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

2. 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

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(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)

8. SCFG + N-gram Main Verb Reduced Relative S NPVP DNVBD Thecoparrestedthedetective S NPVP NPVP DNVBNPP Thecoparrestedby

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… and the reduced relative reading less likely as one hears the word by, the RR reading becomes the more likely one: shift in attention shift in attention → slower reading time 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… and the MV reading less likely no effect as one hears the word by, the ranking of the two readings do not change → no effect on reading time lexical valence probability (semantic fit) agrees with the RR reading The evidence examined by the lawyer

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

13. GOOD LUCK!