Visual Word Recognition
25 important questions on Visual Word Recognition
Feature detection in the nervous system
- Hubel & Wiesel: edge detectors in the visual cortex of the cat. Edges do not occur often in the background 'noise' of the visual environment
- Deel learning: multiple layers of non-linear processing units
Process of visual word recognition
- One-by-one (sequentially)
- Forster's autonomous serial search model
- In parallel (simultaneously) = now assumed
- Morton's logogen model
- Connectionist model (localist/distributed) --> Interactive activation (IA) - McClelland & Rumelhart + PDP model - Seidenberg & McClelland
Pseudoword superiority effect
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IA model: word frequency explanation
IA model: word superiority effect explanation
IA model: neighborhood effects explanation
IA model: orthographic priming effects explanation
IA model: nonword effects explanation
Problems of the IA model
- Model functions only words of one and the same length
- 'Position coding': words with a wrong letter order should not be recognized but they are
- Phonology and meaning are not included
Phonological recoding (sounding out)
- Direct route: directly into the lexicon
- Indirect route: through sounding out
- Meaning-route: via meaning to sound
The Dual-route / 'house' model has all three routes
Types of scripts: abrades / Consonant Alphabets
Arabic, Hebrew
Types of scripts: Alphabets
Latin, Greek
Types of scripts: Syllabic Alphabets / Abugidas
Bengali, Bilanese
Types of scripts: Syllabaries
Hiragana, Katakana
Types of scripts: Logographic writing systems
Ancient Egyptian, form of Chinese
Speech of word recognition (ERP data)
Word's surface form and meaning are first accessed at different times in different brain systems and then processed simultaneously. This supports interactive processing models with different stages.
Principle component analysis
- Word length
- Letter n-gram frequency
- Lexical frequency
- Semantic coherence of a word's morphological family
- Starting round 90 ms: word length and letter n-gram frequency effects
- Starting at 110 ms: lexical frequency effect
- Starting at 160 ms: word - pseudo word differences
- After 200 ms: all variables exhibit simultaneous EEG correlates
Source estimates indicate parieto-temporo-occipital generators for length, letter n-gram, frequency and word frequency. Widespread activation with foci (focus) in the left anterior temporal love and inferior frontal cortex related to Semantic coherence.
Types of priming: Repetition priming
Types of priming: Semantic priming
Types of priming: Orthographic priming
Types of priming: Phonological / homophone priming
Automatic or controlled priming?
Lexical ambiguity: Homographs
Tear
Lexical ambiguity: Homophones
Knight - night
Speed of context effects on visual word recognition (ERP data)
- Hypothesis: sentence comprehension depends on continuous prediction of upcoming words
- Effects of word frequency and contextual predictability on N1 (early visual processing), P200 (perceptual decoding), and N400 (semantics)
- N1 --> low predictable words has a more negative anterior N1 than high predictable words - only for high frequency words
- P200 --> low predictable words has a less positive P200 than high predictable words
- N400 --> low predictable words had a greater N400 than high predictable words.
Context facilitates visual-feature and orthographic processing and semantic integration later
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