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文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて/kisoken3rd

95d5cfc0ce395d0bfedeeb92d34261ce?s=47 Yu Tamura
February 27, 2016

 文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて/kisoken3rd

田村祐(2016)「文法性判断課題における反応時間と主観的測度は正答率を予測するか—文法項目の違いに焦点をあてて—」 外国語教育メディア学会中部支部外国語教育基礎研究部会第三回年次例会,名古屋大学

95d5cfc0ce395d0bfedeeb92d34261ce?s=128

Yu Tamura

February 27, 2016
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  1. จ๏ੑ൑அ՝୊ʹ͓͚Δ൓Ԡ࣌ؒͱ ओ؍తଌ౓͸ਖ਼౴཰Λ༧ଌ͢Δ͔ —จ๏߲໨ͷҧ͍ʹয఺Λ͋ͯͯ— 2016೥2݄27೔ ֎ࠃޠڭҭϝσΟΞֶձத෦ࢧ෦ ֎ࠃޠڭҭجૅݚڀ෦ձୈ3ճ೥࣍ྫձ ԙɹ໊ݹ԰ֶӃେֶ

  2. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 2
  3. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 3
  4. • ൓Ԡ࣌ؒͱओ؍తଌ౓͕จ๏ੑ൑அͷਖ਼౴ɾޡ ౴ʹ༩͑ΔӨڹ͸จ๏߲໨ʹΑͬͯҟͳΔ ͸͡Ίʹ 4 ຊݚڀͷ݁࿦

  5. ాଜ ༞ ໊ݹ԰େֶେֶӃ 5

  6. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 6
  7. • ֶशऀͷ΋ͭݴޠ஌͕ࣝɼ̎छྨͷҟͳΔ஌ࣝ ͔Βߏ੒͞ΕΔͱ͍͏ݟํ • ໊લ͸ҧ͑Ͳ͜͏͍ͬͨݟํ͸ୈೋݴޠशಘݚ ڀʢSLAʣʹ͓͍ͯ͸͔ͳΓҰൠతͱ͍͑Δ (e.g., Anderson, 1992; Bialystok,

    1978; Jiang, 2007ʣ • ͜ΕΛςʔϚʹͨ͠ಛू߸͕͘·ΕΔ͜ͱ΋ • Hulstijn and Ellis (2005), Andringa & Rebuschat (2015) ݚڀഎܠ 7 ໌ࣔత҉ࣔత஌ࣝ
  8. • ໌ࣔత஌ࣝͱ͸ʢEllis, 2004ʣ • ҙࣝత • ͠͹͠͹ϝλݴޠత • ݴޠใࠂ͕Մೳ •

    ΞΫηεʹ͕࣌ؒඞཁ • ※ͨͩ͠஌ࣝͷࣗಈԽΛೝΊΔཱ৔͔ΒΈΕ͹҉ ࣔత஌ࣝͱಉ͡Α͏ͳৼΔ෣͍Λ͢Δ໌ࣔత஌ࣝ ΋͋ΔʢDeKeyser, 2003) ݚڀഎܠ 8 ໌ࣔత҉ࣔత஌ࣝ
  9. • ҉ࣔత஌ࣝͱ͸ • ແҙࣝత • ݴޠӡ༻Λओʹ୲͏΋ͷ • ฼ޠ࿩ऀͷ΋͍ͬͯΔ஌ࣝ • ڱٛͷSLAͷ໨త͸҉ࣔత஌ࣝͷशಘ

    ݚڀഎܠ 9 ໌ࣔత҉ࣔత஌ࣝ
  10. • 2ͭͷ஌ࣝ͸ૢ࡞తʹͲͷΑ͏ʹ۠ผ͞ΕΔͷ͔ • ໌ࣔత஌ࣝͷଌఆ • ੍࣌ؒݶͳ͠ͷจ๏ੑ൑அ՝୊ʢe.g., Tamura & Kusanagi, 2015ʣ

    • ϝλݴޠςετʢe.g., Ellis, 2005ʣ • ޡΓగਖ਼՝୊ • ҉ࣔత஌ࣝͷଌఆ • ੍࣌ؒݶ͋Γͷจ๏ੑ൑அ՝୊ʢe.g., Loewen, 2009ʣ • ࣗݾϖʔεಡΈ՝୊ʢe.g., Jiang, 2007ʣ • ޱ಄໛ൣ՝୊ʢe.g., Erlam, 2006) • ࢹઢܭଌʢe.g., Godfroid et al., 2015ʣ • ϫʔυϞχλϦϯά՝୊ʢe.g., Granena, 2013) ݚڀഎܠ 10 ໌ࣔత҉ࣔత஌ࣝ
  11. • ࣗݾϖʔεಡΈ΍ϫʔυϞχλϦϯάͳͲͷ՝୊ ͕҉ࣔత஌ࣝͷଌఆ۩ͱͯ͠༏Ε͍ͯΔ ʢVafaee et al., 2016ʣ • ΦϯϥΠϯͷ՝୊͸ɼֶशऀ͕໌ࣔత஌ࣝʹΞ Ϋηε͢ΔՄೳੑΛۃྗഉআͰ͖ΔʢSuzuki

    & DeKeyser, 2015ʣ • จ๏ੑ൑அ՝୊ͩͱܗࣜʹ஫ҙ͕޲͘ͷͰͲ ͏ͯ͠΋໌ࣔత஌ࣝΛ࢖ͬͯ͠·͏ʢ੍࣌ؒݶ ͔͚Δ͚ͩ͸ෆे෼ʣ ݚڀഎܠ 11 ໌ࣔత҉ࣔత஌ࣝ
  12. • จ๏ੑ൑அͱ੍࣌ؒݶΛ༻͍ͯ໌ࣔతɾ҉ࣔత஌ࣝͷଌఆΛࢼΈͨݚ ڀʢe.g., Kusanagi & Yamashita, 2013; Tamura & Kusanagi,

    2015ʣ • Tamura and Kusanagi (2015) • ର৅߲໨ • ී௨໊ࢺͱ෺໊࣭ࢺ • ݁Ռ • ʮಡΈͳ͓͠ΛͤͣͰ͖Δ͚ͩૣ͘ʯͱࢦࣔͨ͠৔߹ʹී௨ ໊ࢺͷਖ਼౴཰͕௿Լ • ෺໊࣭ࢺ͸ී௨໊ࢺΑΓ΋ਖ਼౴཰͕௿͘ɼૣ͘൑அ͢ΔΑ͏ ʹͱ͍͏ࢦ͕ࣔ͋ͬͯ΋ਖ਼౴཰͸௿Լ͠ͳ͍ • ී௨໊ࢺ͸҉ࣔత஌͕ࣝशಘ͞Ε͓ͯΒͣɼ෺໊࣭ࢺ͸໌ࣔ త஌ࣝ͢Β΋ͳ͍Մೳੑ ݚڀഎܠ 12 ໌ࣔత҉ࣔత஌ࣝ
  13. • ೝ஌৺ཧֶʹ͓͚Δ໌ࣔతɾ҉ࣔత஌ࣝ • ७ਮʹҙࣝతͰ͋Δ͔ɼແҙࣝతͰ͋Δ͔ͷ ໰୊ʢDiense, 2007ʣ • ํ๏ • ܹࢗ࠶ੜ๏

    • ࢥߟൃ࿩๏ • ओ؍తଌ౓ ݚڀഎܠ 13 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  14. • ೝ஌৺ཧֶʹ͓͚Δ໌ࣔతɾ҉ࣔత஌ࣝ • ७ਮʹҙࣝతͰ͋Δ͔ɼແҙࣝతͰ͋Δ͔ͷ ໰୊ʢDiense, 2007ʣ • ํ๏ • ܹࢗ࠶ੜ๏

    • ࢥߟൃ࿩๏ • ओ؍తଌ౓ ݚڀഎܠ 14 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  15. • ओ؍తଌ౓ • ൑அ஌ࣝͷϦιʔεΛओ؍తʹ൑அͯ͠΋Β͏ • ਪଌʢ·ͬͨ͘ͷ͋ͯͣͬΆ͏ʣ • ௚ײʢͳΜͱͳͦ͘Μͳײ͕͢͡Δʣ • هԱʢલʹͰ͖ͯͨ΍ͭΛ͍֮͑ͯΔʣ

    • نଇʢنଇͰઆ໌Ͱ͖Δʣ ݚڀഎܠ 15 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  16. • ओ؍తଌ౓ • ൑அ஌ࣝͷϦιʔεΛओ؍తʹ൑அͯ͠΋Β͏ • ਪଌʢ·ͬͨ͘ͷ͋ͯͣͬΆ͏ʣ • ௚ײʢͳΜͱͳͦ͘Μͳײ͕͢͡Δʣ • هԱʢલʹͰ͖ͯͨ΍ͭΛ͍֮͑ͯΔʣ

    • نଇʢنଇͰઆ໌Ͱ͖Δʣ ݚڀഎܠ 16 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ ແ ҙ ࣝ త ҙ ࣝ త
  17. • Tamura et al. (in press) • ൓Ԡ࣌ؒͰද͞ΕΔεϐʔυͱݴޠత஌ࣝͷҙ ࣝ࣠͸ࣼߦ͍ͯ͠Δ •

    ૣ͍ˍҙࣝతɼ஗͍ˍ҉ࣔతͱ͍͏஌ࣝ͸ط ଘͷSLAత໌ࣔɾ҉ࣔͷ࿮૊ΈͰͲͷΑ͏ʹઆ ໌͞ΕΔʁʢಛʹޙऀʣ • ҙࣝ࣠ΛऔΓೖΕͨจ๏ੑ൑அ՝୊ͷ෼ੳख ๏ͷఏҊ ݚڀഎܠ 17 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  18. • ຊݚڀͰ࠾༻͢Δओ؍తଌ౓ • ҉ࣔత஌ࣝʢແҙࣝత஌ࣝʣ • ௚ײʢͳΜͱͳͦ͘Μͳײ͕͢͡Δʣ • ໌ࣔత஌ࣝʢҙࣝత஌ࣝʣ • نଇʢنଇͰઆ໌Ͱ͖Δʣ

    ݚڀഎܠ 18 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  19. • จ๏ੑ൑அ՝୊ʹ͓͚Δओ؍తଌ౓ɼ൓Ԡ࣌ؒɼ ਖ਼౴཰ͷ࿈ؔؔ܎Λௐࠪͨ͠ݚڀʢ૲ಽɾ઒ޱ, 2015ʣ • ૣ͍൓Ԡ->ਖ਼౴཰ߴ͍ˍنଇత • ͨͩ͠ɼ͜ͷݚڀͰ͸จ๏߲໨ͷҧ͍͸য఺ʹ͸ ͍ͯ͠ͳ͍ ->ҟͳΔจ๏߲໨ؒͰ͸ɼ൓Ԡ࣌ؒ΍ओ؍తଌ౓͸

    Ͳ͏ͳΔʁ ݚڀഎܠ 19 ૣ͍ʹ҉ࣔతɼ஗͍ʹ໌ࣔతʁ
  20. • จ๏ੑ൑அ՝୊ʹ͓͚Δ൓Ԡ࣌ؒͱओ؍తଌ౓͸ ਖ਼౴཰Λ༧ଌ͢Δ͔ • ൓Ԡ͕࣌ؒૣ͚Ε͹ or ஗͚Ε͹ਖ਼౴͠΍͢ ͍ʁ • ʮنଇΛઆ໌Ͱ͖Δʯor

    ʮ௚ײͰ͋Δʯͱ౴ ͑ͨ৔߹ʹਖ਼౴͠΍͍͢ʁ • ൓Ԡ࣌ؒɼओ؍తଌ౓ɼਖ਼౴཰ͷ࿈ؔؔ܎͸จ๏ ߲໨ʹΑͬͯҟͳΔ͔ ݚڀഎܠ 20 ݚڀ՝୊
  21. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 21
  22. • ೔ຊਓେֶੜɾେֶӃੜʢN = 24ʣ • Tamura et al. (in press)ͱಉ༷

    • ฏۉ೥ྸ • 22.87ࡀʢSD = 1.29, n = 23) • TOEICฏۉείΞ • 704.32 (SD = 95.39, n = 22) • ઐ߈ • ڭҭՊֶɼ޻ֶɼਓจࣾձֶɼԽֶɼetc. ຊݚڀ 22 ࣮ݧࢀՃऀ
  23. • Tamura and Kusanagi (2015) Ͱ༻͍ΒΕͨ΋ͷͱಉ༷ • ී௨໊ࢺɿ12߲໨ • She

    picked three apples out of the bag. • * She picked three apple out of the bag. • ෺໊࣭ࢺɿ12߲໨ • She bought a lot of gold last month. • * She bought many golds last month. • He spilled a wine by accident. • * He spilled wine by accident. • ͦΕͧΕͷ߲໨͝ͱʹจ๏จͱඇจ๏จͷ2৚݅ • 2ͭͷϦετͰΧ΢ϯλʔόϥϯε • 24จʴϑΟϥʔ40จͷ߹ܭ64߲໨ͷจ๏ੑ൑அ ຊݚڀ 23 ࣮ݧࡐྉ
  24. ຊݚڀ 24 ࣮ݧࡐྉ ී௨໊ࢺ ෺໊࣭ࢺ نଇ ෆنଇ apple knife gold

    thread dog child wine rice pen man toast chalk bag mouse stone gas car goose paper timber lake teeth meat mud ද1 ࣮ݧʹ༻͍ΒΕ໊ͨࢺ
  25. 1. ࣮ݧͷઆ໌ 2. ಉҙॻͷهೖ 3. PC൛จ๏ੑ൑அ՝୊ ຊݚڀ 25 ࣮ݧखॱ

  26. 1. ࢀՃऀͷσϞάϥ৘ใͷೖྗ 2. จ๏ੑ൑அ՝୊ 1. ஫ࢹ఺ͷఏࣔ(1000msʣ->ϒϥϯΫը໘ͷఏࣔ ʢ500msʣ 2. ܹࢗจͷఏࣔ 3.

    ΩʔԡԼʹΑΔจ๏ੑ൑அ 4. ओ؍తଌ౓ʢ൑அͷϦιʔεʣͷճ౴ • ࣗ෼ͷ஌͍ͬͯΔنଇ ->ҙࣝత஌ࣝ • ௚ײ ->ແҙࣝత஌ࣝ ຊݚڀ 26 PC൛จ๏ੑ൑அ՝୊
  27. • ҰൠԽઢܗࠞ߹ϞσϧʢGLMM) by R (R Core team, 2014) , lme4

    (Bates et al., 2015) • Ԡ౴ม਺ • ਖ਼౴ɾޡ౴ͷ0/1σʔλ • આ໌ม਺ • ൓Ԡ࣌ؒʢRTʣ • logม׵౳͸ͳ͠Ͱzม׵ • ओ؍తଌ౓ʢنଇ or ௚ײʣ • ίϯτϥετίʔσΟϯάʹมߋʢLinck & Cunnings, 2015) • ෼෍ • ೋ߲෼෍ˍϩδοτϦϯΫؔ਺ ຊݚڀ 27 ෼ੳ
  28. • ߲໨ʢී௨໊ࢺɾ෺໊࣭ࢺʣͰ෼͚ͯσʔληο τΛ2ͭ࡞੒ • 2ͭͷσʔληοτͰ൓Ԡ࣌ؒͱओ؍తଌ౓͕จ ๏ੑ൑அͷਖ਼౴཰ʹٴ΅͢ӨڹΛ୳ࡧతʹ෼ੳ ຊݚڀ 28 ෼ੳ

  29. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 29
  30. • ৴པੑʢCronbach αʣ • ී௨໊ࢺ • α = .60 (k

    = 24) • ෺໊࣭ࢺ • α = .26 (k = 24) ݁Ռ 30 هड़౷ܭ
  31. ݁Ռ 31 هड़౷ܭ ߲໨ M SD Min Max skew kurtsis

    ී௨໊ࢺ .78 .17 .42 1.0 -0.32 -0.76 ෺໊࣭ࢺ .54 .16 .17 .75 -0.52 -0.41 Note. k = 24 for each, N = 24 CNP MNP 0.2 0.4 0.6 0.8 1.0 ฏۉ஋ͷਤࣔɻ੺఺͸ ݸਓͷฏۉ஋ɼ੨఺͸ શମͷฏۉ஋Λࣔ͢ɻ ද2 ߲໨ผͷਖ਼౴཰ͷهड़౷ܭ
  32. Model formula Df AIC BIC logLik deviance 1 judgment ~

    (1|participant)+(1| item) 3 303.55 314.54 -148.77 297.55 2 judgment ~ zrt + (1| participant)+(1| item) 4 303.77 318.43 -147.89 295.77 3 judgment ~ sub + (1| participant)+(1| item) 4 299.05 313.71 -145.53 291.05 4 judgment ~ sub + zrt + (1| participant)+(1| item) 5 300.61 318.93 -145.31 290.61 5 judgment ~ sub*zrt + (1| participant)+(1| item) 6 302.20 324.18 -145.10 290.20 32 Ϟσϧൺֱʢී௨໊ࢺʣ Ϟσϧ1͸NULLϞσϧͰɼAICΛݟΔͱϞσϧ3͕࠾୒͞Ε ΔɻϞσϧ4΋NULLϞσϧͱͷ໬౓ൺݕఆͰ͸༗ҙ͕ͩɼϞσ ϧ3ͱൺֱͯ͠ύϥϝʔλ͕1ͭ૿͍͑ͯΔͷʹAIC͕ߴ͍
  33. ݁Ռ 33 ී௨໊ࢺ Random effects Fixed effects By Subject By

    Items Parameters Estimate SE z p SD SD Intercept 1.24 0.26 4.81 < .001 0.72 0.43 subjective 0.91 0.36 2.55 .01 — — Note. Number of observation = 288, N = 24, K = 24.
  34. Model formula Df AIC BIC logLik deviance 1 judgment ~

    (1|participant)+(1| item) 3 381.23 392.22 -187.61 375.23 2 judgment ~ zrt + (1| participant)+(1| item) 4 383.17 397.82 -187.59 375.17 3 judgment ~ sub + (1| participant)+(1| item) 4 380.85 395.50 -186.43 372.85 4 judgment ~ sub + zrt + (1| participant)+(1| item) 5 382.83 401.14 -186.41 372.83 5 judgment ~ sub*zrt + (1| participant)+(1| item) 6 382.23 404.21 -185.12 370.23 34 Ϟσϧൺֱʢ෺໊࣭ࢺʣ Ϟσϧ1͸NULLϞσϧͰɼAICΛݟΔͱϞσϧ3͕࠾୒͞ΕΔ (͕͔͠͠NULLϞσϧͱͷ໬౓ൺݕఆͰ͸ඇ༗ҙʣ
  35. ݁Ռ 35 ෺໊࣭ࢺ Random effects Fixed effects By Subject By

    Items Parameters Estimate SE z p SD SD Intercept 0.16 0.26 0.63 .53 0.37 0.99 subjective 0.44 0.28 1.53 .13 — — Note. Number of observation = 288, N = 24, K = 24.
  36. ݁Ռ 36 ओ؍తଌ౓ͱਖ਼౴཰ͷؔ܎ ී௨໊ࢺ ෺໊࣭ࢺ sub effect plot sub judgment

    0.55 0.60 0.65 0.70 0.75 0.80 0.85 -0.4 -0.2 0.0 0.2 0.4 sub effect plot sub judgment 0.40 0.45 0.50 0.55 0.60 0.65 0.70 -0.4 -0.2 0.0 0.2 0.4 փ৭ͷ෦෼͸95%৴པ۠ؒ Intuition Intuition explainable explainable
  37. ݁Ռ 37 ओ؍తଌ౓ͱਖ਼౴཰ͷؔ܎ ී௨໊ࢺ ෺໊࣭ࢺ sub effect plot sub judgment

    0.55 0.60 0.65 0.70 0.75 0.80 0.85 -0.4 -0.2 0.0 0.2 0.4 sub effect plot sub judgment 0.40 0.45 0.50 0.55 0.60 0.65 0.70 -0.4 -0.2 0.0 0.2 0.4 փ৭ͷ෦෼͸95%৴པ۠ؒ Intuition Intuition explainable explainable ޡ͕ࠩେ͖͘ ༗ҙͰ͸ͳ͍
  38. ݁Ռ 38 ൓Ԡ࣌ؒͱਖ਼౴཰ͷਪఆ஋ͷؔ܎ 0 20000 40000 60000 0.0 0.2 0.4

    0.6 0.8 1.0 RT Estimated Accuracy 0 20000 40000 60000 0.0 0.2 0.4 0.6 0.8 1.0 RT Estimated Accuracy ී௨໊ࢺ ෺໊࣭ࢺ શମͰΈΔͱɼ࣌ؒͱਖ਼౴཰ͷؔ܎͸ബ͍ʢ࣮ࡍઆ໌ྗ͸൓Ԡ࣌ؒΛϞσϧʹ૊ΈࠐΜͰ΋͕͋Βͳ͍ʣ
  39. ݁Ռ 39 ൓Ԡ࣌ؒͱਖ਼౴཰ͷਪఆ஋ͷؔ܎ έʔε਺ 219 έʔε਺ 69 έʔε਺ 120 έʔε਺

    168 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Intuition RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Intuition RT Estimated Accuracy
  40. ݁Ռ 40 ൓Ԡ࣌ؒͱਖ਼౴཰ͷਪఆ஋ͷؔ܎ έʔε਺ 219 έʔε਺ 69 έʔε਺ 120 έʔε਺

    168 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Intuition RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Intuition RT Estimated Accuracy
  41. ݁Ռ 41 ൓Ԡ࣌ؒͱਖ਼౴཰ͷਪఆ஋ͷؔ܎ έʔε਺ 219 έʔε਺ 69 έʔε਺ 120 έʔε਺

    168 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Intuition RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Intuition RT Estimated Accuracy ༗ҙͰ͸ͳ͍͕…
  42. ֓ཁ • ͸͡Ίʹ • എܠ • ຊݚڀ • ݁Ռ •

    ߟ࡯ • ݁࿦ 42
  43. • ී௨໊ࢺ߲໨ • ओ؍తଌ౓ͷओޮՌ͋Γ • ൓Ԡ࣌ؒͷओޮՌͳ͠ • ओ؍తଌ౓×൓Ԡ࣌ؒͷަޓ࡞༻ͳ͠ • ෺໊࣭ࢺ߲໨

    • ओ؍తଌ౓ͷओޮՌͳ͠ • ൓Ԡ࣌ؒͷओޮՌͳ͠ • ओ؍తଌ౓×൓Ԡ࣌ؒͷަޓ࡞༻ͳ͠ ߟ࡯ 43 ݁Ռͷ·ͱΊ
  44. • ී௨໊ࢺ߲໨ • ʮنଇͰઆ໌Ͱ͖Δʯͱ౴͑ͨ৔߹ʹਖ਼౴͠΍͍͢ • ҙࣝతͳ஌ࣝΛ͍࣋ͬͯΔʁ • ෺໊࣭ࢺ߲໨ • ʮنଇͰઆ໌Ͱ͖Δʯͱ౴͑ͨ৔߹Ͱ΋ਖ਼౴͠΍͍͢

    Θ͚Ͱ͸ͳ͍ • ҙࣝతͳ஌ࣝ͸͍࣋ͬͯͳ͍ʁ • ֓ͶTamura and Kusanagi (2015)ͷ݁ՌͱҰக • ͔͠͠ɼʮҙࣝతͳ஌ࣝʯ͕ඞͣ͠΋ʮ஗͍ʯΘ͚Ͱ͸ ͳ͍ ߟ࡯ 44 ओ؍తଌ౓ͷӨڹ
  45. • ී௨໊ࢺɾ෺໊࣭ࢺͱ΋ʹ • ૣ͚Ε͹ʢ·ͨ͸஗͚Ε͹ʣਖ਼౴͠΍͍͢ͱ͍͏܏޲͸ΈΒ Εͳ͍ • ͔͠͠ɼҙࣝ࣠Ͱ෼͚Δͱ… • ʮ௚ײͰ͋Δʯͱ౴͑ͨ৔߹ʢແҙࣝత஌ࣝʣ •

    ී௨໊ࢺ߲໨ • ࣌ؒͱͱ΋ʹਖ਼౴཰͕Լ͕Δ܏޲ʁ • ෺໊࣭ࢺ߲໨ • ࣌ؒͱͱ΋ʹਖ਼౴཰্͕͕Δ܏޲ʁ • ͲͪΒͷ߲໨΋ɼʮنଇʯͱ౴͑ͨ৔߹ΑΓ΋͹Β͖͕ͭ খ͍͜͞ͱ͸໌ന ߟ࡯ 45 ൓Ԡ࣌ؒͷӨڹ
  46. • ී௨໊ࢺ • ଈ࣌తʹ׆ੑԽ͞ΕΔҙࣝతͳ஌ࣝද৅͕͋Δ • ແҙࣝతͳ஌ࣝද৅͕ଈ࣌తʹ׆ੑԽ͞Ε͍ͯΔՄೳ ੑ • ෺໊࣭ࢺ໊ࢺ •

    ҙࣝతͳ஌ࣝද৅͕͍ܽؕͯ͠Δ • ඇଈ࣌తʹ׆ੑԽ͞ΕΔແҙࣝతͳ஌ࣝද৅͕͋ΔՄ ೳੑ • ͨͩ͠ɼਖ਼౴཰͕શମͰ΋54%Ͱ͋Γɼʮ௚ײʯͱ౴ ͑ͨ৔߹ͷਖ਼౴཰͸50%ऑʢʮنଇʯͷ৔߹͸57%ʣ ߟ࡯ 46 ·ͱΊ
  47. ֓ཁ • ͸͡Ίʹ • ࣮ફഎܠ • ݚڀഎܠ • ࣮ફ಺༰ •

    ݁Ռ • ߟ࡯ • ݁࿦ 47
  48. • ݶք • ͦ΋ͦ΋ͷਖ਼౴཰͕ܾͯ͠ߴ͍ͱ͸͍͑ͳ͍ • ஌ࣝͷ༗ແΑΓ΋ɼ൓Ԡ࣌ؒɼओ؍తଌ౓ͱɼʮਖ਼ ౴͠΍͢͞ʯͷ࿈ؔͱ͍͏ٞ࿦ʹͱͲΊ͓ͯ͘΂͖ • จ๏ੑ൑அ՝୊ͷ৴པੑʹ೉͋Γ… •

    ల๬ • ݴޠ߲໨Λਫ਼ࠪ͠ɼҟͳΔ߲໨ʹ͓͚Δ൓Ԡ࣌ؒɼ ओ؍తଌ౓͕ਖ਼౴཰ʹ༩͑ΔӨڹͷҧ͍Λ໢ཏతʹ ݕূ͢Δ͜ͱʹΑΓɼֶशऀͷจ๏஌ࣝωοτϫʔ ΫΛΑΓਫ਼ඍʹଊ͑Δ ݁࿦ 48 ຊݚڀͷݶքͱࠓޙͷల๬
  49. Anderson, J. R. (1992). Automaticity and the ACT theory. The

    American Journal of Psychology, 105, 165–180. doi:10.2307/1423026 Andringa, S., & Rebuschat, P. (Eds.). (2015). New directions in the study of implicit and explicit learning. [Special issue]. Studies in Second Language Acquisition, 37. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). lme4: Linear mixed-effects models using Eigen and S4. [R package version 1.1-9]. Retrieved from https://cran.r-project.org/package=lme4 Bialystok, E. (1978). A theoretical model of second language learning. Language Learning, 28, 69–83. Dekeyser, R. (2003). Implicit and Explicit Learning. In C. J. Doughty & M. H. Long (Eds.), The handbook of second language acquisition (pp. 313– 348). Blackwell Publishing. Dienes, Z. (2007). Subjective measures of unconscious knowledge. Progress in Brain Research, 168, 49–64. doi: 10.1016/S0079-6123(07)68005-4 Ellis, R. (2004). The definition and measurement of L2 explicit knowledge. Language Learning, 54, 227–275. doi:10.2307/145776 Ellis, R. (2005). Measuring Implicit and explicit knowledge of a second language: A psychometric study. Studies in Second Language Acquisition, 27, 141–172. doi:10.1017/S0272263105050096 Erlam, R. (2006). Elicited imitation as a measure of L2 implicit knowledge: An empirical validation study. Applied Linguistics, 27, 464–491. doi: 10.1093/applin/aml001 Godfroid, A., Loewen, S., Jung, S., Park, J.-H., Gass, S., & Ellis, R. (2015). Timed and untimed grammaticality judgments measure distinct types of knowledge. Studies in Second Language Acquisition, 37, 269–297. doi:10.1017/S0272263114000850 Granena, G. (2013). Individual differences in sequence learning ability and second language acquisition in early childhood and adulthood. Language Learning, 63, 665–703. doi:10.1111/lang.12018 Hulstijn, J. H., & Ellis, R. (Eds.). (2005). Implicit and explicit second language learning. [Special issue]. Studies in Second Language Acquisition, 27. Jiang, N. (2007). Selective integration of linguistic knowledge in adult second language learning. Language Learning, 57(1), 1–33. doi:10.1111/j. 1467-9922.2007.00397.x ૲ಽ๜޿ɾ઒ޱ༐࡞ (2015) ʮจ๏ੑ൑அͷ֬৴౓ͱ໌ࣔత͓Αͼ҉ࣔత஌ࣝʯʰத෦஍۠ӳޠڭҭֶձلཁʱ 44, 65–72. Kusanagi, K., & Yamashita, J. (2013). Influences of linguistics factors on the acquisition and explicit and implicit knowledge: Focusing on agreement type and morphosyntactic regularity in English plural morpheme. Annual Review of English Language Education in Japan, 24, 205–220. Linck, J. A., & Cunnings, I. (2015). The utility and application of mixed-effects models in second language research. Language Learning, 65, 185– 207. doi:10.1111/lang.12117 Loewen, S. (2009). Grammaticality judgment tests and the measurement of implicit and explicit L2 knowledge. In R. Ellis, S. Loewen, C. Elder, R. Erlam, J. Philp, & H. Reinders (Eds.), Implicit and explicit knowledge in second language learning (pp. 94–112). Bristol, UK: Multilingual Matters. R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/ Suzuki, Y., & DeKeyser, R. (2015). Comparing Elicited Imitation and Word Monitoring as Measures of Implicit Knowledge. Language Learning, 65, 860–895. doi:10.1111/lang.12138 Tamura, Y., Harada, Y., Kato, D., Hara, K., & Kusanagi, K. (in press). Unconscious but slowly activated grammatical knowledge of Japanese EFL learners: A case of tough movement. Annual Review of English Language Education in Japan, 27. Tamura, Y., & Kusanagi, K. (2015). Asymmetirical representation in Japanese EFL learners’ implicit and explicit knowledge about the countability of common/material nouns. Annual Review of English Language Education in Japan, 26, 253–268. Vafaee, P., Kachisnke, I., & Suzuki, Y. (2016). Validating grammaticality judgment tests: Evidence from two new psycholinguistic measures. Studies in Second Language Acquisition. Advance Online Publication. doi;10.1017/S0272263115000455 References 49
  50. จ๏ੑ൑அ՝୊ʹ͓͚Δ ൓Ԡ࣌ؒͱओ؍తଌ౓͸ ਖ਼౴཰Λ༧ଌ͢Δ͔ contact info ాଜ ༞ ໊ݹ԰େֶେֶӃੜ yutamura@nagoya-u.jp http://www.tamurayu.wordpress.com/

    50 sub effect plot sub judgment 0.40 0.45 0.50 0.55 0.60 0.65 0.70 -0.4 -0.2 0.0 0.2 0.4 sub effect plot sub judgment 0.55 0.60 0.65 0.70 0.75 0.80 0.85 -0.4 -0.2 0.0 0.2 0.4 ී௨໊ࢺ ෺໊࣭ࢺ ൓Ԡ࣌ؒɿ༧ଌ͠ͳ͍ɼ͕ҙࣝ࣠ʹ Αͬͯ܏޲͕ҟͳΔʁ ओ؍తଌ౓ɿී௨໊ࢺͷΈ༧ଌ͢Δ 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 CNP Intuition RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Explainable RT Estimated Accuracy 0 10000 30000 50000 0.0 0.2 0.4 0.6 0.8 1.0 MNP Intuition RT Estimated Accuracy