Stop-Consonant Perception in 7.5-month-olds: Evidence for gradient categories Bob McMurray & Richard N. Aslin Department of Brain and Cognitive Sciences University of Rochester Title Slide With thanks to Julie Markant & Robbie Jacobs

Understanding spoken language requires that children learn a complex mapping… Learning Language What is the form of this mapping? How do the demands of learning affect this representation? Lexicon All labs Bob’s lab NP the lab S VP produced Meaning Language Understanding

Speech perception and word recognition require mapping… Learning Speech What representations mediate acoustics and lexical or sublexical units? How does learning affect this representation? Syntax, semantics, pragmatics… Speech Recognition …continuous, variable perceptual input to a something discrete, categorical.

1)Acoustic mappings: Categorical and gradient perception in adults and infants. 2)Infant speech categories are graded representations of continuous detail. 3)Statistical learning models and sparse representations. 4)Conclusions and future directions. Overview

What is the nature of the mapping between continuous perception and discrete categories? How are these representations sensitive (or not) to within-category detail? Categorization & Categorical Perception Representation of Speech Detail Empirical approach: Use continuously variable stimuli. Explore response using Discrimination Identification (adults) Habituation (infants)

Categorical Perception 1 B P Subphonemic within-category variation in VOT is discarded in favor of a discrete symbol (phoneme). Sharp labeling of tokens on a continuum. VOT PB % /p/ ID (%/pa/) Discrimination Discrimination poor within a phonetic category. Categorical Perception

Categorical Perception 2 Many tasks have demonstrated within- category sensitivity in adults... Discrimination Task Variations Pisoni and Tash (1974) Pisoni & Lazarus (1974) Carney, Widin & Viemeister (1977) Training Samuel (1977) Pisoni, Aslin, Perey & Hennessy (1982) Goodness Ratings Miller (1997) Massaro & Cohen (1983) BUT… And lexical activation shows systematic sensitivity to subphonemic detail (McMurray, Tanenhaus & Aslin, 2002).

Infant Categorical Perception 1 Infants have shown a different pattern. For 30 years, virtually all attempts to address this question have yielded categorical discrimination. Categorical Perception in Infants Exception: Miller & Eimas (1996). Only at extreme VOTs. Only when habituated to non- prototypical token. GWB

Infant Categorical Perception 3 Nonetheless, infants possess abilities that would require within-category sensitivity. Infants can use allophonic differences at word boundaries for segmentation (Jusczyk, Hohne & Bauman, 1999; Hohne, & Jusczyk, 1994) Infants can learn phonetic categories from distributional statistics (Maye, Werker & Gerken, 2002).

Distributional Learning 2 Speech production causes clustering along contrastive phonetic dimensions. Distributional Learning E.g. Voicing / Voice Onset Time B:VOT ~ 0 P:VOT ~ 40 Result: Bimodal distribution Within a categories, VOT is distributed Gaussian. VOT 0ms40ms

track frequencies of tokens at each value along a stimulus dimension. VOT frequency 0ms50ms Distributional Learning 1 Distributional Learning To statistically learn speech categories, infants must: This requires ability to track specific VOTs. Extract categories from the distribution. +voice-voice

? Question 1 Prior examinations of speech-categories used: Habituation Discrimination not ID. Possible selective adaptation. Possible attenuation of sensitivity. Synthetic speech Not ideal for infants. Single exemplar/continuum Not necessarily a category representation Experiment 1: Reassess this issue with improved methods.

HTPP 1 Misperception 3 Head-Turn Preference Procedure (Jusczyk & Aslin, 1995) Infants exposed to a chunk of language: Words in running speech. Stream of continuous speech (ala statistical learning paradigm). Word list. Head-Turn Preference Procedure After exposure, memory for exposed items (or abstractions) is assessed by comparing listening time to consistent items with inconsistent items.

HTPP 2Misperception 3 Test trials start with all lights off.

HTPP 2 Misperception 3 Center Light blinks.

HTPP 3 Misperception 3 Brings infant’s attention to center.

HTPP 3 Misperception 3 One of the side-lights blinks.

When infant looks at side-light… …he hears a word Beach… Beach… Beach… HTPP 4 Misperception 3

…as long as he keeps looking. HTPP 5 Misperception 3

Experiment 1 Methods Misperception 3 Experiment month old infants exposed to either 4 b-, or 4 p-words. 80 repetitions total. Form a category of the exposed class of words. PeachBeach PailBail PearBear PalmBomb Measure listening time on… VOT closer to boundary Competitors Original words Pear*Bear* BearPear Bear

Experiment 1 Stimuli Misperception 3 B* and P* were judged /b/ or /p/ at least 90% consistently by adult listeners. B*: 97% P*: 96% Stimuli constructed by cross-splicing naturally produced tokens of each end point. B:M= 3.6 ms VOT P:M= 40.7 ms VOT B*: M=11.9 ms VOT P*: M=30.2 ms VOT

Experiment 1 Familiarity vs. Novelty Misperception 3 Novelty/Familiarity preference varies across infants and experiments. 1221P 1636B FamiliarityNovelty Within each group will we see evidence for gradiency? Familiarity vs. Novelty We’re only interested in the middle stimuli (b*, p*). Infants were classified as novelty or familiarity preferring by performance on the endpoints.

Categorical Experiment 1 Fam. vs. Nov. 2 Misperception 3 Gradiency What about in between? After being exposed to bear… beach… bail… bomb… Infants who show a novelty effect… …will look longer for pear than bear. Gradient Bear*BearPear Listening Time

TargetTarget*Competitor Listening Time (ms) Experiment 1 Results Experiment 1 Results Nov B P Exposed to: Novelty infants (B: 36 P: 21) Target vs. Target*: Competitor vs. Target*: p<.001 p=.017

Experiment 1 Results Fam Familiarity infants (B: 16 P: 12) Target vs. Target*: Competitor vs. Target*: P=.003 p= TargetTarget*Competitor Listening Time (ms) B P Exposed to:

Experiment 1 Results Planned P Misperception 3 Planned Comparisons Infants exposed to /p/ Novelty N=21 PP*B.024*.009** PP*B.024*.009** Listening Time (ms) P*B *.028*.018* P Listening Time (ms).028* Familiarity N=12

Novelty N=36 <.001** >.1 <.001** > BB*P Listening Time (ms) Experiment 1 Results Planned B Misperception 3 Infants exposed to /b/ Familiarity N= BB*P Listening Time (ms).06.15

Experiment 1 Conclusions Misperception month old infants show gradient sensitivity to subphonemic detail. Clear effect for /p/ Effect attenuated for /b/. Experiment 1 Conclusions Contrary to all previous work:

Experiment 1 Conclusions 2 Misperception 3 Reduced effect for /b/… But: Bear Pear Listening Time Bear* Null Effect? Bear Pear Listening Time Bear* Expected Result?

Experiment 1 Conclusions 3 Misperception 3 Bear*  Pear Bear Pear Listening Time Bear* Actual result. Category boundary lies between Bear & Bear* Between (3ms and 11 ms). Will we see evidence for within-category sensitivity with a different range?

Experiment 2 Misperception 3 Same design as experiment 1. VOTs shifted away from hypothesized boundary (7 ms). Train 40.7 ms. PalmPear PeachPail 3.6 ms. Bomb*Bear* Beach* Bale* -9.7 ms. BombBear BeachBale Test: BombBear BeachBale -9.7 ms.

Experiment 2 Results Fam Misperception 3 Experiment 2 Results Familiarity infants (34 Infants) B-BP Listening Time (ms) =.05* =.01**

Experiment 2 Results Nov Misperception 3 Experiment 2 Results Novelty infants (25 Infants) =.02* =.002** B-BP Listening Time (ms)

Experiment 2 Conclusions Misperception 3 Experiment 2 Conclusions Within-category sensitivity in /b/ as well as /p/. VOT Adult boundary /b//p/ Category Mapping Strength Adult Categories Shifted category boundary in /b/: not consistent with adult boundary (or prior infant work). Why?

Experiment 2 Conclusions 2 Misperception 3 /b/ results consistent with (at least) two mappings. VOT Adult boundary /b//p/ Category Mapping Strength 1) Shifted boundary Inconsistent with prior literature. Why would infants have this boundary?

Experiment 2 Conclusions 3 Misperception 3 2) Sparse Categories /b/ VOT Adult boundary /p/ Category Mapping Strength unmapped space HTPP is a one-alternative task. Asks:B or not-Bnot:B or P Sparse categories may in fact by a by-product of efficient statistical learning.

Model Intro Misperception 3 Distributional learning model Computational Model 1)Model distribution of tokens as a mixture of gaussian distributions over phonetic dimension (e.g. VOT). 2)After receiving an input, the Gaussian with the highest posterior probability is the “category”.    VOT 3)Each Gaussian has three parameters:

Model Intro 2 Misperception 3 Statistical Category Learning 1) Start with a set of randomly selected Gaussians. 2)After each input, adjust each parameter to find best description of the input. 3)Start with more Gaussians than necessary model doesn’t innately know how many categories.  -> for unneeded categories. VOT

Model Intro 3 Misperception 3

Model Overgen Misperception 3 Overgeneralization large  costly: lose phonetic distinctions…

Model Undergen Misperception 3 Undergeneralization small  not as costly: maintain distinctiveness.

Model err on side of caution To increase likelihood of successful learning: err on the side of caution. start with small  Starting  P(Success) 2 Category Model 3 Category Model

Model Sparseness Sparseness coefficient: % of space not mapped to any category Training Epochs Avg Sparsity Coefficient Starting  VOT.5-1 Unmapped space Small 

Model Sparseness 2 Sparseness coefficient: % of space not mapped to any category Training Epochs Avg Sparsity Coefficient Starting  VOT.5-1

Model Sparseness 3 Sparseness coefficient: % of space not mapped to any category Training Epochs Avg Sparsity Coefficient Starting  VOT

Model Conclusions Small starting  ’s lead to sparse category structure during infancy—much of phonetic space is unmapped. Occasionally model leaves sparse regions at the end of learning. 1) Competition/Choice framework: Additional competition or selection mechanisms during processing allows categorization on the basis of incomplete information. Model Conclusions To avoid overgeneralization… …better to start with small estimates for 

Model Conclusions 2 Similar properties in terms of starting  and the resulting sparseness. 2) Non-parametric models VOT Categories Competitive Hebbian Learning (Rumelhart & Zipser, 1986). Not constrained by a particular equation—can fill space better.

Conclusions 3 Final Conclusions Infants show graded response to within-category detail. /b/-results suggest regions of unmapped phonetic space. Statistical approach provides support for sparseness. Given current learning theories, sparseness results from optimal starting parameters. Empirical test will require a two-alternative task. AEM: train infants to make eye-movements in response to stimulus identity.

Future Work Infants make anticipatory eye-movements along predicted trajectory, in response to stimulus identity. Two alternatives allows us to distinguish between category boundary and unmapped space.

Last Word Early speech categories emerge from an interplay of Exquisite sensitivity to graded detail in the signal. Long-term sensitivity to statistics of the signal. Early biases to optimize the learning problem VOT The last word