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RESEARCH ARTICLE
No Evidence of Association between
Toxoplasma gondii Infection and Financial
Risk Taking in Females
Lasha Lanchava
1☯
, Kyle Carlson
2☯
, Blanka Šebánková
3‡
, Jaroslav Flegr
3‡
, Gideon Nave
4
*
1Center for Economic Research and Graduate Education and Economics Institute (CERGE-EI), Prague,
Czech Republic, 2Department of Humanities and Social Sciences, California Institute of Technology,
Pasadena, United States of America, 3Department of Biology, Faculty of Science, Charles University in
Prague, Prague, Czech Republic, 4Department of Computation & Neural Systems, California Institute of
Technology, Pasadena, United States of America
☯These authors contributed equally to this work.
‡These authors also contributed equally to this work.
*gnave@caltech.edu
Abstract
Background
Past research linked Toxoplasma gondii (TG) infection in humans with neurological and
mental disorders (e.g., schizophrenia, Alzheimer’s disease and attention disorders), irregu-
larities of the dopaminergic and testosterone system, and increased likelihood of being
involved in traffic accidents.
Methodology/Principal Findings
We test for an association between TG infection and financial decision-making (DM) using
a case-control design in a sample of female Czech students (n = 79). We estimate each
subject's risk attitude and loss aversion using an experimental economic task involving real
monetary incentives. We find no significant evidence that either measure of decision-mak-
ing is associated with TG infection.
Conclusion
We were unable to find evidence of an association between TG infection and financial deci-
sion-making in females.
Introduction
Accumulating evidence from the lab and field show that human decision-makers display a
great level of individual heterogeneity in economic preferences [1–6]. Although the diversity of
preferences is a well-documented phenomenon at the descriptive level, its roots are poorly
PLOS ONE | DOI:10.1371/journal.pone.0136716 September 24, 2015 1/17
a11111
OPEN ACCESS
Citation: Lanchava L, Carlson K, Šebánková B,
Flegr J, Nave G (2015) No Evidence of Association
between Toxoplasma gondii Infection and Financial
Risk Taking in Females. PLoS ONE 10(9): e0136716.
doi:10.1371/journal.pone.0136716
Editor: Pablo Brañas-Garza, Middlesex University
London, UNITED KINGDOM
Received: September 30, 2014
Accepted: August 7, 2015
Published: September 24, 2015
Copyright: © 2015 Lanchava et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: The financial support of the Czech Science
Foundation project No. P402/12/G097 DYME
Dynamic Models in Economics is acknowledged.
URL: http://www.gacr.cz/en/. The funders had no role
in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
understood. Accordingly, researchers seek to understand the origin of the observed variability
and whether cultural, educational or biological factors underlie the heterogeneity of economic
preferences [5–11]. In recent years, scholars have suggested that interplay of genetics and cul-
tural influences is responsible for shaping personality traits and other economic preferences
[12–15]. Furthermore, biological factors such as circulating levels of the steroid hormones cor-
tisol [16] and testosterone [17–19], and individual differences in the functionality of neuro-
transmitter systems, e.g. dopamine and serotonin [20–22] have been found to modulate
decision-making (DM) under uncertainty.
A biological factor that might partially explain the heterogeneity of economic preferences
and has so far been overlooked by researchers is the composition of parasitic microorganisms
residing within the human body [23]. Evidence shows that parasites can cause significant
behavioral changes in various species, from insects to mammals [24–31], where a well-known
ethological theory, the ‘behavioral manipulation hypothesis’postulates that parasites specifi-
cally manipulate host behaviors so as to increase the fitness of the parasite [29,32]. For exam-
ple, the parasite Toxoplasma gondii (TG), which is the subject of this study, must be
transmitted from an intermediate host to the definite host, a feline predator, in order to sexu-
ally reproduce. Therefore, TG would increase its fitness by manipulating the intermediate
hosts’behavior so as to increase contact with felids.
We focus on TG for several reasons. First, TG has been shown to manipulate the behavior
of some of its mammalian hosts such that the probability that a feline predator will capture
them increases; for example, TG influences rodents’vigilance and their ability to recognize
novel stimuli, causes prolonged reaction times and even turns rodents’innate fear of the cat
odor into an attraction [33–36]. While it is true that modern humans are rarely eaten by cats
and therefore it is unlikely that TG-related changes in human behavior have an impact on pre-
dation risk, it is plausible that TG targets human brain tissues as a byproduct of its evolution in
rodents. Humans’immune responses to the latent infection may also induce behavioral
changes. Furthermore, since in early stages of evolution apes constituted a significant share of
the prey of feline predators, TG’s act on human behavior could also be an evolutionary rem-
nant [37]. Finally, an estimated 30% of humans worldwide have been infected by TG, but this
rate varies substantially between countries, making the parasite a candidate for generating
behavioral variation between populations.
Although latent TG infection has been considered harmless for many years following its dis-
covery (While latent infection was generally considered to be harmless, it is well-recognized that
the acute TG infection can be harmful, especially for immunocompromised or fetuses [38]), stud-
ies over the past few decades have associated it with various psychiatric and neurological prob-
lems, including schizophrenia, Alzheimer’s disease, and attention disorders [37,39–41]. Many of
the behavioral effects of TG in rodents have been extended to humans. Thus, TG infected humans
show prolonged response times in cognitive tasks and TG infection is suspected to underlie indi-
vidual differences in various personality traits [26,42]. Furthermore, several independent studies
associate TG infection with increased rates of traffic accidents, which might be mediated by
impaired decision-making capabilities or changes in sensitivity to risk and losses [43–45].
TG infection can cause functional changes in the dopaminergic system [37,46–48], which plays a
key role in DM and is particularly involved in shaping one’s attitudes towards risk and loss
[20,22,49,50]. Moreover, the genome of TG contains two genes for the enzyme tyrosine hydroxylase,
which is essential for dopamine synthesis [51]. Finally, TG is also associated with gender-specific
individual differences in the circulating levels of the hormones testosterone and cortisol [52,53],
which are correlated with individual differences in risk taking in economic DM [17,54]. Although
causality has not been established in humans (due to methodological and ethical limitations) experi-
mentsonrodentsshowsthatTGcauseschanges in testosterone and not vice-versa [55].
Toxoplasma gondii and Financial Risk Taking
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Given the growing evidence of behavioral and physiological effects of TG infection in
humans, we hypothesized that TG-infection would have an influence on economic DM. The
current case-controlled study used a well-established experimental task [56,57] to investigate
individual differences in two components of financial DM, namely risk and loss aversion. Risk
aversion is the tendency to prefer a guaranteed payoff to a gamble with equivalent expected
value [58,59]. By and large, people are willing to pay a premium in order to reduce the variance
of their choice outcomes, even when the riskier option has a greater expected value. Loss aver-
sion is human’s tendency to overweight losses relative to gains [60,61], a phenomenon that has
been repeatedly documented in lab experiments using both money and goods [62–64] and in
field data [65–68]. A large body of literature reports that biological factors such as gender,
genetic variability and physiological state (such as hormone levels) underlie individual differ-
ences in people’s attitudes toward risk and loss [17,54,56,69–71]. Furthermore, loss aversion
and risk aversion exist across species [72,73], making them candidate descriptive measures for
decision parameters that might be affected by biological factors.
Materials and Methods
Subjects
Seventy-nine female subjects (mean age, 23.89; S.D. 3.65) participated in the experiment. Par-
ticipants were recruited from the pool of current and former biology students of the Faculty of
Science, Charles University, Prague. All subjects were previously tested for toxoplasmosis and
rhesus (RhD) status (see detailed methods below). All participants provided a written informed
consent. Subjects’recruitment and data handling were performed in compliance with the
Czech legislation in force and were approved by the Institutional Review Boards of the Faculty
of Science, Charles University and the Department of Humanities and Social Science, Caltech.
The student volunteers often take part in many studies, most of which are not related to TG
infection. Crucially, TG was not mentioned in the study recruitment invitation, and the experi-
ments were run at the University of Economics—not the usual location for TG-related experi-
ments. Therefore, it is unlikely that subjects suspected that the experiment was related to TG
research. Moreover, no data or hypotheses concerning possible effects of TG on the risk-taking
behavior had been published before the study started.
Experimental Procedures
The experimental sessions took place in November and December 2013 in the Laboratory of
Experimental Economics (LEE) in Prague. Upon arrival, participants were randomly assigned
to isolated computer stations where they could not interact with each other. Subjects received a
printed copy of the instructions (see supporting information (SI)). After reading the instruc-
tions, subjects were asked to complete a quiz to ensure their understanding of the task. The
experimenter (same person in all sessions) was responsible for verifying the correctness of their
answers and in case of a wrong answer provided an explanation, until subjects fully understood
the task. After all subjects had answered the quiz questions correctly, they proceeded to the
task. The task was designed to explore subjects’attitudes towards monetary risk and potential
losses and was programmed in z-Tree [74] in English. After the completion of the task and an
additional, unrelated task, participants filled out a questionnaire about their socio-economic
background, health, personality, and general attitude towards risk. The experimental sessions
lasted approximately 90 minutes and on average subjects earned 350 Czech korunas (CZK),
equivalent to 17.5 USD according to the exchange rate at that time. After making their deci-
sions, subjects were privately paid in cash.
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Financial decision-making task
Subjects were endowed with 250 CZK and made a series of 140 forced choices between differ-
ent pairs of monetary gambles (S3 Table in SI lists the entire set of pay-offs used). Each pair
contained a sure option (SO) which paid S CZK for sure, and a risky option (RO), which deliv-
ered a gain of G CZK and a loss L CZK each with an equal probability of 50% (Fig 1). The task
was used to measure risk attitudes and loss aversion in previous studies [56,57]. Choices were
presented in four pseudo-randomly ordered blocks of 35 decisions, such that block order was
counterbalanced across participants. Gamble positions (left or right) and gamble outcomes
were randomized across participant. Subjects received no immediate feedback about the lottery
outcomes and were informed that at the end of the experiment, one trial would be selected at
random and the payoff associated with the selected option would be implemented. The payoffs
and initial endowment of 250 CZK were such that no subject could end with negative earnings.
Immunological Tests for Toxoplasmosis
Serological tests took place between 2006 and 2013 in the National Reference Diagnostic Labo-
ratory for Toxoplasmosis, National Institute of Public Health, Prague. Specific IgG and IgM
Fig 1. Risk task: A sample screenshot from the study. The two numbers on the left represent the gamble’s possible gain and loss amounts (Top and
Bottom, respectively). The number on the right represents the guaranteed amount. Participants had to indicate which option they wanted to choose by
clicking the corresponding button.
doi:10.1371/journal.pone.0136716.g001
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antibody titres were determined by ELISA (IgG: SEVAC, Prague, IgM: TestLine, Brno), opti-
mized for early detection of acute toxoplasmosis [75] and by complement fixation tests (CFT,
SEVAC, Prague) which are more sensitive and therefore more suitable for the detection of
latent TG infection [76]. The titre of anti-TG antibodies in sera was measured in dilutions
between 1:8 and 1:1024. The subjects with negative results of IgM ELISA (positivity index<0.9)
and both CFT titres higher than 1:8 and IgG ELISA>250 optical units, i.e. approximately 10
IU/ml, were considered latent toxoplasmosis positive. Individuals with ambiguous diagnosis,
e.g., different result of CFT and ELISA, were excluded from the study.
Six of the non-infected subjects were tested prior to 2010; nine subjects were tested between
April 2010 and November 2010; seven subjects were screened from October 2011 to November
2011. The remaining 18 non-infected subjects were tested between March 2012 and April
2013. The seroprevalence of TG increases from 8% in women aged 12 to 19 to 15% in women
aged 20 to 29 [77]; these numbers are comparable to the Czech Republic population [78]. As
the age range of women in our study was 20 to 26 (mean 23.15; SD 1.64), and given that the
non-TG women tested negative in prior years (12 to 19 years of age), it is safe to deduce that
7% is an upper bound for the likelihood that any of the control group subjects had contracted
the parasite between the time of the serological examination and the behavioral task. All of the
analysis replicates when we exclude the 6 subjects that were tested prior 2010 (see S5–S7 Tables
in supplementary material section).
RhD Examination
Several studies over the past years have that RhD phenotype moderates several effects of TG on
human personality, reaction times and behavior [79–82]: generally, RhD positive subject are
highly resistant to TG related effects [83], where TG has opposite effects on RhD negative
homozygotes and RhD positive heterozygotes [84]. Therefore, it is important to include RhD
phenotype in the analysis as a covariate.
We used a standard agglutination method for RhD examination. A constant amount of
anti-D serum (human monoclonal anti-D reagent; Seraclone, Immucor Gamma Inc.) was
added to a drop of blood on white glass plate. Red cells of RhD-positive subjects were aggluti-
nated within 2–5 minutes.
Results
Summary Statistics
We present summary statistics in Table 1. The experiment was a case-control study; thus, the
prevalence of neither TG-infected nor RhD negative subjects reflected real frequencies in
Czech population: According to a recent study [78] the prevalence of latent toxoplasmosis in
women of childbearing age in the Czech Republic was 20% as of 2007. The rate of RhD negative
Table 1. Summary statistics for the sample.
All Toxo + Toxo - P
a
Variable Obs. Mean SD Obs. Mean SD Obs. Mean SD
Age 79 23.89 3.65 39 24.66 4.84 40 23.15 1.64 0.065
RhD positive 53 NA NA 26 NA NA 27 NA NA NA
RhD negative 26 NA NA 13 NA NA 13 NA NA NA
a
P shows statistical significance for two tailed t test.
doi:10.1371/journal.pone.0136716.t001
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subjects in the Caucasian population was about 16% [76]. Among 79 subjects, 39 were TG-
infected and 40 were TG-free. There were about twice as many RhD positive subjects (N = 53)
as RhD negataive ones (N = 26) and their distribution among TG-infected and TG-free sub-
jects were identical. TG-infected subjects were slightly older (t(77) = -1.87, P<0.07).
Decision-making task
For each subject, we calculated a raw measure of risk attitude by calculating the frequency of
risky choices (i.e., the proportion of trials in which the subject chose the risky option). Fig 2
shows the means of this variable in both TG-infected and TG-free subjects. The difference was
not statistically significant (t(77) = 0.61, P>0.53). To further assess risk-seeking behavior, we
examined subjects’choices when the expected value of the gamble was less than the sure
option; a two tailed t-test shows that the difference was not statistically significant (t(77) =
-0.40, P>0.68, Fig 3, left panel). We also found no significant differences in the frequency of
risky choices when the expected value of the gamble was more than the sure option (t(77) =
1.33, P>0.18, Fig 3, right panel). Additionally, we compared average response times (RTs) in
seconds (per trial) across the two groups (see Fig 4). In accordance with previous studies
Fig 2. Frequency of risky choices in Toxoplasma-infected and Toxoplasma-free subjects. The graph shows arithmetic means, standard errors and a p-
value.
doi:10.1371/journal.pone.0136716.g002
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reporting slower RTs in TG-infected subjects [42] the TG-infected group average RT was larger
than the TG-free counterparts, though the difference was not statistically significant (t(77) =
-1.10, P>0.27). We also found no differences in RTs between the two groups when separately
examining choices of the risky option (t(77) = -1.62, P>0.10, Fig 5, left panel) and choices of
the sure option (t(77) = -1.33, P>0.18, Fig 5, right panel).
We defined the variable incentive as the difference between expected value of the gamble
and the sure amount and further analyzed the data using logistic regression with clustered stan-
dard errors at subject level. The binary outcome variable choice indicated (= 1) if the subject
chose the risky option in a given trial. Column (1) in Table 2 reports the results of basic regres-
sion. As expected, we found a significant effect of incentive on participants’decisions (X
2
(1,
N= 79) = 138.14, P<0.01). Our results show no effect of TG-infection status on subjects’DM
under financial risk (X
2
(1, N= 79) = 0.38, P>0.53). In column (2) we added other explanatory
variables (age, RhD status) to our basic regression. None of the new explanatory variables were
statistically significant, and the magnitude and significance levels of the basic regression coeffi-
cients remained virtually unchanged. Next, we investigated the data using a model that also
accounts for the variance of the risky option, by adding a variable representing the variance of
the risky choice to the original regression. Columns (3) and (4) show the estimation results of
Fig 3. Frequency of risky choices when the expected value of RO is less than SO (left) and the expected value of RO is greater than SO (right) in
Toxoplasma-infected and Toxoplasma-free subjects. The graph shows arithmetic means, standard errors and a p-value.
doi:10.1371/journal.pone.0136716.g003
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the basic regression without interaction terms, with interaction terms, and with additional
explanatory variables. Adding the variance did not affect the significance of the coefficients,
and the results from columns (1) and (2) are not substantially changed.
We analyzed the decision RT data using a mixed-effects linear model. Column (1) in Table 3
reports the results of the basic regression. Across groups, choices of the risky option took con-
siderably longer (z(77) = 10.55, P<0.01). Additionally, when facing higher monetary incentives
to take risks, decisions were slower (z(77) = 6.31, P<0.01). There was no difference in decision
times between TG-infected and TG-free groups (z(77) = 1.17, P>0.24). In column (2) of Table 3
we repeated the regression by including interaction terms. The Toxoplasma-Choice interaction
term was positive but insignificant (z(75) = 1.16, P>0.24). As expected, we also observed a sig-
nificant negative effect of choice x incentive interaction (z(75) = -10.18, P<0.01), implying that
when deciding to take a risk, subjects made the decision faster when the risky option was more
attractive. In column (3) in Table 3 we extend our analysis by including additional explanatory
variables (age, RhD). None of the new controls have a significant effect on RT.
As a final step, we estimated a parametric model inspired by prospect-theory [60] for mea-
suring individual levels of risk aversion (ρ) and loss aversion (λ) using maximum likelihood
estimation, in a similar manner to recent studies that investigated individual differences in risk
Fig 4. Decision response times (RT) in Toxoplasma-infected and Toxoplasma-free subjects. The graph shows arithmetic means, standard errors and a
p-value.
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and loss aversion applying the same experimental task [56,57] (see S2 Appendix for detailed
method. Table 4 reports summary statistics of the parametric data (S1 and S2 Figs illustrate the
model fit). A two-tailed t-test revealed no statistically significant differences in the mean values
of ρand λbetween the TG-infected and TG-free groups. We also performed OLS regression
analysis with additional explanatory variables and found no significant effect of any explana-
tory variable on either risk aversion or loss aversion parameters (columns 1 and 2 of Table 5).
In addition, we excluded parameter values that were more than two standard deviations away
from mean (see S4 Table in SI for individual parameter estimates, nine extreme values were
excluded in total). The number of excluded subjects was significantly lower in the uninfected
group (2 out of 40) compared to the infected group (7 out of 39, p = 0.04). These subjects were
excluded because their risk aversion (ρ) and/or loss aversion (λ) parameters were unusually
high or unusually low. The results were robust to excluding these extreme parameter values in
the analysis (see S1 and S2 Tables).
Discussion
Although latent TG-infection in humans has been considered harmless for many years follow-
ing its discovery, recent studies suggest effects on human behavior. TG is associated with an
Fig 5. Decision response times (RT) when choosing RO (left) and SO (right) in Toxoplasma-infected and Toxoplasma-free subjects. The graph
shows arithmetic means, standard errors and a p-value.
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increased risk of psychological disorder, changes in personality traits, prolonged response
times in cognitive tasks, and a higher likelihood of being involved in a car accident [26,37,39–
45]. TG is also linked to functional changes in the dopaminergic and testosterone systems [51–
53], both of which are known to shape one’s attitudes towards risks and losses and partly
explain individual differences in economic preferences. In light of these findings and based on
the well-established animal literature, the present study is the first to examine a potential link
between latent TG infection and financial DM, namely risk and loss aversion, in humans.
Using a case-controlled design and a well-established experimental task that was previously
used to investigate individual differences in economic DM, we explore two hypotheses relating
TG infection to risk-taking and loss aversion. While our study fails to find such a direct link,
we acknowledge several limitations to our work.
Our sample is comparable in size to other DM studies that used the same experimental para-
digm to explore the influence of psychological and biological factors on financial DM [56,57].
Given the costs of constructing a case-controlled study (e.g. conducting blood-draws and bio-
analysis in a large sample of candidate participants), we find this sample size adequate. Yet, as
this is the first attempt to test the effects of parasitic infection on human DM, we had no means
of conducting detailed power analysis ex-ante. Given the moderate sample size, we cannot rule
out very small effects. However, our results are inconsistent with any large effect of TG infection.
Based on recent studies reporting gender-specific effects of TG on several psychological and
physiological variables [32,52], the current study used only female subjects. Future studies
should further illuminate the relationship between TG-infection and DM in males. In addition,
TG-infection effects on DM could be moderated by several hidden factors that are not mea-
sured in the current study due to methodological limitations. For example, the RhD factor has
Table 2. Logistic regression.
Choice
Dependent Variable (1) (2) (3) (4)
Toxoplasma
a
0.153 0.191 0.154 0.193
(0.248) (0.461) (0.250) (0.466)
Incentive 0.088*** 0.089*** 0.088*** 0.089***
(0.007) (0.007) (0.008) (0.008)
Age 0.001 0.001
(0.031) (0.032)
RhD
b
0.095 0.096
(0.320) (0.323)
Toxoplasma*RhD 0.516 0.520
(0.542) (0.547)
Variance 6.34E-5*** 6.36E-5***
(1.48E-5) (1.48E-5)
Constant 0.538*** 0.569 0.248 0.279
(0.149) (0.786) (0.150) (0.793)
Restricted Log Likelihood 5377 5359 5303 5285
Observations 11060 11060 11060 11060
Coefficients in all columns are logistic regression estimates, clustered standard errors are in parentheses;
*** indicate significance at 1% level.
a
Toxoplasma is a dummy variable and equals 1 for Toxoplasma-infected subjects.
b
RhD is a dummy variable and equals 1 for RhD positive subjects.
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been suggested as a moderator that increases the psychological effects of TG infection [85–88].
Although we have specifically addressed this variable by verifying that the same amount of Rh-
positive and Rh-negative subjects were included in both experimental groups, and by control-
ling for RhD status in our analysis, the low prevalence of RhD-negative types in the population
restricted our capability to recruit a large amount of such subjects. Furthermore, we recognize
that few simple cause and effect relationships exist in nature; thus, future studies should further
explore possible variables that might moderate the effects of TG on human DM, such as the
time since infection, personality, levels of testosterone and cortisol, and genotype.
Table 3. Mixed-effects linear regression.
Response Time
Dependent Variable (1) (2) (3)
Toxoplasma
a
0.286 0.210 -0.118
(0.244) (0.254) (0.435)
Incentive 0.010*** 0.021*** 0.021***
(0.001) (0.001) (0.001)
Choice
b
0.785*** 0.977*** 0.977***
(0.074) (0.097) (0.097)
Toxoplasma*Choice 0.140 0.141
(0.121) (0.121)
Choice*Incentive -0.040*** -0.040***
(0.003) (0.003)
Age 0.038
(0.034)
RhD
c
0.086
(0.368)
Toxoplasma*RhD 0.408
(0.523)
Constant 3.125*** 3.258*** 2.315***
(0.174) (0.180) (0.847)
Log Likelihood -27469 -27597 -27595
Observations 11060 11060 11060
Coefficients in all columns are logistic regression estimates, clustered standard errors are in parentheses;
*** indicate significance at 1% level.
a
Toxoplasma is a dummy variable and equals 1 for Toxoplasma-infected subjects.
b
Choice is a dummy variable and equals 1 if subjects chose risky option.
c
RhD is a dummy variable and equals 1 for RhD positive subjects.
doi:10.1371/journal.pone.0136716.t003
Table 4. Summary statistics of the parametric data.
All Toxo + Toxo - P
a
Variable Obs. Mean SD Obs. Mean SD Obs. Mean SD
ρ
79 0.846 0.145 39 0.829 0.175 40 0.864 0.108 0.29
λ
79 1.612 0.892 39 1.762 1.06 40 1.465 0.672 0.139
a
P shows statistical significance for two tailed t-test.
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Finally, the current study focuses on decisions from description (that is, subjects were told
about the probabilities and payoffs) rather than from experience. As people show different
behavioral patterns in these two types of DM experiments [89,90], future studies should
explore TG-related effects on decisions from experience. Likewise, we only investigated the
link between TG infection and financial DM. Arguably, TG-infection on DM could be
domain-specific (e.g., driving or health risks), or might influence decision parameters that were
not explored in the current experimental setting and might be consistent with previous find-
ings, such as under-weighting of low probabilities [91–93].
Supporting Information
S1 Appendix. Instructions.
(DOCX)
S2 Appendix. Parametric estimation Procedure.
(DOCX)
S1 Dataset.
(XLSX)
S1 Fig. Propensity to choose the RO as a function of its net expected utility (x-axis is trun-
cated to (-25, 25). The figure shows the proportion of risky choices across a range of net
expected utility values. The net expected utility of each binary choice (faced by each subject)
was calculated by subtracting the utility of the sure option from the expected utility of the risky
option. Utilities were calculated according to each subject's estimated prospect theory parame-
ters. The choices were grouped into bins of width 5 for the purpose of calculating the propor-
tions of risky choices. The fitted curves were obtained by estimating logit models (one model
for the pooled data from each group) where the probability of choosing the risky option
Table 5. Regression analysis of individual parameters.
ρλ
Dependent Variable (1) (2)
Toxoplasma
a
-0.042 0.269
(0.056) (0.356)
Age -0.001 0.002
(0.004) (0.028)
RhD
b
0.070 0.155
(0.048) (0.303)
Toxoplasma*RhD 0.017 0.039
(0.068) (0.430)
Constant 0.859*** 1.311*
(0.110) (0.697)
R
2
0.083 0.036
Observations 79 79
Coefficients in all columns are logistic regression estimates, clustered standard errors are in parentheses;
*** indicate significance at 1% level.
*indicate significance at 10% level.
a
Toxoplasma is a dummy variable and equals 1 for Toxoplasma-infected subjects.
b
RhD is a dummy variable and equals 1 for RhD positive subjects.
doi:10.1371/journal.pone.0136716.t005
Toxoplasma gondii and Financial Risk Taking
PLOS ONE | DOI:10.1371/journal.pone.0136716 September 24, 2015 12 / 17
depends only on the net expected utility.
(PNG)
S2 Fig. Propensity to choose the RO as a function of its net expected utility (x-axis is trun-
cated to (-20, 20). The figure shows the proportion of risky choices across a range of net
expected utility values. The net expected utility of each binary choice (faced by each subject)
was calculated by subtracting the utility of the sure option from the expected utility of the risky
option. Utilities were calculated according to each subject's estimated prospect theory parame-
ters. The choices were grouped into bins of width 5 for the purpose of calculating the propor-
tions of risky choices. The fitted curves were obtained by estimating logit models (one model
for the pooled data from each group) where the probability of choosing the risky option
depends only on the net expected utility.
(PNG)
S1 Table. Summary statistics of the parametric data (full sample).
(DOCX)
S2 Table. Regression analysis of individual parameters (full sample).
(DOCX)
S3 Table. Binary choices used in the experiment as measured in CZK.
(DOCX)
S4 Table. Individual parameter estimates. Values in bold were excluded from the analysis.
(DOCX)
S5 Table. Logistic Regression. Robustness analysis.
(DOCX)
S6 Table. Mixed-effects linear regression. Robustness analysis.
(DOCX)
S7 Table. Regression analysis of individual parameters. Robustness analysis.
(DOCX)
Acknowledgments
We would like to thank Miroslav Zajicek, the director of the Laboratory of Experimental Eco-
nomics (LEE) at The University of Economics, Prague (VSE).
Author Contributions
Conceived and designed the experiments: LL KC JF GN. Performed the experiments: LL BS JF.
Analyzed the data: LL GN. Contributed reagents/materials/analysis tools: LL KC BS JF GN.
Wrote the paper: LL KC JF GN.
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