computing segmental and suprasegmental information in lexical … · 2020-01-26 · garcia (mcgill)...
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IntroductionMethods
Results & Discussion
Computing segmental and suprasegmentalinformation in lexical decision
Guilherme D. Garcia
McGill University
24th mfm
Garcia (McGill) Suprasegmental information and lexical decision
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IntroductionMethods
Results & Discussion
Today
Lexical decision & stress
Speakers take longer to recognize words if stress coin-cides with the segmental point of recognition.
Ï Hypothesis: when crucial segmental and suprasegmentalinformation need to be processed at (nearly) at the same time,reaction time slows down
Garcia (McGill) Suprasegmental information and lexical decision
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IntroductionMethods
Results & Discussion
IntroductionRole of stress?Portuguese stress
MethodsLexical decision taskStimuliSpeakersHypothesis
Results & DiscussionDataAnalysisConclusion
Garcia (McGill) Suprasegmental information and lexical decision
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IntroductionMethods
Results & Discussion
Role of stress?Portuguese stress
Known segmental effects on lexical accessCutler et al. 1987, Sommers 1996, Luce and Pisoni 1998, Vitevitch and Luce 1998
Ï Word frequencyMore frequent words → faster RTs
Ï Neighbourhood densityWords with sparse phonological neighbourhoods → faster RTs(cf. Vitevitch and Rodríguez 2005 for Spanish)
Ï PhonotacticsMore frequent patterns → faster RTs
Ï Stress...?Word-initial stress → faster RTs (Vitevitch et al. 1997)
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IntroductionMethods
Results & Discussion
Role of stress?Portuguese stress
Stress & lexical access
Word-initial stress in En → faster RTs (Vitevitch et al. 1997)
Ï Confound: positional bias in English (Cutler and Carter 1987)
∴ The relationship between stress per se and RT is unclear
Today
Lexical decision task in Portuguese+ Unlike English, no bias towards word-initial stress
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IntroductionMethods
Results & Discussion
Role of stress?Portuguese stress
Portuguese stressGeneral patterns
In Pt, a heavy (H) σ has a coda or a diphthong:
Ï Final stress if H]PWd . Else, penult stress (a)Ï Antepenult stress considered ‘irregular/idiosyncratic’ (b)
Bias: penult stress (default)
(a) caracól (‘snail’), caválo (‘horse’)(b) patético (‘pathetic’), fantástico (‘fantastic’)
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IntroductionMethods
Results & Discussion
StimuliSpeakersHypothesis
Auditory lexical decision taskMethods: stimuli
Trisyllabic words (n = 360)Different syllable shapes and segmental qualitiesReal (n = 180) and nonce words pseudorandomly presented
Ï Real word → Final onset → Nonce word∴ Point of recognition (PoR): onset of final syllable
E.g. moletom (‘sweater’) → molerom
Ï Stimuli: article + noun
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IntroductionMethods
Results & Discussion
StimuliSpeakersHypothesis
Auditory lexical decision taskMethods: stimuli
Ï Recorded by a phonetically trained Portuguese speaker (f)Ï No phonetic manipulationÏ Response button only available after each stimulus
Stimuli preceded by question:‘Is this a real word in Portuguese?’
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IntroductionMethods
Results & Discussion
StimuliSpeakersHypothesis
Auditory lexical decision taskMethods: participants & variables
Participants (n = 51): native Portuguese speakersCut-off point: 80% accuracy (n = 37)
Ï Variables examined:StressBigram probability, neighbourhood density, frequency
Ï RTs scaled and centred by speaker (z-score)
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IntroductionMethods
Results & Discussion
StimuliSpeakersHypothesis
1 Hypothesis: when crucial segmental and suprasegmentalinformation need to be processed at (nearly) at the same time,reaction time slows down
2 Are phonetic cues to stress available early on?
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Neighbourhood densityMore neighbours = faster RTs
APU PU U
-1
0
1
2
0 2 4 6 8 0 2 4 6 8 0 2 4 6 8
Neighbourhood density (z-score)
Reac
tion
time
(z-sc
ore)
Ï Unlike English (Sommers 1996)
Ï But consistent with Spanish (Vitevitch and Rodríguez 2005)
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
StressEarlier stress = faster RTs
Real word Nonce word
-0.2
-0.1
0.0
APU PU U APU PU UStress
Reac
tion
time
(z-sc
ore)
Ï Real words = faster RTs, consistent with previous studies
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
OverviewMultilevel linear regression estimates (fixed effects)
Word: nonce
Stress: APU
Stress: U
Frequency
N. density
Bigram prob.
-0.05 0.00 0.05 0.10
← U slower than PU
← APU faster than PU
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
StressBayesian estimation (Kruschke 2013)
APUµ1 vs. PUµ2
-2 -1 0 1 2 3
0.0
0.4
0.8
y
p(y
)
Data Group 1 w. Post. Pred.
N1 = 4327
-2 -1 0 1 2 3
0.0
0.4
0.8
yp
(y)
Data Group 2 w. Post. Pred.
N2 = 6474
Normality
log10(ν)0.34 0.35 0.36 0.37 0.38 0.39 0.40
95% HDI0.343 0.394
mode = 0.369
Group 1 Mean
μ1
-0.32 -0.30 -0.28 -0.26
95% HDI-0.318 -0.289
mean = -0.304
Group 2 Mean
μ2
-0.32 -0.30 -0.28 -0.26
95% HDI-0.283 -0.259
mean = -0.27
Difference of Means
μ1 − μ2
-0.05 -0.04 -0.03 -0.02 -0.01 0.00
95% HDI-0.052 -0.0156
mean = -0.0336100% < 0 < 0%
Group 1 Std. Dev.
σ1
0.34 0.35 0.36 0.37 0.38 0.39 0.40
95% HDI0.355 0.383
mode = 0.367
Group 2 Std. Dev.
σ2
0.34 0.35 0.36 0.37 0.38 0.39 0.40
95% HDI0.361 0.386
mode = 0.373
Difference of Std. Dev.s
σ1 − σ2
-0.02 -0.01 0.00 0.01
95% HDI-0.0206 0.0103
mode = -0.00427
Effect Size
(μ1 − μ2) (σ12 + σ2
2) 2
-0.15 -0.10 -0.05 0.00
95% HDI-0.14 -0.042
mode = -0.0904100% < 0 < 0%
PUµ1 vs. Uµ2
-2 -1 0 1 2 3
0.0
0.4
0.8
y
p(y
)Data Group 1 w. Post. Pred.
N1 = 6474
-2 -1 0 1 2 3
0.0
0.4
0.8
y
p(y
)
Data Group 2 w. Post. Pred.
N2 = 2148
Normality
log10(ν)0.36 0.38 0.40 0.42
95% HDI0.363 0.423
mode = 0.395
Group 1 Mean
μ1
-0.30 -0.25 -0.20 -0.15
95% HDI-0.28 -0.255
mean = -0.267
Group 2 Mean
μ2
-0.30 -0.25 -0.20 -0.15
95% HDI-0.215 -0.169
mean = -0.192
Difference of Means
μ1 − μ2
-0.10 -0.08 -0.06 -0.04 -0.02 0.00
95% HDI-0.101 -0.0497
mean = -0.075100% < 0 < 0%
Group 1 Std. Dev.
σ1
0.36 0.38 0.40 0.42 0.44 0.46
95% HDI0.367 0.394
mode = 0.38
Group 2 Std. Dev.
σ2
0.36 0.38 0.40 0.42 0.44 0.46
95% HDI0.398 0.44
mode = 0.418
Difference of Std. Dev.s
σ1 − σ2
-0.06 -0.05 -0.04 -0.03 -0.02 -0.01
95% HDI-0.0592 -0.016
mode = -0.0384
Effect Size
(μ1 − μ2) (σ12 + σ2
2) 2
-0.25 -0.20 -0.15 -0.10 -0.05 0.00
95% HDI-0.251 -0.124
mode = -0.184100% < 0 < 0%
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Phonetic cues
1 Hypothesis: when crucial segmental and suprasegmentalinformation need to be processed at (nearly) at the same time,reaction time slows down
2 Are phonetic cues to stress available early on?Ï Given the relative nature of stress:
How early can speakers perceive APU stress?
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Phonetic cuesDuration
Main phonetic correlate in Pt is duration: (Major 1985)
APU PU U
0.15
0.20
0.25
0.30
APU PU U APU PU U APU PU U
Syllable
Dur
atio
n (m
s)
Ï Overall, stressed syllables are considerably longer
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Phonetic cuesIntensity
Besides, note amplitude in APU σs:
APU PU U
45
50
55
60
APU PU U APU PU U APU PU U
Syllable
Ampl
itude
(dB
)
∴ APU stress is phonetically cued early
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Conclusion
Ï Results are consistent with the hypothesis:U stress ‘coincides’ with PoR → slower RTStimuli contain sufficiently robust phonetic cues to stress
Ï Stress plays a clear role in lexical access in these dataÏ Distance matters: segment vs. syllable?
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IntroductionMethods
Results & Discussion
DataAnalysisConclusion
Next steps
Ï Prediction: whenever stress is aligned with PoR → slower RTsThus, if PoR is varied within each stress pattern:
APU PU U
APU ↗PU ↗U ↗
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References I
Cutler, A. and Carter, D. M. (1987). The predominance of strong initialsyllables in the English vocabulary. Computer Speech & Language,2(3-4):133–142.
Cutler, A., Mehler, J., Norris, D., and Segui, J. (1987). Phoneme identificationand the lexicon. Cognitive Psychology, 19(2):141–177.
Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal ofExperimental Psychology: General, 142(2):573–603.
Luce, P. and Pisoni, D. (1998). Recognizing Spoken Words: The NeighborhoodActivation Model. Ear and Hearing, 19(1):1–36.
Major, R. C. (1985). Stress and rhythm in Brazilian Portuguese. Language,61(2):259–282.
Sommers, M. S. (1996). The structural organization of the mental lexicon andits contribution to age-related declines in spoken-word recognition.Psychology and aging, 11(2):333.
Garcia (McGill) Suprasegmental information and lexical decision References
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References II
Vitevitch, M. S. and Luce, P. A. (1998). When words compete: Levels ofprocessing in perception of spoken words. Psychological science,9(4):325–329.
Vitevitch, M. S., Luce, P. A., Charles-Luce, J., and Kemmerer, D. (1997).Phonotactics and syllable stress: Implications for the processing of spokennonsense words. Language and speech, 40(1):47–62.
Vitevitch, M. S. and Rodríguez, E. (2005). Neighborhood density effects inspoken word recognition in Spanish. Journal of Multilingual CommunicationDisorders, 3(1):64–73.
Garcia (McGill) Suprasegmental information and lexical decision References
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Acknowledgments
Thanks to Heather Goad, Morgan Sonderegger, Natália Brambatti Guzzo,and the Montreal Language Modelling Lab (MLML).
Thank you!
Slides on guilherme.ca
Garcia (McGill) Suprasegmental information and lexical decision f