Syntactic micro-variation and linguistic theory

1. Syntactic micro-variation and linguistic theory Dick Hudson LAGB 2007

2. The issues • Modularity • Is language integrated into other kinds of knowledge? • Inherent variability • How does context affect choice of forms? • How does it produce statistical effects?

3. The data Buckie Researcher: Jennifer Smith

4. Was or were? 121 247 2 : 1 111 50 2 : 1 435 0

5. Influences on was/were • Subject pronoun • 2/1 (we, you), 0/1 (they) • Speaker age • With you: 9/1 (old), 2/1 (mid), 4/3 (young) • With we: 3/1 (old), 3/4 (middle), 2/1 (young) • Speaker sex • With we: 5/2 (male), 1/1 (female) • All highly significant (p <0.005)

6. The challenge • How to relate: • Was - were • We - you - they • Old – middle - young speaker • Male - female speaker • But not, e.g., married - single • How to explain the ratios

7. In a picture married single old young male female we you they p(was) p(were)

8. Adger (Minimalism) married single old young male female Perturbations in performance we you they p(was) p(were) p(was) = p(was) = p(were) grammar

9. Worries • P(was) varies beyond mere ‘perturbation’ with subject and speaker categories • Quantum analysis is implausible for e.g. 7/2 • How can lexical items vary in performance without varying in competence?

10. Bender (HPSG) social meaning married single old young male female we you they p(was) = 0.7 p(were) = 0.3 grammar

11. Worries • How are probabilities represented mentally? • As numbers? No. • As activation levels? Yes. • But how are they represented in the grammar? • Are they part of ‘knowledge’? • Why separate grammar from the rest of cognition?

12. Me (Word Grammar) social meaning married single old young male female we you they p(was) = 0.7 p(were) = 0.3 was were grammar

13. A glimpse of a network old young male female speaker we you they was were subj

14. Language is a network • So is all long-term memory • and working memory • and society • and ‘I-society’ = our knowledge of society • But don’t panick: networks have plenty of structure.

15. Dissociations in a network • The rail and bus networks are ‘dissociated’. • Networks have: • Sub-networks with rich internal connections and sparse external ones. • ‘Hub’ nodes which influence many other nodes. • Network theory predicts dissociated breakdown so ‘modularity’ isn’t needed.

16. Spreading activation • Language must be a network because it carries spreading activation. • Evidence: • Priming: word 1 primes word 2 if it’s a network neighbour. • Speech errors: substituted word is always activated by target or by context.

17. How activation spreads • Blindly – hence errors. • In any direction, depending on the target. • Speaking or listening or analysing or … • Randomly, so weak activation has a weak effect (rather than no effect) • because nodes only spread activation when they reach their ‘firing’ threshold.

18. No boundaries • Activation flows freely between language and non-language • Even speech errors may have non-linguistic causes • E.g. (By a computer) Do you have a • Social influences on variables are another example. computer? screwdriver?

19. A network analysis old young male female speaker we you they was were subj

20. Spreading activation at work old young male female we you they was were

21. Conclusion • Language is a network. • Its nodes connect not only to other language nodes, but also to social nodes. • The network carries variable activation which spreads blindly. • Quantitative variation is due to activation levels, not quantitative ‘knowledge’.