Statistics and Rules in Language Acquisition: Constraints and the Brain Richard N. Aslin Department of Brain and Cognitive Sciences University of Rochester CALACEI Conference, Trieste, Italy Tools to Study Language Acquisition in Early Infancy May 6, 2006

Outline 1. What is Statistical Learning (SL)? 2. How is SL constrained? 3.Neural correlates of visual SL 4.Implications of SL for rule learning (RL)

1. What is SL? Acquisition of structured information by listening or observing No reinforcement or feedback Sensitivity to frequency or probability distributions

Why is SL interesting? Something like SL must be how language is acquired  no instructor SL appears to be implausible –Computations involved (infinite # statistics) –Limits of information processing (real-time flow of input and demands on working memory)

Why word segmentation? Tractable problem Must be solved early by all language learners (words are defined similarly across languages) Illustrative of distributional learning mechanism that may apply more broadly

Sequence of elements: A-B-C-D-E-F-G-H-I-J-K-L... Test triplets: D-E-F vs. I-J-K Saffran, Aslin & Newport (1996)

Domains and species SL operates on human speech and tones (Saffran et al., 1996a,b; 1999), as well as on visual shapes in temporal (Fiser & Aslin, 2002; Kirkham et al., 2002) and spatial domains (Fiser & Aslin, 2001, 2002). SL operates in human adults, infants, tamarin monkeys (Hauser et al., 2001, 2004), and rats (Toro & Trobalon, 2005); rats fail higher- order SL.

2. How is SL constrained? Gestalt principles –Proximity (Newport & Aslin, 2004; Pena et al, 2002) –Similarity (Creel, Newport & Aslin, 2004) –Good continuation (Fiser, Scholl & Aslin, in press) Social/attentional cues (Yu, Ballard & Aslin, 2005) Preferred units over which statistics are computed (Newport, Weiss, Wonnacott & Aslin, 2004) Redundancy reduction (Fiser & Aslin, 2005) Primacy (Gebhart, Aslin & Newport, in preparation)

Happy birthday to you Twinkle twinkle little star Element similarity Twinkle twinkle little starhappy birthday to you

AgBhCi Creel, Newport & Aslin (2004) TPs between adjacent tones = 0.5 and 0.25 Same octave

ghi …...ABC... Different octaves TPs between adjacent tones = 0.5 and 0.25

Results Same OctaveDiff Octaves

Are syllables (CV) or segments (C and V) the preferred unit for SL? Saffran, Newport & Aslin (1996: adults) and Saffran, Aslin & Newport (1996: infants) assumed that syllable transitional probabilities were the relevant computational unit However, BOTH syllable and segment transitional probabilities in our artificial languages would parse the speech streams in the same way

Syllables AND Segments

Syllables NOT Segments 1.0.5

What about infants? No previous work has examined this question for statistical computations But there is a literature on infant perception of segments and syllables –Jusczyk & Derrah: 2 mos old - syllables –Mehler et al.; Jusczyk: development from syllables  segments? –Kuhl, Hillenbrand: 12 mos old - segments (or acoustic similarity)

Syllables AND Segments

Syllables NOT Segments 1.0.5

Infants: Syllables NOT segments

The Statistical ‘Garden Path’ Two languages with different words and partial overlap of syllables Expose to Lang A + Lang B (5 min each) No pause between languages Post-test: –words vs. partwords in A –words vs. partwords in B

5 min of exposure to Lang A or B alone 5 min of exposure each to Lang A+B chance

Add 30 sec pause between languages Change pitch of synthetic voice Triple duration of 2 nd language (15 min) chance

Eliminate syllable differences (all identical) –5 min exposure and test Lang A or B alone –Test for word vs. partword in each language chance Primacy: learning first structure ‘blocks’ new structure

3. Neural correlates of SL Statistical learning in the visual modality: spatial structure, not temporal structure How are higher-order visual features represented in the brain? –Hemisphere bias in SL and interhemispheric transfer –fMRI activations of brain regions during SL

Background Can mere exposure to a series of scenes enable adult learners to extract features defined by shape-conjunctions? (Fiser & Aslin, 2001)

Six base-pairs Fit three base-pairs into 3 X 3 grid

Testing phase 2AFC task Base-pair vs. Non-base pair E F IJ A B A B Base-pair 70% correct IF Non-base pair

Split the base-pairs Fiser, Roser, Aslin & Gazzaniga (in prep) 2 deg

Modified test phase Ipsilateral: Practice: RHTest: RH Practice: LHTest: LH Contralateral: Practice: RHTest: LH Practice: LH Test: RH Four lateralized test types

Subjects Normal subjects: Sixteen college students Callosotomy patient: V.P. (Corballis et al. Neurology 2001)

Results with normal subjects Equal learning in all conditions  interhemispheric transfer Chance

Contralateral: No interhemispheric information transfer Ipsilateral: Strong right hemisphere advantage * Chance Results with the split brain patient

Event-Related fMRI Design – LEARNING PHASE 2500 Baseline fix (4 TRs) /5000/7500 StimulusJitter Trials /5000/75000 StimulusJitter Trials Instructions 144 Stimuli each presented once – Divided into 3 Runs of 6 min each

TEST PHASE 2500 Baseline fix (4 TRs) /5000/7500 Stimulus + Response Jitter Trials /5000/75000 Stimulus + Response Jitter Trials Base PairNon Base Pair 48 test trials: 24 base-pairs, 24 non base-pairs  yes/no familiarity task

Learning Phase: final 1/3 vs. initial 1/3 Right Parietal Activation Consistent with split-brain findings

4. Implications of SL for RL Generalization to new tokens: Rule-learning –Gomez & Gerken (1999) –Marcus et al. (1999) –Pena et al. (2002) –Saffran & Wilson (2003) Not based on perceptual similarity Could be based on surrounding context (Mintz, 2003) and on category variability (Gomez, 2002; Gomez & Maye, 2005

What enables RL? Obtained with strings, not streams Pauses enable encoding of position info High variability in a sea of stability may induce categories by down-weighting the category exemplars and then enabling their differences to be learned after “frequent frames” (Mintz, 2003; Santelmann & Jusczyk, 1998) are established

RL vs. SL: Different mechanism? RL operates over categories rather than over surface forms. Computation of statistics over categories may involve the same SL mechanism as computation over surface forms  only a difference in input? RL in tamarins (Hauser, Weiss & Marcus, 2002) suggests that RL is not unique to language learning.

Conclusions Statistical learning is ubiquitous and powerful. SL must be constrained to operate efficiently and to extract the “right” structure. The search for neural correlates of SL is ongoing. Whether SL can also operate at the level of categories or whether RL involves a separate mechanism remains unclear.

Thanks to my collaborators and funding sources Elissa Newport Jenny Saffran Jozsef Fiser Andrea Gebhart Sarah Creel Matt Roser Mike Gazzaniga NIH, Packard Foundation, McDonnell Foundation

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Why conditionalized statistics? Element frequency (N-gram) is a poor predictor of underlying structure. –Many high frequency sounds appear in multiple contexts –Conditional probabilities are computable by adults and infants (and in classical conditioning by rats, but not in speech) But element frequency can serve as an “anchor” or a “filter” on how SL operates.

With what fidelity? How much input is needed to compute the relevant statistic(s)? –Brent & Siskind (2001) –Mintz, Newport & Bever (2002) What decision mechanism operates on those stored statistical values? –Local minimum vs. hard threshold –How many bits of resolution? Is a transitional probability difference of 0.43 > 0.39 relevant?

Are SL studies just “toy” demos? Saffran et al. used simple structures Swingley (2005) showed that similar structures are present in IDS.

Which unit? Saffran, Aslin & Newport (1996) presumed the unit was the syllable. Newport et al. (BU: 2004) showed that SL in speech streams is computed over segments (Cs & Vs), not syllables. Other cues are clearly important: Saffran et al. (1996): Although experience with speech in the real world is unlikely to be as concentrated as it was in these studies, infants in more natural settings presumably benefit from other types of cues correlated with statistical information.

Fiser, Scholl & Aslin (in press) Bouncing vs. streaming

Perception of bouncing or streaming biases statistical learning “streaming”

3. What are the limits of SL? Some minimal “attention” is required. –Saffran et al. (1997) –Turke-Brown, Junge & Scholl (2005) –Toro, Sinnett & Soto-Faraco (in press) In streams of syllables, non-adjacent learning is difficult. –Newport & Aslin (2004) –Pena et al. (2002) Unfamiliar elements (noises) are hard to learn. –Gebhart, Newport & Aslin (2004)

Test phase: correct – incorrect