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Bayesian Model of Sentence Processing: Predicting Ambiguity in Language Comprehension

Explore the neural basis of thought and language through the Bayesian model of sentence processing. Learn how it calculates and predicts the meaning of ambiguous sentences, and its implications for reading time and comprehension. Final review, paper, and exam details included.

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Bayesian Model of Sentence Processing: Predicting Ambiguity in Language Comprehension

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  1. The Neural Basis ofThought and Language Week 15 The End is near...

  2. Schedule • Final review Sunday May 8th? • Final paper due Tuesday, May 10th, 11:59pm • Final exam Tuesday, May 10th in class • Last Week • Psychological model of sentence processing • Applications • This Week • Wrap-Up

  3. 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?

  4. 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

  5. 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

  6. 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 Post-verbal that interpreted as determiner is infrequent P(S → SBAR VP) = .00006 P(S → NP ...) = .996

  7. N-gram • P(wi | wi-1, wi-2, …, wi-n) • probability of one word appearing given the preceeding n words • “take advantage” (high probability) • “take celebration” (low probability)

  8. S S NP VP NP VP NP VP D N VBD D N VBN PP The cop arrested the detective The cop arrested by SCFG + N-gram Reduced Relative Main Verb

  9. 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

  10. 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

  11. 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

  12. Direct Object/Sentential Complement Ambiguity. Delay from Expectation. The athlete realized her (exercises | potential) one day might make her a world...

  13. GOOD LUCK!

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