repeated-measure-ANOVA

Date

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t test for independent samples t

Completely Randomized Design (CRD)

Randomized Block Design (RBD)

Latin Square Design (LSD)

Completely Randomized Factorial Design (CRFD)

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DV

1. IV

2.

3.

4./

5.

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(randomization)

(treatments)(order)

(blocking)

(Blocks)

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dependent samples

(threats of internal validity)

(repeated measure)

(blocking, subject matching)

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Completely Randomized Design (CRD, CR-p)

Yij = + !j + "i(j) (i = 1,..., n; j = 1,...,p)

"ij = Yij !j

Randomized Block Design (RBD, RB-p)

Yij = + !j + #i + "ij (i = 1,..., n; j = 1,...,p)

"ij = Yij !j #i

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Repeated measure ANOVA

subjectfactorlevel

F

practice effectcarryover effect

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within factor

between factor

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Awithin factor2Bwithin factor3A1B1A1B2A1B3A2B1A2B2A2B3Y

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Abetween factor2Bwithin factor3A1A2B1B2B3Y

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Lee et al.(2004, 2005).

CI Consistency Index

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Lee et al.(2004, 2005).

CI=1 CI=0.5

CI=0.33 CI=0.01CI Consistency Index

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Lee et al.(2005).

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statistical assumptionY

assumption of sphericity

MauchlyFFFepsilonGreenhouse-Geisser (G-G) Huynh-Feldt (H-F)

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Data from Vasey and Thayer (1987):

(EMG)

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EMG

0

20

40

60

80

A_relax B_posi C_agita D_sad 16

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F (3, 63) = 11.51, p < .001, = 0.48

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t

(normality test) Wilcoxon rank test

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12...

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(linear-mixed effect model)

EMG

0

20

40

60

80

A_relax B_posi C_agita D_sad

21

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EMG

Q 1:

Q 2: ()

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emotion

EM

G a

mpl

itude

0

20

40

60

80

A_relaxB_posiC_agitaD_sad

s1 s10


s11 s12


s13 s14

s15 s16 s17 s18 s19

0

20

40

60

80s2

0

20

40

60

80s20 s21 s22 s3 s4 s5

s6


s7 s8


0

20

40

60

80s9

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s3s4s13s14s2s15s1s12s20s9s18s7s6s17s5s16s8s10s19s11s21s22

-50 0 50

(Intercept) emotionB_posis3s4s13s14s2s15s1s12s20s9s18s7s6s17s5s16s8s10s19s11s21s22

emotionC_agita

-50 0 50

emotionD_sad

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(Intercept)

-20 0 10 20 30 -40 -20 0 20 40

-20

010

2030

-20

010

30

emotionB_posi

emotionC_agita

-20

010

30

-20 -10 0 10 20 30

-40

020

40

-20 0 10 20 30

emotionD_sad

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Tarkiainen et al. (1999) 1: (0 ~ 4)

2: (0% ~ 24%)

0% (symb)

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Tarkiainen et al. (1999)

M100 response

M170 response

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M100 response

M170 response

# M100 response varies in intensity with visual noise

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M100 response

M170 response

# M100 response varies in intensity with visual noise# M170 response varies in intensity with string length

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M100 response

M170 response

# M100 response varies in intensity with visual noise# M170 response varies in intensity with string length# M170 response shows the difference between symbols and letters

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Reading-Related N170 response

150ms~200ms after onsets have been well defined in both ERP & MEG studies. generated from fusiform gyrus

lateralized to the left hemisphere fusiform gyrus (the visual word form area; Cohen et al., 2000)

orthographic word-form detection

(Bentin et al., 1999)

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(adapted from Dehaene et al., 2005)

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In studies of alphabetic languages, there are different measurements for different aspect of orthographic properties.e.g., letter length and bigram frequency

In Chinese orthography, number of strokes is highly correlated with many factorsstrokes and frequency: r = -.14***strokes and phonetic combinability: r = -.14***strokes and semantic combinability: r = -.19*** (3967 phonograms)

N170/ M170 can reflect: Letter length (Tarkiainen et al., 2002) Bigram frequency (Hauk et al., 2006) transition probability (Solomyak and Marantz, 2010) Expertise of words (Bentin et al., 1999; Wong et al., 2005)

limitations of factorial design

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In studies of alphabetic languages, there are different measurements for different aspect of orthographic properties.e.g., letter length and bigram frequency

In Chinese orthography, number of strokes is highly correlated with many factorsstrokes and frequency: r = -.14***strokes and phonetic combinability: r = -.14***strokes and semantic combinability: r = -.19*** (3967 phonograms)

limitations of factorial design

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Solutions:single-trial analyses

Dambacher, Kliegl, Hofmann, & Jacobs, 2006; Hauk et al., 2006; Solomyak & Marantz, 2009, 2009

linear mixed model (Baayen et al., 2008).

Measurement of MEG source activation by minimum-norm estimations

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Experimental Design

400 real characters

400 pseudo-characters and non-characters

Task: lexical decision

Subjects:10 native Chinese speakers, error rate: 9% (S.D.: 3%)5 English speakers, error rate: 50% (45~54)

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(fixed effects)(random effects)

(maximum likelihood)

Baayen et al. (2008) (Markov chain Monte Carlo sampling) Type-1 error

Type I error rates across different methods (64 observations)

Type I error rates across different methods (800 observations)

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Approximately, 80% of characters are phonograms (Zhou, 1978).These are made up of a phonetic radical and a semantic radical.

semantic radical phonetic radical

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Variables for LMM analysis Random Variable

Subjects, Items

Fixed Variablestrial numbers (the rank of trials in the list)number of strokesphonetic combinabilitysemantic combinabilityfrequencynoun-to-verb ratiosemantic ambiguity

physical level

lexical level

orthographic level

semantic level

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Results of M100 analysis

* p < .05; ** p < .01; *** p < .001

Variables Beta Std. Error tvalue pMCMC Beta Std. Error tvalue pMCMC

Chinese Participants LH M100; R2 = .202 = .20

(Intercept) .0709 .0548 1.29 .2trial number .0001 .000 3.29 .001**number of s trokes .0028 .0012 2.35 .018*f requency .0128 .0084 1.53 .126phonetic com binability .002 .0012 1.65 .101semantic com binability .0001 .0001 1.38 .169semantic ambiguity .001 .0131 .08 .958NVratio .0035 .0046 .76 .456

.002 .0012 1.65 .101semantic com binability .0001 .0001 1.38 .169semantic ambiguity .001 .0131 .08 .958NVratio .0035 .0046 .76 .456

English Participants LH M100 ; R = .48

.1517 .1145 1.33 .118

.000 .000 .85 .392

.003 .0015 2.04 .037*.0039 .0077 .51 .607.0008 .0015 .56 .577.000 .0001 .37 .706.0125 .0139 .9 .367.0032 .0056 .57 .58

.0008 .0015 .56 .577.000 .0001 .37 .706.0125 .0139 .9 .367.0032 .0056 .57 .58

Chinese Participants RH M100; R2 = .14

(Intercept) .003 .0445 .07 .965trial number .0001 .000 4.06 < .001***number of s trokes .0045 .0013 3.57 < .001***f requency .0121 .0089

Chinese Participants RH M100; R2 = .14

(Intercept) .003 .0445 .07 .965trial number .0001 .000 4.06 < .001***number of s trokes .0045 .0013 3.57 < .001***f requency .0121 .0089 1.36 .16phonetic com binability .0034 .0013 2.68 .004**semantic com binability .0001 .0001 .79 .411semantic ambiguity .0038 .014 .27 .773NVratio .0063 .0049 1.27 .174

English Participants RH M100; R2 = .25

.1197 .0817 1.47 .177.000 .000 .04 .916.0028 .0017 1.61 .091

English Participants RH M100; R2 = .25

.1197 .0817 1.47 .177.000 .000 .04 .916.0028 .0017 1.61 .091.0052 .009 .58 .542.0012 .0017 .71 .472.0001 .0001 .71 .45.0066 .0164 .4 .661.0057 .0066 .86 .386

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The$contribu-ons$of$bilateral$occipital3temporal$regions$in$the$reading$of$Chinese$words$

The$seman-c$combinability$eects$in$RH$M170$reects$the$decomposi-on$of$characters.$

Eect$of$visual$complexity$in$LH$M170$suggests$that$LH$fusuform$gyrus$is$a$general$mechanism$for$visual$word$recogni-on.$

(Hsu, Lee and Marantz, 2011)

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repeated-measure-ANOVA