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

2. 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 Dependent measure Independent measure

3. 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 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 Dependent measure Independent measure

4. 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) Dependent measure Dependent measure Independent measure something else

5. Possible relationships of independent to dependent measure 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 Dependent measure Independent measure ( often other things)

6. 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 Dependent measure Dependent measure Independent measure

7. 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?

9. 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/

10. 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)

11. 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.

12. 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/

13. Distribution of humidity and languages with 3+ tonal distinctions

14. Humidity & WALS tone categories

15. 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

16. Humidity & WALS tone categories

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

19. Distance to the equator

20. Incorporating distance to the equator

21. 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-humidity- faq/10286.html Also interesting discussion: http://dejonedge.blogspot.com/2015/01/my-first-post-publication- review.html

22. 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.

23. 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.

24. A visual representation of the data

25. 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?

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

27. Is verb phrase ellipsis primed in conversation? Alan Hogue Dept of Linguistics U. of Arizona

28. 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

29. 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)

30. 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)

31. 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.

32. 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?

33. 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?