What are informative answers to the question, ‘Why do zebras have stripes?’

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.

• Example:– Effect of breathing helium on vocal

pitch• Example:

– Dry air desiccates vocal folds (cf. Everett, Blasi & Roberts 2015)

Dependent measure

Independent 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• 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

Independent 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)

Dependent measure

Independent measure

something else


5. Circular causation• Properties:

– Feedback can create large surface differences despite underlying sameness of process.

• 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)

Dependent measure

Independent measure

(often other things)


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

Dependent measure

Independent measure

How does prior expectation influence the value of an observation?

‘Hypothesis discovery’ ‘Hypothesis testing’

Noticing an unexpected pattern in some dataset –

no obvious explanation

Noticing a pattern in some dataset – realizing it has a

plausible explanation

Confirmatory testing of an established model on a fresh,

well-controlled dataset

Initial testing and exploration of a priori theoretical predictions

in a dataset

How likely is a correlation meaningful?

• 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/.NCEP-

NCAR/.CDAS-1/.MONTHLY/.Diagnostic/.above_ground/

Distribution of humidity and languages with 3+ tonal distinctions

Humidity & WALS tone categories

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 ***

Mixed model results



Distance to the equator

Nettle, D. (1998). Explaining global patterns of language diversity. J. Anthropol. Archaeol., 17, 354–374.

Distance to the equator

Incorporating distance to the equator

Some blog posts on Humidity/ToneExplaining 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-humidity-faq/10286.html

Also interesting discussion:• http://dejonedge.blogspot.com/2015/01/my-first-post-publication-

review.html

• http://dejonedge.blogspot.com/2015/01/my-first-post-publication-review.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 ejectivesCaleb 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?

‘Hypothesis discovery’ ‘Hypothesis testing’

Noticing an unexpected pattern in some dataset –

no obvious explanation

Noticing a pattern in some dataset – realizing it has a

plausible explanation

Confirmatory testing of an established model on a fresh,

well-controlled dataset

Initial testing and exploration of a priori theoretical predictions

in a dataset

How likely is a correlation meaningful?

Some blog posts on Altitude/Ejectives

• http://languagelog.ldc.upenn.edu/nll/?p=4685

• http://dlc.hypotheses.org/491• http://dlc.hypotheses.org/507• http://www.replicatedtypo.com/altitude-and-

ejectives-hypotheses-up-in-the-air/6324.html• http://swphonetics.com/2013/08/17/

corrcaus/

Is verb phrase ellipsis primed in conversation?

Alan HogueDept 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 shiftPassive

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] vsgive [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?

Documents

What are informative answers to the question, ‘Why do zebras have stripes?’