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index.Rmd
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---
title: ' Dutch modality exclusivity norms'
author: '<a href="#section-info" style="color:#E4E5E8 !important; text-decoration:none !important; font-size:10px"> Bernabeu (2018) </a>'
output:
flexdashboard::flex_dashboard:
theme: 'spacelab'
orientation: columns
vertical_layout: scroll
favicon: https://i.ibb.co/bB9fCfr/norms-favicon.png
runtime: shiny
---
```{r packages, include = FALSE}
library(arules)
library(car)
library(contrast)
library(corpcor)
library(plyr) # Load before `dplyr` (https://github.com/tidyverse/dplyr/issues/347)
library(dplyr)
library(flexdashboard)
library(gdata)
library(ggplot2)
library(ggrepel)
library(GPArotation)
library(grid)
library(gridExtra)
library(gtools)
library(Hmisc)
library(htmltools)
library(irr)
library(kableExtra)
library(knitr)
library(lattice)
library(ltm)
library(MASS)
library(pander)
library(pastecs)
library(plotly)
library(png)
library(psych)
library(QuantPsyc)
library(RColorBrewer)
library(RCurl)
library(reactable)
library(reshape)
library(rmarkdown)
library(Rmisc)
library(rsconnect)
library(scales)
library(shiny)
library(shinyWidgets)
library(stringr)
library(tibble)
library(tidyr)
panderOptions('keep.trailing.zeros', TRUE)
knitr::opts_chunk$set(cache = FALSE, fig.align='center')
```
<!-- Begin definition of layout parameters -->
<head>
<style type="text/css">
<!-- Tabs format -->
/* inactive tab */
ul.nav.navbar-nav.navbar-left a {
color: white !important;
background-color: none !important;
border-bottom: none !important;
}
/* inactive tab hovered over */
ul.nav.navbar-nav.navbar-left a:hover {
color: white !important;
background-color: #34507D !important;
border-bottom: none !important;
text-shadow: none !important;
}
/* active tab */
ul.nav.navbar-nav.navbar-left li.active a {
color: white !important;
font-weight: bold !important;
background-color: #5472A3 !important;
border-bottom: none !important;
text-shadow: none !important;
}
/* visited links in active tabs */
ul.nav.navbar-nav.navbar-left li.active a:visited {
color: white !important;
font-weight: bold !important;
background-color: #5472A3 !important;
border-bottom: none !important;
text-shadow: none !important;
}
/* visited links in inactive tabs */
ul.nav.navbar-nav.navbar-left a:visited {
color: white !important;
font-weight: bold !important;
background-color: #5472A3 !important;
border-bottom: none !important;
text-shadow: none !important;
}
<!-- Links format -->
/* unvisited link */
section-properties, section-concepts, section-cf-lc-english-norms, section-sound-symbolism, section-info a {
color: #3C6CA7 !important;
border-bottom: 0.03px solid #5277A5 !important;
font-weight: normal !important;
background-color: none !important;
text-shadow: none !important;
}
/* visited link */
section-properties, section-concepts, section-cf-lc-english-norms, section-sound-symbolism, section-info a:visited {
color: #426DA1 !important;
border-bottom: none !important;
font-weight: normal !important;
background-color: none !important;
text-shadow: none !important;
}
/* mouse over link */
section-properties, section-concepts, section-cf-lc-english-norms, section-sound-symbolism, section-info a:hover {
color: #2462B0 !important;
border-bottom: none !important;
font-weight: normal !important;
background-color: none !important;
text-shadow: 1px 1px darkgrey !important;
}
/* selected link */
section-properties, section-concepts, section-cf-lc-english-norms, section-sound-symbolism, section-info a:active {
color: #1964BF !important;
border-bottom: none !important;
font-weight: bold !important;
background-color: none !important;
text-shadow: 1px 1px darkgrey !important;
}
<!-- Define CSS style for customising output to specific screen sizes -->
.desktop-only {display: inline;}
/* Smartphone Portrait and Landscape */
@media only screen
and (max-width : 765px){
.desktop-only {display: none;}
}
.mobile-only {display: inline;}
/* Smartphone Portrait and Landscape */
@media only screen
and (min-width : 766px){
.mobile-only {display: none;}
}
<!-- Define CSS style for fonts -->
body{ /* Normal */
font-size: 16px;
}
td { /* Table */
font-size: 14px;
}
h1.title {
font-size: 38px;
font-weight: bold;
color: #28002E
}
h1 { /* Header 1 */
font-size: 28px;
font-weight: bold;
color: #28002E
}
h2 { /* Header 2 */
font-size: 22px;
font-weight: bold;
color: #28002E
}
h3 { /* Header 3 */
font-size: 18px;
font-weight: bold;
}
h4 { /* Header 4 */
font-size: 16px;
}
h5 { /* Header 5 */
font-size: 15px;
}
h6 { /* Header 6 */
font-size: 14px;
}
code.r{ /* Code block */
font-size: 12px;
}
pre { /* Code-formatted output */
font-size: 14px;
padding-top: 2px;
padding-bottom: 2px;
margin-top: -15px;
margin-bottom: 18px;
}
<!-- Define CSS style for splitting columns -->
* {
box-sizing: border-box;
}
/* Create two equal columns that float next to each other */
.column {
float: left;
padding: 10px;
}
/* Clear floats after the columns */
.row:after {
content: "";
display: table;
clear: both;
}
</style>
<!-- Load library of icons -->
<link rel='stylesheet' href='https://use.fontawesome.com/releases/v5.7.0/css/all.css' integrity='sha384-lZN37f5QGtY3VHgisS14W3ExzMWZxybE1SJSEsQp9S+oqd12jhcu+A56Ebc1zFSJ' crossorigin='anonymous'>
<!-- Javascript function to enable a hovering tooltip -->
<script>
$(document).ready(function(){
$('[data-toggle="tooltip1"]').tooltip();
});
</script>
</head>
```{r global, include = FALSE}
# Since this script contains the dashboard, the code run is minimised,and all the rest--namely, diagnostic commands--is commented out. Fully run code on the proper analysis scripts, at https://osf.io/brkjw/.
# Perform analyses for PCA
# RELATION AMONG MODALITIES
# Below is a Principal Components Analysis (PCA) with plots. Firstly it is performed
# on the Dutch norms, and then on Lynott and Connell's (2009, 2013) English norms
# (leaving out gustatory and olfactory scores and words).
all = read.csv('https://raw.githubusercontent.com/pablobernabeu/Modality-exclusivity-norms-747-Dutch-English-replication/master/all.csv', fileEncoding = 'Latin1')
#nrow(all)
# Set correct numeric class for standard deviation variables
all$SD_Auditory = as.numeric(all$SD_Auditory)
all$SD_Haptic = as.numeric(all$SD_Haptic)
all$SD_Visual = as.numeric(all$SD_Visual)
# PROPERTIES
# Principal component analysis on the three modalities
# Check conditions for a PCA
# matrix
prop = all[all$cat == 'Property' & !is.na(all$word), c('Auditory', 'Haptic', 'Visual')]
#nrow(prop)
prop_matrix = cor(prop, use = 'complete.obs')
#prop_matrix
#round(prop_matrix, 2)
# POOR: correlations not apt for a PCA, with too many below .3
# now on the raw vars:
#nrow(prop)
#cortest.bartlett(prop)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
#KMO(prop_matrix)
# Result: .56 = mediocre. PCA not strongly recommended. But we still do it
# because the purpose is graphical only.
# check determinant
#det(prop_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_prop = psych::principal(prop, nfactors = 3, rotate = "none")
#pc1_prop
# RESULT: Only PC1, with eigenvalue > 1, should be extracted,
# acc to Kaiser's criterion (Jolliffe's threshold of 0.7 way too lax;
# Field, Miles, & Field, 2012)
# Unrotated: scree plot
#plot(pc1_prop$values, type = "b")
# Result: one or two RCs should be extracted, converging with eigenvalues
# Now with varimax rotation, Kaiser-normalized (by default).
# Always preferable because it captures explained variance best.
# Compare eigenvalues w/ 1 & 2 factors
pc2_prop = psych::principal(prop, nfactors = 2, rotate = "varimax", scores = TRUE)
#pc2_prop
#pc2_prop$loadings
# good to extract 2 factors, as they both explain quite the same variance,
# and both surpass 1 eigenvalue
#pc2_prop$residual
#pc2_prop$fit
#pc2_prop$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals OK: fewer than 50% have absolute
# values > 0.05 (exactly 50% do).Model fit good, > .90.
# Communalities good, all > .7 (av = .83).
# subset and add PCs
props = all[all$cat == 'Property' & !is.na(all$word),]
#nrow(props)
props = cbind(props, pc2_prop$scores)
#nrow(props)
# Set to character format
props$word = as.character(props$word)
# Replace NAs in corpora with 0 to allow selection
props[is.na(props$phonemes_DUTCHPOND), 'phonemes_DUTCHPOND'] = 0
props[is.na(props$freq_lg10CD_SUBTLEXNL), 'freq_lg10CD_SUBTLEXNL'] = 0
props[is.na(props$freq_lg10WF_SUBTLEXNL), 'freq_lg10WF_SUBTLEXNL'] = 0
props[is.na(props$inflected_adj_freq_lg10CD_SUBTLEXNL), 'inflected_adj_freq_lg10CD_SUBTLEXNL'] = 0
props[is.na(props$freq_CELEX_lem), 'freq_CELEX_lem'] = 0
props[is.na(props$orth_neighbours_DUTCHPOND), 'orth_neighbours_DUTCHPOND'] = 0
props[is.na(props$phon_neighbours_DUTCHPOND), 'phon_neighbours_DUTCHPOND'] = 0
props[is.na(props$AoA_Brysbaertetal2014), 'AoA_Brysbaertetal2014'] = 0
props[is.na(props$concrete_Brysbaertetal2014), 'concrete_Brysbaertetal2014'] = 0
# Turn modality exclusivity into percentage format
props$Exclusivity = props$Exclusivity * 100
# CONCEPTS
# Principal component analysis on the three modalities
# Check conditions for a PCA
# matrix
conc = all[all$cat == 'Concept' & !is.na(all$word), c('Auditory', 'Haptic', 'Visual')]
#nrow(conc)
conc_matrix = cor(conc, use = 'complete.obs')
#conc_matrix
#round(conc_matrix, 2)
# POOR: correlations not apt for a PCA, with too many below .3
# now on the raw data:
#nrow(conc)
#cortest.bartlett(conc)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
#KMO(conc_matrix)
# Result: .49 = poor. PCA not strongly recommended. But we still do it
# because the purpose is graphical really.
# check determinant
#det(conc_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_conc = psych::principal(conc, nfactors = 3, rotate = "none")
#pc1_conc
# RESULT good: PC1 and PC2, with eigenvalue > 1, should be extracted,
# acc to Kaiser's criterion (Jolliffe's threshold of 0.7 way too lax;
# Field, Miles, & Field, 2012)
# Unrotated: scree plot
#plot(pc1_conc$values, type = "b")
# Result: with no point of inflexion along the y axis, two PCs would obtain.
# Now with varimax rotation, Kaiser-normalized (by default):
# Always preferable because it captures explained variance best.
# Compare eigenvalues w/ 1 & 2 Principal Components
pc2_conc = psych::principal(conc, nfactors = 2, rotate = "varimax", scores = TRUE)
#pc2_conc
#pc2_conc$loadings
# good to extract 2 Principal Components, as they both explain quite the same variance,
# and both surpass 1 eigenvalue
#pc2_conc$residual
#pc2_conc$fit
#pc2_conc$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals bad: over 50% have absolute
# values > 0.05. Model fit good, > .90. Communalities good, all > .7 (av = .82).
# subset and add PCs
concs = all[all$cat == 'Concept' & !is.na(all$word),]
#nrow(concs)
concs = cbind(concs, pc2_conc$scores)
#nrow(concs)
# Set to character format
concs$word = as.character(concs$word)
# Replace NAs in corpora with 0 to allow selection
concs[is.na(concs$phonemes_DUTCHPOND), 'phonemes_DUTCHPOND'] = 0
concs[is.na(concs$freq_lg10WF_SUBTLEXNL), 'freq_lg10WF_SUBTLEXNL'] = 0
concs[is.na(concs$freq_lg10CD_SUBTLEXNL), 'freq_lg10CD_SUBTLEXNL'] = 0
concs[is.na(concs$freq_CELEX_lem), 'freq_CELEX_lem'] = 0
concs[is.na(concs$orth_neighbours_DUTCHPOND), 'orth_neighbours_DUTCHPOND'] = 0
concs[is.na(concs$phon_neighbours_DUTCHPOND), 'phon_neighbours_DUTCHPOND'] = 0
concs[is.na(concs$AoA_Brysbaertetal2014), 'AoA_Brysbaertetal2014'] = 0
concs[is.na(concs$concrete_Brysbaertetal2014), 'concrete_Brysbaertetal2014'] = 0
# Turn modality exclusivity into percentage format
concs$Exclusivity = concs$Exclusivity * 100
# Colors for plots
colours = c('Auditory' = 'firebrick1', 'Haptic' = 'dodgerblue', 'Visual' = 'forestgreen')
```
<i class="fas fa-code" aria-hidden="true"></i> Info {style="background-color: #FCFCFC; data-width: 100%; width: 900px; margin: 0 auto;"}
=======================================================================
Column {style="height:1300px; background-color:#FCFCFC;"}
--------------------------------------------------------
<h2> Information </h2>
<!-- Links -->
<div style = "text-align: right; padding-top: 35px; padding-bottom: 30px; padding-right: 60px;">
<a href="https://osf.io/58gzs/" style="color:#5C746D; border-bottom:none;font-size: 20px; font-weight: bold; font-family: 'Courier New', Courier, monospace;"> <i class='fas fa-database' aria-hidden="true" style='font-size:20px; color:#577787'></i> Data</a>
<a href="https://github.com/pablobernabeu/Modality-exclusivity-norms-747-Dutch-English-replication/blob/master/Shiny-app%2FDutch-modality-exclusivity-norms.rmd" style="color:#5C746D; border-bottom:none;font-size: 20px; font-weight: bold; font-family: 'Courier New', Courier, monospace;"> <i class='fab fa-r-project' aria-hidden="true" style='font-size:20px; color:#526772'></i><i class='fas fa-code' aria-hidden="true" style='font-size:12px; color:#526772'></i> Code</a>
<a href="https://mybinder.org/v2/gh/pablobernabeu/Modality-exclusivity-norms-747-Dutch-English-replication/master?urlpath=rstudio" style="color:#5C746D; border-bottom:none; font-size: 15px; font-weight: bold; font-family: 'Courier New', Courier, monospace;"> <img src='https://img.shields.io/badge/Binder-RStudio-579ACA.svg?logo=data:image/png;base64,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' alt='Launch Binder RStudio'></img> Edit and run code online</a>
<a href="http://creativecommons.org/licenses/by/4.0/" rel="Attribution licence" style="color:#9AA7A3; font-size:15px"> Licence <img style="border-width:0;" src="https://i.creativecommons.org/l/by/4.0/80x15.png" alt="Creative Commons License CC BY"></img></a>
</div>
<div style = "padding-left: 60px; padding-right: 60px; text-align: justify; background-color:#FCFCFC; font-size: 16px; line-height: 1.6;">
This dashboard presents the data and analyses of a set of modality exclusivity norms for Dutch. Various tabs and interactive plots are available. In addition, the data set is available in [CSV format](https://osf.io/ge7pn/) and [Excel format including column definitions](https://osf.io/58gzs/).
The norms, which are ratings of linguistic stimuli, served a twofold purpose: first, the creation of linguistic stimuli ([Bernabeu, 2018](https://psyarxiv.com/s2c5h); see also Speed & Majid, 2017), and second, a conceptual replication of Lynott and Connell's (2009, 2013) analyses. In the collection of the ratings, forty-two respondents completed surveys for the properties or the concepts separately. Each word was rated by eight participants on average, with a minimum of five (e.g., for *bevriezend*) and a maximum of ten ratings per word (e.g., for *donzig*). The [instructions to participants](https://osf.io/ungey/) were similar to those used by Lynott and Connell (2009, 2013), except that we elicited three modalities (auditory, haptic, visual) instead of five.
> <div style = "text-align: justify; background-color: #FCFCFC; font-size: 15px;"> 'This is a stimulus validation for a future experiment. The task is to rate how much you experience everyday' [properties/concepts] 'using three different perceptual senses: feeling by touch, hearing and seeing. Please rate every word on each of the three senses, from 0 (not experienced at all with that sense) to 5 (experienced greatly with that sense). If you do not know the meaning of a word, leave it blank.' </div>
<h3> Definitions (as in Lynott & Connell, 2009, 2013) </h3>
* Dominant modality: highest-rated modality;
* Modality exclusivity: range of the three modality ratings divided by the sum;
* Perceptual strength: highest rating across modalities.
<br>
```{r}
allNL = all[!is.na(all$main),]
allNL$Range = floor(allNL$Exclusivity * 4)
allNL$Range = mapvalues(allNL$Range, from = c(0, 1, 2, 3, 4),
to = c("0-20%", "20-40%", "40-60%", "60-80%", "80-100%"))
allNL$cat = dplyr::recode(allNL$cat, Concept = "Concepts", Property = "Properties")
# Set order to display properties first, instead of alphabetical 'Concepts-Properties' order
allNL$cat = as.factor(allNL$cat)
allNL$cat = factor(allNL$cat, levels = rev(levels(allNL$cat)))
p = ggplot(allNL) +
geom_bar(mapping = aes(x = main, fill = Range), position = position_stack(reverse = TRUE)) +
scale_fill_grey(start=.9, end=0, labels = c("0-20%", "20-40%", "40-60%", "60-80%", "80-100%"),
guide = guide_legend(reverse = TRUE, override.aes = list(size = 11))) +
scale_x_discrete(expand = c(.24,0)) + scale_y_continuous(expand = expand_scale(mult = c(0, .05))) +
facet_grid(. ~ cat) + labs(fill = "Modality\nExclusivity", x = 'Dominant Modality', y = 'Number of Words') +
theme_bw() + theme(legend.position = c(.17, .6), legend.title = element_text(size = 20, face = 'bold'),
legend.text = element_text(size = 18), legend.background = element_rect(fill=alpha('white', 0)),
axis.title = element_text(size = 21, face = "bold"), axis.text = element_text(size = 19),
axis.title.x = element_text(margin = margin(.09, 0, 0, 0, "cm")),
axis.text.x = element_text(margin = margin(.09, 0, 0, 0, "cm")),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text = element_text(size = 21, face = 'bold', vjust = .7,
margin = margin(.3, 0, .3, 0, "cm")),
axis.ticks.x = element_blank())
renderPlot(p)
```
<h5 style='text-align:justify; padding-left:5px; padding-right:5px; padding-bottom:10px; line-height: 1.4;'> Figure 1. Number of words in the norms per word category, dominant modality, and modality exclusivity. <br> [<i class='fas fa-external-link-alt' aria-hidden=TRUE style='font-size:7'></i> Github](https://raw.githubusercontent.com/pablobernabeu/Modality-exclusivity-norms-747-Dutch-English-replication/master/stacked_exc.png) </h5>
These norms were validated in an experiment demonstrating that shifts across trials with different dominant modalities incurred semantic processing costs ([Bernabeu, Willems, & Louwerse, 2017](https://doi.org/10.31234/osf.io/a5pcz)). All data for that study are [available](https://osf.io/97unm/wiki/home/), including a [dashboard](https://pablobernabeu.shinyapps.io/ERP-waveform-visualization_CMS-experiment/) (in case of downtime of the dashboard site, please see [this alternative](https://mybinder.org/v2/gh/pablobernabeu/Modality-switch-effects-emerge-early-and-increase-throughout-conceptual-processing/master?urlpath=shiny/Shiny-app/)).
The [**properties**](#properties){style='background-color:#FDFFFF'} and the [**concepts**](#concepts){style='background-color:#FDFFFF'} may also be consulted together on a [**table**](#table){style='background-color:#FDFFFF'}. Distinct relationships emerged among the modalities, with the visual and haptic modalities being more related to each other than to the auditory modality. This ties in with findings that, in conceptual processing, modalities can be collated based on language statistics (Louwerse & Connell, 2011). Furthermore, properties were found to be more strongly perceptual than concepts ([**cf. English norms by Lynott & Connell, 2009, 2013**](#section-cf-lc-english-norms){style='background-color:#FDFFFF'}).
<br>
```{r}
# ENGLISH PROPERTIES
# check conditions for a PCA
# matrix
eng_prop = all[all$cat == 'Property', c('English_Auditory_Lynott_Connell_2009_2013', 'English_Haptic_Lynott_Connell_2009_2013', 'English_Visual_Lynott_Connell_2009_2013')]
# nrow(eng_prop)
eng_prop_matrix = cor(eng_prop, use = 'complete.obs')
# eng_prop_matrix
# round(eng_prop_matrix, 2)
# OK: correlations good for a PCA, with enough < .3
# now on the raw vars:
# nrow(eng_prop)
# cortest.bartlett(eng_prop)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
# KMO(eng_prop_matrix)
# Result: .56 = mediocre. PCA not strongly recommended. But we still do it
# because the purpose is graphical only.
# check determinant
# det(eng_prop_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_eng_prop = psych::principal(eng_prop, nfactors = 3, rotate = "none")
# pc1_eng_prop
# RESULT: Extract either one PC, acc to Kaiser's criterion, or two RCs, acc to
# Joliffe's (Field, Miles, & Field, 2012)
# Unrotated: scree plot
# plot(pc1_eng_prop$values, type = "b")
# Result: again one or two RCs should be extracted
# Now with varimax rotation, Kaiser-normalized (by default)
pc2_eng_prop = psych::principal(eng_prop, nfactors = 2, rotate = "varimax", scores = TRUE)
# pc2_eng_prop
# pc2_eng_prop$loadings
# two components are good, as they both have eigenvalues over 1
# pc2_eng_prop$residual
# pc2_eng_prop$fit
# pc2_eng_prop$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals bad: more than 50% have absolute
# values > 0.05. Model fit good, > .90. Communalities good, all > .7.
# subset and add PCs
eng_props = all[all$cat == 'Property', ]
# nrow(eng_props)
eng_props = cbind(eng_props, pc2_eng_prop$scores)
# nrow(eng_props)
#
# head(eng_props)
# Finally, plot
# Set sample words to show on plot (first word in each modality)
auditory_w = as.character(sort(eng_props[eng_props$English_Main_Lynott_Connell_2009_2013=='Auditory', 'English_Word_Lynott_Connell_2009_2013'])[1])
haptic_w = as.character(sort(eng_props[eng_props$English_Main_Lynott_Connell_2009_2013=='Haptic', 'English_Word_Lynott_Connell_2009_2013'])[1])
visual_w = as.character(sort(eng_props[eng_props$English_Main_Lynott_Connell_2009_2013=='Visual', 'English_Word_Lynott_Connell_2009_2013'])[1])
w_set = c(auditory_w, haptic_w, visual_w)
eng_props$English_Main_Lynott_Connell_2009_2013 = dplyr::recode(eng_props$English_Main_Lynott_Connell_2009_2013, Auditory = "a", Haptic = "h", Visual = "v")
Engprops = ggplot(eng_props,
aes(RC1, RC2, label = as.character(English_Main_Lynott_Connell_2009_2013))) + stat_density2d (color = "gray87") +
geom_text(size = ifelse(eng_props$English_Word_Lynott_Connell_2009_2013 %in% w_set, 8, 5),
fontface = ifelse(eng_props$English_Word_Lynott_Connell_2009_2013 %in% w_set, 'bold', 'plain')) +
geom_point(data=eng_props[eng_props$English_Word_Lynott_Connell_2009_2013 %in% w_set,], pch=21, fill=NA, size=8, stroke=2, alpha=.6) +
ggtitle('English properties (Lynott & Connell, 2009)') +
labs(x = "", y = "Varimax-rotated Principal Component 2") + theme_bw() +
theme( plot.background = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.title.x = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.title.y = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.text.x = element_text(size=9), axis.text.y = element_text(size=9),
plot.title = element_text(hjust = 0.5, size = 17, margin=margin(1,1,1,1)) ) +
geom_label_repel(data = eng_props[eng_props$English_Word_Lynott_Connell_2009_2013 %in% w_set,], aes(label = English_Word_Lynott_Connell_2009_2013), size = 6,
alpha = 0.77, color = 'black', box.padding = 1.5 )
# ENGLISH CONCEPTS
# check conditions for a PCA
# matrix
eng_conc = all[all$cat == 'Concept', c('English_Auditory_Lynott_Connell_2009_2013', 'English_Haptic_Lynott_Connell_2009_2013', 'English_Visual_Lynott_Connell_2009_2013')]
# nrow(eng_conc)
eng_conc_matrix = cor(eng_conc, use = 'complete.obs')
# eng_conc_matrix
# round(eng_conc_matrix, 2)
# POOR: correlations not apt for a PCA, with too many below .3
# now on the raw data:
# nrow(eng_conc)
# cortest.bartlett(eng_conc)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
# KMO(eng_conc_matrix)
# Result: .48 = poor. PCA not strongly recommended. But we still do it
# because the purpose is graphical really.
# check determinant
# det(eng_conc_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_eng_conc = psych::principal(eng_conc, nfactors = 3, rotate = "none")
# pc1_eng_conc
# RESULT: Extract either one PC, acc to Kaiser's criterion, or two RCs, acc to
# Joliffe's (Field, Miles, & Field, 2012)
# Unrotated: scree plot
# plot(pc1_eng_conc$values, type = "b")
# Result: two PCs obtain.
# Now with varimax rotation, Kaiser-normalized (by default):
# always preferable because it captures explained variance best.
pc2_eng_conc = psych::principal(eng_conc, nfactors = 2, rotate = "varimax", scores = TRUE)
# pc2_eng_conc
# pc2_eng_conc$loadings
#
#
# pc2_eng_conc$residual
# pc2_eng_conc$fit
# pc2_eng_conc$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals bad: over 50% have absolute
# values > 0.05. Model fit good, > .90. Communalities good, all > .7.
# subset and add PCs
eng_concs = all[all$cat == 'Concept', ]
# nrow(eng_concs)
eng_concs = cbind(eng_concs, pc2_eng_conc$scores)
# summary(eng_concs$RC1, eng_concs$RC2)
eng_concs = eng_concs[eng_concs$normed == 'Dut_Eng' | eng_concs$normed == 'English',]
# nrow(eng_concs)
# summary(eng_concs$RC1, eng_concs$RC2)
# Finally, plot
# Set sample words to show on plot (first word in each modality)
auditory_w = as.character(sort(eng_concs[eng_concs$English_Main_Lynott_Connell_2009_2013=='Auditory', 'English_Word_Lynott_Connell_2009_2013'])[1])
haptic_w = as.character(sort(eng_concs[eng_concs$English_Main_Lynott_Connell_2009_2013=='Haptic', 'English_Word_Lynott_Connell_2009_2013'])[1])
visual_w = as.character(sort(eng_concs[eng_concs$English_Main_Lynott_Connell_2009_2013=='Visual', 'English_Word_Lynott_Connell_2009_2013'])[1])
w_set = c(auditory_w, haptic_w, visual_w)
eng_concs$English_Main_Lynott_Connell_2009_2013 = dplyr::recode(eng_concs$English_Main_Lynott_Connell_2009_2013, Auditory = "a", Haptic = "h", Visual = "v")
Engconcs = ggplot(eng_concs,
aes(RC1, RC2, label = as.character(English_Main_Lynott_Connell_2009_2013))) + stat_density2d (color = "gray87") +
geom_text(size = ifelse(eng_concs$English_Word_Lynott_Connell_2009_2013 %in% w_set, 8, 5),
fontface = ifelse(eng_concs$English_Word_Lynott_Connell_2009_2013 %in% w_set, 'bold', 'plain')) +
geom_point(data=eng_concs[eng_concs$English_Word_Lynott_Connell_2009_2013 %in% w_set,], pch=21, fill=NA, size=8, stroke=2, alpha=.6) +
ggtitle('English concepts (Lynott & Connell, 2013)') +
labs(x = "Varimax-rotated Principal Component 1", y = "Varimax-rotated Principal Component 2") +
theme_bw() +
theme( plot.background = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.title.x = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.title.y = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.text.x = element_text(size=9), axis.text.y = element_text(size=9),
plot.title = element_text(hjust = 0.5, size = 17, margin=margin(1,1,1,1)) ) +
geom_label_repel(data = eng_concs[eng_concs$English_Word_Lynott_Connell_2009_2013 %in% w_set,], aes(label = English_Word_Lynott_Connell_2009_2013), size = 6,
alpha = 0.77, color = 'black', box.padding = 1.5 )
# DUTCH
# Properties
# check conditions for a PCA
# matrix
Property = all[all$cat == 'Property' & !is.na(all$word), c('Auditory', 'Haptic', 'Visual')]
# nrow(Property)
prop_matrix = cor(Property, use = 'complete.obs')
# prop_matrix
# round(prop_matrix, 2)
# POOR: correlations not apt for a PCA, with too many below .3
# now on the raw vars:
# nrow(Property)
# cortest.bartlett(Property)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
# KMO(prop_matrix)
# Result: .56 = mediocre. PCA not strongly recommended. But we still do it
# because the purpose is graphical only.
# check determinant
# det(prop_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_prop = psych::principal(Property, nfactors = 3, rotate = "none")
# pc1_prop
# RESULT: Only PC1, with eigenvalue > 1, should be extracted,
# acc to Kaiser's criterion (Jolliffe's threshold of 0.7 way too lax;
# Field, Miles, & Field, 2012)
# Unrotated: scree plot
# plot(pc1_prop$values, type = "b")
# Result: one or two RCs should be extracted, converging with eigenvalues
# Now with varimax rotation, Kaiser-normalized (by default).
# Always preferable because it captures explained variance best.
# Compare eigenvalues w/ 1 & 2 factors
pc2_prop = psych::principal(Property, nfactors = 2, rotate = "varimax", scores = TRUE)
# pc2_prop
# pc2_prop$loadings
# good to extract 2 factors, as they both explain quite the same variance,
# and both surpass 1 eigenvalue
# pc2_prop$residual
# pc2_prop$fit
# pc2_prop$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals OK: fewer than 50% have absolute
# values > 0.05 (exactly 50% do).Model fit good, > .90.
# Communalities good, all > .7 (av = .83).
# subset and add PCs
properties = all[all$cat == 'Property' & !is.na(all$word), ]
# nrow(properties)
properties = cbind(properties, pc2_prop$scores)
# nrow(properties)
# Finally, plot: letters+density (cf. Lynott & Connell, 2009, 2013)
# Set sample words to show on plot (first word in each modality)
auditory_w = as.character(sort(properties[properties$main=='Auditory', 'word'])[1])
haptic_w = as.character(sort(properties[properties$main=='Haptic', 'word'])[1])
visual_w = as.character(sort(properties[properties$main=='Visual', 'word'])[1])
w_set = c(auditory_w, haptic_w, visual_w)
properties$main = dplyr::recode(properties$main, Auditory = "a", Haptic = "h", Visual = "v")
NLprops = ggplot(properties,
aes(RC1, RC2, label = as.character(main))) + stat_density2d (color = "gray87") +
geom_text(size = ifelse(properties$word %in% w_set, 8, 5),
fontface = ifelse(properties$word %in% w_set, 'bold', 'plain')) +
geom_point(data=properties[properties$word %in% w_set,], pch=21, fill=NA, size=8, stroke=2, alpha=.6) +
ggtitle('Dutch properties') + labs(x = "", y = "") + theme_bw() +
theme( plot.background = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.title.x = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.title.y = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.text.x = element_text(size=9), axis.text.y = element_text(size=9),
plot.title = element_text(hjust = 0.5, size = 17, margin=margin(1,1,1,1)) ) +
geom_label_repel(data = properties[properties$word %in% w_set,], aes(label = word), size = 6,
alpha = 0.77, color = 'black', box.padding = 1.5 )
# CONCEPTS
# check conditions for a PCA
# matrix
Concept = all[all$cat == 'Concept' & !is.na(all$word), c('Auditory', 'Haptic', 'Visual')]
# nrow(Concept)
conc_matrix = cor(Concept, use = 'complete.obs')
# conc_matrix
# round(conc_matrix, 2)
# POOR: correlations not apt for a PCA, with too many below .3
# now on the raw data:
# nrow(Concept)
# cortest.bartlett(Concept)
# GOOD: Bartlett's test significant
# KMO: Kaiser-Meyer-Olkin Measure of Sampling Adequacy
# KMO(conc_matrix)
# Result: .49 = poor. PCA not strongly recommended. But we still do it
# because the purpose is graphical really.
# check determinant
# det(conc_matrix)
# GOOD: > 0.00001
# start off with unrotated PCA
pc1_conc = psych::principal(Concept, nfactors = 3, rotate = "none")
# pc1_conc
# RESULT good: PC1 and PC2, with eigenvalue > 1, should be extracted,
# acc to Kaiser's criterion (Jolliffe's threshold of 0.7 way too lax;
# Field, Miles, & Field, 2012)
# Unrotated: scree plot
# plot(pc1_conc$values, type = "b")
# Result: with no point of inflexion along the y axis, two PCs would obtain.
# Now with varimax rotation, Kaiser-normalized (by default):
# Always preferable because it captures explained variance best.
# Compare eigenvalues w/ 1 & 2 Principal Components
pc2_conc = psych::principal(Concept, nfactors = 2, rotate = "varimax", scores = TRUE)
# pc2_conc
# pc2_conc$loadings
# good to extract 2 Principal Components, as they both explain quite the same variance,
# and both surpass 1 eigenvalue
# pc2_conc$residual
# pc2_conc$fit
# pc2_conc$communality
# Results based on a Kaiser-normalizalized orthogonal (varimax) rotation
# (by default in psych::stats). Residuals bad: over 50% have absolute
# values > 0.05. Model fit good, > .90. Communalities good, all > .7 (av = .82).
# subset and add PCs
concepts = all[all$cat == 'Concept' & !is.na(all$word), ]
# nrow(concepts)
concepts = cbind(concepts, pc2_conc$scores)
# nrow(concepts)
# Finally, plot
# Set sample words to show on plot (first word in each modality)
auditory_w = as.character(sort(concepts[concepts$main=='Auditory', 'word'])[1])
haptic_w = as.character(sort(concepts[concepts$main=='Haptic', 'word'])[1])
visual_w = as.character(sort(concepts[concepts$main=='Visual', 'word'])[1])
w_set = c(auditory_w, haptic_w, visual_w)
concepts$main = dplyr::recode(concepts$main, Auditory = "a", Haptic = "h", Visual = "v")
NLconcs = ggplot(concepts,
aes(RC1, RC2, label = as.character(main))) + stat_density2d (color = "gray87") +
geom_text(size = ifelse(concepts$word %in% w_set, 8, 5),
fontface = ifelse(concepts$word %in% w_set, 'bold', 'plain')) +
geom_point(data=concepts[concepts$word %in% w_set,], pch=21, fill=NA, size=8, stroke=2, alpha=.6) +
ggtitle('Dutch concepts') +
labs(x = "Varimax-rotated Principal Component 1", y = "") + theme_bw() +
theme( plot.background = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.title.x = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.title.y = element_text(colour = 'black', size = 14, margin=margin(1,1,1,1)),
axis.text.x = element_text(size=9), axis.text.y = element_text(size=9),
plot.title = element_text(hjust = 0.5, size = 17, margin=margin(1,1,1,1)) ) +
geom_label_repel(data = concepts[concepts$word %in% w_set,], aes(label = word), size = 6,
alpha = 0.77, color = 'black', box.padding = 1.5 )
# Four plots on a grid. This CSS Flexbox has to be used in Shiny instead of the usual gridextra, multiplot, etc.
# (https://plotly-r.com/arranging-views.html)
div(
style = "display: flex; flex-wrap: wrap; justify-content: center",
div(renderPlot(Engprops), style = "width: 50%; border: none;"),
div(renderPlot(NLprops), style = "width: 50%; border: none;"),
div(renderPlot(Engconcs), style = "width: 50%; border: none;"),
div(renderPlot(NLconcs), style = "width: 50%; border: none;")
)
```
<h5 style='text-align:justify; padding-left:5px; padding-right:5px; padding-bottom:3px; line-height:1.4;'> Figure 2. Dutch norms compared to English norms (reanalysis of Lynott & Connell, 2009, 2013, narrowed to three modalities) based on a principal component analysis of the auditory, haptic, and visual ratings for each word. Letters indicate the dominant modality of each word (*a* = auditory, *h* = haptic, *v* = visual), and contours further display the degree of consistency of the modalities. [<i class='fas fa-external-link-alt' aria-hidden=TRUE style='font-size:7'></i> Github](https://raw.githubusercontent.com/pablobernabeu/Modality-exclusivity-norms-747-Dutch-English-replication/master/allfour_lowres.png)
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The norms also served to investigate [**sound symbolism**](#sound-symbolism){style='background-color:#FDFFFF'}, which is the relation between the form of words and their meaning. The form of words rests on their sound more than on their visual or tactile properties (at least in spoken language). Therefore, auditory ratings should more reliably predict the lexical properties of words (length, frequency, distinctiveness) than haptic or visual ratings would. Lynott and Connell's (2013) findings were replicated, as auditory ratings were either the best predictor of lexical properties, or yielded an effect that was opposite in polarity to the effects of haptic and visual ratings. The present analyses and further ones will be reported in a forthcoming paper.
All data and code are [available for re-use](https://osf.io/brkjw/wiki/home/) under a [CC BY licence](https://creativecommons.org/licenses/by/4.0/), by citing the source:
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<div style = "text-align: center !important; padding-bottom: 0 !important;"><img src="https://upload.wikimedia.org/wikipedia/commons/e/e7/Booba-Kiki.svg" alt="Bouba-Kiki" width=80px height=55px></img></div>
<div style="text-align: justify !important; padding-top: 0 !important; padding-right: 5px; padding-left: 5px;"> Sound symbolism is a psycholinguistic effect whereby the pronunciation and the meaning of words bear a non-arbitrary relationship. For instance, when people are asked to match the pseudowords *bouba* and *kiki* to the above objects, the vast majority name the angular object *kiki* and the smooth one *bouba* (Köhler, 1929; Sourav et al., 2019). </div>
<div style="font-size:65%; padding-top:4px; padding-right: 5px; padding-left: 5px; text-align:left;"> Image: <a href="https://commons.wikimedia.org/wiki/File:Booba-Kiki.svg" title="via Wikimedia Commons">Monochrome version 1 June 2007 by BendžVectorized with Inkscape --Qef (talk) 21:21, 23 June 2008 (UTC)</a> [<a href="http://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA</a>].</div>
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> <div style = 'text-align: justify; text-indent: -1.5em; margin-left: 1.5em; font-size: 16px; background-color: #FCFCFC; font-size: 15px;'> Bernabeu, P. (2018). Dutch modality exclusivity norms for 336 properties and 411 concepts [Data dashboard]. Retrieved from https://pablobernabeu.shinyapps.io/Dutch-Modality-Exclusivity-Norms/.</div>
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<h3> External corpora </h3>
* Concreteness and age of acquisition: norms by Brysbaert, Warriner, and Kuperman (2014);
* Phonological and orthographic neighbours: DutchPOND (Marian et al., 2012);
* Word frequency and contextual diversity: SUBTLEX-NL (Keuleers, Brysbaert, & New, 2010);
* Lemma frequency: CELEX (Baayen, Piepenbrock, & van Rijn, 1993).
<h3 style='padding-top:5px;'> Acknowledgements </h3>
This research was greatly supported by the supervision from Max Louwerse and Roel Willems; the financial help from Tilburg University; Wendy Leijten's help with the translations; and the forty-two students from Tilburg University and Radboud University who completed the surveys.
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<a name="section-references"></a>
<h3 style='padding-top:5px;'> References </h3>
<div style = "text-align: justify; text-indent:-2em; margin-left:2em; background-color:#FCFCFC; font-size:15px;">
Baayen, R. H., Piepenbrock, R., & van Rijn, H. (1993). *The CELEX Lexical Database* [CD-ROM]. Philadelphia: Linguistic Data Consortium, University of Pennsylvania
Bernabeu, P. (2018). *Dutch modality exclusivity norms for 336 properties and 411 concepts*. PsyArXiv. https://psyarxiv.com/s2c5h
Bernabeu, P., Willems, R. M., & Louwerse, M. M. (2017). Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), *Proceedings of the 39th Annual Conference of the Cognitive Science Society* (pp. 1629-1634). Austin, TX: Cognitive Science Society. https://doi.org/10.31234/osf.io/a5pcz
Brysbaert, M., Warriner, A.B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. *Behavior Research Methods, 46*, 3, 904-911. <br>
[https://doi.org/10.3758/s13428-013-0403-5](https://doi.org/10.3758/s13428-013-0403-5)
Field, A. P., Miles, J., & Field, Z. (2012). *Discovering Statistics Using R*. London, UK: Sage
Keuleers, E., Brysbaert, M. & New, B. (2010). SUBTLEX-NL: A new frequency measure for Dutch words based on film subtitles. *Behavior Research Methods, 42*, 3, 643-650. [https://doi.org/10.3758/BRM.42.3.643](https://doi.org/10.3758/BRM.42.3.643)
Köhler, W. (1929). *Gestalt Psychology*. New York: Liveright
Louwerse, M., & Connell, L. (2011). A taste of words: Linguistic context and perceptual simulation predict the modality of words. *Cognitive Science, 35*, 2, 381-98. [https://doi.org/10.1111/j.1551-6709.2010.01157.x](https://doi.org/10.1111/j.1551-6709.2010.01157.x)
Lynott, D., & Connell, L. (2009). Modality exclusivity norms for 423 object concepts. *Behavior Research Methods, 41*, 2, 558-564. [https://doi.org/10.3758/BRM.41.2.558](https://doi.org/10.3758/BRM.41.2.558)
Lynott, D., & Connell, L. (2013). Modality exclusivity norms for 400 nouns: The relationship between perceptual experience and surface word form. *Behavior Research Methods, 45*, 2, 516-526. <br>
[https://doi.org/10.3758/s13428-012-0267-0](https://doi.org/10.3758/s13428-012-0267-0)
Marian, V., Bartolotti, J., Chabal, S., & Shook, A. (2012). CLEARPOND: Cross-Linguistic Easy-Access Resource for Phonological and Orthographic Neighborhood Densities. *PLoS ONE, 7*, 8: e43230. <br>
[https://doi.org/10.1371/journal.pone.0043230](https://doi.org/10.1371/journal.pone.0043230)
Sourav, S., Kekunnaya, R., Shareef, I., Banerjee, S., Bottari, D., & Röder, B. (2019). A protracted sensitive period regulates the development of cross-modal sound-shape associations in humans. *Psychological Science, 30*, 10, 1473-1482. [https://doi.org/10.1177/0956797619866625](https://doi.org/10.1177/0956797619866625)
Speed, L. J., & Majid, A. (2017). Dutch modality exclusivity norms: Simulating perceptual modality in space. *Behavior Research Methods, 49*, 6, 2204-2218. [https://doi.org/10.3758/s13428-017-0852-3](https://doi.org/10.3758/s13428-017-0852-3)
</div>
<h2 style='padding-top:5px;'> Contact </h2>
Pablo Bernabeu. Email: pcbernabeu@gmail.com