L1 French mcgill- RT data
Coughlin
September 23, 2015
set working directory load behavioral data
setwd("C:/Users/Katie/Desktop/Research/Dissertation/STUDIES/Studies 3 and 4 EEG/EEG Native French/Behavioral data")
bdata<-read.csv("For R native french eeg presentation data.csv",header=TRUE)
dim(bdata)## [1] 119190 31head(bdata)## Subject Trial Event.Type Code Time TTime RT
## 1 KC02 1 Response 87 1843884 1834618 NA
## 2 KC02 2 Response 87 1960442 116471 11655.8
## 3 KC02 3 Picture fix_cross 1960531 0 8.9
## 4 KC02 3 Picture nothing 1970661 10130 1013.0
## 5 KC02 3 Picture forward_mask 1980791 20260 1013.0
## 6 KC02 3 Picture 99 1985856 25325 506.5
## Uncertainty Duration Uncertainty.1 ReqTime ReqDur Stim.Type Pair.Index
## 1 1 NA NA NA NA NA
## 2 1 NA NA NA NA NA
## 3 1 10130 2 0 10000 other 0
## 4 1 10130 2 10000 10000 other 0
## 5 1 5065 2 20000 5000 other 0
## 6 1 533 2 25000 500 other 0
## order List Lookup UseInRT TriggerType Condition Related Item
## 1 1 v1-1 KC02-1 #N/A response correct reject
## 2 2 v1-1 KC02-2 #N/A response correct reject
## 3 3 v1-1 KC02-3 #N/A
## 4 4 v1-1 KC02-3 #N/A
## 5 5 v1-1 KC02-3 #N/A
## 6 6 v1-1 KC02-3 #N/A
## First.or.Second respond.yes respond.no Word Accuracy
## 1 NA 1 0 0 0
## 2 NA 1 0 0 0
## 3 NA 0 0 0 1
## 4 NA 0 0 0 1
## 5 NA 0 0 0 1
## 6 NA 0 0 0 1
## Accuracy.for.target.trigger Cond.Above. Rel.Above Item.Above
## 1 0
## 2 1 correct reject 0
## 3 1 correct reject 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 1 0 0 0Limit dataframe to just response lines
bdata<-bdata[bdata$TriggerType=="response",]
bdata$TriggerType<-factor(bdata$TriggerType)
bdata$Code<-factor(bdata$Code)
bdata<-bdata[bdata$Cond.Above.=="id"| bdata$Cond.Above.=="morph"| bdata$Cond.Above.=="orth"| bdata$Cond.Above.=="sem"| bdata$Cond.Above.=="nonce",]
head(bdata)## Subject Trial Event.Type Code Time TTime RT Uncertainty
## 82 KC02 25 Response 88 2671792 2570 883.5 1
## 89 KC02 27 Response 77 2713631 1757 802.2 2
## 96 KC02 29 Response 88 2761070 6544 1280.8 2
## 103 KC02 31 Response 77 2802190 5012 1127.6 1
## 110 KC02 33 Response 77 2841950 2120 838.4 1
## 117 KC02 35 Response 77 2885069 2587 885.1 1
## Duration Uncertainty.1 ReqTime ReqDur Stim.Type Pair.Index order List
## 82 NA NA NA NA NA 82 v1-1
## 89 NA NA NA NA NA 89 v1-1
## 96 NA NA NA NA NA 96 v1-1
## 103 NA NA NA NA NA 103 v1-1
## 110 NA NA NA NA NA 110 v1-1
## 117 NA NA NA NA NA 117 v1-1
## Lookup UseInRT TriggerType Condition Related Item First.or.Second
## 82 KC02-25 yes response incorrect reject NA
## 89 KC02-27 yes response correct accept NA
## 96 KC02-29 yes response incorrect reject NA
## 103 KC02-31 yes response correct accept NA
## 110 KC02-33 yes response correct accept NA
## 117 KC02-35 yes response correct accept NA
## respond.yes respond.no Word Accuracy Accuracy.for.target.trigger
## 82 0 1 0 1 0
## 89 1 0 0 0 1
## 96 0 1 0 1 0
## 103 1 0 0 0 0
## 110 1 0 0 0 0
## 117 1 0 0 0 1
## Cond.Above. Rel.Above Item.Above
## 82 nonce unrel N044
## 89 sem unrel D073
## 96 nonce rel N017
## 103 orth unrel D055
## 110 orth rel D043
## 117 orth rel D069dim(bdata)## [1] 16519 31limit dataframe to relevant columns
library(dplyr)
bdata<-bdata[,c(1,7,15,27,29:31)]
colnames(bdata)[5] <- "Condition"
colnames(bdata)[6] <- "Related"
colnames(bdata)[7] <- "Item"
bdata$Condition<-factor(bdata$Condition)
head(bdata)## Subject RT order Accuracy Condition Related Item
## 82 KC02 883.5 82 1 nonce unrel N044
## 89 KC02 802.2 89 0 sem unrel D073
## 96 KC02 1280.8 96 1 nonce rel N017
## 103 KC02 1127.6 103 0 orth unrel D055
## 110 KC02 838.4 110 0 orth rel D043
## 117 KC02 885.1 117 0 orth rel D069limit to only items correctly identified as words
bdata<-bdata[bdata$Accuracy==1,]Summarize RTs for each condition
library(plyr)
RT <- ddply(bdata, c("Condition", "Related"), summarize,
N = length(RT),
mean = round(mean(RT)),
sd = round(sd(RT),2),
se = round(sd / sqrt(N),2)
)
RT## Condition Related N mean sd se
## 1 id rel 553 550 154.42 6.57
## 2 id unrel 466 575 151.77 7.03
## 3 morph rel 551 541 112.70 4.80
## 4 morph unrel 452 559 103.81 4.88
## 5 nonce rel 2634 778 223.17 4.35
## 6 nonce unrel 5249 778 215.25 2.97
## 7 orth rel 488 590 183.08 8.29
## 8 orth unrel 492 588 151.11 6.81
## 9 sem rel 479 559 124.00 5.67
## 10 sem unrel 478 561 109.04 4.99Giving priming effect for each condition (related-unrelated)
id<-RT[1,4]-RT[2,4]
morph<-RT[3,4]-RT[4,4]
nonce<-RT[5,4]-RT[6,4]
orth<-RT[7,4]-RT[8,4]
sem<-RT[9,4]-RT[10,4]
effects<-rbind(id,morph,orth,sem,nonce)
effects## [,1]
## id -25
## morph -18
## orth 2
## sem -2
## nonce 0relevel to set unrelated as baseline
bdata$Related<-relevel(bdata$Related,ref="unrel")plotting RTs
library(ggplot2)
ggplot(bdata, aes(x = Related, y = RT, fill = Related)) + geom_boxplot() +
facet_wrap(~Condition, ncol = 5)+ylim(500,1500)
add lexical properties
load in properties .csv file
props<-read.csv("stim properties.csv",header=TRUE)
head(props)## Item Condition Word Letters Frequency Syllables tar.rel.unrel
## 1 D001 Morph OSE 3 28.51 1 target
## 2 D002 Morph TUE 3 16.96 1 target
## 3 D003 Morph ARME 4 1.15 1 target
## 4 D004 Morph CITE 4 4.80 1 target
## 5 D005 Morph ERRE 4 2.64 1 target
## 6 D006 Morph JOUE 4 40.47 1 target
## rel.overlap word.nonce
## 1 NA word
## 2 NA word
## 3 NA word
## 4 NA word
## 5 NA word
## 6 NA wordtargets<-props[props$tar.rel.unrel=="target" & props$word.nonce=="word",]
target_length<-targets[,c(1,4)]
target_freq<-targets[,c(1,5)]
target_syllables<-targets[,c(1,6)]integrate lexical properties into bdata dataframe
library(qdap)
bdata$targetfreq<-lookup(bdata$Item,target_freq)
bdata$targetlength<-lookup(bdata$Item,target_length)
bdata$targetsyllables<-lookup(bdata$Item,target_syllables)RT analyses
The below model includes all test conditions and nonce items to look at the effect of condition and prime relatedness on RTs
library(lme4)
library(lmerTest)
test1<-lmer(RT~Condition*Related+(1|Subject)+(1|Item),bdata)
summary(test1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT ~ Condition * Related + (1 | Subject) + (1 | Item)
## Data: bdata
##
## REML criterion at convergence: 152747.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0154 -0.5862 -0.1451 0.3559 6.3987
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 2687 51.84
## Subject (Intercept) 16210 127.32
## Residual 22271 149.23
## Number of obs: 11842, groups: Item, 288; Subject, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 637.799 25.890 40.000 24.635 < 2e-16
## Conditionmorph -1.713 14.729 903.000 -0.116 0.907414
## Conditionnonce 140.517 11.394 752.000 12.333 < 2e-16
## Conditionorth 14.994 14.589 873.000 1.028 0.304357
## Conditionsem -6.490 14.550 922.000 -0.446 0.655653
## Relatedrel -31.463 9.444 11600.000 -3.332 0.000866
## Conditionmorph:Relatedrel 7.870 13.413 11597.000 0.587 0.557368
## Conditionnonce:Relatedrel 29.889 10.259 11708.000 2.913 0.003583
## Conditionorth:Relatedrel 30.214 13.471 11605.000 2.243 0.024921
## Conditionsem:Relatedrel 34.783 13.545 11602.000 2.568 0.010244
##
## (Intercept) ***
## Conditionmorph
## Conditionnonce ***
## Conditionorth
## Conditionsem
## Relatedrel ***
## Conditionmorph:Relatedrel
## Conditionnonce:Relatedrel **
## Conditionorth:Relatedrel *
## Conditionsem:Relatedrel *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cndtnm Cndtnn Cndtnr Cndtns Rltdrl Cndtnm:R Cndtnn:R
## Conditnmrph -0.283
## Conditinnnc -0.372 0.643
## Conditinrth -0.287 0.508 0.654
## Conditionsm -0.284 0.502 0.648 0.508
## Relatedrel -0.201 0.356 0.458 0.358 0.356
## Cndtnmrph:R 0.142 -0.508 -0.323 -0.251 -0.251 -0.704
## Cndtnnnc:Rl 0.185 -0.328 -0.467 -0.329 -0.328 -0.920 0.648
## Cndtnrth:Rl 0.142 -0.250 -0.322 -0.484 -0.251 -0.701 0.494 0.645
## Cndtnsm:Rlt 0.140 -0.245 -0.318 -0.248 -0.488 -0.697 0.490 0.641
## Cndtnr:R
## Conditnmrph
## Conditinnnc
## Conditinrth
## Conditionsm
## Relatedrel
## Cndtnmrph:R
## Cndtnnnc:Rl
## Cndtnrth:Rl
## Cndtnsm:Rlt 0.488- The results show an effect of condition for nonce only, which means overall, RTs for nonce targets were longer than for ID targets.
- There was also an effect of relatedness, meaning that in the ID condition related primes elicited faster RTs than unrelated
- There were also interactions of condition x relatedness for orth x related, sem x related, and nonce x related, meaning that the priming effect of related primes found in the ID condition was also found in the morph condition, but not found in the other conditions.
Temporarily remove participant KC06 from analysis. This is because there was a problem mapping the responses to specific target items, causing trouble with lexical properties. Once I am able to fix the item mapping, I’ll add her back into analyses
bdata<-bdata[bdata$Subject!="KC06",]Remove nonce items from dataframe to allow checking for the effects of target lexical properties
bdata<-bdata[bdata$Condition!="nonce",]
test1<-lmer(RT~Condition*Related+(1|Subject)+(1|Item),bdata)
test2<-lmer(RT~Condition*Related+targetfreq+(1|Subject)+(1|Item),bdata)
summary(test2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT ~ Condition * Related + targetfreq + (1 | Subject) + (1 |
## Item)
## Data: bdata
##
## REML criterion at convergence: 46872.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0403 -0.4449 -0.0788 0.3269 9.3883
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 4279 65.41
## Subject (Intercept) 6668 81.66
## Residual 9508 97.51
## Number of obs: 3871, groups: Item, 144; Subject, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 632.4129 19.7892 64.0000 31.958 < 2e-16
## Conditionmorph -18.2270 16.7746 163.0000 -1.087 0.278822
## Conditionorth 6.3458 16.7344 162.0000 0.379 0.705034
## Conditionsem -10.7747 16.7926 162.0000 -0.642 0.522016
## Relatedrel -35.7290 6.2453 3708.0000 -5.721 1.14e-08
## targetfreq -0.5396 0.1370 135.0000 -3.939 0.000131
## Conditionmorph:Relatedrel 14.7032 8.8868 3708.0000 1.654 0.098111
## Conditionorth:Relatedrel 34.7918 8.9213 3709.0000 3.900 9.79e-05
## Conditionsem:Relatedrel 33.5692 8.9609 3709.0000 3.746 0.000182
##
## (Intercept) ***
## Conditionmorph
## Conditionorth
## Conditionsem
## Relatedrel ***
## targetfreq ***
## Conditionmorph:Relatedrel .
## Conditionorth:Relatedrel ***
## Conditionsem:Relatedrel ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cndtnm Cndtnr Cndtns Rltdrl trgtfr Cndtnm:R Cndtnr:R
## Conditnmrph -0.425
## Conditinrth -0.419 0.499
## Conditionsm -0.409 0.497 0.502
## Relatedrel -0.173 0.202 0.203 0.202
## targetfreq -0.167 0.013 -0.030 -0.079 0.003
## Cndtnmrph:R 0.121 -0.290 -0.143 -0.142 -0.703 -0.001
## Cndtnrth:Rl 0.120 -0.141 -0.277 -0.141 -0.700 -0.001 0.492
## Cndtnsm:Rlt 0.121 -0.141 -0.142 -0.279 -0.697 0.002 0.489 0.487Summarizing the reaction time data
- The nonce targets overall had longer reaction times than real word targets
- There was an effect of relatedness in the ID condition, where identical primes elicited faster RTs compared to unrelated primes
- The lack of interaction for of morph x related means that the effect of relatedness that was found in the ID condition is the same as the pattern found in the morph condition
- The interactions of orth x related and sem x related indicate that the facilitation of relatedness found in the ID condition is different in the orth condition and the sem condition