1. Linguistic Society of America Annual Meeting
Vsevolod Kapatsinski
Indiana University
The influence of syllabic constituency on learning CV-affix vs. VC-affix associations: Constituency is more than dependency
Linguistic Society of America Annual Meeting
January 3-6, 2008.
Chicago, IL

2. Syllabic constituency in English
English syllables tend to have an onset-rime structure, rather than body-coda structure
Treiman and Danis (1988), Lee (2006):
If English speakers are presented with a list of syllables to memorize, they tend to make errors in which the onset of one syllable is combined with the rime of another syllable, rather than the body of one syllable being recombined with the coda from a different syllable.
On1 On2
Cd1 Cd2
N1 Cd1
N2 Cd2
b   d  g
b  g d  
On1 N1
On2 N2
b   d  g
d   b  g

3. Main question How should we represent syllabic constituency? N1 Cd1
Tree-structure On N Cd
st a nd Rm
Dependency
On1 On2
N1 Cd1
N2 Cd2
b   d  g

4. Main question What is constituency? Tree-structural view
(e.g., Fudge 1987)
Constituency is unithood.
A constituent is allocated
a node in the hierarchy.
Dependency-based view
(e.g., Vennemann 1988)
Constituency is connection strength.
Connections between parts of a
constituent are stronger than
connections that cross constituent
boundaries. On N Cd
st a nd Rm On N Cd
st a nd

5. What does it mean to say that the rime is allocated a node?
Under both tree-structural and dependency-based accounts, linguistic structures are represented with a network
A network is a set of nodes connected by links

6. What does it mean to say that the rime is allocated a node?
In a network, links must connect nodes.
 A node is something that can be linked to / associated with something else *

7. What does it mean to say that the rime is allocated a node?
Thus, to say that the rime is allocated a node while the body is not means to say that the rime can be associated with something else while the body cannot (or at least cannot as easily)
I.e., the rime is more associable than the body *

8. What does it mean to say that the rime is allocated a node?
So, the tree-structural account predicts that learning rime-affix associations should be easier than learning body-affix associations
For instance, learning that Cg stems take the affix /mn/ while C stems take /num/ should be easier than learning that gC stems take /mn/ while C stems take /num/.
The dependency-based account does not make this prediction.

9. An analytical issue
How do we know that a particular association is an association of the whole and not an association of the parts?
If we find that the rime is more easy to associate with an affix
than the body is, we need to make sure that this is not because
the coda is more associable than the onset before we argue for a
tree-structural model.

10. Solution: XOR learning
The parts and the whole should not be associated with the same affix.
For instance, if // is paired with /num/ than /C/ would be paired with /mn/, as would /V/.
In general,
Parts of the stem Affixes
A X B
A B
C D C
D Y ¬B ¬A ¬D ¬C
On N
N Cd

11. XOR learning: making parts and wholes compete
Stage I
Prefix Suffix
mn-C C-mn
Stage II
CC  num
CC  mn
CV  num
CVg  mn
CC  noom
VC  num
gVC  mn
C  mn
Cg  num
C  mn
gC  num
Subjects exposed to rime-affix pairings
Subjects exposed to body-affix pairings
Prefix Suffix
mn-C C-mn
17 native English speakers in each condition (68 total)

12. Methodological Details
During training, minimal pairs are presented to aid acquisition
ma – mn; mi – mn
ma – mn; mag – num
During test, min/noom replaced with noise
Amplitude contour, length average of mn/num
Tests involve generalization to novel syllables
During feedback, the entire phrase is pronounced when the subject makes the guess

13. Prediction Tree-structural Rime exists, body does not
Rime more associable
Accuracy on rimes should be better than on bodies
Even if syllables containing familiar rimes or bodies are novel and
Even if accuracy on codas and onsets does not differ
//-/mn/ //-/mn/
/g/-/num/ /g/-/num/
and
/mn/-// /mn/-// /num/-/g/ /num/-/g/
//-/num/
/g/-/mn/
/num/-//
/mn/-/g/

14. Rimes are more associable than bodies
Complete generalization:
Accuracy with novel syllables
containing familiar rimes (72%)
is not lower than accuracy with
familiar syllables containing
familiar rimes (69%).
Subjects are not just memorizing
syllables.
No evidence for an adjacency effect: rime-prefix associations learned as well as rime-suffix ones.
/mn/-// /mn/-//
//-/mn/ //-/mn/

15. Even though codas are as associable as onsets and subjects assigned to rime and body conditions are equally good learners
The lack of a difference between onset and coda also suggests that the rime/body difference is not
due to the fact that the rime occurs after the body.

16. Summary Rimes are more associable than bodies This is not due to
Adjacency
Differences in associability between onsets and codas
Serial order
Differences in learning ability between subjects

17. Theoretical implications
Rimes are more associable than the bodies
This is predicted by the tree-structural account of constituency but not by the dependency-based account
In general, the tree-structural account proposes that constituents are processing units: the chunks that are likely to be parsed out of the signal (the rime is more likely to be parsed out of the signal than the body)
This claim needs to be verified separately at different levels in the linguistic hierarchy.
One way in which this can be done is using XOR learning and related configural learning paradigms (e.g., biconditional discrimination, Saavedra 1975) whose essential feature is that the associations of the whole are not associations of the parts.

18. An open question
What makes something likely to be extracted from the signal?
Overall statistics of the language
Overall, nucleus-coda co-occurrences are much less random than onset-nucleus co-occurrences in English while the opposite is true in Korean (Kessler and Treiman 1997, Lee 2006).
Accordingly, English speakers tend to recombine onsets and rimes in serial recall while Korean speakers tend to recombine bodies and codas (Lee 2006, Yoon and Derwing 2001)
even when statistics of co-occurrence within the syllables presented for memorization do not favor either the body or the rime (Lee 2006).
Tree-structural model:
Rimes extracted in English
Bodies extracted in Korean

19. An open question
What makes something likely to be extracted from the signal?
Between-part co-occurrence (Lee 2006)
Lee (2006): English speakers can show a tendency to recombine bodies and codas if syllables in the to-be-memorized list are carefully selected so that the nucleus co-occurs with the coda more often than it co-occurs with the onset
Potential lexicality (e.g., Cutler et al. 2001)
In an English syllable with a lax vowel, the body cannot be a free-standing word while the rime can. Thus, the rime is a possible word while the body is not
 Not all rimes are equally likely to be extracted from the signal

20. An open question
What makes something likely to be extracted from the signal?
Degree of coarticulation (e.g., Goldinger and Azuma 2003)
Usefulness in the task (e.g., Nosofsky et al. 1994)
A node representing a linguistic chunk may be parsed out when needed (when it has properties that are not inherited from its parts).
 Not all instances of the same rime or body are equally likely to be extracted from the signal

21. Conclusion
The results of the present study provide support for the view that syllabic constituents are more likely to be parsed out of the signal than non-constituents, the idea inherent in tree-structural representations of constituency.
There are multiple factors that can make something likely to be parsed out of the signal
Language-specific, syllable-specific, token-specific factors
Open question: do all factors that contribute to constituency contribute to parseability?
XOR learning, and configural learning more generally, presents a new way to assess the nature of constituent structure in various domains.

22. Acknowledgments NIH for funding
Nicholas Altieri for pronouncing the stimuli
Tessa Bent, Adam Buchwald and Rob Felty for helpful comments
Luis Hernandez for programming help

23. References
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