Dwell Time Analyses for the Discrimination Session

Data Files

#Data are in two files, one for the category-learning group, and one for the paired-associate-learning group
#Category learning
data1<-read.table("FixRep_StimPresentationB_CAT.txt", header=T)
colnames(data1)[2]<-"group"

#Paired-Associate Learning
data2<-read.table("FixRep_StimPresentationB_PA.txt", header=T)
colnames(data2)[2]<-"group"

data<-rbind(data1,data2)
#Combine data into one data frame 
data<-rbind(data1,data2)
library(dplyr)
#Convert all chr columns to factor
data <- mutate_if(data, is.character, as.factor)

#Rename group levels
levels(data$group)<-c("CAT", "PA")
#Following Hendreson et al. (1991) we exclude fixations with duration <90 or >1000
#We are only interested in critical (encoded as "mixed") trials, presenting one label and one ideogram shape
#Finally, we are interested in fixations falling within an interest area (i.e., within a shape)

data<-droplevels(data[data$CURRENT_FIX_DURATION>90 & data$CURRENT_FIX_DURATION<1000 & data$cnd=='mixed' & data$CURRENT_FIX_INTEREST_AREA_LABEL!='.',])

Dwell Time by Learning Group and Shape

#Calculate Dwell Time (i.e., sum of duration of fixations during one trial)
data_dw<-aggregate(CURRENT_FIX_DURATION~sbj+trial+group+cnd+CURRENT_FIX_INTEREST_AREA_LABEL+Response,data,sum,na.rm=T)
data_dw<-data_dw[order(data_dw$sbj, data_dw$trial,data_dw$CURRENT_FIX_INTEREST_AREA_LABEL),]
colnames(data_dw)[5]<-'IA_LABEL'
colnames(data_dw)[7]<-'DWELL_TIME'
data_dw$IA_LABEL<-as.factor(data_dw$IA_LABEL)
#Calculate Dwell Time by Learning Group and Shape (coded as IA_Label: "label" vs. "ideog")
data_mean<-aggregate(DWELL_TIME~sbj+group+IA_LABEL,data_dw, mean,na.rm=T)

#Category Learning
#Dwell Time on label shapes
mean(data_mean[data_mean$group=='CAT'&data_mean$IA_LABEL=='label',]$DWELL_TIME)
## [1] 668.2807
sd(data_mean[data_mean$group=='CAT'&data_mean$IA_LABEL=='label',]$DWELL_TIME)
## [1] 213.0915
#Dwell Time on ideogram shapes
mean(data_mean[data_mean$group=='CAT'&data_mean$IA_LABEL=='ideog',]$DWELL_TIME)
## [1] 683.8226
sd(data_mean[data_mean$group=='CAT'&data_mean$IA_LABEL=='ideog',]$DWELL_TIME)
## [1] 220.485
#Paired-Associate Learning 
#Dwell Time on label shapes
mean(data_mean[data_mean$group=='PA'&data_mean$IA_LABEL=='label',]$DWELL_TIME)
## [1] 726.596
sd(data_mean[data_mean$group=='PA'&data_mean$IA_LABEL=='label',]$DWELL_TIME)
## [1] 225.2125
#Dwell Time on ideogram shapes
mean(data_mean[data_mean$group=='PA'&data_mean$IA_LABEL=='ideog',]$DWELL_TIME)
## [1] 727.5203
sd(data_mean[data_mean$group=='PA'&data_mean$IA_LABEL=='ideog',]$DWELL_TIME)
## [1] 205.9584

Dwell Time by Learning Group, Shape and Block

library(sciplot)
library(gplots)

#Create "block" variable 
data$block<-ifelse(data$trial<25,1,ifelse(data$trial<49,2,ifelse(data$trial<73,3,4)))
#Calculate dwell time 
data_dw<-aggregate(CURRENT_FIX_DURATION ~ sbj+trial+group+cnd+block+CURRENT_FIX_INTEREST_AREA_LABEL+Response,data,sum,na.rm=T)
data_dw<-data_dw[order(data_dw$sbj, data_dw$trial,data_dw$CURRENT_FIX_INTEREST_AREA_LABEL),]
colnames(data_dw)[6]<-'IA_LABEL'
colnames(data_dw)[8]<-'DWELL_TIME'
data_dw$IA_LABEL<-as.factor(data_dw$IA_LABEL)

#Calculate dwell time by block, shape, and learning group
data_bl<-aggregate(DWELL_TIME~ sbj+block+IA_LABEL+group,data_dw,mean,na.rm=T)
data_bl<-data_bl[order(data_bl$sbj, data_bl$block),]
3-way ANOVA
library(ez)
data_bl$block<-as.factor(data_bl$block)
ezANOVA(data=data_bl, dv=.(DWELL_TIME), wid=.(sbj), within=.(IA_LABEL,block), between = .(group), detailed=TRUE, type=3)
## $ANOVA
##                 Effect DFn DFd          SSn      SSd            F            p
## 1          (Intercept)   1  46 1.885028e+08 15705006 552.12507948 2.897620e-27
## 2                group   1  46 2.812385e+05 15705006   0.82374810 3.688169e-01
## 3             IA_LABEL   1  46 5.007966e+03  1581865   0.14562966 7.045044e-01
## 5                block   3 138 4.575167e+04  3978573   0.52897775 6.631204e-01
## 4       group:IA_LABEL   1  46 2.387688e+03  1581865   0.06943303 7.933409e-01
## 6          group:block   3 138 2.422953e+05  3978573   2.80140208 4.229036e-02
## 7       IA_LABEL:block   3 138 1.809948e+05  2567114   3.24323847 2.402422e-02
## 8 group:IA_LABEL:block   3 138 2.015125e+05  2567114   3.61089400 1.499358e-02
##   p<.05          ges
## 1     * 0.8877598126
## 2       0.0116629689
## 3       0.0002100871
## 5       0.0019160346
## 4       0.0001001760
## 6     * 0.0100642479
## 7     * 0.0075371945
## 8     * 0.0083844517
## 
## $`Mauchly's Test for Sphericity`
##                 Effect         W         p p<.05
## 5                block 0.8275720 0.1325377      
## 6          group:block 0.8275720 0.1325377      
## 7       IA_LABEL:block 0.8860819 0.3681421      
## 8 group:IA_LABEL:block 0.8860819 0.3681421      
## 
## $`Sphericity Corrections`
##                 Effect       GGe      p[GG] p[GG]<.05       HFe      p[HF]
## 5                block 0.8972648 0.64375505           0.9583583 0.65551753
## 6          group:block 0.8972648 0.04847193         * 0.9583583 0.04469171
## 7       IA_LABEL:block 0.9280550 0.02729440         * 0.9938931 0.02428543
## 8 group:IA_LABEL:block 0.9280550 0.01751123         * 0.9938931 0.01519208
##   p[HF]<.05
## 5          
## 6         *
## 7         *
## 8         *
#There is a triple interaction. We analyze each group separately
2 -way ANOVA on data from the Category-Learning Group
data_cat<-droplevels(data_bl[data_bl$group=="CAT",])
ezANOVA(data=data_cat, dv=.(DWELL_TIME), wid=.(sbj), within=.(IA_LABEL,block), detailed=TRUE, type=3)
## $ANOVA
##           Effect DFn DFd          SSn       SSd          F            p p<.05
## 1    (Intercept)   1  23 87110922.429 8154167.3 245.708868 9.063632e-14     *
## 2       IA_LABEL   1  23     7155.784  529664.0   0.310731 5.826170e-01      
## 3          block   3  69   105401.140 2298417.9   1.054737 3.740832e-01      
## 4 IA_LABEL:block   3  69   290220.873  918227.4   7.269528 2.626766e-04     *
##            ges
## 1 0.8798070050
## 2 0.0006009409
## 3 0.0087791282
## 4 0.0238067489
## 
## $`Mauchly's Test for Sphericity`
##           Effect         W          p p<.05
## 3          block 0.5661863 0.03039813     *
## 4 IA_LABEL:block 0.8200044 0.50602696      
## 
## $`Sphericity Corrections`
##           Effect       GGe       p[GG] p[GG]<.05       HFe        p[HF]
## 3          block 0.7299433 0.360908924           0.8097951 0.3655852161
## 4 IA_LABEL:block 0.8727392 0.000546225         * 0.9949608 0.0002703815
##   p[HF]<.05
## 3          
## 4         *
# Interaction of block by shape
# Post-hoc multiple comparisons
# See https://cran.r-project.org/web/packages/multcomp/vignettes/multcomp-examples.pdf

library(nlme)
library(lmerTest)
library(multcomp)

# Same analysis as with ezANOVA, but using lme this time
mod<-lme(DWELL_TIME ~ IA_LABEL* block, random=list(sbj=pdBlocked(list(~1, pdIdent(~IA_LABEL-1), pdIdent(~block-1)))), data=data_cat)

tmp <- expand.grid(IA_LABEL = unique(data_cat$IA_LABEL), block = unique(data_cat$block))
X <- model.matrix(~ block * IA_LABEL, data =tmp)

Tukey <- contrMat(table(data_cat$IA_LABEL), "Tukey")
mat<-matrix(0, nrow = nrow(Tukey), ncol = ncol(Tukey))
K1 <- cbind(Tukey, mat,  mat,  mat)
rownames(K1) <- paste(levels(data_cat$block)[1], rownames(K1), sep = ":")
K2 <- cbind(mat, Tukey, mat, mat)
rownames(K2) <- paste(levels(data_cat$block)[2], rownames(K2), sep = ":")
K3 <- cbind(mat,mat, Tukey,mat)
rownames(K3) <- paste(levels(data_cat$block)[3], rownames(K3), sep = ":")
K4 <- cbind(mat, mat, mat, Tukey)
rownames(K4) <- paste(levels(data_cat$block)[4], rownames(K4), sep = ":")
K <- rbind(K1, K2, K3, K4)
colnames(K) <- c(colnames(Tukey), colnames(Tukey), colnames(Tukey), colnames(Tukey))

summary(glht(mod, linfct = K %*% X))
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Fit: lme.formula(fixed = DWELL_TIME ~ IA_LABEL * block, data = data_cat, 
##     random = list(sbj = pdBlocked(list(~1, pdIdent(~IA_LABEL - 
##         1), pdIdent(~block - 1)))))
## 
## Linear Hypotheses:
##                      Estimate Std. Error z value Pr(>|z|)  
## 1:label - ideog == 0   118.27      44.07   2.684   0.0249 *
## 2:label - ideog == 0   -45.58      55.24  -0.825   0.8074  
## 3:label - ideog == 0   -83.12      55.24  -1.505   0.3481  
## 4:label - ideog == 0   -44.26      44.07  -1.004   0.6856  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
2 -way ANOVA on data from the Paired-Associate-Learning Group
data_pa<-droplevels(data_bl[data_bl$group=="PA",])
ezANOVA(data=data_pa, dv=.(DWELL_TIME), wid=.(sbj), within=.(IA_LABEL,block), detailed=TRUE, type=3)
## $ANOVA
##           Effect DFn DFd          SSn     SSd            F            p p<.05
## 1    (Intercept)   1  23 1.016731e+08 7550839 3.096982e+02 7.707641e-15     *
## 2       IA_LABEL   1  23 2.398709e+02 1052201 5.243323e-03 9.429010e-01      
## 3          block   3  69 1.826458e+05 1680155 2.500277e+00 6.663352e-02      
## 4 IA_LABEL:block   3  69 9.228645e+04 1648886 1.287286e+00 2.856826e-01      
##            ges
## 1 8.949689e-01
## 2 2.010261e-05
## 3 1.507635e-02
## 4 7.674952e-03
## 
## $`Mauchly's Test for Sphericity`
##           Effect         W         p p<.05
## 3          block 0.8316550 0.5492052      
## 4 IA_LABEL:block 0.6885652 0.1509226      
## 
## $`Sphericity Corrections`
##           Effect       GGe     p[GG] p[GG]<.05       HFe      p[HF] p[HF]<.05
## 3          block 0.8843313 0.0748483           1.0103346 0.06663352          
## 4 IA_LABEL:block 0.7926132 0.2867039           0.8901128 0.28652113
# No effect, no interaction

Graph

#Data for graph
data_gr<-aggregate(DWELL_TIME~ block+IA_LABEL+group,data_bl,mean,na.rm=T)
temp<-aggregate(DWELL_TIME~ block+IA_LABEL+group,data_bl,se)
data_gr$se<-temp$DWELL_TIME
data_gr<-data_gr[order(data_gr$block),]

data_gr$block<-as.numeric(data_gr$block)
par(mfrow=c(1,2))

col<-c("red" ,"blue")
lintyp<-c("solid", "dashed")
linwid=c(2,2)
pchar=c(15,17)
leg<-c("Label Shapes", "Ideogram Shapes")
main="Category-Learning Group"
offset=0.02
ylim=c(0,1000)

#Category-Learning Group
#Label Shapes
plotCI(data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='CAT',]$block, data_gr[data_gr$IA_LABEL=='label' & data_gr$group=='CAT',]$DWELL_TIME, uiw=data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='CAT',]$se, gap=0, col=col[1], pch=pchar[1],  xlab="Block", ylab="Dwell Time (msec)", main = main, las=2, bty="n", cex.main=1, cex.axis=0.9, ylim=ylim, xaxt="n")
lines(data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='CAT',]$ block,data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='CAT',]$DWELL_TIME, col=col[1], lty=lintyp[1], lwd=linwid[1])
#Ideogram Shapes
plotCI(data_gr[data_gr$IA_LABEL=='ideog'& data_gr$group=='CAT',]$block+offset, data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='CAT',]$DWELL_TIME, uiw=data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='CAT',]$se, gap=0, col=col[2], pch=pchar[2],  xaxt="n", yaxt="n", las=2, add=T)
lines(data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='CAT',]$block+offset,data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='CAT',]$DWELL_TIME, col=col[2], lty=lintyp[2], lwd=linwid[2])

axis(side = 1, at = 1:4, labels = 1:4, cex.axis = 0.8)
legend(x=2.2, y=450, legend=leg, pch=pchar, lty=lintyp, bty="n", col=col, lwd=linwid, seg.len=4.0)

#Paired-Associate Group
col<-c("red","blue")
lintyp<-c("solid", "dashed")
linwid=c(2,2)
pchar=c(15,17)
leg<-c("Label Shapes", "Ideogram Shapes")
main="Paired-Associate Group"

#Label Shapes
plotCI(data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='PA',]$block+2*offset, data_gr[data_gr$IA_LABEL=='label' & data_gr$group=='PA',]$DWELL_TIME, uiw=data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='CAT',]$se, gap=0, col=col[1], pch=pchar[1],  xlab="Block", ylab="Dwell Time (msec)", main = main, las=2, bty="n", cex.main=1, cex.axis=0.9, ylim=ylim, xaxt="n")
lines(data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='PA',]$block+2*offset,data_gr[data_gr$IA_LABEL=='label'& data_gr$group=='PA',]$DWELL_TIME, col=col[1], lty=lintyp[1], lwd=linwid[1])
#Ideogram Shapes
plotCI(data_gr[data_gr$IA_LABEL=='ideog'& data_gr$group=='PA',]$block+3*offset, data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='PA',]$DWELL_TIME, uiw=data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='CAT',]$se, gap=0, col=col[2], pch=pchar[2],  xaxt="n", yaxt="n", las=2, add=T)
lines(data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='PA',]$block+3*offset,data_gr[data_gr$IA_LABEL=='ideog' & data_gr$group=='PA',]$DWELL_TIME, col=col[2], lty=lintyp[2], lwd=linwid[2])

axis(side = 1, at = 1:4, labels = 1:4, cex.axis = 0.8)
legend(x=2.2, y=450, legend=leg, pch=pchar, lty=lintyp, bty="n", col=col, lwd=linwid, seg.len=4.0)

mtext('Discrimination Session, Average Dwell Time', outer = TRUE, cex = 1, font =2)

par(mfrow=c(1,1),  oma = c(0, 0, 0, 0))

Session Information

sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Greek_Greece.1253  LC_CTYPE=Greek_Greece.1253   
## [3] LC_MONETARY=Greek_Greece.1253 LC_NUMERIC=C                 
## [5] LC_TIME=Greek_Greece.1253    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] multcomp_1.4-17 TH.data_1.0-10  MASS_7.3-54     survival_3.2-11
##  [5] mvtnorm_1.1-2   lmerTest_3.1-3  lme4_1.1-27.1   Matrix_1.3-4   
##  [9] nlme_3.1-152    ez_4.4-0        gplots_3.1.1    sciplot_1.2-0  
## [13] dplyr_1.0.7    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7          lattice_0.20-44     zoo_1.8-9          
##  [4] gtools_3.9.2        digest_0.6.27       utf8_1.2.2         
##  [7] R6_2.5.1            cellranger_1.1.0    plyr_1.8.6         
## [10] evaluate_0.14       highr_0.9           ggplot2_3.3.5      
## [13] pillar_1.6.2        rlang_0.4.11        curl_4.3.2         
## [16] readxl_1.3.1        minqa_1.2.4         data.table_1.14.0  
## [19] car_3.0-11          nloptr_1.2.2.2      rmarkdown_2.10     
## [22] splines_4.1.1       stringr_1.4.0       foreign_0.8-81     
## [25] munsell_0.5.0       numDeriv_2016.8-1.1 compiler_4.1.1     
## [28] xfun_0.25           pkgconfig_2.0.3     mgcv_1.8-36        
## [31] htmltools_0.5.2     tidyselect_1.1.1    tibble_3.1.4       
## [34] codetools_0.2-18    rio_0.5.27          fansi_0.5.0        
## [37] crayon_1.4.1        bitops_1.0-7        grid_4.1.1         
## [40] gtable_0.3.0        lifecycle_1.0.0     magrittr_2.0.1     
## [43] scales_1.1.1        KernSmooth_2.23-20  zip_2.2.0          
## [46] stringi_1.7.4       carData_3.0-4       reshape2_1.4.4     
## [49] ellipsis_0.3.2      generics_0.1.0      vctrs_0.3.8        
## [52] sandwich_3.0-1      boot_1.3-28         openxlsx_4.2.4     
## [55] tools_4.1.1         forcats_0.5.1       glue_1.4.2         
## [58] purrr_0.3.4         hms_1.1.0           abind_1.4-5        
## [61] fastmap_1.1.0       yaml_2.2.1          colorspace_2.0-2   
## [64] caTools_1.18.2      knitr_1.33          haven_2.4.3