Statistical Language Learning: Mechanisms and Constraints

1. Statistical Language Learning:Mechanisms and Constraints Jenny R. Saffran Department of Psychology & Waisman Center University of Wisconsin - Madison

3. What kinds of learning mechanisms do infants possess? • How do infants master complex bodies of knowledge? • Learning requires both experience & innate structure - bridge between nature & nurture? • Constraints on learning: computational, perceptual, input-driven, maturational… all neural, though we are not working at that level of analysis

4. Language acquisition:Experience versus innate structure • How much of language acquisition can be explained by learning? • Language-specific linguistic structures • Learning does not offer transparent explanations… • How is abstract linguistic structure acquired? • Why are human languages so similar? • Why can’t non-human learners acquire human language?

5. Today’s talk: Consider a new approach to language learning that may begin to address some of these outstanding central issues in the study of language & beyond

6. Statistical Learning freq XY freq X pr Y|X =

7. Statistical Learning freq XY freq X pr Y|X = What computations are performed? What are the units over which computations are performed? Are these the right computations & units given the structure of human languages?

8. Breaking into language

9. Word segmentation

10. Word segmentation cues • Words in isolation • Pauses/utterance boundaries • Prosodic cues (e.g., word-initial stress in English) • Correlations with objects in the environment • Phonotactic/articulatory cues • Statistical cues

11. Statistical learning High likelihood High likelihood Low likelihood PRE TTYBA BY Continuations within words are systematic Continuations between words are arbitrary

12. Transitional probabilities PRETTYBABY (freq) pretty (freq) pre .80 versus (freq) tyba (freq) ty .0002

13. Infants can use statistical cues to find word boundaries • Saffran, Aslin, & Newport (1996) • 2 minute exposure to a nonsense language (tokibu, gopila, gikoba, tipolu) • Only statistical cues to word boundaries • Tested on discrimination between words and part-words (sequences spanning word boundaries)

14. Experimental setup

15. Headturn Preference Procedure

16. tokibugikobagopilatipolutokibu gopilatipolutokibugikobagopila gikobatokibugopilatipolugikoba tipolugikobatipolugopilatipolu tokibugopilatipolutokibugopila tipolutokibugopilagikobatipolu tokibugopilagikobatipolugikoba tipolugikobatipolutokibugikoba gopilatipolugikobatokibugopila

17. tokibugikobagopilatipolutokibu gopilatipolutokibugikobagopila gikobatokibugopilatipolugikoba tipolugikobatipolugopilatipolu tokibugopilatipolutokibugopila tipolutokibugopilagikobatipolu tokibugopilagikobatipolugikoba tipolugikobatipolutokibugikoba gopilatipolugikobatokibugopila

18. tokibugikobagopilatipolutokibu gopilatipolutokibugikobagopila gikobatokibugopilatipolugikoba tipolugikobatipolugopilatipolu tokibugopilatipolutokibugopila tipolutokibugopilagikobatipolu tokibugopilagikobatipolugikoba tipolugikobatipolutokibugikoba gopilatipolugikobatokibugopila

19. Results Looking times (sec) *

20. Detecting sequential probabilities • Statistical learning for word segmentation • Infants track transitional probabilities, not frequencies of co-ocurrence (Aslin, Saffran, & Newport, 1997) • The first useable cue to word boundaries: Use of statistical cues precedes use of lexical stress cues (Thiessen & Saffran, 2003) • Statistical learning is facilitated by the intonation contours of infant-directed speech (Thiessen, Hill, & Saffran, 2005) • Infants treat “tokibu” as an English word (Saffran, 2001) • Emerging “words” feed into syntax learning (Saffran & Wilson, 2003) • Other statistics useful for learning phonetic categories, lexical categories, etc. • Beyond language: Domain generality • Tone sequences (Saffran et al., 1999; Saffran & Griepentrog, 2001)golabupabikututibudaropi... AC#EDGFCBG#A#F#D#… • Visuospatial & visuomotor sequences (Hunt & Aslin, 2000; Fiser & Aslin, 2003) • Even non-human primates can do it! (Hauser, Newport, & Aslin, 2001)

21. So does statistical learning really tell us anything about language learning?

22. Language acquisition:Experience versus innate structure • How much of language acquisition can be explained by learning? • Language-specific linguistic structures  • Learning does not offer transparent explanations… • How is abstract linguistic structure acquired? • Why are human languages so similar? • Why can’t non-human learners acquire human languages?

23. Acquisition of basic phrase structure • Words occur serially, but representations of sentences contain clumps of words (phrases) How is this structure acquired?Where does it come from? • Innately endowed as part of Universal Grammar (X-bar theory)? • Prosodic cues? (probabilistically available) • Predictive dependencies as cues to phrase units cross-linguistically (c.f. mid-20th-century structural linguistics: phrasal diagnostics) • Nouns often occur without articles, but articles usually require nouns: *The walked down the street. • NP often occurs without prepositions, but P usually requires NP *She walked among. • NP often occurs without Vtrans, but Vtrans usually requires object NP *The man hit.

24. Statistical cue to phrase boundaries • Unidirectional predictive dependencies  high conditional probabilities • Can humans use predictive dependencies to find phrase units? (Saffran, 2001) • Artificial grammar learning task • Dependencies were the only phrase structure cues • Adults & kids learned the basic structure of the language

25. Statistical cue to phrase boundaries • Predictive dependencies assist learners in the discovery of abstract underlying structure.  Predicts better phrase structure learning when predictive dependencies are availablethan when they are not. **Constraint on learning: Provides potential learnability explanation for why languages so frequently contain predictive dependencies**

26. Do predictive dependencies enhance learning? • Methodology: Contrast the acquisition of two artificial grammars (Saffran, 2002) • Predictive language • - Contains predictive dependencies between word classes as a cue to phrasal units • Non-predictive language • - No predictive dependencies between word classes

27. Predictive language S  AP + BP + (CP) AP  A + (D) BP  CP + F CP  C + (G) A = BIFF, SIG, RUD, TIZ Note: Dependencies are the opposite direction from English (head-final language) A, AD C, CG

28. Non-predictive language S  AP + BP AP  {(A) + (D)} BP  CP + F CP  {(C) + (G)} e.g., in English: *NP  {(Det) + (N)} A, D, AD C, G, CG Det, N, Det N

29. Predictive vs. Non-predictive language comparison • PN • Sentence types 12 9 • Five word sentences 33% 11% • Three word sentences 11% 44% • Lexical categories 5 5 • Vocabulary size 16 16

30. Experiment 1 • Participants: Adults & 6- to 9-year-olds • Predictive versus Non-predictive phrase structure languages • Language: Between-subject variable • Incidental learning task • 40 min. auditory exposure, with descending sentential prosody • Auditory forced-choice test • Novel grammatical vs. novel ungrammatical • Same test items for all participants RUD BIFF HEP KLOR LUM CAV DUPP. LUM TIZ.

31. Results * * Mean score (chance = 15)

32. Experiment 2: Effect of predictive dependencies beyond the language domain? • Same grammars, different vocabulary: • Nonlinguistic materials: Alert sounds • Exp. 1 materials (Predictive & Non-predictive grammars and test items), translated into non-linguistic vocabulary • Adult participants

33. Linguistic versus non-linguistic * * Mean score (chance = 15)

34. New auditory non-linguistic task: Predictive vs. Non-predictive languages

35. Non-linguistic replication * * * Mean score (chance = 15)

36. Predictive language > Non-predictive language • Predictive dependencies play a role in learning • For both linguistic & non-linguistic auditory materials • Also seen for simultaneous visual displays • But not sequential visual displays  modality effects • Human languages may contain predictive dependencies because they assist the learner in finding structure. • The structure of human languages may have been shaped by human learning mechanisms.  Predict different patterns of learning for appropriately aged human learners versus non-human learners.

37. Infant/Tamarin comparison: Methodology(with Marc Hauser @ Harvard) Headturn Preference Procedure:Orienting Procedure: Laboratory exposure Home cage exposure Test: Measure looking times Test: Measure % orienting responses Paired methods previously used in studies of word segmentation, simple grammars, etc. (Hauser, Newport, & Aslin, 2001; Hauser, Weiss, & Marcus, 2002; etc.)

38. Materials • Predictive vs. Non-Predictive languages (between Ss) • Small Grammar: Used to validate methodology • Grammars written over individual words, not categories (one A word, one C word, etc.) • 8 sentences, repeated • 2 min. exposure (infants) or 2 hrs. exposure (tamarins) • Grammatical (familiar) vs. ungrammatical test items • Large Grammar: Languages from adult studies • Grammars written over categories (category A, C, etc.) • 50 sentences, repeated • 21 min. exposure (infants) or 2 hrs. exposure (tamarins) • Grammatical (novel) vs. ungrammatical test items

39. Tamarin results * Small grammar Large grammar

40. Tamarin results * Small grammar Large grammar

41. Infant results (12-month-olds, 12 per group) Looking times (sec) * Small grammar * Looking times (sec) Large grammar

42. Cross-species differences • Small grammar vs. large grammar • Tamarins only learned the small grammar • Difficulty with generalization? Memory for sentence exemplars? • Can learn patterns over individual elements but not categories? • Infants learned both systems, despite size of large grammar • Availability of predictive dependencies • Only affected the tamarins learning the small grammar • Affected the infants regardless of the size of the grammar • Consistent with constrained statistical learning hypothesis human learning mechanisms may have shaped the structure of natural languages

43. Constrained statistical learning as a theory of language acquisition? • Word segmentation, aspects of phonology, aspects of syntax • Developing the theory • Scaling up:Multiple probabilistic cues in the input (e.g., prosodic cues), multiple levels of language in the input, more realistic speech (e.g., IDS) • Mapping to meaning:Are statistically-segmented ‘words’ good labels? • Critical period effects: Exogenous constraints on statistical learning • Modularity: Distinguishing domain-specific & domain-general factors • e.g., statistical learning of “musical syntax” • Bilingualism: Separating languages & computing separate statistics • Relating to real acquisition outcomes: Individual differences • Patients with congenital amusia with Isabelle Peretz, U. de Montreal • Specific Language Impairment study with Dr. Julia Evans, UW-Madison

44. Conclusions • Infants are powerful language learners: Rapid acquisition of complex structure without external reinforcement • However, humans are constrained in the types of patterns they readily acquire • Understanding what is *not* learnable may be just as valuable as cataloging what infants *can* learn  These predispositions may be among the factors that have shaped the structure of human language

45. Acknowledgements Infant Learning Lab UW-Madison • National Institutes of Health RO1 HD37466, P30 HD03352 • National Science Foundation PECASE BCS-9983630 • UW-Madison Graduate School • UW-Madison Waisman Center • Members of the Infant Learning Lab • All the parents and babies who have participated!