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Psycholinguistics I

Psycholinguistics I. LING 640. What is psycholinguistics about?. Guiding Questions. What do speakers of a language mentally represent? How did those representations get there? How are those representations constructed? How are those representations encoded?.

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Psycholinguistics I

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  1. Psycholinguistics I LING 640

  2. What is psycholinguistics about?

  3. Guiding Questions • What do speakers of a language mentally represent? • How did those representations get there? • How are those representations constructed? • How are those representations encoded?

  4. Language is a Human Specialization • Species specificity • Within-species invariance • Spontanous development, insensitivity to input • Independence of general intelligence • Selective brain damage • The ‘Language Instinct’ [Pinker 1994]; see Gleitman & Newport chapter [readings] for nice summary • These arguments suggest that there’s a coherent object of study, but tell us very little about its form

  5. We need explicit answers… • What do speakers of a language mentally represent? • How did those representations get there? • How are those representations constructed? • How are those representations encoded?

  6. Explicit models quickly reveal surprising complexity

  7. A Simple(-ish) Example • Distribution of pronouns/reflexives • John likes him/himself. • John thinks that Mary likes him/himself. • Infinitival clauses • John appeared to Bill to like himself. • John appeared to Bill to like him. • But… • John appealed to Bill to like himself. • John appealed to Bill to like him. • Abstract solution… • Johni appealed to Billj [PROj to like himselfj ]

  8. Abstraction is a double-edged sword

  9. Abstraction • Abstraction is valuable • Provides representational power • Provides representational freedom • Abstraction is costly • Linguistic representations are more distant from experience • This places a burden on the learner - motivation for innate knowledge • This places a burden on comprehension/production systems • (and it makes it harder to know what to look for in the brain)

  10. Sensory Maps Internal representations of the outside world. Cellular neuroscience has discovered a great deal in this area.

  11. Lab #1

  12. Acoustic Continua andPhonetic Categories

  13. Frequency - Tones

  14. Frequency - Tones

  15. Frequency - Tones

  16. Frequency - Tones

  17. Frequency - Complex Sounds

  18. Frequency - Complex Sounds

  19. Frequency - Vowels • Vowels combine acoustic energy at a number of different frequencies • Different vowels ([a], [i], [u] etc.) contain acoustic energy at different frequencies • Listeners must perform a ‘frequency analysis’ of vowels in order to identify them(Fourier Analysis)

  20. Frequency - Male Vowels

  21. Frequency - Male Vowels

  22. Frequency - Female Vowels

  23. Frequency - Female Vowels

  24. Synthesized Speech • Allows for precise control of sounds • Valuable tool for investigating perception

  25. Timing - Voicing

  26. Voice Onset Time (VOT) 60 msec

  27. English VOT production • Not uniform • 2 categories

  28. Perceiving VOT ‘Categorical Perception’

  29. Discrimination A More Systematic Test Same/Different D D 0ms 60ms 0ms 20ms D T 20ms 40ms Same/Different 0ms 10ms T T 40ms 60ms Same/Different Within-Category Discrimination is Hard 40ms 40ms

  30. Quantifying Sensitivity

  31. Quantifying Sensitivity • Response bias • Two measures of discrimination • Accuracy: how often is the judge correct? • Sensitivity: how well does the judge distinguish the categories? • Quantifying sensitivity • Hits MissesFalse Alarms Correct Rejections • Compare p(H) against p(FA)

  32. Quantifying Sensitivity • Is one of these more impressive? • p(H) = 0.75, p(FA) = 0.25 • p(H) = 0.95, p(FA) = 0.45 • A measure that amplifies small percentage differences at extremesz-scores

  33. Dispersionaround mean Mean (µ) √( ) ∑(x - µ)2 n Normal Distribution Standard Deviation A measure of dispersionaround the mean.

  34. The Empirical Rule 1 s.d. from mean: 68% of data 2 s.d. from mean: 95% of data 3 s.d. from mean: 99.7% of data

  35. Quantifying Sensitivity • A z-score is a reexpression of a data point in units of standard deviations.(Sometimes also known as standard score) • In z-score data, µ = 0,  = 1 • Sensitivity score d’ = z(H) - z(FA)

  36. See Excel worksheetsensitivity.xls

  37. Quantifying Differences

  38. (Näätänen et al. 1997) (Aoshima et al. 2004) (Maye et al. 2002)

  39. Dispersionaround mean Mean (µ) √( ) ∑(x - µ)2 n Normal Distribution Standard Deviation A measure of dispersionaround the mean.

  40. The Empirical Rule 1 s.d. from mean: 68% of data 2 s.d. from mean: 95% of data 3 s.d. from mean: 99.7% of data

  41. Normal Distribution Standard deviation  = 2.5 inches Heights of American Females, aged 18-24 Mean (µ) 65.5 inches

  42. If we observe 1 individual, how likely is it that his score is at least 2 s.d. from the mean? • Put differently, if we observe somebody whose score is 2 s.d. or more from the population mean, how likely is it that the person is drawn from that population?

  43. If we observe 2 people, how likely is it that they both fall 2 s.d. or more from the mean? • …and if we observe 10 people, how likely is it that their mean score is 2 s.d. from the group mean? • If we do find such a group, they’re probably from a different population

  44. Standard Erroris the Standard Deviation of sample means.

  45. If we observe a group whose mean differs from the population mean by 2 s.e., how likely is it that this group was drawn from the same population?

  46. Development of Speech Perception in Infancy

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