Seeing ‘water’ in ‘desert’:Semantic radical activation invisual Japanese compound recognition

Koji Miwa, Gary Libben & R. Harald Baayen
University of Alberta
Canadian Linguistics Association Conference
Vancouver, June 1st 20081

Semantic Radicals
 What is a semantic radical?

‘sea’‘liquid’‘pond’‘tear’‘broad’‘law’: Sanzui-hen (‘water’ radical)

 Semantic radicals are orthographic morphemes that have nophonological counterpart.
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Research Question

Do these anomalous morphemes play a detectable role
during reading of two-character Japanese compounds ?

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Past Studies
 Decompositional models: Multi-level lexical representation
Stroke features

RadicalCharacter

Compound‘sand - broad’ = ‘desert’4

Past Studies
 Evidences for radical processing in visual word recognition
Flores d’Arcais & Saito (1993)
 Flores d’Arcais, Saito, Kawakami (1995)
 Feldman & Siok (1997)
 Feldman & Siok (1999)
 Saito, Yamazaki, & Masuda (2002)
 Yencken & Baldwin (2006)
 Feldman & Siok (1997) and Feldman & Siok (1999)- Radicals with large families elicit shorter RTs

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Issues to be Considered
The present study extends previous research using lexical decision by:

Presenting two-character compounds- No isolated presentation of radicals- Most Japanese words are two-character compounds- Less likely to induce strategic radical decomposition

Using better non-words- NOT illegal combinations of radicals- BUT illegal combinations of characters* Use of illegal combinations of radicals as non-words may
encourage participants to decompose characters in an unnatural way
6Experiment: LD with Priming
Original Hypothesis:
Repetition of semantic radicals induces a facilitatory priming effect.

Participants- 30 native speakers of Japanese

Material- 46 prime-target pairs of two-character compounds- 46 prime-nonword pairs7Experiment: LD with Priming(1,000 ms)*
Prime Stimulus(230 ms)

Backward Masks##(200 ms)

Target Stimulus(word or non-word ?)8

Interim Result

Facilitatory priming effect (25 ms) was not significant (p=0.09).

But, fortunately, we can make use of analyses of covariance using awide range of lexical predictors.

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Lexical Predictors: Sanzui-hen (‘water’ radical)


Radical type frequency:

103



Radical token frequency:

503,833



Radical stroke:3

Radical transparency:

5.14 (7-point ratings about
how meaningful the radical is): eki ‘liquid’Character AoA:

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Character type frequency:

20

Character token frequency:

22,792

Character stroke1110

Lexical Predictors
CompoundCompound Token Freq

Character(Log) Right/ Left Characters Type Freq
(Log) Right/ Left Characters Token Freq
(Log) Right/ Left Characters AoA

Radical(Log) Right/ Left Characters Radical Type Freq
(Log) Right/ Left Characters Radical Token Freq
Right Character’s Radical Transparency

Stroke features
Right/ Left Characters Radical Stroke
Right/ Left Characters Stroke11Multi-collinearity



Many of the 21 variables
are highly correlated.
(e.g. radical properties)

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Principal Component Regression

1.

We orthogonalized the predictors with principal component analysis(Uncorrelated new parameters were created).

2.

We used the principal components as predictors.

3.

We interpreted significant principal components by looking at theloadings of the original predictors.

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Principal Component RegressionIn our principle component regression analysis, three principalcomponents emerged as significant predictors (PC1, PC3, and PC5).

Estimate

Std. Errort valuep value(Intercept)

6.59859

0.02960

222.9

0.000

PC1 inhibitory

0.031180.00862

3.62

0.000

PC3 facilitatory-0.04017

0.01366

-2.94

0.003

PC5 facilitatory-0.03333

0.01646

-2.03

0.043

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Principal Component Regression: PC1
LogRadicalFreq
LogRadicalTypeFreq
LogTargetRightTypeFreq
LogPrimeRightTokenFreq
LogTargetRightTokenFreq
LogPrimeWordFreq
LogPrimeRightTypeFreq
LogTargetLeftTokenFreq
LogTargetLeftTypeFreq
LogTargetWordFreq
LeftRadicalStrokeTargetLeftStroke
LogLeftRadicalFreq
RadicalTransparency
LogLeftRadicalTypeFreq
TargetRightStroke
TargetLeftAoA
PrimeRightAoA
RadicalStroke
PrimeRightStroke
TargetRightAoA-0.32595441-0.32461342
Type/ token frequencies of-0.25719829
the right character,-0.24851236
the right character’s radical,-0.22849805-0.20400900
and the prime.
-0.19793693-0.19694687-0.16765199-0.06674472-0.06558624
0.03508257
0.05533828
0.18117849
0.18936575
0.22199603
Number of strokes and AoA
0.22712781
0.23350434
of the right characters and0.25461382
the right character’s radical
0.26371383
150.29843572

Principal Component Regression: PC3
LeftRadicalStroke-0.31890508
LogTargetLeftTypeFreq-0.27377317
TargetRightAoA-0.27015008
LogPrimeRightTokenFreq-0.21593121
RadicalTransparency-0.19802870
LogPrimeRightTypeFreq-0.15852085
LogPrimeWordFreq-0.15197259
TargetRightStroke-0.13537410
LogTargetLeftTokenFreq-0.06331644
RadicalStroke-0.05751754
TargetLeftStroke-0.05340011
LogRadicalTypeFreq-0.03122143
LogLeftRadicalFreq
0.05025043
LogRadicalFreq
0.09020049
LogLeftRadicalTypeFreq
0.12549673
PrimeRightStroke
0.12576490
PrimeRightAoA
0.24687072
LogTargetRightTypeFreq
0.25513908
TargetLeftAoA
0.26796378
LogTargetWordFreq
0.39901548
LogTargetRightTokenFreq
0.43306385

 A number of strokes ofthe left character’s radical
 Type frequencies of theleft character Right character’s AoA

 Token frequencies of rightcharacters
 Token frequencies of the16compoundPrincipal Component Regression: PC5LogLeftRadicalTypeFreq
LogLeftRadicalFreq
TargetLeftStroke
LogRadicalTypeFreq
LogPrimeRightTypeFreq
TargetLeftAoA
RadicalTransparency
LogRadicalFreq
LogPrimeRightTokenFreq
LogPrimeWordFreq
LeftRadicalStroke
PrimeRightStroke
LogTargetRightTypeFreq
TargetRightAoA
LogTargetRightTokenFreq
LogTargetWordFreq
RadicalStroke
LogTargetLeftTokenFreq
TargetRightStroke
LogTargetLeftTypeFreq
PrimeRightAoA-0.590242019-0.526935150-0.377893898-0.151640066-0.149656442-0.090099401-0.063713697-0.061873990-0.054440805-0.030480989-0.014648391-0.004027343
0.028660268
0.038024481
0.045912640
0.078954418
0.080092633
0.096903705
0.152684837
0.157344826
0.301901441

 Type/ token frequenciesof the left character’s radical
 Number of the left character’s
strokes17
AoA of the prime’s right character

Influential Lexical Predictors
CompoundTarget Word Freq

Character
Log Right Character Type/ Token Freq,
Left Character AoA
Log Left Character Type Freq,

Right Character AoA

RadicalLog Right Character Radical Type/ Token Freq
Log Left Character Radical Type/ Token Freq

Stroke features
Left/ Right Character Radical Stroke
Left Character Stroke18

What We Have Learned
CompoundTarget Word Freq

 Word frequency effectCompounds that occur more frequently are recognized faster.

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What We Have Learned
Character
Log Right Character Type/ Token Freq,
Log Left Character Type Freq

 When a compound has a right character with high frequenciesof occurrence or with many type neighbors,the compound is recognized faster.
 When a compound has a left character with many type neighbors,the compound is recognized more slowly.
=> Modifier – head competition for head status
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What We Have Learned
Character
Left Character AoA,

Right Character AoA

 When a compound’s right character was learned early in school,
the compound is recognized faster.
 When a compound’s left character was learned early in school,
the compound is recognized more slowly.
=> Modifier – head competition for head status

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What We Have Learned
RadicalLog Right Character Radical Type/ Token Freq
Log Left Character Radical Type/ Token Freq

 When a compound’s right character contains a semantic radicalwith high frequencies of occurrence or with many typeneighbors, the compound is recognized faster.
 When a compound’s left character contains a semantic radicalwith high frequencies of occurrence or with many typeneighbors, the compound is recognized more slowly.
=> Modifier – head competition for head status
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What We Have Learned
Stroke features
Left/ Right Character Radical Stroke
Left/ Right Character Stroke When characters of a compound are made of many strokes,
the compound is recognized slowly.
=> Orthographic complexity leads to slower word recognition.

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What We Have Learned
Prime Properties
Prime Right Character Token Freq
Prime Right Character Stroke Presentation of a prime compound with highly frequent rightcharacter speeds up the recognition of a target compound.
 Presentation of a prime compound with a orthographicallycomplex right character slows down the recognition ofa target compound.
=> The primes were processed.
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ConclusionThe priming manipulation failed, but regression analyses showthat semantic radicals are at work.

Inclusion of distributional properties of words provides a betterpicture. When distributional data are not available, primingis a good option. Otherwise, regression offers a more powerful
methodology.

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Thank You
Correspondence
Koji Miwa: kmiwa@ualberta.ca
Acknowledgment
I would like to express my special thanks to members ofthe Centre for Comparative Psycholinguistics for theirfeedbacks on the earlier version of this presentation andfor their casual advices and insights.

This research was supported by a Major Collaborative Research Initiative Grant(#412-2001-1009) from the Social Sciences and Humanities Research Council of Canada toGary Libben (Director), Gonia Jarema, Eva Kehayia, Bruce Derwing, and Lori Buchanan (Co26
investigators).