The Neural Basis of Thought and Language Week 15 The End is near...

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The Neural Basis of

Thought and Language

Week 15

The End is near...

Schedule

• Final review Sunday May 8 th ?

• Final paper due Tuesday, May 10 th , 11:59pm

• Final exam Tuesday, May 10 th in class

• Last Week

– Psychological model of sentence processing

– Applications

• This Week

– Wrap-Up

Bayesian Model of Sentence Processing

• What is it calculating?

• What computational components is it composed of?

• What is it used to predict?

What phenomena does it explain?

Bayesian Model of Sentence Processing

• Situation

– You’re in a conversation.

Do you wait for sentence boundaries to interpret the meaning of a sentence?

• No!

– After only the first half of a sentence...

• meaning of words can be ambiguous

• but you still have an expectation

• Model

– Probability of each interpretation given words seen

– Stochastic CFGs, Lexical valence probabilities, N-Grams

Lexical Valence Probability

• Syntactic Category:

– S-bias verbs (e.g. suspect) / NP-bias verbs (e.g.

remember)

– Transitive (e.g. walk the dog)/ Intransitive (e.g. walk to school)

– Participle-bias (VBD; perfect tense) (e.g. selected)/

Preterite-bias (VBN; simple past tense) (e.g.

searched)

• Semantic Fit (Thematic Fit):

– cop, witness: good agents

– crook, evidence: good patients

SCFG

• “that” as a COMP (complementizer):

– [OK] The lawyer insisted that experienced diplomats would be very helpful

– That experienced diplomats would be very helpful made the lawyer confident.

• “that” a DET (determiner):

– The lawyer insisted that experienced diplomat would be very helpful

– [OK] That experienced diplomat would be very helpful to the lawyer.

Sentence-initial that interpreted as complementizer is infrequent

P(S → SBAR VP) = .00006

P(S → NP ...) = .996

Post-verbal that interpreted as determiner is infrequent

N-gram

• P(w i

| w i-1

, w i-2

, …, w i-n

)

• probability of one word appearing given the preceeding n words

• “take advantage” (high probability)

• “take celebration” (low probability)

Main Verb

S

NP VP

SCFG + N-gram

Reduced Relative

S

NP VP

NP VP

D N VBD

D N VBN PP

The cop arrested the detective The cop arrested by

Predicting effects on reading time

• Probability predicts human disambiguation

• Increase in reading time because of...

– Limited Parallelism

• Memory limitations cause correct interpretation to be pruned

The horse raced past the barn fell

– Attention

• Demotion of interpretation in attentional focus

– Expectation

• Unexpected words

A good agent (e.g. the cop, the witness) makes the main verb reading more likely initially… as one hears the word

by, the RR reading becomes the more likely one: shift in attention → slower reading time and the reduced relative reading less likely lexical valence probability

(semantic fit) predicts slower reading time The witness examined by the lawyer

A good patient (e.g. the crook, the evidence) makes the RR reading more likely initially… as one hears the word

by, the ranking of the two readings do not change → no effect on reading time and the MV reading less likely lexical valence probability

(semantic fit) agrees with the RR reading The evidence examined by the lawyer

Direct Object/Sentential Complement Ambiguity. Delay from Expectation .

The athlete realized her (exercises | potential) one day might make her a world...

GOOD LUCK!

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