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This analysis is open source, which means that the code used to produce the graphs and outputs below is openly available. To toggle on/off the raw code, click here.

Aphantasia survey data analysis¶

This is a report on the answers to the TypeForm surveys on Aphantasia.

Background¶

Aphantasia is the inability to form images or visual representation of ideas in the mind. It is not a disease, and we name it a condition just for lack of a better word. In fact, most people with aphantasia are unaware of it. They are able to imagine and recall people, things and situations just as anybody, but their representation will be entirely abstract, a list of properties rather than a concrete image.

In 2009, a paper was published by Dr. Zeman of the Peninsula Medical School in Exeter, UK. Following a medical intervention, a patient had complained that they had lost the capacity to form images in their head. While he was investigating, Dr. Zeman noticed that some of the people he was talking to described a similar incapacity, only they seemed to have had since they were born.

Long story short, Dr. Zeman decided to put a lot more effort in a condition he named aphantasia, or the inability to form mental images. This effort culminated on a 2015 paper in Cortex.

He analysed about 20 individuals who suffered from aphantasia and 120 controls. The paper describes the questionnaire he used to screen individuals: the VVIQ test was created in 1973 and is composed of 16 questions. It builds a score that describes a person's ability to form mental images. The score ranges from 16 to 80; people who have the minimun score of $16$ have a complete absence of imagery, scores between $17$ and $30$ denote a milder form of aphantasia.

For a psychological trait study, that sample size was relatively small. Previous estimates put the prevalence of aphantasia at approximately 2-3% of the population. We wanted to give a bit more confidence to this number, so we decided to use social networks to get people to fill VVIQ questionnaires. This way, we hope to gather a bit more data on this understudied trait.

Surveys¶

There were two surveys, both adapted from the original one found in the paper's methods. We kept the spirit of the original questions, while rephrasing some of them to make them suitable for social networks. The first one was in English, the second is a translation into French.

The first survey was broadcast on the Wellcome Trust Sanger Institute and European Bioinformatics Institute mailing lists, as well on several other health-related campuses in the UK. It was also sent out on the two surveyers' Facebook accounts and on the internal mailing list of UNEP-WCMC, an environment policy consulting agency.

The second survey was also advertised on Facebook, on one of the surveyers' accounts as well as the EESI Art School's account and the ENSIMAG Computer Science School. Respondents on the French survey are mostly students from these two universities (in approximately equal parts).

Both surveys each gathered about 200 answers in two weeks, for a total of 395 answers. The data was frozen on Sunday 16th August 2015. Ten days later, the BBC did a piece on aphantasia, and people started retweeting our study quite a lot. However, there was an unusual concentration of cases in this most recent data, which is why we decided to keep the frozen data until the sample size has increased enough to cover that bias.

Data overview¶

We start by examining the distribution of the data, pooling data from both surveys.

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%load_ext rpy2.ipython

In [7]:

%%R -w 1000 -o d
library(data.table)
d=data.frame(fread("uk.csv"))
d$b=as.factor(d$b)
#levels(d$b)=c("female","male",NA)
#barplot(table(d$b))
d$c=as.factor(d$c)
#levels(d$c)=c("-15","16-25","26-35", "36-45", "46-55", "56-65", "65+")
d$x=rep("uk", nrow(d))
e=data.frame(fread("fr.csv"))
e$b=as.factor(e$b)
#levels(e$b)=c("male","female")
#barplot(table(d$b))
e$c=as.factor(e$c)
#levels(e$c)=c("16-25","26-35", "36-45")
e$x=rep("fr", nrow(e))
d=rbind(d,e)
d$a=NULL;d$t=NULL;d$v=NULL
colnames(d)=c("sex","age","1q1","1q2","1q3","1q4","2q1","2q2","2q3","2q4","3q1","3q2","3q3","3q4","4q1","4q2","4q3","4q4","date", "score","cohort")
means=apply(d, 1, function(x){mean(as.numeric(x[3:18]), na.rm=T)})
for(i in 3:18){
    d[is.na(d[,i]),i]=means[is.na(d[,i])]
}
d$score=rowSums(d[,3:18])
hist(d$score, breaks=20, col="steelblue", main="Pooled scores, both cohorts", xlab="Score", prob=T, cex.main=1.5)
lines(density(d$score), lwd=2, col="darkred")
#boxplot(d$w~d$c)
#barplot(table(d$c))

NB: An interactive version is viewable on plot.ly

This distribution looks pretty similar to the one described in the Cortex paper, except for the two upper bars ($VVIQ>70$). We think that this might be caused by how our participants were selected: mostly from universities and visual arts schools, which could enrich for people with extreme vividness.

Now let's examine the distribution of our two main covariates, sex and age.

In [22]:

%%R -w 1000 -o d
library(RColorBrewer)
d$sex[d$sex != "Male" & d$sex!="Female"]=NA
d$age[d$age==""]=NA
print(length(d$age[is.na(d$age)])) 
d=droplevels(d)
par(mfrow=c(1,2))
counts=table(d$sex, d$cohort)
barplot(counts, beside=T, legend=rownames(counts), main="Sex vs cohorts", ylab="Number of respondents", col=rev(c("cornflowerblue", "mediumspringgreen")))
counts=table(d$age, d$cohort)
barplot(counts, beside=F, legend=levels(d$age), main="Age vs cohorts", ylab="Number of respondents", col=brewer.pal(7, "Spectral"))

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We are strongly enriching for females in the UK cohort, and for males in the French one. The French one is also biased towards younger individuals, mostly in the 16-25 age category.

Influence of sex and cohort¶

We then stratify per cohort and sex. People who answered "I'd rather not say" to the gender question were excluded from this analysis.

In [11]:

%matplotlib inline
from rpy2.robjects import pandas2ri
import matplotlib.pyplot as plt
from IPython.core.pylabtools import figsize
figsize(20,10)
pandas2ri.activate()
e=pandas2ri.ri2py(d)
fig, ax = plt.subplots()
import seaborn as sns
sns.set_style("darkgrid")
sns.set(font_scale=1.5)
s=sns.violinplot(x="sex", y="score",hue="cohort", data=e, split=True, inner="stick", palette=sns.color_palette("hls", 6))


#sns.countplot(x="sex", hue="cohort", data=e)
#sns.barplot(x="age", y="score", hue="cohort", data=e)

From the violin plots, we can make a certain number of observations:

most aphantasia cases, i.e. people with $VVIQ<30$ come from the UK cohort;
in the UK cohort, males seem to have an average lower score than females; no difference can be spotted in the French cohort;
the overall distributions are similar for both surveys, with a mode around 70 (more precise statistics are given below).

In [11]:

%%R
cat("For the UK questionnaires:\n==========================\n")
print(summary(d[d$cohort=="uk",]$score))
cat("\nFor the FR questionnaires:\n==========================\n")
summary(d[d$cohort=="fr",]$score)

For the UK questionnaires:
==========================
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  16.00   54.00   63.00   60.75   72.00   80.00 

For the FR questionnaires:
==========================
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  16.00   55.00   65.00   62.34   71.87   80.00

Let's examine the overall score depending on the gender of respondents.

In [10]:

s=sns.factorplot(data=e, x="sex", y="score", col="cohort", kind="bar", palette="pastel")

We see opposing but insignificant differences in gender responses across cohorts.

Influence of age¶

Next, we look at whether age has an influence over the VVIQ scores, in both cohorts and independently of sex.

In [12]:

figsize(20,10)
s=sns.boxplot(x="age", y="score", order=["16-25", "26-35", "36-45", "46-55", "56-65", "65+"], data=e, palette=sns.color_palette("husl", 7))

Given the very low number of respondents in the last category, we are unable to say anything about very old people's scores, as the distribution might not accurately represent reality. The distributions for other age categories do not seem different from each other, since their median always lies in the bulk of the others' distribution.

This can be confirmed by the Kruskal-Wallis test (generalisation of Mann Whitney's U for more than 2 categories; this test is superior to the t-test when the population distributions are non-normal, which we cannot assume here):

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%%R
kruskal.test(d$score~d$age, data=d)

	Kruskal-Wallis rank sum test

data:  d$score by d$age
Kruskal-Wallis chi-squared = 3.7357, df = 5, p-value = 0.5881

The p-value is very unsignificant, meaning that no age effect can be detected from the data.

We used all age categories here, including those for which we know we don't have enough samples to draw conclusions. If we remove the first and last categories, the p-value remains almost unchanged.

Looking at individual questions¶

The VVIQ questionnaire tests visual imagery in four different areas : a known relative, an imagined scene (sunrise), a known place, and a landscape. These areas are probed using four sub-questions. Some of these scenes appeal more to the imagination (question 2: imagine a sunrise, question 4: imagine a mountain landscape), others more to memory (question 1 is about a known relative, question 3 about a known place).

We regress overall score on scores aggregated on individual questions. If questions have a different effect, we expect slopes to be different between them.

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e["q1"]=e["1q1"]+e["1q2"]+e["1q3"]+e["1q4"]
e["q2"]=e["2q1"]+e["2q2"]+e["2q3"]+e["2q4"]
e["q3"]=e["3q1"]+e["3q2"]+e["3q3"]+e["3q4"]
e["q4"]=e["4q1"]+e["4q2"]+e["4q3"]+e["4q4"]
s=sns.pairplot(e, x_vars=["q1", "q2", "q3", "q4"], y_vars=["score"], size=8, aspect=0.5, hue="sex", palette="pastel", kind="reg")

Responses to all four questions seem to predict the final score equally well. We also draw partial regression lines depending on sex. Men and women seem to answer the first and last question differently. Let's look at the per-question histograms.

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%%R -o f
d$q1=rowSums(d[,3:6]);d$q2=rowSums(d[,7:10]);d$q3=rowSums(d[,11:14]);d$q4=rowSums(d[,15:18])
f=reshape(d, varying=c("q1", "q2", "q3", "q4"), v.names="partial_score", timevar="question", times=c("q1", "q2", "q3", "q4"), direction="long")

In [21]:

from rpy2.robjects import pandas2ri
import matplotlib.pyplot as plt
pandas2ri.activate()
g=pandas2ri.ri2py(f)
figsize(20,10)
s=sns.violinplot(x="question", y="partial_score", hue="sex", split="True", data=g, palette="pastel", inner="quartile")