What are informative answers to the question,
‘Why do zebras have stripes?’
Possible relationships of
independent to dependent measure
2. Relatively direct causation
• Properties:
– Tend to be easy to study
– Robust to replication
– Because relationship is direct
and data is robust, tends to be
easier to build a predictive
model for.
Independent
measure
• Example:
– Effect of breathing helium on
vocal pitch
• Example:
– Dry air desiccates vocal folds (cf.
Everett, Blasi & Roberts 2015)
Dependent
measure
Possible relationships of
independent to dependent measure
3. More indirect causation
• Properties:
– More often context dependent
– More often fails replication, until
chain of causation better
understood
– Theory, and requisite testing more
complex
Independent
measure
• Example:
– Effect of predators and evolution of
physical properties
• Lions and zebra stripes
• Ground predators and loss of flight in
New Zealand
• Example:
– Humidity and the development of
linguistic tone (Everett, Blasi &
Roberts 2015)
Dependent
measure
Possible relationships of
independent to dependent measure
4. Shared cause
• Properties:
– Direct causal theory
more often difficult to
articulate – which can
be a clue…
– Positing joint cause can
help generate new
hypotheses about
direct causes.
• Example:
– correlation between
population size and
grammatical complexity
(Lupyan & Dale 2010)
something else
Independent
measure
Dependent
measure
Possible relationships of
independent to dependent measure
5. Circular causation
• Properties:
– Feedback can create large surface
differences despite underlying
sameness of process.
Independent
measure
• Example:
– Morpho-phonological relationships in
e.g., Turkish, Maltese, English
• Learners tend to extend patterns that
already exist…
• causing the pattern to become more
entrenched … (e.g. Hare & Elman 1995)
• Example:
– Co-evolution of the lexicon and
phonology (Nettle 1998, Graff 2012,
Wedel 2012)
(often other things)
Dependent
measure
Possible relationships of
independent to dependent measure
1. No broader predictive relationship;
correlation is sample specific.
• Properties:
– Due to sampling bias
• Often through contingent historical
factors
– Not robust to replication
– Often ‘discovered’, rather than
predicted by existing models
• Example:
– Correlation between number of
pirates and global temperature
Independent
measure
Dependent
measure
How does prior expectation influence the value of an observation?
Noticing an
unexpected pattern
in some dataset –
no obvious
explanation
Initial testing and
exploration of a priori
theoretical predictions
in a dataset
How likely is a correlation meaningful?
‘Hypothesis discovery’
Noticing a pattern in
some dataset –
realizing it has a
plausible explanation
‘Hypothesis testing’
Confirmatory testing
of an established
model on a fresh,
well-controlled
dataset
• Null hypothesis: the distribution of sounds across the world’s
languages is random with respect to geographic factors.
• ‘There is no correlation whatever between . . . any aspect of
linguistic structure and the environment. Studying the
structure of a language reveals absolutely nothing about either
the people who speak it or the physical environment in which
they live’.
– Kaye, J. D. 1989 Phonology: a cognitive view. Hillsdale, NJ: Erlbaum,
p. 48.
• (But physical factors are thought to matter:)
• inverse correlation of rounding and height in vowels
• correlation of stop voicing distinction with intra-oral volume
– typological probability of /d/ > /g/
• Tested hypothesis: lower atmospheric
moisture correlates with lack of complex tone
• Prior evidence: it’s harder to produce precise
pitch in drier air.
• Background assumption: Ceteris paribus,
better cue quality correlates with a higher
probability of sound contrast evolution. (e.g.
Blevins 2004, etc)
Prior evidence for the hypothesis
• Desiccation of the vocal folds through
inhalation of dry air quickly results in greater:
– ‘Jitter’: variation in pitch
– ‘Shimmer’: variation in amplitude
• Prior evidence for these effects exist from
multiple kinds of experiments.
Data sources
• Tone and Location
– World Atlas of Linguistic Structures (WALS)
• Somewhat areally, genetically balanced
– Phonotactics Database of the Australian National
University (ANU)
• exhaustive sample
• Mean Humidity
– Kalnay E, et al. (1996) The NCEP/NCAR 40-Year Reanalysis
Project. Bulletin of the American Meteorological Society.
– http://iridl.ldeo.columbia.eduSOURCES/.NOAA/.NCEPNCAR/.CDAS-1/.MONTHLY/.Diagnostic/.above_ground/
Distribution of humidity and languages
with 3+ tonal distinctions
Tone Category (WALS)
Humidity & WALS tone categories
complex tone
simple tone
no tone
0.005
0.010
Humidity
0.015
0.020
Mixed model results
Mixed logistic regression, tone vs. no tone
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.175044
0.001080
-2014
<2e-16 ***
specH.mean 80.827246
0.001079
74882
<2e-16 ***
Mixed logistic regression, simple vs. complex tone (>3)
Estimate Std. Error z value Pr(>|z|)
(Intercept)
-8.618
2.277 -3.784 0.000154 ***
specH.mean
474.825
142.735
3.327 0.000879 ***
Tone Category (WALS)
Humidity & WALS tone categories
complex tone
simple tone
no tone
0.000
0.005
0.010
Humidity
0.015
0.020
Tone Category (WALS)
Humidity & WALS tone categories
complex tone
simple tone
no tone
0.000
0.005
0.010
Humidity
0.015
0.020
Distance to the equator
Nettle, D. (1998). Explaining global patterns of language diversity. J. Anthropol. Archaeol., 17, 354–374.
Distance to the equator
EquatorDistance
80
60
40
20
0
0.005
0.010
Humidity
0.015
0.020
Tone Category (WALS)
Incorporating distance to the
equator
complex tone
EquatorDistNormed
1.00
0.75
simple tone
0.50
0.25
0.00
no tone
0.005
0.010
Humidity
0.015
0.020
Some blog posts on Humidity/Tone
Explaining the stats of the tone and humidity paper:
• http://www.replicatedtypo.com/tone-and-humidity/10207.html
Tone and humidity FAQ:
• http://www.replicatedtypo.com/tone-and-humidityfaq/10286.html
Also interesting discussion:
• http://dejonedge.blogspot.com/2015/01/my-first-post-publicationreview.html
• http://dejonedge.blogspot.com/2015/01/my-first-post-publicationreview.html
• Null hypothesis: the distribution of sounds across
the world’s languages is random with respect to
geographic factors.
• Tested hypothesis: lower atmospheric pressure
correlates with presence of ejectives.
– Rationale: less effort is required to produce a
sufficiently high oral pressure for an ejective when
ambient air-pressure is lower.
– Background assumption: Ceteris paribus, better cue
quality correlates with a higher probability of sound
contrast evolution.
Altitude and ejectives
Caleb Everett (2014). PLoS1 pp x-y
• Test: is there a correlation between the
presence of ejectives and altitude?
• Datasets:
– Ejectives: UPSID (ejectives: yes = 92/no = 475)
– Location: WALS
– Altitude: Google Earth + ArcGis + WALS
• Reported result:
– There is a correlation between presence of
ejectives and altitude.
A visual representation of the data
How does prior expectation influence the value of an observation?
Noticing an
unexpected pattern
in some dataset –
no obvious
explanation
Initial testing and
exploration of a priori
theoretical predictions
in a dataset
How likely is a correlation meaningful?
‘Hypothesis discovery’
Noticing a pattern in
some dataset –
realizing it has a
plausible explanation
‘Hypothesis testing’
Confirmatory testing
of an established
model on a fresh,
well-controlled
dataset
Some blog posts on Altitude/Ejectives
• http://languagelog.ldc.upenn.edu/nll/?p=468
5
• http://dlc.hypotheses.org/491
• http://dlc.hypotheses.org/507
• http://www.replicatedtypo.com/altitude-andejectives-hypotheses-up-in-the-air/6324.html
• http://swphonetics.com/2013/08/17/corrcaus
/
Is verb phrase ellipsis
primed in conversation?
Alan Hogue
Dept of Linguistics
U. of Arizona
1. Structural Priming
Processing a syntactic structure – makes subsequent processing of the same
structure faster
– makes production of that structure more likely
Well-explored structures:
Dative shift
Passive
Priming of Dative Shift
"... I don’t feel we should loan [them] [money]...
I wish our leaders were really seeking the Lord on
these things, and if we feel led to give
[a country] [money] to help them, fine”
give [money][to a country] vs
give [a country] [money]
(Jaeger & Snider 2007)
Structural Priming
• Operates across people within discourses
• (Even operates across languages for similar
structures)
• Significant priming effect persists for up to 10
sentences (Bock & Griffin 2000)
2. Verb phrase ellipsis (VPE)
Frankie [baked a pie]i and George did ____i too
• Characterized by missing structure and lexical
material
• Anaphoric relationship between the gap and
some other material
• In order to reconstruct the intended meaning,
listener must
– detect the gap
– scan backwards/forwards through the discourse
context for possible filler.
Questions
• Is VPE structurally-primed?
– It’s different from previously studied structures in
that it involves a long-distance dependency
• If so, how long does it persist?
Pick a VPE:
is there a VPE in one of the previous 10
clauses spoken by the other person?
Is this structural priming? Alternate hypotheses?