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The Neural Basis of Thought and Language

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.

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The Neural Basis of Thought and Language

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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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