psychology 85-419/719 january 30, 2001
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Processing and Constraint Satisfaction: Psychological Implications The Interactive-Activation (IA) Model of Word Recognition. Psychology 85-419/719 January 30, 2001. Background: Fodor’s Definition of Modularity. Input systems are composed of distinct processing modules - PowerPoint PPT PresentationTRANSCRIPT
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Processing and Constraint Satisfaction:
Psychological ImplicationsThe Interactive-Activation (IA)
Model of Word Recognition
Psychology 85-419/719January 30, 2001
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Background: Fodor’s Definitionof Modularity
• Input systems are composed of distinct processing modules
• Modules have certain properties:– Partial results not shared between modules.
Communication is all-or-nothing.– Information is encapsulated in a module; only
results are shared. Access to global data is limited.
• Contrast with view we’ve seen so far...
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More Background: Models and Demonstrations
• A model is our best attempt at simulating a system. We think it’s basically true (to a certain level of approximation, anyway).
• A demonstration is a simulation that we know isn’t right, but that demonstrates a useful point; say, about computational principles.
• Which one is the IA model?
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Letter Perception In Context:The Phenomena
DOG#### BNR####
O or U? N or M?
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The Empirical Findings...
• Subjects are more accurate in identifying letters in briefly presented stimuli if the letter was in a word (as opposed to random letters, or individual letters)
• Nonwords that are pronounceable (e.g., MAVE) show advantage over non-pronounceable strings (e.g., MVAE)
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Assumptions of the Model• Fodor’s wrong.
– Processing is interactive, parallel, with partial results feeding different representational areas.
• There are (at least) 3 levels of analysis: features, letters, and words.
• The levels inhibit or excite each other depending on whether they are consistent with each other.
• Context effects can emerge from interactions between levels of representation
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The Overall Model
Word Level
Letter Level
Feature Level
Visual Input
“Context”
Acoustic Level
Phoneme Level
Acoustic Input
ImplementedModel
Spelling Speech
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Representations of Visual Features
16 features, each corresponding to a linesegment.
4 slots, one for each letter.
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Levels of Representation
Word Level:Inhibitoryconnections
cat dog lake
cLetter level:Inhibitory andexcitatory connections
a
Feature Level(still more connections)
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Pre-Set Weights
• Negative, inhibitory weights between word nodes. All same value.
• Positive or negative weights between letter nodes and word nodes, and between feature nodes and letter nodes. Same values for all weights.
• Biases on word nodes a function of word’s frequency.
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Processing
• Generally, the same formulation that we’ve been working with:– Network of weights, activities for units over
time.
• … with additional mathematics to simulate a forced-choice response
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The Running Average(Eq. 5)
t rxt
ii dxexata )()()(
rxte )(
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14 16 18 20
Time
Weighting
r=1
r=0.05
r=0.01
In simulations, r=0.05
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Response Probability(Eq. 6 & 7)
)()( taui
iets Strength of option i is:
Probability of Response for i is:
jj
ii ts
tstRp
)(
)(),(
In simulations, u = 10
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Model Behavior: Degraded Input
work
word
wear
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Letter Activations Too:
k
a
r
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The Word Preference Effect
• When stimuli is masked, letters embedded in words are perceived more accurately than letters standing alone
“e” in “read”
“e” alone
Mask applied
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Probabilities...P
roba
bili
ty
0
1
“e” in “read”
“e” alone
Subjects: 80% for word 65% alone
Model: 81% for word 66% alone
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Difference Between Maskedand Degraded Stimuli
• When stimuli is masked, there is actual information actively disrupting the visual system– By hypothesis, this actively turns off the letter
representations
• In contrast, when stimuli is simply degraded, there is still some activity in letter units. It’s noisy, but not obliterated.
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Simulating Masking andDegraded Stimuli
• Masking: Present stimuli in reliable fashion for period of time. Then, activate all segments (corresponds to mask)– Result: suppress all letter nodes
• Degraded stimuli: Present stimuli where features have a probability of being detected.
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Interactions...
Empirical Data
80 78
6573
0
20
40
60
80
100
Masked Degraded
In WordAlone
Simulation Results
81 7866 68
0
20
40
60
80
100
Masked Degraded
In WordAlone
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Why?
• In masked condition, letters lose all visual excitation.
• All activity, then, is a result of top down influences. For words, this is much larger than for single letters.
• In degraded stimuli, there is still some visual information. So single letters not so reliant on top down information.
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Letter Perception in Nonwords
• Nonwords that look like words (e.g., MAVE) show letter advantage over letters in isolation too.
• IA model account:– Even though MAVE may not “win” with any
word nodes, it overlaps with enough word nodes (GAVE, SAVE, HAVE) for the letters to get some top-down support
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Neighbors, Friends and Enemies
• A neighbor of a word is one that differs only by one letter
• A letter (e.g., M) in a spelling pattern like MAVE has friends; words that are neighbors and have an M in the 1st position (MOVE, MAKE, MADE)
• There are also enemies. Words that are neighbors but don’t have an M in the 1st position (HAVE, GAVE, SAVE)
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The “Rich Get Richer” Effect
Have (high frequency)
Gave (medium frequency)
Save (low frequency)
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The “Gang Effect”
Save. Part of large gang
Male. Also, large gang
Move. Member of smaller gang
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Other Phenomena?
Word Level
Letter Level
Feature Level
Visual Input
Acoustic Level
Phoneme Level
Acoustic Input
Lesch & Pollatsek ‘93:TOWED primes FROG
Semantic Priming:TOAD primes FROG
Impairments?
Two-hop: STRIPESprimes LION
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The “Slot Problem”
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Wrapping Up Section I: Constraint Satisfaction
• Complex patterns of behavior arise from “simple” interactions between processing units
• Weights encode knowledge about relationships between atomic facts, propositions, perceptions
• Networks are dynamic; representations evolve over time
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Next Section: Simple Learning
• For next class: read handout (from Handbook, Chapter 4, pages 83-89; see web page)
• Homework 1 due (but two day grace period)
• Next homework handed out. Due Feb 15.