1. Speech Comprehension: Decoding meaning from speech

2. Disambiguating Homophones All meanings accessed at first, in all parts of speech (Swinney, 1979) –Heard: “Rumor had it that, for years, the government building had been plagued with problems. The man was not surprised when he found several spiders, roaches, and other bugs [1] in the [2] corner of the room.” –Seen: ant or spy or control sew –Task: Lexical decision –Facilitated for both meanings at position [1], but only for the appropriate meaning at position [2] Context disambiguates after lexical retrieval

3. A few pieces of trivia: The average college-educated adult has a speaking vocabulary of 75,000-100,000 words (Oldfield, 1963) If I guessed what word you said, and all words in a language were equally probable, my chances of guessing right would be between 0.00001 and 0.000013, but not all words are equally frequent.

4. Sentence parsing and syntactic ambiguity (a few definitions) Sentence parsing: assigning words to appropriate linguistic categories in order to determine the syntactic structure (figuring out who did what to whom) Syntactic ambiguity: more than one interpretation given the potential grammatical functions of the individual words

5. 2 kinds of syntactic ambiguity Local ambiguity: the sentence is ambiguous to a point –The bus driven past the school stopped Standing ambiguity: either reading/parsing of the sentence is acceptable –The old books and magazines were on the beach –I saw the man with the binoculars

6. 2 Models of Sentence Parsing The Garden Path Model The Constraint-Satisfaction Model

7. The old man the boats.

8. The old man the boats

9. The horse raced past the barn fell

10. A less dramatic example: Self-sealing bubble cushioned mailer

11. The Garden Path Model Perform one syntactic analysis, and if it doesn’t work, go back and start again 2 principles: –late closure Because Jay always jogs a mile … –minimal attachment (simplest syntactic structure) –Because Jay always jogs a mile, this seems like a short distance to him –NOT: Because Jay always jogs, a mile seems like a short distance to him

12. The Constraint Satisfaction Model More than one syntactic analysis of a sentence may be generated, but one is dominant If we discover we’ve made a parsing error, we activate an alternative interpretation

13. Pauses in speech Syntax, not CO 2, dictates when we pause! –(constituent boundaries) Most pauses come before words of low probability –Gives speaker time to retrieve the word –Warns listener that something unexpected is coming 40-50% of speaking time occupied with pauses

14. Verbatim Recall? Memory for actual surface structure fades quickly Memory for propositional content much stronger –The gentleman picked the cat up. –The gentleman picked up the cat. –The gentleman picked the bat up. Verbatim recall influenced by the nature of the message –personal criticism recalled fairly accurately

15. What do we remember? Sachs (1967) – subjects listened to paragraph-length stories that contained a critical test sentence, “He sent a letter about it to Galileo, the great Italian scientist” 4 conditions: –identical sentence –active/passive change: “A letter about it was sent to Galileo, the great Italian scientist” –formal change: “He sent Galileo, the great Italian scientist, a letter about it” –semantic change: “Galileo, the great Italian scientist, sent him a letter about it” Task: Determine Same/different?

16. Sachs (1967) - Results 100% accuracy with no intervening material for all conditions 80 syllables later: –Performed at chance on identical sentences –Detected active/passive changes and formal changes with 60-70% accuracy –Detected semantic changes with 85% accuracy The Moral: we store meaning more accurately than we store structure!

17. Context effects Shadowing (Marslen-Wilson, 1975) –Subjects rarely lag behind –With lags, word recognition within 200 ms of a word’s onset (in context) Gating (Grosjean, 1980) –Played sentences, and then the first __ms of the final word –What’s the final word? –Gates at 50, 100, 150 ms … until word is recognized 175-200 ms with context 333 ms average out of context

18. “I was going to take a train to New York, but I decided it would be too heavy.”

19. Clausal Processing: breaking up language into bite-sized chunks “I was going to take a train to New York, but I decided it would be too heavy.” We interpret the first clause before we hear the second!

20. How do we know? Reading – 2 techniques: –Self-paced reading Read one word at a time. Press a button to advance –Eye-tracking Shine an infrared light on retina, and this shows where the eye is moving

21. Self-paced reading: chunk effects Stine (1990) “The Chinese, who used to produce kites, used them in order to carry ropes across the rivers” –Long pause on 2 nd “used” –Longer pauses on “carry” and “ropes” Slow down at the beginning of a new clause to integrate new information with the information from the previous clause(s)

22. Time flies like an arrow