Visual word recognition

Question 1

What is the word superiority effect?

Answer 1

It is the fact that knowledge of words facilitates recognition: so seeing a letter within a word makes it easier to recognise, and you have a bias towards identifying partially obscured letters as letters which would fit into the word given.

Question 2

What increases recognition?

Answer 2

High frequency, short length, age of acquisition, recent experience of word.

Question 3

What decreases recognition?

Answer 3

Low frequency, more neighbour words, more semantic ambiguity.

Question 4

What is Forster’s serial search model?

Answer 4

2 stage:
1) Search mental lexicon in serial to find right entry
2) Retrieve meaning from lexical entry.
E.g. ‘do’ = a bin. ‘dog’ ‘doll’ ‘dodo’ are entries in the bin organised by frequency.

Question 5

Does Forster’s serial search model think that knowing the meaning of a word can help you to recognise it?

Answer 5

No: it is an AUTONOMOUS model. Form and meaning are considered to be totally separate.

Question 6

What is the interactive activation model?

Answer 6

It’s inspired by neural networks. In V1 neurons have preference for lines of different orientations, which works with how written letters might be perceived.
Feature units –> letter units –> word units.
Letter position taken into account between letter-word units.

Question 7

Does the interactive activation model think visual word recognition is serial or parallel?

Answer 7

Parallel.

Question 8

How does the IAC code letter position?

Answer 8

The IAC has different unites for each ABSOLUTE position.

Question 9

Does this account of letter position fit with the evidence?

Answer 9

Nope! Masked priming still happens when relative position is used (garden –> grdn). And when small changes in absolute position are made (garden –> gadren).
This doesn’t fit with the IAC’s proposed coding of letter position, as it seems like we code by RELATIVE position.

Question 10

Is there any effect of the sound of the word on recognition?

Answer 10

Yes: in categorizing tasks, high error rates are found for homophones like ‘pair’/’pear’.
In semantic relatedness tasks, (‘are these two words semantically related?’) there are slower judgements for pairs which SOUND related (sand-beech).

Question 11

So is the sound of the word the key determinant of visual word recognition?

Answer 11

NO. Does seem like the primary route is direct (i.e. based on phonology), but just that semantics has some role to play too.