Speech Comprehension:Decoding meaning from speech

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

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.

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

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

2 Models of Sentence Parsing

• The Garden Path Model

• The Constraint-Satisfaction Model

The old man the boats.

The old man the boats

The horse raced past the barn fell

A less dramatic example:

Self-sealing bubble cushioned mailer

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

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

Pauses in speech

• Syntax, not CO2, 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

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

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?

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!

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

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

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!

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

Self-paced reading: chunk effectsStine (1990)

• “The Chinese, who used to produce kites, used them in order to carry ropes across the rivers”– Long pause on 2nd “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)

Time flies like an arrow