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Flashcards in Visual word recognition Deck (68)
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Why is reading an important cultural invention?

Because it allows the transmission and storage of information and therefore the endurance of ideas.


What was the first written language, and what was the 'giant leap for mankind'?

Akkadian, developed in Ancient Mesopotamia - first purely images, then symbols were used to represent sound, which was a giant leap for mankind as it allowed the symbolic representation of abstract thoughts.


How does reading relate to visual word recognition?

Visual word recognition is one of the cornerstones for reading.


What are the main features of visual word recognition?

- Fast and automatic
- Flexible
- Precise


How many words can we process per minute?

250-300 words per minute.


How quickly can the brain distinguish between words and non-words?

Within 200ms.


In what way is visual word recognition flexible?

We can read different fonts, scripts, case, handwriting, etc.


In what way is visual word recognition precise?

We can distinguish between words that are similar e.g. trail and trial.


What did Hauk et al. (2006) do and find?

Investigated the speed of visual word recognition through EEG. Found that the typicality effect occurs 100ms after onset and the lexicality effect 200ms after onset.


What did Stroop (1935) do?

Demonstrated the fast and automatic nature of visual word recognition - found that it's impossible to ignore the word in a colour-naming stroop task, therefore it's highly automatic.


What did Bisson et al. (2012) do?

Demonstrated the automatic nature of visual word recognition through eye movements and subtitles - found that we try to read subtitles whenever they're there regardless of whether we know the language or need subtitles.


Describe the masked priming paradigm.

Mask – prime – target:
- Prime is presented briefly (e.g. 60ms)
- Prime duration (prime-target SOA): 30-250ms
- Participants to decide whether a target word is a correct English word (LDT)


How can the masked priming paradigm be used to support the automatic nature of visual word recognition?

A masked non-word prime differing from the target by one letter facilitates target word processing compared to non-words without similar letters. E.g. bontrast facilitates CONTRAST according to Forster and Davis (1984). This shows that to some extent we read words even when they're presented for a very brief period of time and we're not aware of them.


What did Ferrand and Grainger (1993) do and find?

Manipulated orthography and phonology in different priming conditions and found that:
- Phonology: facilitation increases when prime duration increases (although drops after optimum of 67ms)
- Orthography: facilitation decreases when prime duration increases.


What did Perfetti and Tan (1998) investigate and find?

Graphic, phonological and semantic priming in Chinese. The effect of graphic priming decreases over prime duration, phonological increases then stabilises, and semantic is non-existent until 85ms, after which it increases.


How can letter order demonstrate the flexibility of visual word recognition?

Because to a certain extent, letter order in a word doesn't matter as long as the first and last letter remain correct, as the brain reads the whole word. However this often doesn't work for longer/more uncommon/unexpected words, for example magltheuansr isn't easily recognisable as manslaughter.


How do writing systems demonstrate the flexibility of visual word recognition?

There are 7,105 languages spoken in the world. As shown by Cook & Basetti (2005), languages can be categorised as being either meaning- or sound-based, then divided into whether they use morphemes, syllables or phonemes, what script they use, etc. This wide variety of written languages demonstrates the flexibility of our brain, as we are capable of learning all of them!


What experimental tasks can be used to investigate word recognition?

- Lexical decision task (LDT) [very powerful when combined with masked priming]
- Word naming
- Perceptual identification
- Priming


What can be measured in experimental investigation of word recognition?

- Response times (RTs) and accuracy
- Eye movements
- Brain imaging:
• Event-related potentials (ERP)
• functional Magnetic Resonance Imaging (fMRI)


Define orthographic input coding.

How we recognise letters and words.


How do we distinguish between anagrams such as leap, pale, peal and plea?

Through position specific coding - each letter is assigned a position number in the word.


What are the three main computational models of visual word recognition?

• Interactive Activation (IA) model (McCelland & Rumelhart, 1981; Rumelhart & McCellend, 1982)
• DRC model (Coltheart et al., 2001)
• MROM (Grainger & Jacobs, 1996)


Describe the IA model.

Localist connectionist neural network model.


What representations are involved in the IA model?

(Visual input) --> letter features --> letters --> words


What do representations do in the IA model?

Each representation has the ability to inhibit or excite the next, and words can excite similar words (orthographic neighbours).


What does the resting level activation of word nodes in the IA model reflect?

Word frequency.


What is the IA model very good at?

Predicting speed of word recognition.


How did Coltheart et al. (1997) define orthographic neighbours?

The number of words that can be created by changing one letter of a target word, for example the 29 orthographic neighbours of 'mine' include pine, line, mind, mint etc.


What effect does neighbourhood size/density (number of neighbours) have on word recognition?

The results are mixed - some suggest facilitation (Andrews, 1989, 1992); other suggest null (Coltheart et al., 1997); inhibition (Carreiras et al., 1997).


How does neighbourhood frequency (number of lower or higher neighbours) affect word recognition?

Results suggest that more frequent neighbours result in inhibition (Carerias et al., 1997; Grainger, 1989; Grainger et al., 1990).