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Decision-making in children in the Hungry Donkey Test: A behavioral
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Article in Developmental Neuropsychology · November 2017
DOI: 10.1080/87565641.2017.1404065
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Developmental Neuropsychology
ISSN: 8756-5641 (Print) 1532-6942 (Online) Journal homepage: http://www.tandfonline.com/loi/hdvn20
Decision-making in children in the Hungry Donkey
Test: A behavioral analysis
Diana Milena Cortes-Patino, Paulo Sergio Dillon Soares-Filho & Maria Rocio
Acosta-Barreto
To cite this article: Diana Milena Cortes-Patino, Paulo Sergio Dillon Soares-Filho & Maria Rocio
Acosta-Barreto (2017): Decision-making in children in the Hungry Donkey Test: A behavioral
analysis, Developmental Neuropsychology, DOI: 10.1080/87565641.2017.1404065
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Date: 27 November 2017, At: 11:52
DEVELOPMENTAL NEUROPSYCHOLOGY
https://doi.org/10.1080/87565641.2017.1404065
Decision-making in children in the Hungry Donkey Test: A
behavioral analysis
Diana Milena Cortes-Patino, Paulo Sergio Dillon Soares-Filho, and Maria Rocio Acosta-Barreto
Psychology Department, Universidad de San Buenaventura sede Bogotá, Programa de Psicología, Bogotá, Colombia
Downloaded by [190.131.242.66] at 11:52 27 November 2017
ABSTRACT
This study analyzed choice behavior in the Hungry Donkey Task, with a
focus on learning trajectories and the stability of preference, in 100 children
of different ages (8–9, 10–11, 12–13, 14–15, and 16–17 years old). The
results indicated that (a) learning occurred as the task progressed, (b)
early adolescents performed poorly during the task, and (c) most of the
participants did not reach the stability criterion during the task. The present
study suggests that decision-making in children and adolescents varies with
age, and that the inclusion of an operant-based approach (e.g., the use of
stability criterion) may improve methods for evaluating decision-making in
children.
Introduction
Decision-making is a prerequisite for human adaptation to environmental conditions that commonly requires deliberation about future consequences with some level of uncertainty. The process
of deliberation involves cognitive aspects, sensitivity to reward and punishment contingencies, and
emotional signs that are related to possible outcomes (Tom, Fox, Trepel, & Poldrack, 2007).
The Iowa Gambling Card Task (IGCT) is extensively used to study decision-making because it
resembles real-life decisions in the face of uncertain outcomes. In this task, the participant makes
successive choices among four decks of cards (A, B, C and D) with different outcomes (wins or losses
of money). Reward and punishment contingencies are arranged by the experimenter such that the
choice of decks that provide larger wins in each trial results in a long-term net loss (A and B,
disadvantageous), whereas choosing decks that provide smaller wins in each trial results in a longterm net win (C and D, advantageous). Decks also differ in the frequency and magnitude of
punishment delivered: choice of decks B and D yields infrequent but larger losses, meanwhile choice
of decks A and C yields frequent but small losses (Bechara, Damasio, Damasio, & Anderson, 1994).
Performance in this task allows the assessment of fundamental aspects of decision-making in
complex situations, such as sensitivity to reward, sensitivity or aversion to punishment, or sensitivity
to future outcomes (Bechara, Tranel, & Damasio, 2000).
Studies that have used the IGCT have reported that healthy individuals gradually learn to
maximize net winnings. At the beginning of the task, they choose the decks that provide larger
wins in each trial. Over the course of the task, they switch their preference to the decks that provide
smaller wins in each trial but a long-term net win. Patients with brain injury in the ventromedial
prefrontal cortex (vmPFC) do not change their preference to maximize net wins; instead, they
continue to choose the disadvantageous decks for an immediate gain, regardless of the frequency of
CONTACT Diana Milena Cortes-Patino
[email protected]
Universidad de San Buenaventura sede Bogotá, Programa
de Psicología, Carrera 8H #172 −20, Bogotá, Colombia.
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/HDVN.
© 2017 Taylor & Francis
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2
D. M. CORTES-PATINO ET AL.
punishment that is delivered (Bechara, Damasio, Tranel, & Damasio, 1997; Bechara et al., 2000;
Bechara, Tranel, Damasio, & Damasio, 1996).
Developmental studies have shown that children and adolescents behave similarly to patients with
vmPFC lesions. They appear to behave based on more immediate rewards and neglect the long-term
consequences (Blair, Colledge, & Mitchell, 2001; Ernst et al., 2003). These findings are consistent
with anatomical and physiological data that show that development of the prefrontal cortex
continues until late adolescence (Caviness, Kennedy, Richelme, Rademacher, & Filipek, 1991;
Durston et al., 2001; Sowell, Delis, Stiles, & Jernigan, 2001; Spear, 2000). Poor performance of
children and adolescent on the IGCT has been explained based on behavioral processes that include
low sensitivity to loss, failure to anticipate the outcomes of each choice, and hypersensitivity to
immediate rewards. Most of these studies have relied on physiological measures to examine the
sensitivity to reward and loss based on emotional autonomic reactions, such as skin conductance and
heart rate (Burnett, Bault, Coricelli, & Blakemore, 2010; Crone & van der Molen, 2007). An
alternative approach to explain the poor performance of children and adolescents on the IGCT is
to focus on learning about contingencies.
Based on operant approaches, learning in choice situations involves gradual changes in preference
for the available options. Initially, participants are expected to switch among the alternatives to learn
contingencies. As individuals learn, their preference becomes biased toward one of the alternatives
until exclusive preference develops for the best alternative. Thus, changes in preference or stable
preference for an alternative is a measure of learning that can inform about the decision-making
process. Although performance in the IGCT depends on learning about contingencies, it is noteworthy that learning indexed as stable preference has not been used as a measure of this task in
children.
Recently, Bull, Tippett, and Addis (2015) used a more behavior analytic approach to analyze
performance on the IGCT in healthy adults relying on stable preference as a measure of learning.
These authors used an extended 200-trial version of the IGCT. In addition to assessing regular
measures of the IGCT, these authors evaluated the participant’s learning rates using a stability
criterion for the choice behavior. In general terms, choice behavior was considered stable when
participants maintained strong preference for a deck or a pair of decks for at least three blocks (60
trials). The results suggested that healthy adults who performed poorly on the IGCT had slower
learning rates than other participants. When considering only the first 100 trials (like in most
studies), most of the participants did not exhibit stable preference for any deck, implying that
learning about the contingencies was not yet complete. In fact, nearly 30% of the participants
performed similarly to patients with vmPFC damage. When the task was extended to 200 trials,
most of the participants reached the stability criterion, and only 16% of the participants did not
reach the criterion. The authors concluded that different learning rates observed in healthy adults
could be key to understanding interindividual variability that is found in decision-making tasks.
Despite the importance of considering individual learning rates in the IGCT, most studies with
healthy children have prioritized group analysis of net score by age (a score that collapses the overall
proportion of choices between advantageous and disadvantageous decks). Steingroever, Wetzels,
Horstmann, Neumann, and Wagenmakers (2013) suggested that regular analysis of the IGCT that
aggregates the proportion of choices for good and bad decks might lead to the loss of potentially
diagnostic information. Therefore, an analysis of performance that focuses on individual learning
trajectories, specifically changes in choice that lead to stable preference, might be a good index of the
decision-making process in children and adolescents.
Research goal
Developmental studies have consistently shown that children and adolescents perform poorly than
adults in decision-making tasks. These studies have explained the disadvantageous performance of
children based on deficits on aspects of decision-making such as sensitivity to reward or punishment
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DEVELOPMENTAL NEUROPSYCHOLOGY
3
(Burnett et al., 2010; Crone & van der Molen, 2007). Despite the importance of these studies, they (a)
do not evaluate learning about contingencies as indexed by stable preference, which may confound
the effects of differential learning rates with deficits in decision-making and (b) use aggregated data
of performance in the IGCT, which might lead to a loss of information about the sources of
variability between groups of age (Steingroever, Wetzels, Horstmann, Neumann, & Wagenmakers,
2013).
As asserted by Bull et al. (2015) a different and more complete approach to understand variability
in decision-making could focus on individual learning about contingencies. Taking this into account,
the present study aims to evaluate decision-making of healthy children and adolescents using an
operant approach. In addition to measures based on group analysis used in most experiments, we
analyzed individual learning trajectories in an extended version of the IGCT to describe learning
rates and the stability of preference as an index of learning in children of different ages.
Decision-making was assessed in children and adolescents using an appropriate age version of the
IGCT, the Hungry Donkey Task (Crone, Bullens, van der Plas, Kijkuit, & Zelazo, 2008; Crone,
Bunge, Latenstein, & van der Molen, 2005; Crone & van der Molen, 2004, 2007). This task utilizes
the same principle as the four-deck IGCT with regard to the arrangement of rewards and punishments. The participants had to choose among four doors to feed a hungry donkey. The first two
doors (A and B) provided higher wins (i.e., more apples for the donkey) in the short term but
resulted in greater long-term losses. The other two doors (C and D) provided smaller gains in each
trial but were more advantageous in the long term. Similar to the IGCT, children are expected to
switch their preference to doors C and D to maximize net wins. A 200-trial version of the task was
used to assess changes in preference and choice stability.
Methods
Participants
One hundred children (N = 100) from Bogotá, Colombia, participated in the study. Five age groups
comprised the sample: 8–9, 10–11, 12–13, 14–15, and 16–17 years old (n = 20 per group, 10 of each
sex). The participants were students who were between the third year of primary school and last year
of high school. Children and adolescents were recruited by contacting schools of the urban area of
Bogotá. Participants were selected with the help of their teachers. In order to be part of the study,
children primary caregivers had to sign an informed consent form and complete a questionnaire that
included a pre-, peri-, and postnatal history of the participants. All participants were reported to be
health. Children reported with impaired arm motor function, uncorrected visual problems or
learning disabilities were excluded from the study.
Hungry donkey task
The experimental task was based on Crone and van der Molen (2004). In this version of the task,
four doors (A, B, C, and D) were displayed horizontally on a computer screen. Below the doors, on
the left side of the monitor, an image of a sitting donkey was presented. A gain and loss counter was
presented on the right. The name of the participant and a timer were displayed above the doors.
The participants were told to assist the donkey with obtaining apples by clicking on the doors (see
detailed instructions below). By clicking the doors with the computer mouse, the participants could
gain or lose apples, depending of the programmed outcome for each door. A click on a door
produced replacement of the door with a white square that showed the number of apples gained or
lost for 2.0 s. The total number of apples that were won and lost was updated in the counter and
continuously presented on the bottom right of the screen. The computer recorded subsequent
responses after presenting the outcome and updating the counter, with a minimum interresponse
time of approximately 1 s. The participants had no maximum time to click one of the doors.
4
D. M. CORTES-PATINO ET AL.
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All of the participants performed a 200-trial version of the task. Each door had a different
programmed outcome of gains and losses. Doors A and B had a higher programmed amount of
gains but also a higher programmed amount of losses. Doors C and D had a lower programmed amount
of gains but also a lower programmed amount of losses. Clicking door A always produced a gain of four
apples but produced a loss of 10 apples with a 30% probability and a loss of 12 and eight apples with a
10% probability each. Clicking door B also always produced a gain of four apples but produced a loss of
50 apples with a 10% probability. Clicking door C always produced a gain of two apples but produced a
loss of two apples with a 30% probability and a loss of one and three apples with a 10% probability each.
Clicking door D always produced a gain of two apples but produced a loss of 10 apples with a 10%
probability. As a function of the different programmed gains and losses, if the participant selected door
A or B 10 times, then he/she would receive a sum of 40 apples but would lose a sum of 50 apples,
resulting in a net score of −10 apples. If the participant selected door C or D 10 times, then he/she would
gain a sum of 20 apples and would lose 10 apples, resulting in a net score of +10 apples.
Procedure
The experiment was conducted individually in a room with multiple computers. A total of 15
participants could complete the task at the same time. The participants sat in front of a 15-inch
computer screen at a distance of approximately 75 cm. Before beginning the task, the participants
were presented the following instructions:
“This task is called the Hungry Donkey Game. In this task, you need to help the donkey find the largest number of
apples. You will find the apples behind each of the four doors. However, you will find that behind each door there is
a different number of apples that you can gain or lose. Your task is to find the largest number of apples that you can
feed the hungry donkey! You can choose one of the doors by clicking the door with the mouse’s left bottom. TRY
TO PICK UP AS MANY APPLES AS YOU CAN BY CHOOSING THE CORRECT DOORS! GOOD LUCK!”
After reading the instructions and receiving an explanation of the task from the experimenter, the
participants began the task. The task took 5–10 min to complete per participant.
Data analysis
The net score was calculated as the difference between net apples gained/lost by choosing the good
doors (C + D) and bad doors (A + B; i.e., [C + D] – [A + B]). Group data were analyzed using
mixed-model analysis of variance (ANOVA). When significant violation of sphericity was found in
the ANOVA, the degrees of freedom (df) were reduced according to the Greenhouse-Geisser
correction. Follow-up comparisons were conducted using one-way ANOVA and the Tukey
Honestly Significant Difference (HSD) test. Statistical significance for all of the analyses was
determined using an α = .05.
Additional individual analyses of learning trajectories and preference stability were performed
based on the criteria of Bull et al. (2015). A participant was considered to have a strong preference
when the proportion of choices for one door was at least .5 (i.e., responses for that door comprised
50% of the total responses) and the preference for this door was .25 greater than the proportion of
choices for any other door. A participant’s performance was considered stable when he/she maintained a strong preference over three consecutive blocks of trials. For participants who did not reach
the above criterion (i.e., did not show a strong preference for a particular door over three consecutive
blocks), a criterion of preference for a pair of doors was used. The participants were considered to
have a preference for a pair of doors when the preference for both doors was at least .75 and the
difference between pairs of doors was less than .25.
DEVELOPMENTAL NEUROPSYCHOLOGY
5
Results
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Effects of block on net score
Figure 1 shows the net scores for the different blocks, both overall (Figure 1(a)) and separately by age
group (Figure 1(b)). A mixed ANOVA (block × age) revealed a significant main effect of block on
net score (F5.7, 546.6 = 95.64, p < .001, η2 = .5). Significant linear and quadratic trends were found
(F1,95 = 511.87, p < .001, η2 = .84, and F1,95 = 34.39, p < .001, η2 = .26, respectively), suggesting that
performance improved as the blocks increased and had a curvilinear shape. Pairwise comparisons
showed significant differences in net scores for most of the blocks (p < .05). Only the differences
between blocks 6 and 7, 7 and 8, and 8 and 9 did not reach statistical significance.
A significant main effect of age on net score was also found (F4,95 = 10.27, p < .001, η2 = .3).
Pairwise comparisons between age groups showed that the 12–13 year old participants had lower net
scores than all of the other age groups (all p < .001). The difference between the 8–9 and 10–11 year
old age groups was also significant (p = .05). Differences between others age groups was not
statistically significant. The block × age interaction was not statistically significant (F36, 360 = 1.15,
p = .25), however, examination of simple main effects of age on net score for each block showed that
the effect of age was significant for the blocks 5 (F4,95 = 2.47, p = .04, η2 = .09); 7 (F4,95 = 4.75,
p = .002, η2 = .16]; 8 (F4,95 = 3.01, p = .02, η2 = .11); 9 (F4,95 = 6.62, p < .001, η2 = .21), and 10
(F4,95 = 6.42, p < .001, η2 = .21). Pos hoc analysis showed that in those blocks differences were
significant between 12–13 years old and all the other age groups (p < 0.05)
The analysis of net scores separately for trials 1–100 (epoch 1) and trials 101–200 (epoch 2)
showed an effect of epoch on net score (F1,95 = 315.81, p < .001, η2 = .76). The mean net scores by
epoch are shown in Table 1. For participants of all ages, performance improved during epoch 2,
indicating that the number of participants who presented impaired performance diminished when
Figure 1. Plots of group data (n = 100) in the Hungry Donkey Task. (A) Mean net score by blocks of 20 trials each. (B) Mean net
scores by blocks of trials according to age. Error bars represents the 1þ
standard errors.
Table 1. Mean net scores (expressed as a proportion of the number of trials), variability statistics, and proportion of participants
who met the criterion for impaired performance per epoch 1 (trials 1–100), epoch 2 (trials 101–200), and the entire study (trials
1–200).
Net scoresa
Trials
1–100
101–200
1–200
a
M
−.02
.09
.02
SD
.04
.05
.02
Min
−.17
−.07
−.03
Max
.11
.34
.08
Range (Difference between
Min and Max)
.28
.41
.11
Net scores expressed as a proportion of the number of trials.
Proportion of participants with
impaired performance (net score < 0)
.72
.05
.19
6
D. M. CORTES-PATINO ET AL.
200 trials were conducted. The standard deviations (relative to the means) indicated that net scores
had high interindividual variability.
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Door preference
Figure 2 shows door preference (proportion of choices for each door) separately for trials 1–100
(epoch 1) and trials 101–200 (epoch 2). The mixed ANOVA (epoch × door × age) revealed a main
effect of door on preference (F2.6,251.7 = 142.3, p < .001, η2 = .6). An epoch × door interaction was
also found (F2.3,224.1 = 216.18, p < .001, η2 = .69). Planned comparisons showed that preference for
the different doors changed during epochs 1 and 2. Preference for doors A and B decreased during
epoch 2 (both p < .001), and preference for doors C and D increased during epoch 2 (both p < .001).
Figure 3 shows the mean difference in the proportion of choices between the advantageous and
disadvantageous doors for the different age groups. Zero values indicate no differences in preference
between the advantageous and disadvantageous doors. Values greater than zero indicate preference
for advantageous doors. Values less than zero indicate preference for disadvantageous doors. During
epoch 1, most of the age groups preferred the disadvantageous doors. In epoch 2, this preference was
reversed, in which all of the age groups preferred the advantageous doors. The mixed ANOVA (age
× gain × frequency × epoch; 5 × 2 × 2 × 2) was used to evaluate whether the proportion of choices
for each door was affected by the gain (advantageous vs. disadvantageous) or the frequency of
punishment (low vs. high frequency of punishment). A main effect of gain on choice was found
(F1,95 = 123.32, p < .001, η2 = .565), with no effect of frequency of punishment (p > .05), suggesting
that the participants preferred advantageous doors. The frequency × gain interaction was significant
(F1,95 = 28.4, p < .001, η2 = .23), suggesting that the effect of frequency of punishment was higher for
disadvantageous choices. No frequency of punishment × age interaction was found.
A significant Gain × Age interaction was found (F4,95 = 11.36, p < .001, η2 = .32). Post hoc
comparisons revealed differences between the 12–13 year old participants and the other age groups
in the preference for the advantageous and disadvantageous doors, suggesting that preference for the
advantageous doors was less pronounced in this age group (p < 0.05 for all comparisons). (Table 2).
Analysis by epoch, showed that age had a significant effect on preference for advantageous doors
during both epochs, (F4,95 = 3.67, p = .008, η2 = .13 for Epoch 1, and F4,95 = 10.17, p < .0001, η2 = .3
for Epoch 2). Pos hoc analysis comparing preference for advantageous doors in the different ages
showed that 12–13 years old exhibited less preference for advantageous doors than the other age
Figure 2. Mean proportion of choices by door for each 100-trial epoch.
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DEVELOPMENTAL NEUROPSYCHOLOGY
7
Figure 3. Mean difference in the proportion of choices for the advantageous and disadvantageous doors ([C + D] – [A + B]) by age
per epoch.
Table 2. Age differences in the proportion of choices for each door by epoch.
Door A
Disadvantageous
(High gain
High frequency)
Age
8–9 years
10–11 years
12–13 years
14–15 years
16–17 years
Epoch
Epoch
Epoch
Epoch
Epoch
Epoch
Epoch
Epoch
Epoch
Epoch
1
2
1
2
1
2
1
2
1
2
.24
.05
.23
.06
.29
.12
.23
.09
.22
.06
(.1)
(.05)
(.04)
(.03)
(.08)
(.07)
(.06)
(.07)
(.04)
(.04)
Door B
Disadvantageous
(High gain
Low frequency)
.26
.16
.32
.2
.32
.28
.27
.17
.31
.17
(.08)
(.07)
(.08)
(.1)
(.07)
(.08)
(.07)
(.06)
(.05)
(.06)
Door C
Advantageous
(Low gain High frequency)
.29
.42
.28
.42
.24
.33
.3
.42
.3
.42
(.1)
(.07)
(.06)
(.07)
(.1)
(.07)
(.05)
(.05)
(.05)
(.05)
Door D
Advantageous
(Low gain
Low frequency)
.2
.35
.14
.35
.13
.26
.17
.32
.15
.32
(.07)
(.07)
(.05)
(.07)
(.03)
(.08)
(.06)
(.07)
(.05)
(.07)
The data are presented as means (standard deviation).
groups mainly during the second epoch (p < .001 for all comparisons). During the first epoch
differences were significant only between 8–9 years old and 12–13 years old (p = .007) and
12–13 years old and 14–15 years old (p = .02).
Stability analysis (individual learning trajectories)
Figure 4(a) shows the proportion of participants who reached the stability criterion by block. Only
11% of the participants met the stability criterion by trial 100. By trial 200, this percentage increased
to 36%. A 64% of the participants did not meet the criterion by the end of the task. Figure 4(b) shows
the preference for each door and each pairwise combination. Although most of the participants did
not exhibit stable preference, those who developed a preference did so for the advantageous pair
(C + D, 18% of the participants).
Figure 5 shows the proportion of participants who developed stable preference by age. The
12–13 year old group had the lowest proportion of participants who reached the criterion (1%
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8
D. M. CORTES-PATINO ET AL.
Figure 4. (A) Proportion of participants who reached the stability criterion by door. (B) Proportion of participants who reached the
stability criterion by block. The dotted line indicates the division between the first (0–100) to the second (101–200) epoch.
Figure 5. Proportion of participants who reached the stability criterion by age.
DEVELOPMENTAL NEUROPSYCHOLOGY
9
[two participants]). For the remaining age groups, the percentage of participants who met the
criterion approached 40%.
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Discussion
Based on the procedure of Bull et al. (2015), the present study analyzed operant choice behavior in
children using an extended version of the Hungry Donkey Task with 200 trials. In addition to the
analysis of net scores, performance was analyzed in terms of learning and the stability of choice
behavior. The study yielded three main conclusions. First, for all of the age groups, learning occurred
as the task progressed. Second, an inverted U-shaped curve was observed between age and net score,
with early adolescents performing worse than children and late adolescents in the decision-making
task. Third, most of the participants did not develop stable preference for an option.
We found an important effect of block on net score. In all of the age groups, performance
improved as the number of blocks increased, indicating that learning about contingencies occurred
in all of the age groups. Previous studies reported similar results (Crone et al., 2005; Overman et al.,
2004). However, other studies reported that the effect of block was absent in younger children and
adolescents, in which 8–10 and 11–13 year old children continued to make disadvantageous choices
as the number of blocks increased (i.e., a flat learning rate) and late adolescents and adults improve
their performance as the number of blocks increases (Crone & van der Molen, 2007: Hooper,
Luciana, Conklin, & Yarger, 2004). Such variability between studies is common in research that
utilizes decision-making tasks and indicates the importance of considering procedural details when
assessing decision-making processes (Areias, Paixão, & Figueira, 2013; Bull et al., 2015; Fernie &
Tunney, 2006).
One procedural variation that consistently affects results in the Hungry Donkey Task is the number
of trials. In the present study, extending the number of trials to 200 (in contrast to the 100-trial version
that is typically used) considerably decreased the number of participants who would be considered to
exhibit impaired performance. In fact, preference for the advantageous doors switched between epoch 1
and epoch 2. During epoch 1, most of the participants preferred the disadvantageous doors or showed
indifference in the choice. During epoch 2, the participants in all of the age groups preferred the
advantageous doors. The latter result is similar to Bull et al. (2015) who studied healthy adults.
In the present study, we found a quadratic relationship between age and performance in the
Hungry Donkey Task. Compared with children and late adolescents, early adolescents (12–13 years
old) exhibited the lower net scores. The fine-grained analysis indicated that performance in early
adolescents was unaffected by the frequency of punishment. Instead, their choice was affected by the
magnitude of gains, in which they consistently chose disadvantageous decks that resulted in more
immediate gains but long-term losses. We also found that young adolescents had the fewest
participants who met the stability criterion. Our results are consistent with previous studies that
used the IGCT to evaluate decision-making in children. For example, Smith, Xiao, and Bechara
(2012) assessed performance in the IGCT in children of a similar age range (8–17 years old).
Compared with children of other ages, 10–13 year old children exhibited the poorest performance,
and 12 year old children had the lowest net scores.
The pattern of choosing disadvantageous options despite the higher probability of losing is
consistent with the literature that shows that early adolescents engage in risky behaviors that are
associated with a high potential of harm (Hooper et al., 2004; Laird, Pettit, Bates, & Dodge, 2003;
Lejuez, Aklin, Zvolensky, & Pedulla, 2003). For example, Burnett et al. (2010) assessed risk-taking
behavior in children, adolescents, and adults using a behavioral economic task whereby the participants chose between two gambles that differed in risk (i.e., outcome variance). The proportion of
risky choices followed an inverted U-shaped pattern peaking in young adolescents. Steinberg et al.
(2008) reported similar age-related risk-taking behavior. They assessed risk-taking based on two
dimensions: sensation seeking and impulsivity. Sensation seeking but not impulsivity followed an
inverted U-shaped pattern.
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10
D. M. CORTES-PATINO ET AL.
Deficits in decision-making by early adolescents have been explained by a potential developmental imbalance between two neural systems: an affective reward-driven system (Nucleus
Accumbens and Ventral Tegmental Area) and a cognitive control system that taps into executive
areas (dorsolateral and ventromedial prefrontal cortices; Defoe, Dubas, Figner, & Van Aken, 2015).
Several studies have shown that activity of the reward system peaks during early adolescence, which
is consistent with hyperresponsiveness to rewards and greater risk-taking behavior in this age group
(Galván, 2013; Somerville, Jones, & Casey, 2010; Spear, 2000). In contrast to reward-related brain
areas, areas that modulate cognitive control are not fully developed until the early 20s. The
immaturity of these areas in adolescents would make cognitive and emotional control more difficult.
Based on the imbalance model, the differential development of reward and cognitive control areas
would result in heightened sensitivity to appetitive (i.e., greater and more immediate) payoffs in early
adolescents (Smith et al., 2012). The poor performance by early adolescents (12–13 years old) in the
present study is consistent with the imbalance model. Nonetheless, our data do not allow us to
reliably conclude whether sensitivity to reward is the mechanism that is responsible for differences
among early adolescents and children or adults.
As highlighted by Bull et al. (2015), sensitivity to reward can be quantified using behavioral
models of choice. Typically, operant behavioral research evaluate choice behavior using a concurrent-schedule procedure in which participants have two or more options and each one produce
consequences with different probabilities. The studies have shown that the relative preference to one
of the options approximately matches the relative distribution of the consequences (Davison &
Baum, 2000; Herrnstein, 1961; Lie, Harper, & Hunt, 2009; Takahashi & Shimakura, 1998; Zars &
Zars, 2009). The mathematical description of this regularity was named as The Matching Law
(Davison & Mccarthy, 1988; Grace & Hucks, 2013; Herrnstein, 1970).
One of the most important developments of the matching law is that the mathematical description includes parameters that allow to quantify sensitivity to reinforcement/punishment and bias,
that might explain deviations from the matching law (Baum, 1974; Lie & Alsop, 2009; Mcdowell,
1989; see Grace & Hucks, 2013; for review). Recently, Bull et al. (2015, Experiment II) used the
Matching Law model to quantify the sensitivity to reward magnitude, sensitivity to punishment
frequency, and sensitivity to punishment magnitude in participants who performed poorly on the
IGCT. They used the Auckland Card Task (ACT), a task that resembles concurrent schedules, to
evaluate choice behavior and estimate the sensitivity to reinforcers and distribution of punishment.
Participants who performed poorly on the IGCT exhibited significantly lower sensitivity to the
magnitude (but not frequency) of reward in the IGCT. To evaluate sensitivity to reward and
punishment in children and adolescents, further studies should adapt the approach that is used in
operant behavioral studies with adult participants (Lie et al., 2009).
Finally, our results showed that more than half of the participants (63%) did not reach the
stability criterion. Our analysis considered that the choice was stable when the participants showed a
strong preference for a door (or pair of doors) over three consecutive blocks. Previous studies that
used a similar stability criterion with healthy adults showed that the percentage of participants who
did not reach the stability criterion approached 30% (e.g., Bull et al., 2015).
The high proportion of children who presented no stability might indicate that learning was still
occurring for most of the participants. The presence of fast and slow learners has been a recurrent
finding in studies of decision-making (Buelow, Okdie, & Blaine, 2013). Although one assumption is that
slow rates of learning are related to atypical decision-making, studies with healthy adults have shown
that slow learners represent nearly 30% of the participants, suggesting that slow learning rates do not
necessarily indicate impaired decision-making ability but rather reflect normal individual variability in
learning rates (Bull et al., 2015). Taking into account developmental differences between children and
adults, it could be expected that the proportion of slow learners in children might be higher and might
vary by age. Further studies should explore this issue by (a) evaluating the effect of increasing the
number of learning trials on performance in children of different ages, (b) incorporating measures of
DEVELOPMENTAL NEUROPSYCHOLOGY
11
stable preference in the task, and (c) measuring sensitivity based on stable preference using operant
choice models in order to explore the relation between slow learning rates and sensitivity to reward.
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Limitations and future research perspectives
The present study has limitations. First, we did not control the attention of the participants during
the task. We used an extended version of the Hungry Donkey Task (200 trials), and poor attentional
control may have interfered with learning in the task. Second, the relatively small number of
participants per group (20 per age) did not allow a statistical analysis of the profiles of “slow” and
“fast” learners. Further investigations could form subgroups to determine which characteristics are
associated with the rate of learning in the Hungry Donkey Task.
Notwithstanding these limitations, the present study incorporated new forms of data analysis in
the Hungry Donkey Task to provide additional information regarding the pattern of decisionmaking in children and adolescents. We suggest that future experiments interested in evaluate
decision-making in children and adolescents should consider using an operant approach by incorporating measures of stable preference and adjusting procedures allowing to proper evaluating
sensitivity based on stable preference using operant choice models.
Acknowledgments
We thank to Daniel Arguilles for the programing support during the data analysis.
Funding
The elaboration of this paper was supported bySan Buenaventura research grant (Nº 012-2017). The authors declare
no conflicts of interest.
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