Psychology 85-419/719 January 30, 2001

1. Processing and Constraint Satisfaction: Psychological ImplicationsThe Interactive-Activation (IA)Model of Word Recognition Psychology 85-419/719 January 30, 2001

2. 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...

3. 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?

4. O or U? N or M? Letter Perception In Context:The Phenomena DOG #### BNR ####

5. 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)

6. 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 Assumptions of the Model

7. “Context” Word Level Letter Level Phoneme Level Feature Level Acoustic Level Spelling Speech Visual Input Acoustic Input The Overall Model Implemented Model

8. Representations of Visual Features 16 features, each corresponding to a line segment. 4 slots, one for each letter.

9. cat dog lake Letter level: Inhibitory and excitatory connections c a Feature Level (still more connections) Levels of Representation Word Level: Inhibitory connections

10. 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.

11. 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

12. The Running Average(Eq. 5) In simulations, r=0.05

13. Strength of option i is: Probability of Response for i is: Response Probability(Eq. 6 & 7) In simulations, u = 10

14. work word wear Model Behavior: Degraded Input

15. k r a Letter Activations Too:

16. “e” in “read” “e” alone Mask applied The Word Preference Effect • When stimuli is masked, letters embedded in words are perceived more accurately than letters standing alone

17. Probabilities... Subjects: 80% for word 65% alone 1 Model: 81% for word 66% alone “e” in “read” Probability “e” alone 0

18. 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.

19. 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.

20. Interactions...

21. 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.

22. 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

23. 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)

24. Have (high frequency) Gave (medium frequency) Save (low frequency) The “Rich Get Richer” Effect

25. Save. Part of large gang Male. Also, large gang Move. Member of smaller gang The “Gang Effect”

26. Word Level Letter Level Phoneme Level Feature Level Acoustic Level Visual Input Acoustic Input Other Phenomena? Semantic Priming: TOAD primes FROG Two-hop: STRIPES primes LION Lesch & Pollatsek ‘93: TOWED primes FROG Impairments?

27. The “Slot Problem”

28. 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 Wrapping Up Section I: Constraint Satisfaction

29. 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.