Behavioral andBrain Functions

Furukawa et al. Behav Brain Funct (2017) 13:13
DOI 10.1186/s12993-017-0131-6

Open Access

SHORT REPORT

Behavioral sensitivity of Japanesechildren with and without ADHD to changing
reinforcer availability: an experimental study
using signal detection methodology
Emi Furukawa1* , Shizuka Shimabukuro1, Brent Alsop2 and Gail Tripp1*

Abstract
Background: Most research on motivational processes in attention deficit hyperactivity disorder (ADHD) has been
undertaken in Western Europe and North America. The extent to which these findings apply to other cultural groups
is unclear. The current study evaluated the behavioral sensitivity of Japanese children with and without ADHD tochanging reward availability. Forty-one school-aged children, 19 diagnosed with DSM-IV ADHD, completed a signaldetection task in which correct discriminations between two stimuli were associated with different reinforcement
frequencies. The response alternative associated with the higher rate of reinforcement switched twice during the task
without warning.
Findings: Both groups of children developed an initial bias toward the more frequently reinforced response alternative. When the reward contingencies switched the response allocation (bias) of the control group children followed
suit. The response bias scores of the children with ADHD did not, suggesting impaired tracking of reward availability
over time.
Conclusions: Japanese children with ADHD adjust their behavioral responses to changing reinforcer availability lessthan their typically developing peers. This is not explained by poor attention to task or a lack of sensitivity to reward.
The current results are consistent with altered sensitivity to changing reward contingencies identified in non-Japanese samples of children with ADHD. Irrespective of their country of origin, children with ADHD will likely benefit frombehavioral expectations and reinforcement contingencies being made explicit together with high rates of reinforcement for appropriate behaviors.
Keywords: ADHD, Positive reinforcement, Signal detection, Japan
Background
Attention deficit hyperactivity disorder (ADHD) is acommon neurodevelopmental disorder characterized byelevated levels of inattention, hyperactivity and/or impulsivity that impair daily functioning. Upwards of 5% of elementary school-age children are diagnosed with ADHD[1], 50–65% of whom continue to meet diagnostic criteria*Correspondence: furukawa@oist.jp; tripp@oist.jp
1
Human Developmental Neurobiology Unit, Okinawa Institute of Scienceand Technology Graduate University, 1919‑1 Tancha, Onna, Okinawa
904‑0495, Japan
Full list of author information is available at the end of the article

for the disorder in adulthood [2, 3]. Attention deficit
hyperactivity disorder has been identified across countries and cultures [4], with similar prevalence rates andsymptom presentation, including in Japan [5, 6].
The disorder is known to be highly heritable (see [7] forreview), however its precise etiology remains uncertain.
Altered sensitivity to reward has been hypothesized tocontribute to symptoms of ADHD (e.g., [8–13]). Evidence
continues to accumulate in support of altered motivational processes in ADHD, although findings across studies are not entirely consistent, highlighting the complex
nature of motivational processes [14]. At the behavioral© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Furukawa et al. Behav Brain Funct (2017) 13:13

level the most consistent finding is that children with
ADHD are more likely to choose small immediate overlarger delayed rewards compared with typically developing children (see [15] for a review). A number of studies report the performance enhancing effects of reward,
on a range of cognitive tasks, are larger in children with
ADHD than controls (see [16, 17] for reviews). Resultsfrom some studies also suggest the performance of children with ADHD is more similar to that of controls whenreinforcement for correct responses is continuous ornear continuous (e.g., [12, 18–22]).
Alsop et al. [16] recently demonstrated that children
with ADHD are less sensitive to changing reward availability than typically developing children, when rates
of reinforcement are low and changes are not signaled.
Their findings are consistent with earlier reports thatthe behavior of children with ADHD does not match thereinforcement contingencies operating as well as that ofcontrols [23, 24]. These findings may help explain the difficulty children with ADHD have in adapting their behavior to shifting situational demands in everyday life, whereexpectations for their behavior often change without
warning (e.g., playing outside vs. sitting in a restaurant)
and appropriate behavior is not continuously reinforced.
To date, the majority of studies assessing motivational
processes in children with ADHD have been undertaken
in Western countries. Only a small number of studies
have evaluated reward sensitivity in children with ADHD
from other cultural groups (e.g., South Africa [25], Japan[26], China [27]). Aase et al. [25] described reinforcers
as being less efficient in establishing stimulus control
in children with ADHD compared with controls in the
Limpopo district of South Africa. Masunami et al. [26]reported Japanese children with ADHD paid more attention to reward than their typically developing peers while
completing a variant of the Iowa Gambling Task. Most
recently, Yu et al. [27] demonstrated Chinese children
with ADHD show a stronger preference than controls forsmaller immediate rewards over larger delayed rewards
in temporal discounting and choice delay paradigms.
The results of these studies suggest the association
between ADHD and altered reward processing is nota Western cultural phenomenon. However, further
research is needed to confirm the presence of altered
motivational processing in children with ADHD acrosscultures, preferably using the same paradigms so thatfindings can be directly compared. This becomes increasingly important as Western-styled behavior management
programs are progressively adopted in non-Westerncountries [28].
In the current study we evaluate the sensitivity ofJapanese children with and without ADHD to unequal
frequency of reward and to changing reinforcement

Page 2 of 9availability, using the signal detection task described by
Alsop et al. [16]. Participating children were required to
identify which of two stimuli were presented by making the appropriate response on a two-button response
panel. The task began with one type of correct response
being rewarded four times as often as the other. Suchunequal arrangement of reward typically produces a
response bias (preference) for the more frequently reinforced alternative [29, 30]; that is, the task examines theeffects of reward on subsequent behavior, by exploiting
instances of uncertainty regarding the correct discrimination between stimuli. In this study the ratio of available reinforcers for correct discriminations on the two
response alternatives switched twice during the task.
Based on the findings of Alsop et al. [16], and the available evidence of altered reward sensitivity in children
with ADHD in non-Western cultures [25–27], we predict the response allocation of Japanese children with
ADHD will match reward contingencies less closely thanthat of their typically developing peers. Such an outcome
would be of particular clinical relevance in Japan, where
conformity is highly valued and the ability to adapt ones
behavior to situational demands is especially important,
but where behavioral expectations are seldom madeexplicit [31].
The overall sensitivity of the two groups of Japanesechildren to the asymmetric reward distribution is moredifficult to predict. Praise is used sparingly in Japan [32–34] and how this experience will impact the children’s
sensitivity to reward availability in the current task isunclear. Any differences in the reinforcement sensitivity of typically developing Japanese children, comparedto their Western peers, would need to be considered ininterpreting the data from the children with ADHD.

Methods
Participants

Data from 41 children, 19 meeting DSM-IV diagnostic
criteria for ADHD (all boys) and 22 typically developing children (50.0% boys) living in Okinawa, Japan, areincluded in the study. Within the ADHD group, 10 children were diagnosed with inattentive type, one withhyperactive/impulsive type and eight with combined
type ADHD. Four children were prescribed stimulant
medication, which was discontinued for at least 24 hprior to study participation. Table 1 presents the sample
characteristics.
Inclusion criteria for the study were estimated IQ
scores above 70, participation in regular education
classes1, normal or corrected vision, no past or current
1

Special education classrooms or day services in Japan tend to serve children with significant functional impairments.

Furukawa et al. Behav Brain Funct (2017) 13:13

Page 3 of 9

Table 1 Participant characteristics
Control

ADHD(n = 22)(n = 19)

Mean

sd

Range

Mean

sd

Range

Age (months)a122.73

20.48

86–149107.7414.78

89–137

Estimated IQ

98.00

12.29

84–121104.47

14.91

77–126

Boys, n (%)

11 (50%)19 (100%)

Stimulant medication (concerta), n–

4

Subtype, n: inattentive/hyperactivity_impulsivity/combined–

10/1/8Comorbidity, n: oppositional defiant disorder3

Language/Tic/asperger ­disorderb

1/1/2

a
b

The mean age was significantly higher for the control than ADHD group (t(39) = 2.65, p < .05)

Given that DSM-5 allows comorbid diagnosis of ASD with ADHD, children demonstrating symptoms of DSM-IV asperger disorder were included in the study ifthey satisfied other inclusion criteria. However, those demonstrating symptoms consistent with autistic disorder were excluded from the study due to accompanying
cognitive impairments

head injury, neurological disorder or psychosis. Comorbid conditions were allowed if these inclusion criteria
were met. Children in the ADHD group were recruited
through a university research clinic, where they completed multi-method, multi-informant research diagnostic assessments. Data from semi-structured diagnostic
interviews (K-SADS-PL, disruptive behavior disordersection) [35, 36], parent and teacher completed rating
scales for ADHD symptoms (SNAP) [37, 38], and observations of the child’s behavior were used to make a clinical diagnosis of ADHD. Parent and teacher completed
broadband rating scales (CBCL/TRF) [39–42] and background questionnaires screened for other behavioral andemotional problems, neurological and medical conditions. Cognitive functioning was assessed with the
WISC-III or WISC-IV [43–46].
Children were required to display six or more symptoms of inattention and/or hyperactivity/impulsivity inat least one setting, evidence of symptoms in a second
setting, and functional impairment. Symptoms were
not summed across informants. Assessments were carried out by a team including a US-licensed, Japanesespeaking clinical psychologist (EF), and clinicians withadvanced counseling or other relevant degrees, all fluent in Japanese and experienced in working with children with ADHD. Control group children were recruited
through invitation letters sent home to parents throughpublic schools. These children completed an abbreviated
IQ assessment (WISC-III Vocabulary/Block Design).
Their parents and teachers completed the behavior rating scales, which were used to rule out the presence of
ADHD or other significant behavioral or emotional disorders. Those demonstrating fewer than four symptoms
of inattention or hyperactivity/impulsivity were included.

Parent and teacher reports were reviewed for other inclusion criteria.
Experimental task

The experimental task and procedure used is describedin detail in Alsop et al. [16] and illustrated in Fig. 1. After
instructions and a short demonstration of the task, children indicated whether there were “more red” or “moreblue” characters in a checkerboard pictured on a computer screen (10 × 10 arrays containing either more redor more blue faces in a ratio of 54:46), using a two-button
response panel. They were advised that correct responses
only sometimes earn rewards. A multi-element reinforcement system was used to maximize reward effectiveness: the message “Well done!” and an animated cartoon
appearing on screen, verbal praise from the examiner,and colored tokens placed in a clear plastic container
next to the child. Following incorrect and non-rewarded
correct responses, the screen was blank and the experimenter remained silent. All children received a prize at
the end of the session irrespective of their performanceon the task.
Successive blocks of eight trials contained an equal
number of each array type, randomized within blocks.
The computer determined quasi-randomly which correct
responses were reinforced. At the start of the session,correct identifications of one stimulus (“more blue”) wererewarded four times more often than correct identifications of the other stimulus (“more red”). After the child
had received 20 rewards with this distribution, the computer reversed the contingencies and correct identifications of “more red” stimuli were reinforced four times asoften. After another 20 rewards, the original reward distribution was reinstated, with the game ending after theFurukawa et al. Behav Brain Funct (2017) 13:13

Page 4 of 9

Fig. 1 Experimental task timeline

children received a further 20 rewards. Each successive
block of ten reinforcements contained eight reinforcements for correct identification of one array type and twofor correct identification of the other, randomized withineach block.
Data collection and analysis

Three measures of performance were calculated for eachchild: median response time, discriminability between
stimuli (logd) i.e., accuracy, and response bias (logb) i.e.,the systematic preference for the more frequently reinforced alternative [29].
Discriminability between the stimuli was calculated by
the equation:

RedCorrect
BlueCorrect1·
log d = log
2
BlueIncorrect RedIncorrect
and response bias by:
BlueCorrect RedIncorrect1·
log b = log
2
BlueIncorrect RedCorrectwhere ­BlueCorrect denotes the number of correct
responses following presentations of the “more blue”
array, ­RedIncorrect denotes the number of incorrect
responses following presentation of the “more red” array,and so forth. Response bias scores were calculated relative to the response alternative that was reinforced more
frequently during the initial phase. Mean response discriminability and response bias and median responsetime scores were calculated for all trials completed toreceive reinforcements 1–10 and 11–20 (reward distribution 4:1), reinforcements 21–30 and 31–40 (reward
distribution 1:4), and reinforcements 41–50 and 51–60(reward distribution 4:1).
Mean scores for response bias, discriminability andmedian response time for the six blocks (2 blocks each forthe initial, reversal and reinstatement phases) were analyzed with SPSS GLM. Mixed ANOVA was conducted to
examine the main effects of Block (i.e., changes in bias,
discriminability or response time over the 6 blocks) andGroup (i.e., the difference in bias, discriminability or
response time between the ADHD and control group),
and the interaction effects. The bias scores were expected
to change with the two shifts in the contingency schedule, therefore polynomial contrasts examined quadratic
and linear trends in the bias scores over the six blocks,
separately for the ADHD and control groups. Post-hoc
pairwise comparisons were performed when significant
univariate effects were observed. There were differencesin the mean age and IQ, and gender, between the ADHD
and control groups. Although the difference in IQ scores
did not reach statistical significance, given the small sample size, IQ was entered as a covariate along with age andgender, when the two groups were compared on the performance measures.

Results
Response bias

Mixed ANOVA indicated a significant univariate
Block × Group interaction effect (F(5, 180) = 2.64,

Furukawa et al. Behav Brain Funct (2017) 13:13

p < .05) for bias, controlling for age, IQ and gender(Fig. 2). For both groups, their bias scores were significantly different from zero during the first block(p < .001). The bias scores of the two groups were significantly different from one another during blocks twoand six (p < .05, Bonferroni correction). Two polynomial
contrasts were performed to assess quadratic and linear
trends in the bias scores over the six blocks, separately
for the control and ADHD groups. For the control group,
there were significant quadratic (F(1, 21) = 9.50, p < .01)
and linear (F(1, 21) = 12.02, p < .01) effects. Pairwise
comparisons indicated a significant difference in the bias
scores for the second and forth blocks (the second halfof the initial and reversal phases; p < .01, Bonferroni correction). Mean bias scores decreased across both blocksof the reversal phase, increasing during the first block of
the reinstatement phase. For the ADHD group, only thelinear trend was significant (F(1, 18) = 31.68, p < .001).
For this group the bias score for the first block was significantly larger than the bias scores for the two reinstatement blocks (p < .05).
Discriminability

Mixed ANOVA indicated a significant main effect ofGroup (F(1, 36) = 10.81, p < .01) after controlling forage, IQ and gender (Fig. 3). Discriminability scores weresignificantly higher for the control group for the second

Page 5 of 9through sixth blocks of the task (p < .05, Bonferroni
correction).
Response time

Mixed ANOVA indicated a trend toward a main effect ofGroup (F(1, 36) = 3.50, p < .10), after controlling for age,
IQ and gender (Fig. 4). Pairwise comparisons show thatchildren in the ADHD group responded more quickly
than those in the control group during the first block of
the initial phase (p < .05, Bonferroni correction).

Discussion
The current study assessed the behavioral sensitivity ofJapanese children with ADHD and their typically developing peers to unequal frequency of reward and changing reinforcement contingencies using signal-detection
methodology. Both groups of children initially developed
a preference (response bias) for the more frequently reinforced alternative, demonstrating their behavioral sensitivity to the asymmetric reward distribution.
As the task progressed the response allocation of thetypically developing children reflected the availabilityof reward, i.e., as the reward contingencies changed thechildren’s response bias scores changed with them. The
overall response pattern that emerged was very similar tothat reported by Alsop et al. [16] for their New Zealandcontrol subjects. Cross-culturally, typically developing

Fig. 2 Mean response bias scores and standard errors for the ADHD and control groups during the initial, reversal and reinstatement phasesFurukawa et al. Behav Brain Funct (2017) 13:13

Page 6 of 9

Fig. 3 Mean discriminability scores and standard errors for the ADHD and control groups during the initial, reversal and reinstatement phases

Fig. 4 Median response time and standard errors for the ADHD and control groups during the initial, reversal and reinstatement phasesFurukawa et al. Behav Brain Funct (2017) 13:13

children respond similarly to changing reinforcer availability, altering their response allocation to match theprevailing reinforcement contingencies. In both studies,
the lower response bias scores seen in the reinstatement
phase of the task likely reflect the children’s accumulated
experience of reward over the initial and reversal phasesof the task (i.e., their “reinforcement history” on the task).
The similarity of the control group findings from New
Zealand and Japan suggests the signal detection task isrelevant to children from different cultural groups.
The response bias pattern of the Japanese children with
ADHD suggests their ability to track changing reward
availability is impaired. After developing a significant
response bias toward the more frequently reinforced
alternative during the first half of the initial phase, thechildren’s bias score dropped to near zero. This was followed by a slight increase in bias during the first halfof the reversal phase, then a slow decrease across theremainder of the task. These data suggest a loss of behavioral control by the asymmetric reward distribution.
Although the exact response pattern differed, Alsop et al.
[16] also reported impaired tracking of reinforcement
contingencies in New Zealand and American children
with ADHD. In the current study, it is not clear why thechildren stopped responding to the asymmetric reward
distribution so early in the task. However, the failure of
the children to match their response allocation to thereinforcement contingencies operating is consistent withthe difficulty children with ADHD have adapting their
behavior when setting demands change (e.g., “often runs
about or climbs in situations where it is inappropriate”[47]).
Poorer behavioral tracking of the reinforcement contingencies by the ADHD group does not reflect a lackof sensitivity to reinforcement. As noted, both groupsof children developed a clear bias toward the more frequently reinforced response alternative early in the task.
Nor does it reflect difficulty completing the signal detection task. Although discriminability scores were significantly lower for the children with ADHD, possiblyreflecting less attention to the stimuli, their mean accuracy exceeded 70% across all phases of the task. The children with ADHD initially responded more quickly on thetask than controls, the latter’s response times decreasing
to a similar level with time on task. While the response
speed of the ADHD group may have impacted their discriminability, it does not show a consistent relationshipwith bias scores, arguing against impulsive responding
leading to poorer tracking of reward availability.
While the mechanisms underlying the poor alignment between reward availability and response allocation in children with ADHD remains to be determined,
the current study provides further evidence that children

Page 7 of 9with ADHD have difficulty adapting their behavior tosituational demands when levels of reinforcement are
relatively low and contingency changes are not signaled.
We previously suggested such a disparity might arise
as children with ADHD have difficulty adjusting theirinternal representations of reinforcement contingencies
under low rates of partial reinforcement [8, 48]. Additional studies are needed to confirm the robustness ofthe observed effect together with theoretical and empirical research to address its underlying neurobiology. Theory-driven computational models could be developed toaddress questions regarding how prior experience withreward interacts with the prevailing contingencies toinfluence subsequent actions in those with and without
ADHD. Alongside such theoretical work, neuroimaging
and animal studies might examine frontostriatal activity
patterns in response to changing contingencies during a
conditional-discrimination task such as the signal-detection paradigm.
The overall similarity of the results to those reported
by Alsop et al. [16], lends weight to the current findings. The study does, however, have limitations, including small sample sizes. The final ADHD sample is smaller
than expected given the number of families who initiallyvolunteered to participate. Compared with many countries, ADHD has a shorter history of recognition in Japan[5, 49]. This coupled with the stigma attached to mental health concerns may have increased the severity andcomplexity of the problems seen in children whose families volunteered for the study. Families of children experiencing pronounced difficulties may have been more
willing to participate in research offering comprehensive
multi-method, multi-informant assessments. The result
being the exclusion of a number of children who did notmeet the study criteria.
Groups in the current study were not well matched forage, IQ or gender, in part reflecting the challenges of datacollection. As potential confounds, all three variables
were included as covariates in the analyses, resulting in
more conservative probability estimates, but no change
in the findings. Importantly, within the control group
there were no significant gender differences for age, IQ,
response bias or discriminability.2 The assessment anddiagnostic practices followed international guidelines
and care was taken to rule out any psychiatric or neurodevelopmental disorders amongst the control group.
We are therefore confident of group membership in our
sample. In addition, the rate and pattern of comorbidity
in the ADHD sample argues against other disorders
explaining the current findings.
2

The mean response time was faster for females throughout the task (F (1,20) = 4.69, p < .05).

Furukawa et al. Behav Brain Funct (2017) 13:13

The signal detection task used in the current study has
good ecological validity. Just as in children’s every day
experiences, not all instances of correct behavior (i.e.,
successful discriminations) were rewarded and changes
in reward availability were unsignaled [16]. These conditions may be especially relevant to the study of ADHDin Japan, where the use of praise is less normative [32–34]. As a more homogeneous population, societal roles
are strongly tied to an individual’s identity and behavior[50], with children and adults expected to know the rules
without them always being explicitly stated. Behavioral
sensitivity to situational demands would be important inmaintaining group harmony.
The present findings suggest similarities between Japanese and Western children with ADHD that go beyond
symptom presentation. The results are consistent with
reduced sensitivity to unsignaled contingency changes
identified in non-Japanese samples of children with
ADHD. Parents and teachers of Japanese children with
ADHD should be advised of the importance of reducing
ambiguity regarding behavioral expectations, irrespective of normative practices. Although Japanese culture has
many daily rituals marking the transition from one setting to another (e.g., all students stand up and bow as theteacher enters the classroom), children with ADHD would
benefit from explicit information regarding behavioral
expectations in different settings and when these change.
Furthermore, it should not be assumed that children with
ADHD will carry this information forward to new environments, or that a failure to adapt behavior to situational
demands is evidence of misbehavior. For children with
ADHD, frequent, immediate and consistent reward forappropriate behavior is recommended across cultures.

Conclusion
The current results provide additional evidence that children with ADHD do not track reward contingencies as
consistently as typically developing children, when rates
of reinforcement are low and the contingencies change
without warning. The similarity of the findings, acrossstudies and cultures, using the same paradigm and diagnostic procedures, offers further evidence for alteredreward sensitivity playing a role in the pathophysiologyof ADHD. Altered motivational processes may be a common, defining characteristic of the disorder expressedsimilarly across cultures. These findings also suggest
recommendations to alert children with ADHD to situational expectations, and the consequences of theiractions, are relevant across cultures. Further experimental research in non-Western countries, across therange of neurodevelopmental and psychiatric disorders,is important to confirm the cross-cultural continuity ofcore deficits and underlying neurobiology. Such efforts

Page 8 of 9

will help refine behavioral and pharmacological treatments and ensure their appropriateness for different cultural groups.
Authors’ contributions
GT and BA designed the original paradigm, EF and SS supervised data collection, GT, BA and EF analyzed and interpreted the data. All authors contributed to the writing of manuscript. All authors read and approved the final
manuscript.
Author details
1
Human Developmental Neurobiology Unit, Okinawa Institute of Scienceand Technology Graduate University, 1919‑1 Tancha, Onna, Okinawa 904‑0495,
Japan. 2 Department of Psychology, University of Otago, Dunedin, New
Zealand.
Acknowledgements
The authors thank all the children, families, and teachers who took part, as wellas members of the OIST Children’s Research Center in Japan who assisted withassessment and data collection.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding author on reasonable request.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Ethical approval for the study was obtained from the Human Subjects
Research Review Committee at the Okinawa Institute of Science and Technology (OIST) Graduate University in Japan. Participating parents, teachers, andchildren were volunteers and provided written consent.
Funding
The study was supported by the internal subsidy funding from the OIST
Graduate University.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Received: 10 June 2017 Accepted: 18 September 2017

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