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Liina Pylkkänen Department of Linguistics/ Center for Neuromagnetism New York University

LP, Aug 03, Tateshina. MEG, the Mental Lexicon and Morphology. Liina Pylkkänen Department of Linguistics/ Center for Neuromagnetism New York University. LP, Aug 03, Tateshina. MEG, the Mental Lexicon and Morphology. Day 1 Lexical access 1: The M350 as an MEG index of lexical activation

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Liina Pylkkänen Department of Linguistics/ Center for Neuromagnetism New York University

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  1. LP, Aug 03, Tateshina MEG, the Mental Lexicon and Morphology Liina Pylkkänen Department of Linguistics/ Center for Neuromagnetism New York University

  2. LP, Aug 03, Tateshina MEG, the Mental Lexicon and Morphology Day 1 Lexical access 1: The M350 as an MEG index of lexical activation Day 2 Lexical access 2: The M350 and mechanisms of recognition Day 3 Morphology 1: The M350 as a tool for investigating similarity and identity Day 4 Morphology 2: Electrophysiological and behavioral evidence for early effects of morphology

  3. LP, Aug 03, Tateshina Day 3 Morphology 1: The M350 as a tool for investigating similarity and identity • Do effects of morphology reduce to combined effects of phonological and semantic similarity? • Fiorentino & Poeppel: M350 evidence for decomposition in compounding. • Stockall et al.: M350 evidence for decomposition in irregular inflectional morphology.

  4. LP, Aug 03, Tateshina Day 3 Morphology 1: The M350 as a tool for investigating similarity and identity • Do effects of morphology reduce to combined effects of phonological and semantic similarity? • Fiorentino & Poeppel: M350 evidence for decomposition in compounding. • Stockall et al.: M350 evidence for decomposition in irregular inflectional morphology.

  5. Same vs. similar TEACHER vs. TEACH BROTHEL vs. BROTH SORCERY vs. MAGIC

  6. Morphological decomposition TEACH ER vs. TEACH BROTHEL vs. BROTH SORCERY vs. MAGIC

  7. Alternative(?): “emergent morphology” • Morphology is similarity at the extreme. • Effects of morphology should reduce to combined effects of semantic and phonological/formal similarity. (Seidenberg and Gonnerman, 2000) Gonnerman and Plaut (2000)

  8. To test the theories: • What are the effects of phonological and semantic similarity? • Do effects of morphology reduce to combined effects of semantic and phonological similarity?

  9. Dependent measures • Behavioral lexical decision times to visually presented words • Measurements of brain activity using MagnetoEncephaloGraphy (MEG) • In particular the M350 -- an MEG index of automatic lexical activation

  10. Magnetoencephalography (MEG) EEG http://www.ctf.com/Pages/page33.html

  11. Magnetoencephalography (MEG) EEG MEG http://www.ctf.com/Pages/page33.html

  12. Outgoing Ingoing Magnetoencephalography (MEG) Distribution of magnetic field at 93 ms (auditory M100) Averaged epoch of activity in all sensors overlapping on each other.

  13. Magnetoencephalography (MEG)

  14. What happens in the brain when we read words? 150-200ms (M170) 200-300ms (M250) 300-400ms (M350) 400-500ms 200 [fT] 0 200 -100 0 100 200 300 400 500 600 700 [msec] Pylkkänen and Marantz, Trends in Cognitive Sciences, in press. Letter string processing (Tarkiainen et al. 1999) Lexical activation (Pylkkänenet al. 2002)

  15. Activation Competition Selection TURN TURNIP level of activation TURF TURTLE resting level time Stimulus: TURN M350 (i) 1st component sensitive to lexical factors (such as lexical frequency) (ii) not affected by competition

  16. Repetition (i) (ii) Frequency • Stimuli that • speed up early lexical processing • but induce intense competition, delaying RT • elicit faster, not slower M350’s. (Embick, Hackl, Shaeffer, Kelepir, Marantz, Cognitive Brain Research, 2001) (Pylkkänen, Stringfellow, Flagg, Marantz, Biomag2000 Proceedings, 2000) M350 (i) 1st component sensitive to lexical factors (such as lexical frequency) (ii) not affected by competition Methods: RMS

  17. Effect of probability/density (n=10) * * * * n.s. n.s. n.s. n.s. (Pylkkänen, Stringfellow, Marantz, Brain and Language, 2002)

  18. Activation Competition Selection TURN TURNIP level of activation TURF TURTLE resting level time Stimulus: TURN M350 (i) 1st component sensitive to lexical factors (such as lexical frequency) (ii) not affected by competition

  19. To test the theories: • What are the effects of phonological and semantic similarity? • Behaviorally? • On the M350? • Do effects of morphology reduce to combined effects of semantic and phonological similarity?

  20. Crossmodal priming (materials adapted from Gonnerman (1999)) • SOA: • Duration of prime • Task • Lexical decision • 21 subjects

  21. Phonological similarity single subject n=21 (Pylkkänen, Stringfellow & Marantz, submitted)

  22. Semantic similarity • Behaviorally facilitory • NURSE primes DOCTOR • Would the M350 show semantic priming?

  23. n=21 single subject (RMS) M250 Results • M350 = First component affected by semantic relatedness (Pylkkänen, Stringfellow, Gonnerman, Marantz, in prep.)

  24. Phonological and semantic relatedness affect the same component, the M350 • Consistent with recent ERP results showing that phonological and semantic relatedness affect the same ERP component, the N400 (Radeau et al. 1998)

  25. IDEA – NOTION • Priming in M350 and RT. • SPINACH – SPIN • Inhibition in M350 and RT. So far: • Onset matching phonological similarity and semantic similarity have opposite effects:

  26. What about TEACHER-TEACH? • Decomposition view: • Relationship is one of identity. • TEACHER contains TEACH • Morphemes are emergent(e.g. Seidenberg and Gonnerman 2000): • Relationship is one of similarity. • TEACHER and TEACH are only semantically and phonological similar

  27. What about TEACHER-TEACH? • Decomposition view: • Relationship is one of identity. • Morphemes are emergent(e.g. Seidenberg and Gonnerman 2000): • Relationship is one of similarity. • M350 & RT should show repetition priming • M350 & RT should show added effects of phonological and semantic similarity

  28. Materials (crossmodal) (part of previous experiment) • AUDITORY PRIME VISUAL TARGET • RELATED teacher teach • UNRELATED ocean teach

  29. Results Repetition priming (Pylkkänen et al 2000)

  30. Results: like repetition priming, not additive similarity effects

  31. What about TEACHER-TEACH? • Decomposition view: • Relationship is one of identity. • Morphemes are emergent(e.g. Seidenberg and Gonnerman 2000): • Relationship is one of similarity. • M350 & RT should show repetition priming • M350 & RT should show added effects of phonological and semantic similarity

  32. Possible objection: • Phonological similarity is only inhibitory in the absence of semantic similarity. • Prediction: ritzy – glitzy should prime very much like morphologically related pairs.

  33. Behavioral data from Gonnerman (1999) EXPERIMENT 1 Prime-target example Priming • Low sem, no morph: spinach-spin -19 • Low sem: corner-corn -24 • Mid sem: dresser-dress 19* • High sem: teacher-teach 40* • Hi sem, no phon: idea-notion 13* Morphological priming exceeds semantic priming EXPERIMENT 4 Prime-target example Priming Psychology undergrads • High sem & phon: ritzy - glitzy -16 • mid sem & phon: dismal-dismay -12 • low sem & phon: rankle-rank 12 • High sem, no phon: idea - notion 21* • Hi sem, no phon: pumpkin-pump -19 semantic priming exceeds ritzy – glitzy priming (Gonnerman, 1999, PhD thesis, USC)

  34. Behavioral data from Gonnerman (1999) EXPERIMENT 1 Prime-target example Priming • Low sem, no morph: spinach-spin -19 • Low sem: corner-corn -24 • Mid sem: dresser-dress 19* • High sem: teacher-teach 40* • Hi sem, no phon: idea-notion 13* Morphological priming exceeds semantic priming EXPERIMENT 4 Prime-target example Priming Honors students • High sem & phon: ritzy - glitzy 24* • mid sem & phon: dismal-dismay -5 • low sem & phon: rankle-rank 19 • High sem, no phon: sorcery-magic 54* • Hi sem, no phon: pumpkin-pump -39* semantic priming exceeds ritzy – glitzy priming (Gonnerman, 1999, PhD thesis, USC)

  35. Possible objection: • Phonological similarity is only inhibitory in the absence of semantic similarity. Makes the wrong predictions

  36. What about TEACHER-TEACH? • Decomposition view: • Relationship is one of identity. • Morphemes are emergent(e.g. Seidenberg and Gonnerman 2000): • Relationship is one of similarity. • M350 & RT should show repetition priming • M350 & RT should show added effects of phonological and semantic similarity

  37. Crossmodal priming (materials adapted from Gonnerman (1999)) • SOA: • Duration of prime • Task • Lexical decision • 21 subjects

  38. Transparent morphological: teacher-teach * ** **

  39. Opaque morphological: dresser-dress *

  40. Pseudoaffixed: corner-corn p = 0.06

  41. Opaque morphological: dresser-dress ** ** *

  42. Pseudoaffixed: corner-corn ** * * ** **

  43. LP, Aug 03, Tateshina Day 3 Morphology 1: The M350 as a tool for investigating similarity and identity • Do effects of morphology reduce to combined effects of phonological and semantic similarity? • Fiorentino & Poeppel: M350 evidence for decomposition in compounding. • Stockall et al.: M350 evidence for decomposition in irregular inflectional morphology.

  44. Decomposition of compound words: an MEG measure of early access to constituents Robert Fiorentino1 and David Poeppel1,2 1Department of Linguistics University of Maryland, College Park 2Department of Biology, Neuroscience and Cognitive Sciences Program University of Maryland, College Park

  45. The psychological reality of the morphological complexity difference between compounds (‘flagship’) and single words (‘crescent’) is controversial • Does lexical decomposition occur online? • If so, then under what conditions, and on what time course? • Experiment: test pairwise-matched compounds and single words in a classic visual lexical decision paradigm, using both RT and brain-level magnetoencephalography (MEG) measures to characterize the deployment and time course of lexical decomposition in English noun-noun compounds

  46. Measuring decomposition online: • Response time (RT) may reflect differential processing of compounds and single words, if decomposition is present either early or late--possibly subject to lexicalization, length or other constraints (e.g. Andrews, 1986; Van Jaarsveld & Rattink, 1988; Bertram & Hyönä, 2003, among others) • MEG offers a millisecond/millimeter spatiotemporal resolution measure of activation along the way to decision • An MEG component reflecting lexical activation should track RT when stimulus properties affect lexical access, but contrast with RT in cases where stimuli elicit early access but delayed post-access processing (e.g. Embick et al., 2001; Pylkkänen et al., 2002, among others) • In this way, a combined RT and MEG experiment can test for decomposition effects and place them early or late in time course

  47. Example: ‘flagship’ versus ‘crescent’ • Items in the compound (CW) and single word (SW) conditions are pairwise matched for overall log frequency, letter-length, and syllabicity. • In whole-word properties:In internal structure: • flagship = crescentflagshipcrescent • Log Freq. .68 .69 1.49 1.95 .69 • # Letters 8 8 4 4 8 • # Syllables 2 2 1 1 2

  48. Stimuli 60 CW and 60 pairwise matched SW, all nouns from Cobuild Corpus (320 million words); 120 disyllabic pronounceable nonwords, including 18 W-NW foils (1:1 word:nonword ratio) Condition Mean Log. Freq. Mean # Letters Example Compound (CW) .455 7.82 flagship CW 1st/2nd constituents 1.96/1.96 3.82/4.0 flag/ship Single Wd (SW) .455 7.78 crescent Nonword (NW) 7.81 nishpern W-NW Foil (WNW) 7.94 crowskep Measures RT (Visual Lexical Decision); Peak Latency from RMS analysis on 10 left-hemisphere sensors from source distribution (MEG)

  49. CW SW W-NW CW SW W-NW CW SW W-NW M350RT

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