question 23

Learning to Read

What does the DRC predict about how children learn to read?

Answer 23

• DRC Predicts that readers start by decoding words using the non-lexical route (breaking words down)
• Gradually build up an orthographic lexicon
• Enabling direct access of meaning via the lexical route without accessing phonology

SO the DRC model predicts that the link between the orthographic lexicon and the phonological lexicon becomes weakened over time as readers become more skilled

Question 24

Learning to Read

Self-teaching hypothesis (Share, 1995)

What he said

Answer 24

Share said children use Grapheme-Phoneme correspondences to teach themselves to read, and orthographic (whole word) representations are added after 1-6 exposures of the same word

Question 25

Learning to Read

Self-teaching hypothesis (Share, 1995)

Research

Answer 25

Participants - Second grade primary school - Mean age 8
Stories read out loud
Target word presented 4 – 6 times

“In the middle of Australia is the hottest town in the world. This town is called Akunia and it’s right in the middle of the desert. In Akunia, the temperature can reach 60 degrees. It’s so hot that even the flies drop dead and the rubber tires on the cars start to melt. You can even fry an egg on the roof of your car. The houses in Akunia are under the ground, far away from the heat of the sun. The people also dig for gold deep under the ground. In Akunia, they drink lots of beer to stay cool. They drink beer in the morning, in the afternoon, and in the evening. The beer in Akunia is very strong. If you’re not used to drinking beer you’d better watch out!

Would you like to live in Akunia?”

Tests of orthographic learning:
1. Children asked to select the target word from 4 options
Used an alternative forced choice task to ask the children to select the spelling of the town, the kids were really good at this

2. Naming (timed reading out loud)
   Each item named twice embedded in a list of 60 known items
   The original target spelling “Akunia”
   A homophonic spelling “Acunea”
   Kids were quicker in saying the word Akunia than the homophone
3. Spelling (child asked to spell the ‘town’)
   Although the target was produced 50% of the time, they came up with alternative spellings a lot – suggests spelling was a matter of chance

Question 26

Evidence of self-teaching

Cunningham (2006)

Answer 26

Cunningham (2006) – started to question Share’s hypothesis on the basis of self-teaching works well for transparent languages, but not opaque ones like English

• Children chose the correct spelling for the 4 AFC (alternative forced choice)
• Spelling test showed at chance levels of correct spelling
• Orthographic learning equivalent for items correctly decoded and items incorrectly decoded
• Concludes that recognition of target is not the same as having an orthographic representation that facilitates correct spelling of the target

This research showed that the orthographic learning, the degree to which the kids were able to recognise the correct spelling of an item, did not correlate with their ability to decode. If orthographic learning relies on decoding of graphemes and phonemes then these two things should correlate but they didn’t. Suggests orthographic learning may not utilise the non-lexical route to such a degree (maybe just for transparent languages?).

Question 27

Evidence of self-teaching

Nation et al (2007)

Answer 27

Nation et al (2007)
• No correlation between successful de-coding of pseudowords and selection in 4AFC task
• Concludes that de-coding is not necessary for recognition of target

Question 28

Evidence of self-teaching

Extra studies

Answer 28

Goswami et al (2001) transparent orthographies use grapheme-phoneme correspondence (GPC), opaque orthographies require combination of GPC and orthographic recognition

Nation & Snowling (1998) Irregular words may be learnt using top down knowledge from oral vocabulary

Ziegler et al (2013) computational model of self- learning hypothesis, suggests that irregular words can be learnt by rote and 80% of words can be learnt via de-coding

Question 29

Self Teaching and the DRC model overview after looking at evidence

Answer 29

• The DRC model provides a useful framework that matches Share’s hypothesis
• The evidence suggests that decoding ability supports, but may not be necessary, for the development of orthographic lexicons
• Learning to read opaque languages may require more support from orthographic lexicons than transparent languages

Castles et al. (2018) suggest that the question of how we transition from novice to skilled reader remains unanswered

Question 30

Conclusions

Answer 30

> The quirks of visual word recognition provide us with insight into lexical organisation and evidence that we have an orthographic lexicon that is distinct from our phonological lexicon
Reading is a skill that we have to learn (and it’s not easy)
Share’s self teaching hypothesis and the DRC model of reading provide an account of how we learn to read
Some questions remain unanswered in relation to the shift from novice to skilled reader