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ORIGINAL RESEARCH
published: 03 July 2020
doi: 10.3389/fpsyg.2020.01381
Edited by:
Guomei Zhou,
Sun Yat-sen University, China
Reviewed by:
Jordan Litman,
University of Maine at Machias,
United States
Hong Li,
Shenzhen University, China
*Correspondence:
Jifan Zhou
Hui Chen
Specialty section:
This article was submitted to
Cognition,
a section of the journal
Frontiers in Psychology
Received: 06 December 2019
Accepted: 25 May 2020
Published: 03 July 2020
Citation:
Shen M, Liu P, Li X, Zhou J and
Chen H (2020) The Gilding-the-Lily
Effect: Explorator y Behavior Energized
by Curiosity. Front. Psychol. 11:1381.
doi: 10.3389/fpsyg.2020.01381
The Gilding-the-Lily Effect:
Exploratory Behavior Energized by
Curiosity
Mowei Shen
1
, Pengpeng Liu
1
, Xinyu Li
2
, Jifan Zhou
1
*
and Hui Chen
1
*
1
Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China,
2
Department of Psychology,
Zhejiang Normal University, Jinhua, China
The widespread metaphor “to gild the lily” suggests that people usually engage in
superfluous behaviors. Understanding the cognitive mechanism underlying superfluous
behaviors helps individuals to reduce possible waste and even disasters incurred by
unnecessary actions. Here, we assumed that curiosity for new information partly pushes
people to make needless efforts. This hypothesis was tested through three experiments.
In three experiments, we found that when participants knew that expending more
efforts than task requirements brought no better results, they still exerted various
exploratory activities to fulfill curiosity. These results imply that the impulsion to satisfy the
desire for information could partly drive individuals to indulge in unnecessary activities
over mission demands. Present research improves the comprehension of irrational
superfluous behavior and provides directions to reduce loss and waste caused by
gilding the lily.
Keywords: gild the lily, superfluous behavior, curiosity, interest, deprivation, exploration
INTRODUCTION
People often engage in activities superfluous to the requirements of a task or mission, which
have the potential to induce disasters, instead of being beneficial. For example, in January 2012,
32 passengers were killed in the Costa Concordia cruise ship disaster in Tuscany, Italy. Later,
investigations showed that the captain’s additional manual change in the route was a major cause
of the accident. The ship’s navigation system had planned a safe route for the voyage, which
kept the ship away from rocks, and additional manual adjustment was not required.However,
the captain took control of the ship and sailed away from the planned route. The new route
was too close to the shore, and the ship eventually struck some rocks (Levs, 2012). The captain’s
behavior could be described by the proverb, “to gild the lily,” which is defined as making superfluous
additions to what is already complete (American Heritage, 2011), or engaging in unnecessary and
usually wasteful activities (Price, 2011). Because automated technologies are extensively employed,
needless intervention in human-automation interaction systems potentially entail grave losses or
catastrophes (Hoff and Bashir, 2015).
Current research refers to the phenomenon of immersing in superfluous behaviors as the
gilding-the-lily effect (abbreviated as GL effect), and it is usually regarded as irrational (Pielstick,
1970; Lidstone, 1995). The rationale behind gilding the lily being regarded as superfluous is twofold.
First, given that gilding the lily adds no more beauty to lilies, it is waste of gold foil. Second,
redundant gold probably undermines the lily’s natural appearance. In the Costa Concordia tragedy,
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Shen et al. Curiosity Energizes Superfluous Behaviors
since that a safe route had already been provided by
the navigation system, additional manual control not only
guaranteed no more safety but also might cause danger. Thus, the
disaster could be interpreted as a consequence of the GL effect.
Both the irrationality of the GL effect and its potential harmful
effects prompted us to examine the cognitive mechanism that
underlies the GL effect.
Some accounts have been proposed to explain the GL effect.
The account of boredom suggests that superfluous behaviors
may derive from an individual’s tendencies to dispel boredom,
which is perceived to be torturous (Koerth-Baker, 2016). Koerth-
Baker (2016) found in their study that when the participants
were told to wait for a while, with nothing to do, and they had
the chance to receive electric shocks, participants chose to give
themselves an electric shock. Receiving painful electric shocks
was superfluous, since the task did not require the participants
to do this. Koerth-Baker (2016) suggested that when left with
nothing to do, participants felt bored and tortured, and it was
the impulsion to dispel boredom that induced participants to get
an electric shock.
Another account that has been proposed to explain the
GL effect is the account of curiosity. Curiosity is defined as
the desire for new information, knowledge, experiences, or
sensory stimulation, and it motivates exploratory behavior to
resolve uncertainty or experience the unknown (Berlyne, 1978;
Loewenstein, 1994; Spielberger and Starr, 1994; Litman, 2005;
Grossnickle, 2016). When curiosity is aroused, individuals often
soak themselves in impulsive activities to seek information (Arkes
and Ayton, 1999; Shani and Zeelenberg, 2007; Kruger and Evans,
2009; Barkan et al., 2016; Hsee and Ruan, 2016). The curiosity
account suggests that, to satisfy their curiosity, humans possibly
tend to perform additional actions over the task requirements
(Loewenstein, 1994, 2006; Kruger and Evans, 2009; Hsee and
Ruan, 2016). Specifically, Hsee and Ruan (2016) designed an
experiment in which pictures of disgusting insects were projected
on a screen. The participants did not know the specific content
of each picture, and they could simply press keys to skip pictures
in order to complete the task (Hsee and Ruan, 2016). However,
the researchers found that participants still chose to view the
pictures, even though they knew that the pictures were disgusting
(Hsee and Ruan, 2016). Viewing these pictures was unnecessary
for the task, and these superfluous behaviors were attributed
to eliminating the uncertainty caused, and curiosity aroused,
by the pictures left unseen. Overall, both these accounts offer
explanations for the motivation to engage in needless activities.
Traditional curiosity theories and empirical evidence
distinguish between two types of curiosity: the interest
type and the deprivation type (Berlyne, 1978; Litman, 2005;
Litman et al., 2010; Mussel, 2010). The interest type, or I-type
curiosity, is induced by opportunities to gain information
and is associated with acquiring knowledge simply for the
intrinsic joy associated with it (Litman, 2008; Litman et al.,
2010; Chang and Shih, 2019). The deprivation curiosity, or
D-type curiosity, is stimulated when humans lack a specific
piece of information, which is relevant to the reduction of
undesirable uncertainty (Loewenstein, 1994; Litman, 2008;
Litman et al., 2010; Chang and Shih, 2019). I-type curiosity
is usually accompanied with positive emotions and diverse
exploratory behavior, and D-type curiosity is accompanied
with negative feelings and specific exploration for the missing
information (Litman and Spielberger, 2003; Litman, 2008, 2010).
Previous research mainly focused on the driving force of D-type
curiosity. For instance, participants in the above-mentioned
study did not have the information about the content of the
pictures, and thus, they chose to view the photos to eliminate that
aspect of uncertainty (Hsee and Ruan, 2016). In such a situation,
the motivation is understood to be the D-type curiosity, which
partly drove participants to make impulsive exploring acts.
I-type curiosity mostly emerges in fresh and novel situations,
without obvious specific uncertainty, and pushes individuals to
seek possible information (Litman, 2008; Grossnickle, 2016). It
remains to be examined whether I-type curiosity can stimulate
superfluous behavior.
The present study had two aims. First, to examine the
possibility that I-type curiosity energizes the GL effect. Previous
studies on the subject usually studied the notion of specific
uncertainty and showed that D-type curiosity plays an important
role in the GL effect (Kruger and Evans, 2009; Hsee and Ruan,
2016). I-type curiosity differs from D-type curiosity in many
aspects, and the outcomes that result from D-type curiosity
cannot be directly applied to I-type curiosity (Litman et al.,
2005, 2010; Litman, 2008). Thus, investigating the role of
I-type curiosity in inducing the GL effect helps to enhance the
comprehension of the GL effect and could potentially guide
individuals to mitigate the possible losses triggered by GL effect.
The second aim of the study was to compare the influence of
I-type curiosity and D-type curiosity on exploratory activities.
Previous research has found that D-type curiosity possibly has
a more sustainable impetus for exploratory behavior (Litman
et al., 2005). However, the two types of curiosity were measured
as traits by the questionnaires, and the conclusion was inferred
from a multiple correlation analysis (Litman et al., 2005). The
facets of curiosity as a trait and a state refer to the different
properties of curiosity. Trait facets reflect the frequency with
which individuals experience curiosity, while state facets refer
to the intensity of the curiosity experienced at a certain time,
as an emotional and motivational state (Spielberger and Starr,
1994; Collins et al., 2004). In the present study, we tried to
separately evoke the two types of state curiosities in the same
situation and compared their effect on exploratory behavior.
Thus, we could directly test the theorical assumption that D-type
curiosity drives exploratory behavior more acutely (Litman, 2008;
Litman et al., 2010).
We examined and compared the impact of I-type curiosity
and D-type curiosity through three studies. Each study comprised
of two different conditions. One was the unknown condition
where specific uncertainty was created. The other was the
known condition where participants were given key information
about the task. This was done because the I-type curiosity
is usually involved in fresh and novel situations without
obvious specific uncertainty, and the D-type is often involved
in situations with a specific information gap (Litman, 2008;
Grossnickle, 2016; Chang and Shih, 2019). We inferred that
I-type curiosity was mainly related to the known condition,
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Shen et al. Curiosity Energizes Superfluous Behaviors
and D-type curiosity was more concerned with the unknown
condition. Thus, we could identify the exploring activities in
the known condition and contrast it with behaviors in the
unknown condition.
STUDY 1
Study 1 simulated a scene where a spaceship and space
station were docked. Participants played the role of astronauts
to monitor a spaceship that was going to dock at a space
station. Before docking, they should make sure that docking
requirements were fulfilled. If the automatic system on the
spaceship failed to accomplish preparatory work, participants
needed to manually finish docking preparation. Half of the
participants were assigned to the known condition, in which they
were clearly informed that, when preparatory work for docking
was done, extra manipulation no longer helped to improve the
success rate of docking. The other half of the participants were
assigned to the unknown condition, in which they were not
aware of whether extra manipulation would be useful. In both
conditions, requirements for docking would always be finished
by the automatic system, and we examined whether participants
would still sail the spaceship manually.
The information gap in the unknown condition was expected
to activate D-type curiosity, because participants lacked the
information to understand whether shrinking deviation could
further improve the docking success rate. In the known condition,
such an uncertainty was removed; therefore, I-type curiosity was
expected to emerge to impel participants to search for any new
information in the scenario. Due to the influence of D-type
and I-type curiosities, we expected that participants would make
extra manipulations in both conditions. And given that D-type
curiosity possibly exhibits a larger effect on exploration than
I-type curiosity, the proportion of participants who manually
controlled the spaceship would be higher in the unknown
condition. All measures, manipulations, and exclusions in Study
1 are reported below. No more data were collected contingent on
initial analysis.
Materials and Methods
Participants
Based on the results of the pilot studies, we predicted an effect
size of w = 0.34 for our experimental design. We performed
a power analysis with G
∗
power 3 (Faul et al., 2007), which
determined that at an α level of 0.05, 69 participants were needed
to detect an effect with a power of 0.80.Sixty-nine participants
were finally recruited: 34 in the unknown condition [19 women;
mean age (M
age
) = 23.35, standard deviation (SD) = 2.40; age
range = 19–30 years] and 35 in the known condition(21 women;
M
age
= 22.89, SD = 2.24; age range = 20–27 years). Participants
were paid to take part in the experiment and signed consent
forms. All participants had normal or corrected-to-normal visual
acuity. The study was approved by the Research Ethics Board
at the Department of Psychology and Behavioral Sciences,
Zhejiang University.
Procedure and Design
The participants were tested in a dark room individually. All
displays were presented on a Cathode Ray Tube (CRT) monitor
of a 17-inch computer. Participants were informed that they
would take part in an ergonomics study in which they played the
role of an astronaut. A spaceship was going to dock at a space
station under the control of an automatic system. The mission
for the astronaut was to monitor whether the automatic system
had successfully prepared for docking. If the automatic system
failed to prepare per the standards, the astronaut needed to make
manual corrections. In the first part of experiment, the spaceship
sped up to reach the space station. Participants gradually saw
the space station on the screen. When the spaceship caught up
with space station, the second part began. During the second
part, two systems worked together to adjust the position deviation
between the spaceship and the space station: the self-calibration
system of the spaceship and the manual system controlled by
astronaut. The self-calibration system worked first and then
came the manual system. Before the adjustment period ended,
participants had to ensure that the position deviation between
the spaceship and the space station was less than a required
threshold (Figure 1).
Half of the participants were assigned to the unknown
condition arbitrarily, and they were informed that once the
green cross center was in the red circle, the spaceship could
dock at the space station. The other half participants in
the known condition were further informed that once the
position deviation meets the docking requirement, any additional
manual control would not change the success rate of docking.
In both conditions, before the manual operation phase, the
self-calibration system would successfully adjust the green cross
center into the red circle. Specifically, the intersection finally
randomly lay in a concentric circle within the red circle. The
radius of this concentric circle was 15 pixels. Therefore, in
both conditions, the qualification for docking was met. This
experiment took about 5 min. Before the experiment, each
participant would view an accelerated docking process and
manually control the spaceship to reduce uncertainties about
the course and operation of study. After the simulation task,
participants received a questionnaire about their understanding
of the task and the reason they chose (or not) to take
extra control. Those questions were mixed with other filler
questions to collect participants’ subjective experiences with
the docking system.
Analysis and Results
In the unknown condition, two participants who reported that
the self-calibration system had not adjusted the cross center
into the red circle were excluded; in the known condition, three
participants were excluded for the same reason. Consequently,
the data from 64 participants (32 in each condition) were
analyzed. We used SPSS 20.0 for data processing.
In the unknown condition, 27 participants manually
controlled the spaceship, and 5 participants gave up the chance
of manipulation. The number of participants who manually
controlled the spaceship was significantly higher than those who
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FIGURE 1 | The scene that participants saw on the monitor. When the spaceship was far from the space station, they saw nothing but darkness. When the
spaceship gradually caught up with the space station, participants could see its outline. When the spaceship was near enough for docking, participants could see
the mark for docking clearly. The dashed green cross is the location mark of the spaceship, and the white cross is the mark of the space station. When the
intersection of the green cross was in the red circle within the white cross, the position deviation between those two aircrafts met the requirement for docking. The
radius of the red circle was 23 pixels.
did not control the spaceship [χ
2
(1) = 15.13, p < 0.001, w = 0.49].
In the known condition, 31 participants manually controlled the
spaceship, and 1 participant gave up the chance to manipulate the
spaceship. The number of participants who manually controlled
the spaceship was also significantly higher than the number
of participants who did not control [χ
2
(1) = 28.13, p < 0.001,
w = 0.66, respectively]. However, the proportion of participants
who chose to control the spaceship in the unknown condition
was not significantly higher than the proportion in the known
condition [χ
2
(1) = 1.66, p > 0.05,w = 0.16].
In the unknown condition, among the five participants who
did not control the spaceship, four participants thought that
work had been done by the automatic system, and there was
no need for manual control; one participant reported that the
automatic system was more precise, and manual manipulation
might lead to a bigger deviation. Of the 27 participants who
manually manipulated, one participant stated that she/he just
needed to do something, one felt obsessive if he/she gave
up, and 25 participants reported that they wanted to improve
success rate. In the known condition, all 32 participants reported
that the automatic system had completed preparatory work for
docking, and extra efforts did not help to improve success
rate of mission. The participant who gave up manipulating the
spaceship stated that the docking requirements were already
achieved and there was no need for further corrections. Among
the remaining 31 participants, 3 participants wanted to have a
try, 12 participants clearly reported that they felt obsessive if they
gave up manipulating, and 16 participants stated that they shrunk
the deviation because they wanted to have a try to improve
the success rate.
Discussion
In the unknown condition, the D-type curiosity drove
participants to control the spaceship. We also detected
significant human intervention in the known condition.
When participants explicitly knew that extra manipulation
brought no additional benefits, they still shrank the position
deviation. The GL effect in the known condition indicated
that I-type curiosity partly pushed participants to indulge in
unnecessary behaviors beyond task requirements. However,
we detected no significant differences between the unknown
and known conditions. This could partly be because there was
only one test without feedbacks in Study 1. Participants had
no information to revise their exploratory behavior. Both the
force of D-type and I-type curiosities possibly reached the
ceiling. Therefore, more trials might be needed to exhibit the
intensity and persistence of D-type in the unknown condition.
Despite the absence of the manipulation difference between the
unknown and known conditions, we detected different levels of
goal diversities in each condition. In the unknown condition,
most participants reported that they wanted to improve the
success rate. In the known condition, participants’ reasons for
manipulating the spaceship were more diverse. This is consistent
with findings that D-type curiosity involves a desire for success,
and I-type curiosity is associated with various exploration
(Litman, 2008).
It is also worth noting that some subjects reported being
obsessive if they gave up controlling the spaceship. The results
of questionnaire showed that participants fully understood that
manual manipulation did not help improve the success rate when
docking requirements were fulfilled by the automatic system,
and they did not know the exact reason why they felt obsessive
and why they chose to manually control the spaceship when
the preparation had been done. Such reports of feeling obsessive
might be the rational post hoc attribution of superfluous behaviors
in the current study. Considering that subjective reports were
vague and lagging and in all three studies, we mainly focused
on the analysis of behaviors during the task. To further test
the effectiveness of I-type curiosity and compare its intensity
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with D-type curiosity, we designed a multiple-trial situation with
feedbacks in Study 2.
STUDY 2
The design of Study 2 was abstracted from popular mobile
role-playing games such as “Onmyoji.” In such games, players
usually need to draw summon circles to send for different roles
and spend lots of resources to cultivate those roles. We were
inspired by players’ extra behaviors during games. The probability
for summoning different roles is actually constant and unrelated
with the type of summon circles. However, we have observed
that players often rack their brains for various summon circles
to send for desired roles. Similar behaviors are frequently seen
in daily life. For example, some people usually blow on the dice
before they roll them, even when they know that blowing does
not have any specific benefit because the outcome is randomly
generated. In Study 2, we simulated the summoning mechanism
and designed a drawing game. We manipulated the existence
of specific uncertainty and analyzed explorative activities in the
unknown and known conditions. All measures, manipulations,
and exclusions in Study 2 are subsequently reported. No more
data were collected contingent on initial analysis.
Materials and Methods
Participants
Based on the results of our pilot studies, we predicted an effect
size of Cohen’s d = 0.69 for our experimental design. To ensure
adequate power (0.80), we performed a power analysis with
G
∗
power 3 (Faul et al., 2007), which determined that when
α = 0.05, we required a sample size of ˜67. Sixty-seven participants
were finally recruited: 33 in the unknown condition (16 women;
M
age
= 21.09 years, SD = 2.43; age range = 18–28 years) and 34 in
the known condition (25 women; M
age
= 20.84 years, SD = 2.79;
age range = 18–28 years). Participants were paid to participate
in the experiment and signed consent forms. All participants
had normal or corrected-to-normal visual acuity. The study was
approved by the Research Ethics Board at the Department of
Psychology and Behavioral Sciences, Zhejiang University.
Procedure and Design
The participants were tested in a dark room individually. All
displays were presented on a CRT monitor of a 17-inch computer.
Participants were instructed to play a game. In this game, they
needed to draw circles on the screen by continually pressing
the mouse button and dragging the mouse. The center of the
circle was the point of the cursor where the button was pressed,
and the length of radius was determined by how far the cursor
was dragged away from the center point. The circle drawing
was completed when participants released the mouse button.
Participants would obtain a score ranging from 1 to 100 for
each circle they had drawn (Figure 2). Whenever they got
“100,” the game ended immediately, and participants got paid
the maximum; otherwise, they needed to draw 500 circles and
finally got paid according to their average score. At the end
of every trial, participants would get a reward feedback, which
was based on the average score they obtained till that point.
Thus, participants could know how much money they could
eventually get according to their current performance. Half of
the participants were assigned to the unknown condition, and
they were not informed any information about the relationship
between the circles they drew and the scores they received.
The other half of the participants were assigned to the known
condition and explicitly informed that the scores were randomly
generated (i.e., the scores were totally unrelated to the position
and size of circles and the way of drawing the circle). The
unknown condition created an information gap because the
participants did not know if there was a relationship between
the circle and the score; thus, D-type curiosity was expected to
be triggered. Without such uncertainty, in the known condition,
I-type curiosity would emerge. Before the experiment, each
participant would take several trials for practice to eliminate
their uncertainties about operation factors. After the game,
FIGURE 2 | The procedure used in Study 2. For each trial, a fixation cross appeared on the screen for 500 ms; participants were required to draw a circle on the
screen by continually pressing the mouse button and dragging the mouse. The center of the circle was located where the mouse button was pressed. The circle
enlarged as the mouse was dragged away from the initial location, or shrank when the mouse was dragged toward the initial location. When the mouse button was
released, the drawing of the circle was finished. The radius was the distance between the pointer and the center. When the circle drawing phase ended, the score
they received was presented for 1 s. Then, the reward amount appeared on the screen for 750 ms. The reward was calculated based on the average of all the
scores participants got till that point and served as an immediate reward feedback. Participants could know how much money they would eventually earn based on
their current level of performance.
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participants received a questionnaire about their thoughts during
the task, for example “please describe your thoughts during this
game.” Actually, scores were randomly chosen from 0 to 99.
Therefore, “100” never appeared, and each participant needed
to draw 500 circles. Five hundred trials were considered to be
enough to examine the tendency of exploring behaviors.
In Study 2, drawing time and radii of circles were used
to analyze participants’ behaviors. The drawing time could
partly reflect the mental efforts that participants expended for
drawing circles, for example, thinking about what kinds of circles
to draw. The radii intuitively reflected participants’ operation
efforts. In the known condition, if participants finished the
game according to task demands, they should just draw circles
without thinking too much. Also, to save time and energy, they
could draw circles as small as possible. Thus, both the drawing
time and radii of circles would be stable and at a low level.
We named this the optimal solution. If participants engaged
in unnecessary actions beyond task requirements, the drawing
time or radii possibly increased and fluctuated with time. We
hypothesized that participants in the known condition would
engage in explorative activity even when it was unnecessary; while
participants in the unknown condition would exhibit a stronger
tendency to explore. Further, we hypothesized that when they did
not reach any conclusion, they would gradually give up exploring
and draw circles that were easy to complete (i.e., smaller circles).
Thus, the M and SD of both drawing time and size of radii
would decrease with the number of trials, and it would take
more trials for participants in the unknown condition to reach
the optimal solution.
Analysis and Results
To examine the tendency of drawing time and radii, data
from each participant were equally divided into 10 blocks in
chronological order. The M (Figures 3, 4) and standard variance
(Figures 5, 6) of drawing time and M radius were calculated
for each block. A regression analysis was performed separately
for the M and the SD of each dependent variable to reveal
the trend of exploration behaviors. Two participants in the
unknown condition were excluded because their average time
FIGURE 3 | The M of drawing time in Study 2. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
FIGURE 4 | The M of radii size in Study 2. The solid and dotted lines represent
the regression curves for the unknown and known conditions, respectively.
FIGURE 5 | The SD of drawing time in Study 2. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
FIGURE 6 | The SD of radii size in Study 2. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
used to draw circles lay more than three SDs from the M. Three
participants in the known condition were excluded owing to the
same reason. Therefore, the data from 62 participants (31 in each
condition) were analyzed.
In the unknown condition, the regression coefficient of
the average value for drawing time was significantly different
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from zero, and the drawing time decreased significantly with
blocks [M = –138.52, SD = 103.86, t(30) = 7.43, p < 0.001,
d = 1.33]. In the known condition, drawing time also decreased
significantly with blocks [M = –59.12, SD = 139.31, t(30) = 2.36,
p < 0.05, d = 0.42]. And as shown in Figure 3, drawing
time of participants in the known condition got to a stable
level sooner than those in the unknown condition[t(60) = 2.54,
p < 0.05, d = 0.65]. This indicated that participants in the
known condition gave up exploring sooner. Figure 4 shows
that the regression coefficient of radius size for the unknown
condition is significantly different from zero, but the coefficient
in the known condition is not significantly different from zero
[M
unknown
= –47.33, SD
unknown
= 75.11, t(30) = 3.45, p < 0.01,
d = 0.63; M
known
= –2.44, SD
known
= 45.79, t(30) = 0.29,
p > 0.05, d = 0.05]. Furthermore, the regression the coefficient
of radius in the known condition was significantly larger than
the coefficient in the unknown condition [t(60) = 2.84, p < 0.01,
d = 0.72]. Participants in the unknown condition gradually
drew smaller circles, and the average size of the circles from
participants in the known condition remained stable. In both
conditions, the regression coefficients of the SD for drawing
time [M
unknown
= –81.17, SD
unknown
= 64.65, t(30) = 6.88,
p < 0.001, d = 1.26; M
known
= –37.84, SD
known
= 55.76,
t(30) = 3.72, p < 0.001, d = 0.68] and radius [M
unknown
= –39.99,
SD
unknown
= 53.06, t(30) = 4.13, p < 0.001, d = 0.75;
M
known
= –18.05, SD
known
= 35.98, t(30) = 2.75, p < 0.05,
d = 0.50] were significantly different from zero. The drawing time
and size of radius gradually converged with blocks. As shown
in Figures 5, 6, participants in the known condition tended to
approach the stable level sooner than those in the unknown
condition[SD of duration, t(60) = 2.78, p < 0.01, d = 0.72; SD
of radius, t(60) = 1.88, p = 0.065, d = 0.48].
In the unknown condition, a questionnaire from one
participant was lost and finally 30 valid questionnaires remained.
All 30 participants clearly stated that they thought there remained
some rules at the beginning and tried different ways to search.
Twenty-seven participants became frustrated and gradually gave
up. Two participants stuck with exploring until the end, and one
participant reported that he/she was in a cycle of giving up and
exploration. In the known condition, among the 31 participants,
twenty-nine participants reported that they wondered about
possible rules and tired to explore, and 2 participants just
drew circles without thinking. Thirty participants reported they
gradually felt frustrated and tried, gave up thinking and just drew
circles casually, and 1 participant stuck to exploring until the end.
Discussion
The M and SD of the drawing time and radius provided different
perspectives on the exploring behaviors. When participants
gradually gave up exploring, the decrease in mental and physical
investment were possibly reflected by the M. It was noted that
they spent less time on thinking about how to draw circles,
and smaller circles were drawn to save energy. Meanwhile,
the SD reflected the shrink of variation in the behavior when
participants moderated their acts of exploration. We detected
exploration behavior in both conditions, and participants in
the unknown condition stuck to exploring activities for longer
time. The present finding corresponds with former research,
which revealed that D-type curiosity trait is associated with
more exploration (Litman et al., 2005, 2010; Litman, 2008, 2010;
Lauriola et al., 2015). This also indicates that the manipulation
to differentiate two types of curiosity was partly valid. We also
noted that participants in the known condition reported that they
tried to understand the possible rules. This statement contradicts
the report that they knew that there was no relationship between
the scores and circles. Thus, the statement about “seeking rules”
is more likely to be a post hoc attribution of the superfluous
behaviors to account for the fact that they did not know
the exact reasons.
Nevertheless, we found no significant decrease in average size
of radii for participants in the known condition. We assumed
that the absence of trend in radii was partly due to the mode
of operation in Study 2. In the circle-drawing task, participants
were always required to draw a circle. Theoretically, the optimal
solution was to draw a circle with a radius approaching
zero,however, it was technically difficult for participants to draw
such small circles. Thus, they might choose to draw a circle that is
normal without thinking too much. Actually, many participants
stated that they finally drew circles casually. Moreover, the
length of radii recorded were the final consequence of the
adjustments, and they did not precisely represent the amount
of manual operation. This could also shrink the differences.
For example, participants might zoom in and out of the circles
repeatedly. However, the radius recorded here only reflects the
consequence of modulation but not how many pixels had been
changed in total.
Fatigue might be partly responsible for the decline of
time. When participants were tired, they naturally spent less
time thinking and drew smaller circles. To further test our
hypotheses and distinguish from the effect of other factors, we
designed Study 3, in which a clear distinction between “doing”
and “not doing” was made with more sensitive-dependent
variables than Study 2.
STUDY 3
Study 3 simulated mobile role-playing games slightly different
than Study 2. Rather than drawing circles, participants were
informed to assess circles on the screen. They could choose
to directly accept circles or to adjust the size and location of
circles and then accepted them. Participants would get a score
for each circle. If participants adjusted the circles, every pixel
they modulated would be recorded. If participants opted not to
adjust circles and directly accepted them, the operation amount
would be 0 pixels. Therefore, we had an accurate variate to reflect
participants’ operation efforts. We also recorded assessment
time, which reflected how much time participants used before
accepting circles. Other settings were similar with Study 2.
We hypothesized that in both conditions, the M and SD of
each dependent variable would decrease with time and finally
converge at a certain level. Participants in the unknown condition
would show more exploring activities than participants in the
known condition. All measures, manipulations, and exclusions in
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Study 3 are subsequently reported. No more data were collected
contingent on initial analysis.
Materials and Methods
Participants
Based on the results of Study 2, we predicted an effect size of
Cohen’s d = 0.68 for Study 3. Power analysis showed that given
α = 0.05, 70 individuals were sufficient to detect an effect with a
power of 0.8 (Faul et al., 2007). Seventy participants were finally
recruited in this experiment: 34 in the unknown condition (25
women; M
age
= 19.66 years, SD = 1.30; age range = 17–23 years)
and 36 in the known condition (23 women; M
age
= 20.28 years,
SD = 1.80; age range = 18–24 years). Participants were paid
to participate in the experiment and signed consent forms.
All participants had normal or corrected-to-normal visual
acuity. The study was approved by the Research Ethics Board
at the Department of Psychology and Behavioral Sciences,
Zhejiang University.
Procedure and Design
The participants were tested in a dark room individually. All
displays were presented on a CRT monitor of a 17-inch computer.
In the current game, participants were showed a circle with a
fixed radius (close to the average size of the radius in Study 2, 150
pixels) at a random position. They could adjust the position and
size of the circle by dragging the mouse (the left mouse button
for size and the right mouse button for position). If participants
gave up adjusting circles or had finished their adjustment, they
could press the space bar to end the adjustment phase. After that,
participants received a score ranging from 1 to 100. The unknown
condition offered an information gap because the participants
were uninformed of the relation between scores and circles.
Other settings were similar with Study 2. During this game,
participants could make no adjustment and just press the space
bar to finish the game as soon as possible (Figure 7). The assessing
time (from the onset of the circles to pressing the space bar)
and amount of operation were analyzed. The operation amount
was measured by the total pixels that participants adjusted.
For example, if a participant moved a circle 6 pixels rightward,
4 pixels leftward, and 8 pixels downward, the adjustment amount
was 18 (6 + 4 + 8) pixels. If they further magnified the radius for 2
pixels and then shrank for 2 pixels, the adjustment amount was 22
(18 + 2 + 2) pixels. Before the experiment, each participant would
take several trials for practice to eliminate their uncertainties
about operation factors. After the game, participants received a
questionnaire about their thoughts during the task, for example,
“please describe your thoughts during this game.”
Analysis and Results
The data analysis methods were exactly the same as those
employed in Study 2. There were 34 and 36 participants in the
unknown and the known conditions, respectively. In the unknown
condition, five were excluded because their assessment time lay
more than three SDs from the M, and four were excluded from
the known condition for the same reason. One participant in the
known condition was excluded from further analysis because he
forgot that there was no relationship between circles and scores.
Therefore, the data from 60 participants (29 in the unknown
condition and 31 in the known condition) were analyzed.
In both conditions, the regression coefficient of M of assessing
time significantly differed from zero [M
unknown
= –1020.90,
SD
unknown
= 809.54, t(28) = 6.79, p < 0.001, d = 1.26;
M
known
= –618.84, SD
known
= 589.68, t(30) = 5.84, p < 0.001,
d = 1.05]. This indicated that all participants explored at the
beginning and gradually gave up, spending less time assessing
circles. And as shown in Figure 8, assessing time of participants
in the known condition reached a lower level sooner than those in
the unknown condition [t(58) = 2.21, p < 0.05, d = 0.57]. Figure 9
shows that the regression coefficient of operation amount was
significantly different from zero in the unknown condition [M = –
145.00, SD = 94.98, t(28) = 8.22, p < 0.001, d = 1.53] and in the
known condition [M = –87.95, SD = 92.01, t(30) = 5.32, p < 0.001,
d = 0.96]. The regression coefficient of operation amount in the
known condition was significantly larger than the coefficient in
the unknown condition [t(58) = 2.36, p < 0.05, d = 0.61]. This
indicated that participants in the known condition started to
FIGURE 7 | The procedure used in Study 3. For each trial, a fixation cross was presented for 500 ms. Then participants entered the adjustment phase. They could
adjust the position and size of the circle by dragging the mouse (the left mouse button for size and the right mouse button for position). Participants pressed the
space key to end the adjustment phase. They could also directly press the space key without making any adjustment. After the adjustment phase, the score they
received appeared for 1 s. Then, their reward amount appeared on the screen for 750 ms. The reward, which served as an immediate reward feedback, was based
on the M of all the scores participants had.
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FIGURE 8 | The M of decision time in Study 3. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
FIGURE 9 | The M of operation amount in Study 3. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
make no adjustment sooner. In both conditions, the regression
coefficients of the SD for assessing time [M
unknown
= –479.22,
SD
unknown
= 251.65, t(28) = 10.08, p < 0.001, d = 1.90; M
known
= –
235.37, SD
known
= 315.10, t(30) = 4.09, p < 0.001, d = 0.75]
and operation amount [M
unknown
= –106.64, SD
unknown
= 95.68,
t(28) = 5.90, p < 0.001, d = 1.11; M
known
= –59.12,
SD
known
= 86.71, t(30) = 3.73, p < 0.001, d = 0.68] were
significantly different from zero. This indicated that the variance
of exploring activities gradually declined with blocks. As shown
in Figures 10, 11, participants in the known condition were
inclined to converge earlier than participants in the unknown
condition[SD of assessing time, t(58) = 3.24, p < 0.01, d = 0.86;
SD of operation amount, t(58) = 1.98, p = 0.052, d = 0.52].
In the unknown condition, 28 participants reported that they
were curious and tried to figure out the rules that might exist
in Study 3. One participant felt puzzled and gradually bored.
Sixteen participants had blocks with no adjustment, and 13
participants adjusted circles in all blocks. In the known condition,
questionnaires of one participant was lost, and there finally
remained 30 valid questionnaires. Twenty-eight participants
stated that they wondered about possible rules and tried to
explore. The remaining two participants reported that they
FIGURE 10 | The SD of decision time in Study 3. The solid and dotted lines
represent the regression curves for the unknown and known conditions,
respectively.
FIGURE 11 | The SD of operation amount in Study 3. The solid and dotted
lines represent the regression curves for the unknown and known conditions,
respectively.
mainly just pressed the space key. During Study 3, 18 participants
had blocks with no adjustment, and 12 participants adjusted
circles in all blocks.
Discussion
In Study 3, we obtained significant exploring behaviors from
time and operation amount in both conditions. We also repeated
the outcome in previous research (Litman et al., 2005, 2010;
Litman, 2008, 2010; Lauriola et al., 2015) that D-type curiosity
had a larger impact than I-type curiosity. Participants in the
unknown condition generally devoted longer time and more
physical efforts during the study. In Studies 2 and 3, facing
random feedbacks, participants in the unknown condition were
tougher and kept exploring more than participants in the known
condition. We inferred that they possibly restrained emotion in
order to endure so much random feedback and kept exploring
(Lauriola et al., 2015).
The outcome in Study 3 could not be totally interpreted as the
effect of fatigue. Fatigue indeed affected exploration behaviors.
When individuals were tired, participants had less energy to
support the execution of exploration, and they reduced and
even stopped exploring for rest. However, explorative activities
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Shen et al. Curiosity Energizes Superfluous Behaviors
did not derive from fatigue. Fatigue accumulated during Study
3 only served to reduce the amount of adjustment. Similarly,
frustration due to random feedback accounted for the decline
instead of the presence of exploration. Hence, we assumed that
curiosity played an important role in the GL effect. Fatigue and
frustration served to partly reduce the GL effect. In addition,
the explorative behaviors at the beginning of Study 3 was less
likely caused by boredom. Most participants stated that they felt
interested about game at the beginning. Actually, we detected
the effect of boredom in later blocks. We found that many
participants still manually adjusted circles. They reported to be
bored during latter blocks. To dispel boredom, they possibly
adjusted circles. Thus, we assumed that boredom could drive the
GL effect in later blocks.
GENERAL DISCUSSION
Current studies demonstrated that I-type curiosity could
partly induce the GL effect. When no obvious specific
uncertainty existed, participants still indulged in unnecessary
activities beyond the task requirements. The present research
also found that D-type curiosity had a greater impact on
exploratory behavior.
Curiosity and Boredom Account for the
GL Effect
The present study detected the effect of both accounts. In the
unknown condition, when participants did not know how to
improve the success rate of docking or get a score of 100,
D-type curiosity triggered by the specific uncertainty pushed
them to look for rules. In the known condition, knowing that
exploration did not work in current studies, participants still
chose to explore in various ways. This reflected the effect of
I-type curiosity. Combining the results of three experiments,
we concluded that D-type curiosity could have a greater effect
than I-type curiosity. In Study 1, D-type and I-type curiosities
both drove most participants to manually control the spaceship.
In Studies 2 and 3, D-type curiosity pushed participants in the
unknown condition to devote more efforts and stick to exploring
longer. These results indicated that, in the beginning, the two
types of curiosities might have similar influences. Both types
of curiosities induced people to begin their exploration. It was
seen that I-type curiosity exhibited greater vulnerability toward
exploration. When individuals failed to find any interesting new
information, they gradually gave up. Meanwhile, as random
feedback accumulated, instead of feeling interested, participants
felt frustrated and bored. They reduced exploration to a stable
level. We assumed that it was boredom that partly prevented
participants from completely giving up exploring. It was clearer
in Study 3, in which the efforts needed to accomplish the task
and to explore were distinguished, and the amount of manual
operation did not eventually reach zero. In latter blocks, it
seemed that it was boring to just wait till the end. Thus, to
dispel boredom, participants chose to do something to arouse
themselves, which was similar to taking electric shocks in a
previous research (Koerth-Baker, 2016).
Many factors such as goals, self-regulatory strategies, and
metacognitive experiences possibly contribute to the findings.
D-type curiosity is associated with desire to approach success
and avoid failure (Litman, 2008). With such goals, participants
are reported to be correlated with emotional restraint and
greater thoughtfulness regarding knowledge search (Lauriola
et al., 2015). Moreover, the nearer participants thought they
were with resolving the uncertainty, the higher intensity of
curiosity would be (Loewenstein, 1994; Litman, 2005, 2019;
Litman et al., 2005). And when answers are partially retrieved
(on the tip of the tongue), D-type curiosity is activated
(Litman, 2019). In the unknown condition when D-type curiosity
was activated, participants’ desires for success were energized
(Litman, 2008), and they were inclined to restrain emotion and
immerse themselves in the pursuit of goals (Lauriola et al.,
2015). Whenever participants figured out possible answers for
the specific uncertainty, they might think they were near with
the success and triggered more attempts (Litman, 2005, 2019;
Litman et al., 2005). Correspondingly, participants in the state
of I-type curiosity are associated with desire for new knowledge
and fun (Litman, 2005, 2019; Litman et al., 2005; Lauriola et al.,
2015). When failing to discover interesting findings, they just
gave up. In the future, detailed measurements of those factors are
needed to clarify the relations among factors and how they are
corresponded with behaviors.
Implications and Limitations of Current
Study
Through the presentation of the GL effect, we do not suggest
that humans are irrational or stupid. Exploring the reason for the
GL effect improves the comprehension of superfluous behavior.
Here we clarify both the positive and negative aspects of the GL
effect. We do not know everything about this world. The GL effect
actually reserves the chance to break knowledge barriers and
obtain novel gains in many domains (Day, 1982; Tomkins and
Tway, 1985; Simon, 1992, 2001; Kang et al., 2009; Kashdan and
Silvia, 2009; Gruber et al., 2014; Ruan et al., 2015). For instance,
Koichi Tanaka received the Nobel Prize in chemistry due to
the discovery obtained by his “needless” operation. Meanwhile,
unnecessary activities beyond task demand possibly incur waste
and loss (Kolko and Kazdin, 1989; Green, 1990; Kruger and
Evans, 2009; Hoff and Bashir, 2015; Hsee and Ruan, 2016). We
found that after dozens of trials, when participants found no
interesting discoveries, they gradually gave up. Thus, in situations
where extra manipulation might lead to great loss, thorough
knowledge and sufficient training experiences are recommended.
This finding provides some implications to both social
theories and practices. On one hand, current research
could provide implications for social attribution theories.
Classical attribution theories assume that people combine all
information to understand others’ behaviors, which are probably
attributed to personalities, attitudes, situations, and so on
(Kelley and Michela, 1980; Surian et al., 2007; Malle, 2008).
However, implicit tendencies (such as the exploration driven by
curiosity) are usually neglected in social attributions, leading
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to misunderstanding of others’ (and even our own) behaviors.
Therefore, it is necessary to reveal the detailed mechanism of
such implicit tendencies that affect social behavior, to get a better
interpretation of the human’s “irrational” behavior. On the other
hand, our discovery could afford guidance for social information
inquiry (Coenen et al., 2018). To better interact with each
other’s, humans often need to dynamically track others’ social
information, such as attitudes, traits, and motivations. In such
cases, this current study suggests that individuals could focus
on effective exploration behaviors and minimize unnecessary
impulsive attempts to reduce misapprehension.
The awareness of the knowledge gap is necessary for the
feeling of deprivation in the D-type curiosity. The presentation of
questions or turned-over pictures spontaneously incur curiosity
about the answer of the questions (Litman et al., 2005) or the
content of pictures (Hsee and Ruan, 2016). Current research,
by contrast, did not present the knowledge gap in such a
self-evident way. In the unknown condition, participants were
aware that there was room for operation. For example, in
Study 1, participants could further shrink the deviation when
the automatic system finished adjusting; in Studies 2 and
3, participants could adjust the size and location of circles.
Meanwhile, the responsibility as astronauts in Study 1 and
more awards associated with higher scores in Studies 2 and 3
could encourage participants to improve task performance. Such
situations possibly offered the knowledge gap about whether
manipulation would improve performance. The knowledge
gap generated by the combination of experimental settings
was indirect; thus, the effect of D-type curiosity might be
underestimated. Further studies with more direct manipulation
of the information gap are needed to clarify the differences
between D-type and I-type curiosities.
In the present research, we distinguished D-type and I-type
curiosities by manipulating the existence of obvious specific
uncertainties according to the theoretical differences between two
types of curiosities (Loewenstein, 1994; Litman, 2008; Litman
et al., 2010; Chang and Shih, 2019). The finding that the D-type
state curiosity exerted a stronger effect on energizing exploration
behaviors than the I-type state curiosity was consistent with
the outcome from research on trait curiosity (Litman et al.,
2005). This indicates that current manipulation has differentially
involved D-type and I-type curiosities. Considering that the
measurement of curiosity states possibly encourages participants
to guess the purpose of the research, we did not make direct
manipulation checks to avoid potential confounding from high-
level cognition. However, the lack of direct measure of curiosity
can lead to alternative comprehension of the results, especially
in Study 1. Considering the existence of the automatic system,
participants possibly controlled the spaceship due to their belief
that they could outperform the automatic system (Sieck and
Arkes, 2005; Moore and Healy, 2008). Therefore, findings in
the current research, particularly in Study 1, could be limited
in revealing the peril of curiosity. In the future, systematic
measurement of all these factors are needed to further clarify the
effect of curiosity and other underlying motives. Trait curiosity
measurements are also recommended to further improve the
comprehension of the trait-state interactions in exploration.
CONCLUSION
Individuals possibly make superfluous efforts just for interest.
Three studies revealed participants’ multiple but unnecessary
exploration behaviors when specific uncertainty was removed
and boredom did not dominate. Those GL effects were partly
interpreted as the effect of I-type curiosity. Such superfluous
behaviors on one hand leads to waste and loss, and on the other
hand also retain the opportunity to obtain novel gains. To make
the best use of advantages and bypass the disadvantages of similar
“irrational” behaviors, we hope for more efforts on uncovering
the veil of them.
DATA AVAILABILITY STATEMENT
All datasets generated for this study are included in the
article/Supplementary Material.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Research Ethics Board of Zhejiang University.
The patients/participants provided their written informed
consent to participate in this study.
AUTHOR CONTRIBUTIONS
JZ, HC, PL, and MS conceived and designed the experiments,
performed the data analysis and results interpretation. PL
and XL performed the experiments and collected the data,
drafted the manuscript. JZ, HC, and MS provided the critical
revisions. All authors contributed to the article and approved the
submitted version.
FUNDING
This research was supported by the National Natural
Science Foundation of China (Grant Nos. 31871096 and
31600881), awarded to JZ and the Fundamental Research Funds
for the Central Universities.
ACKNOWLEDGMENTS
We also thank the reviewers for helpful comments on earlier
versions of this manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2020.01381/full#supplementary-material
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Frontiers in Psychology | www.frontiersin.org 12 July 2020 | Volume 11 | Article 1381
