A presentation by Kevin McMullin given on June 7, 2013 at UVic for the annual Canadian Linguistics Association conference.

1. Learning consonant harmony
in artificial languages
Kevin McMullin
University of British Columbia
CLA Conference
June 3, 2013 1

2. The project
2
Phonological
Theory
Formal
Learning
Typology Experimental
Learning

3. Today’s talk
3
Phonological
Theory
Formal
Learning
Typology Experimental
Learning

4. Overview
• Background
• Phonology and learning
• Consonant harmony
• Typology – locality
• Experimental design
• Results and analysis
• Discussion
• Future research
4

5. Phonology and learning
• We know a lot about the typology of phonological systems
• Why are some patterns quite common, while others are rare
or non-existent?
• Systematic sound change through misperception/misproduction
• OT constraint rankings
• Learning biases may also play a role
• Rare patterns should correspond to those that are harder to learn
• A pattern that is difficult to learn would be less likely to persist
over time in a language
• Do humans learn and generalize new phonological patterns in
a way that matches the typology?
5

6. Consonant harmony
• Two consonants are required to agree for some feature
• Example: Yaka has a form a nasal harmony
• The imperfective suffix /-ili-/ will surface with a nasal [n] if the verb
stem also has a nasal
• /jan-a/ ‘cry out in pain’ surfaces as [jan-ini] (*[jan-ili])
• Consonant harmony can have different
directionalities, domains, levels of locality, etc
• The most common form of consonant harmony targets sibilants
• [s…s] and [ʃ…ʃ] sequences are permitted, but not [s…ʃ] or [ʃ…s]
• There at least 130 languages that exhibit consonant harmony
(Hansson 2001/2010a)
• Classified as long-distance dependencies
6

7. Typology
• With respect to locality, there are just two types of consonant
harmony languages (Hansson 2001/2010a, Rose & Walker
2004)
• There are languages that apply harmony to two consonants only
when they are in adjacent syllables (or CVC sequences)
• CvCv, but not CvcvCv
• There are languages that apply harmony at all levels locality, no
matter the distances between them (within some domain)
• CvCv, CvcvCv, CvcvcvCv, etc
• There are no other types of consonant harmony languages
• No languages in between
• CvCv and CvcvCv dependencies, but not CvcvcvCv
• No language with nonlocal harmony, but not local harmony
• CvcvCv but not CvCv 7

8. Predictions
• Humans should learn and generalize consonant harmony
patterns in the same way
• If exposed to nonlocal harmony, they should generalize the
process to all contexts
• If exposed to local harmony, they should not apply the pattern (or
at least be less likely to) at nonlocal distances
8

9. Methodology
• 30 participants – 10x3 conditions
• Adult native English speakers
• Stimuli
• Recorded by a male speaker of English, unaware of the pattern in
the language
• Consisted of 'verbs’ that were 3 syllables (cvcvcv) and conjugated
verbs that added a suffix -su or -ʃi
• Consonants included stops {p,t,k,b,d,g} and sibilants {s, ʃ}
• Vowels included {i,e,a,o,u}
• All items had word-initial stress
• The language contained sibilant harmony, not allowing [s] and [ʃ] to
co-occur within a word
• If a word would otherwise have one of each, the suffix would trigger an
alternation in the root 9

10. Methodology
• 3 phases
• Practice phase
• Participants learned how to conjugate in the past tense using -su and
in the future tense using -ʃi
• 6 roots that were two syllables in length and contained no sibilants
• Training phase
• Participants heard and repeated 120 verb triplets twice each for a
total of 720 productions per subject
• Each triplet always began with the verb root and was followed by its
two suffixed forms, which were counterbalanced for order
• Testing phase
• Participants heard a verb root followed by two choices for the
“correct” conjugation. They were asked to pick which was correct by
pressing one of two buttons. 10

11. Methodology
• 3 conditions that differed only in their training
• Local – This group was trained with half of the verb roots being
cvcvSv, and the other half cvcvcv
• As a result, half of the triplets they produced showed evidence of a
sibilant alternation when the suffixes were added
• Nonlocal – This group was trained with half of the roots being
cvSvcv and half cvcvcv
• Control – This group was trained only on cvcvcv roots that
contained no sibilants
• As a result, this group did not see any evidence for or against sibilant
harmony
• This will reveal any biases that English speakers come in with, or that
arise from the design of the experiment
11

12. Methodology
Example stimuli
• Training phase
• Local: bugaso, bugaso-su, bugaʃo-ʃi
• Nonlocal: busago, busago-su, buʃago̠ʃi
• Testing phase
• patisu: patisu-ʃi, patiʃu̠ʃi
• ʃakabiːʃakabi̠̠ʃi, sakabi̠ʃi
• 10 testing items at each distance
12

13. Results in percentages
Control
(cvcvcv-Sv)
Local
(cvcvSv-Sv)
Nonlocal
(cvSvcv-Sv)
Locality levels (test items)
1 = cvcvSv-Sv
2 = cvSvcv-Sv
3 = Svcvcv-Sv
Training condition
Proportionofharmonyresponses
0.00.10.20.30.40.50.60.7
13

14. Intro to logistic regression
• A statistical method for analyzing categorical data
• Did the subject choose the test item with or without harmony?
• Finds the best fit for the log odds of choosing harmony on any
given trial based on some predictor variables
• Fixed: Group, testing distance, triggering suffix, pair order
• Random: Subject
• Solves problems introduced by using a linear model on
percentages
14

15. Log odds example
15
• As an example, imagine that we have 100 choices in total
• Unbounded dependent variable
• Does not violate assumptions about uniform variance
1st vs. 2nd item
choices
Probability of
choosing 1st
Odds of choosing
1st
Log odds of
choosing 1st
1 vs. 99 0.01 1/99 -4.60
25 vs. 75 0.25 1/3 -1.10
50 vs. 50 0.50 1/1 0
75 vs. 25 0.75 3/1 1.10
99 vs. 1 0.99 99/1 4.60
Range: 0 to 1 0 to ∞ -∞ to ∞

16. Logistic regression model
16
N=827, # of subjects=30, log-likelihood= -456.8
• Random intercepts and slopes for each subject

17. Discussion
• Subjects who were exposed to a local pattern did not
generalize the pattern outwards
• Subjects exposed to a nonlocal pattern generalized both
inwards to local contexts and outwards other nonlocal
contexts
• This matches the typology of consonant harmony systems
• No subjects had any experience with a harmony language
• There must be some sort of bias that influences how we learn
phonological patterns
17

18. Discussion
• These results are not the only way subjects could have learned
• Local group
• Could have applied harmony to all distances
• Nonlocal group
• Could have learned harmony and applied it only to distance 2
• Could have only generalized inwards
• The results have shown that an arbitrary split occurs both
typologically and in learning experiments
• Evidence that humans have certain learning biases that can
contribute to the shape of the world’s phonological patterns
18

19. Future work
• Immediately
• Is there a difference between learning consonant harmony and
long-distance consonant dissimilation?
• Can a formal model of learning account for the properties of
natural language and the results of learning experiments?
• A precedence model of learning (Heinz 2010)
• Not immediately
• Can subjects learn a consonant harmony pattern with blocking?
• Until recently no such patterns were thought to exist
• Recently several languages are thought to exhibit blocking
• e.g. Some Berber dialects (Elmedlaoui 1995, Hansson 2010b)
• How does similarity play a role?
• Are these biases restricted to language learning, or are they more
general cognitive biases? 19

20. Selected references
Elmedlaoui, Mohamed. 1995. Aspects des respesentations phonologiques dans
certains languages chamito-semitiques. Rabat: Faculté des Lettres et des
Sciences Humaines. [Doctoral dissertation, Université Mohammed V, 1992.]
Finley, Sara. 2011. The privileged status of locality in consonant harmony. Journal of
Memory and Language 65: 74–83.
Finley, Sara. 2012. Testing the limits of long-distance learning: Learning beyond the
three-segment window. Cognitive Science 36: 740–756.
Hansson, Gunnar Ólafur. 2001. Theoretical and typological issues in consonant
harmony. Doctoral dissertation, University of California, Berkeley.
Hansson, Gunnar Ólafur. 2010a. Consonant harmony: long-distance interaction in
phonology. Berkeley, CA: University of California Press.
Hansson, Gunnar Ólafur. 2010b. Long-distance voicing assimilation in Berber:
spreading and/or agreement? Actes du Congrès de l'ACL 2010 / 2010 CLA
Conference Proceedings, ed. by M. Heijl. Association canadienne de
linguistique / Canadian Linguistic Association. Available online at:
[http://homes.chass.utoronto.ca/~cla-acl/actes2010]
Heinz, Jeffrey. 2010. Learning long-distance phonotactics. Linguistic Inquiry 41 (4):
623–661.
Rose, Sharon, and Rachel Walker. 2004. A typology of consonant agreement as
correspondence. Language 80: 475–531.
20

21. Acknowledgements
• A big thank you to the following people:
• Gunnar Hansson
• Kathleen Hall
• Carla Hudson Kam
• Doug Pulleyblank
21

22. Individual responses
22
Control
Distance
Proportionharmony
0.0
0.2
0.4
0.6
0.8
1.0
1 2 3
2 3
1 2 3
4 5
6 10 11
0.0
0.2
0.4
0.6
0.8
1.0
12
0.0
0.2
0.4
0.6
0.8
1.0
13
1 2 3
14
Observed probabilities Fitted probabilities

23. Individual responses
23
Local
Distance
Proportionharmony
0.0
0.2
0.4
0.6
0.8
1.0
1 2 3
101 102
1 2 3
104 105
106 107 108
0.0
0.2
0.4
0.6
0.8
1.0
109
0.0
0.2
0.4
0.6
0.8
1.0
110
1 2 3
111
Observed probabilities Fitted probabilities

24. Individual responses
24
Nonlocal
Distance
Proportionharmony
0.0
0.2
0.4
0.6
0.8
1.0
1 2 3
201 202
1 2 3
203 204
205 206 207
0.0
0.2
0.4
0.6
0.8
1.0
208
0.0
0.2
0.4
0.6
0.8
1.0
210
1 2 3
211
Observed probabilities Fitted probabilities