Journal of Cognitive Psychology

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Transferability and sustainability of task-switching
training in socioeconomically disadvantaged
children: a randomized experimental study
Kean Poon , Mimi S. H. Ho , Patrick C. K. Chu & Kee-Lee Chou
To cite this article: Kean Poon , Mimi S. H. Ho , Patrick C. K. Chu & Kee-Lee Chou (2020)
Transferability and sustainability of task-switching training in socioeconomically disadvantaged
children: a randomized experimental study, Journal of Cognitive Psychology, 32:8, 747-763, DOI:
10.1080/20445911.2020.1839082
To link to this article: https://doi.org/10.1080/20445911.2020.1839082

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JOURNAL OF COGNITIVE PSYCHOLOGY
2020, VOL. 32, NO. 8, 747–763
https://doi.org/10.1080/20445911.2020.1839082

Transferability and sustainability of task-switching training in
socioeconomically disadvantaged children: a randomized experimental
study
Kean Poon

a

, Mimi S. H. Hoa, Patrick C. K. Chua and Kee-Lee Chou

b

a

Department of Special Education and Counselling, The Education University of Hong Kong, Tai Po, Hong Kong; bDepartment of
Asian and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong
ABSTRACT

ARTICLE HISTORY

Most empirical studies on executive function (EF) and socioeconomically disadvantaged
children are largely restricted to understanding and confirming the link between them.
The current study extended previous research by examining the near-transfer of taskswitching training to a structurally similar new switching task, and far-transfer to a
structurally dissimilar EF task (i.e. inhibition and working memory) and academic
performance through a 4-week task-switching training programme using a
randomised experimental design. Fifty low SES primary school students (Mage = 8.6
years, SD = 0.7) in Hong Kong participated in pretest, posttest, and a one-year followup on task-switching performance, EF, and academic performance in three core
subjects. Results showed that compared to an inactive control group, the app training
group showed improved performance in similar (untrained task-switching) and
dissimilar (inhibition) EF tasks even at one-year follow-up. No training effect was
found at posttest and in academic performance. Potential implications for future
research in task-switching training are discussed.

Received 22 March 2020
Accepted 14 October 2020

1. Introduction
Numerous studies (e.g. Chung et al., 2016; Lawson
et al., 2018) have investigated the links between
family socioeconomic status (SES) and early cognitive
development in children. Socioeconomic adversity
was negatively related to developmental outcomes
in children such as language acquisition, nonverbal
reasoning, general intelligence, and executive function (EF) (de Rosa Piccolo et al., 2016). A wide range
of conceptual frameworks of child development
have examined the mediating role of early cognitively-enriching environments in the relationship
between poverty and children’s early outcomes.
Socioeconomically disadvantaged children are less
likely to be exposed to early childhood cognitive
stimulation or experience high-quality early education and social interaction than their socioeconomically advantaged counterparts (Haft & Hoeft, 2017;
Lugo-Gil & Tamis-LeMonda, 2008). This finding
CONTACT Kean Poon
keanpoon@gmail.com
Lo Ping Road, Tai Po, NT, Hong Kong

KEYWORDS

Executive function; poverty;
switching cost; taskswitching; working memory

suggests that children with low SES are likely to
have reduced social and cognitive functioning
before their crucial school years, as well as later in
life. Among the cognitive disparities related to SES,
disparities in EF appear to be larger than those in
other cognitive abilities. EF refers to a set of cognitive
abilities to perform tasks that guide goal-directed
behaviours (Ackerman & Friedman-Krauss, 2017;
Zelazo et al., 2013). Many researchers have posited
cognitive flexibility, inhibition, and working memory
as the main components of EF (Diamond, 2012).
Previous findings suggest that poverty and
poverty-related stressors are associated with lower
EF ability and compromised self-regulation in
young children (e.g. Blair et al., 2011). A study of
Chinese-American children between six and nine
years of age (Chen et al., 2015), showed that SES
was significantly associated with effortful control,
which overlapped with EF (Bridgett et al., 2013).

Department of Special Education and Counselling, The Education University of Hong Kong, 10

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/
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altered, transformed, or built upon in any way.

748

K. POON ET AL.

SES was also significantly associated with behavioural
regulation (heads-toes-knees-shoulders) in children
aged six to nine in Taiwan (Wanless et al., 2013),
which involves inhibitory control, cognitive flexibility,
and working memory (Eisenberg et al., 2004). Poon
and Ho (2014) also revealed a significant association
between family income and EF, specifically interference control, among 117 Hong Kong Chinese adolescent boys comprising both delinquents and nondelinquents. Structural and functional brain
imaging suggests a link between SES and the thickness and surface area of the prefrontal cortex of children (Lawson et al., 2013), a direct and accurate
measurement confirming SES disparities in EFs.

1.1. EF and academic skills
EFs have been found to be significantly related to an
array of academic skills in children (Fuhs et al., 2015;
Sektnan et al., 2010; Wanless et al., 2011). For instance,
the domain of working memory is widely recognised
as being critical to reading and mathematical abilities
(Birgisdóttir et al., 2015; Peng et al., 2016; Peng et al.,
2018). Similar results were found for inhibition associated with vocabulary and reading, ranging from
medium to highly significant (Clark et al., 2013;
Clark et al., 2014; Miller et al., 2013; Nesbitt et al.,
2013; Viterbori et al., 2015; Wanless et al., 2011;
Welsh et al., 2010). Meta-analysis suggests that cognitive flexibility is associated with performance in both
mathematics and reading (Yeniad et al., 2013). Given
these findings, which show a strong link between EF
and academic capability, it is natural to assume that
EF improvement might have a beneficial impact on
the academic performance of children.

1.2. EF plasticity
Many studies suggested that EF can be enhanced
with appropriate training (Buitenweg et al., 2012;
Diamond, 2012; Diamond et al., 2007; Karbach &
Schubert, 2013; Lillard & Else-Quest, 2006; MelbyLervåg & Hulme, 2016; O’Connor et al., 2000). Knowledge concerning the malleability of EF comes from a
growing body of literature which shows that EF can
be trained through extensive practice requiring the
prefrontal cortical circuits (cf. Hebb, 1949; Hsu et al.,
2014; Wass et al., 2012; Zhang et al., 2019). According to research into neural systems, EF develops
rapidly in early childhood (Blair, 2016) and shows
continuous development well into young adulthood (Diamond, 2013).

Moreover, research suggested that EF skills
develop significantly when children begin to participate in formal learning environments, as the brain
becomes more adaptive to change and functionally
responsive to the environment, suggesting that this
might be a period of high malleability. For example,
in a study into the malleability of EF in childhood,
Zhang et al. (2019) showed that a first-grade schooling group outperformed a pre-school group in
working memory and inhibitory control at baseline.
After receiving a 15-minute computerised training
four times per week for five weeks, the children in
the pre-school group not only showed improvements in the trained tasks but were also able to
achieve a performance comparable to the participants in the school group both at posttest and
follow-up. This provides evidence for the malleability of EF, with even a short-term intervention facilitating the acquisition of important EF skills during
childhood.

1.3. Cognitive training and EF
Computerised training programmes have become
widespread in the training of EF. These typically
employ game tasks that specifically require one or
more EF skills (e.g. Alloway et al., 2013; Bennett
et al., 2013). One training regime commonly used
to test executive control functioning is the taskswitching paradigm (e.g. Anguera et al., 2013;
Karbach & Kray, 2009; Kiesel et al., 2010; Kray et al.,
2012; Kray & Fehér, 2017; Minear & Shah, 2008;
Pereg et al., 2013; White & Shah, 2006; Zinke et al.,
2012). The task-switching training paradigm is a
process-based computerised training regime
which specifically trains the ability to switch
rapidly between two or more cognitive demands.
The core EF in this switching ability are inhibition,
cognitive flexibility, and working memory (e.g.
Koch et al., 2010) and enables adaptation to a
rapidly changing environment (Miyake et al.,
2000). It allows for separate measures of executive
control processes within the same experimental
paradigm (Kiesel et al., 2010). Although there are a
few variants, most studies have the participants
switch between two tasks (A and B) within a
mixed-task block and perform only one of the
tasks within a single-task block. Two types of costs
are thus determined: the switching cost resulting
from reconfiguration of the task set and overcoming
the interference effect between the previous and
current task execution (e.g. Kiesel et al., 2010;

JOURNAL OF COGNITIVE PSYCHOLOGY

Monsell, 2003); and the mixing cost resulting from
resolving conflict or stimulus ambiguity during
mixed-task trials or differences in arousal level or
working memory load between single-task and
mixed-task blocks (e.g. Rubin & Meiran, 2005).
Both the switching cost (e.g. Berryhill & Hughes,
2009; Karbach & Kray, 2009; Kray et al., 2012; Kray
& Fehér, 2017; White & Shah, 2006; Zinke et al.,
2012) and mixing cost (e.g. Minear & Shah, 2008;
Soveri et al., 2013; Strobach et al., 2012) can, in
general, be reduced through practicing taskswitching. However, there are studies of traininginduced transfer effects which have yielded
mixing findings; the near-transfer effects do
reduce switching and/or mixing costs for
untrained switching tasks (e.g. Anguera et al.,
2013; Karbach & Kray, 2009; Kray et al., 2012;
Kray & Fehér, 2017; Minear & Shah, 2008; Pereg
et al., 2013; White & Shah, 2006; Zinke et al.,
2012) but far-transfer effects were negligible. For
instance, von Bastian and Oberauer (2013)
reported no evidence for far-transfer effects on
reasoning, inhibition, and working memory after
cue-based task-switching training in adults. Zhao
et al. (2018) discovered no far-transfer effects on
untrained EF tasks and general IQ after extensive
task-switching training in adults, while shortlived transfer effects on working memory were
found for young children. On the contrary,
Karbach and Kray (2009) studied children aged
from seven to nine years, asking them to
perform two simple decision tasks (A and B) and
switch between them at a specific signal. In task
A, children were asked to indicate whether the
object presented on the computer was a fruit or
a vegetable and in task B to indicate whether
the object was small or large. Although the training duration was short with four training sessions
once a week over four weeks, the results showed
that those who received training performed significantly better, not only in similar (untrained)
switching tasks, but also in dissimilar EF
domains, such as inhibition, verbal and visualspatial working memory, and reasoning. A similar
task-switching training paradigm to Karbach and
Kray (2009), Kray et al. (2012) was used and this
confirmed a training-induced transfer effect on
children with attention deficit hyperactivity disorder (ADHD), with children in an intervention
group not only showed improvement in taskswitching performance, but also in inhibitory
control and verbal working memory.

749

1.4. Task-switching training and academic
achievement
Several studies examined the role of task-switching
abilities in the context of academic achievement,
with most reporting positive correlations (e.g. Arán
Filippetti & Richaud, 2017; Cantin et al., 2016; Clark
et al., 2010; Gerst et al., 2017). Critically, two metaanalyses have confirmed this association. Firstly,
Yeniad et al. (2013) reported that switching was
positively correlated achievement in mathematics
(r = .26) and reading (r = .21). Secondly, Jacob and
Parkinson (2015) reported similar results, but with
a slightly higher estimation of effect sizes, i.e.
switching ability was positively correlated with
achievement in mathematics (r = .34), and reading
(r = .32). A recent study examined the association
between task switching ability and academic
achievement among 10th grade Chinese adolescents (N = 221) suggested that task-switching
ability was positively related to achievement in
sciences and mathematics but not humanities (Li
et al., 2020).
Considering that the aforementioned findings
between task-switching and academic abilities are
consistent, it is assumed that task-switching training
has the potential to improve academic performance. While there is plenty of evidence of a transfer
to structurally similar tasks and other EF domains, as
well as the child’s reasoning ability (e.g. Karbach &
Kray, 2009; Kray et al., 2012; Liu et al., 2015), the
transfer of skills to academic abilities has not been
established as research has mostly focused on computerised working memory training.
Lastly, some studies have examined the role of
individual differences in baseline performance in
EF training success. Past evidence showed that individuals with low EF benefitted most from the intervention, resulting in compensation effects (e.g.
Bherer et al., 2008; Cepeda et al., 2001; Karbach
et al., 2015; Karbach et al., 2017; Zinke et al., 2014).
Hence, research into whether EF could be enhanced
or even whether the EF training effect could be
transferred to other areas for low SES children
would be extremely meaningful and provide valuable information for early intervention.

1.5. Research gaps and study objectives
Although consistent research findings on the
relationships between EF and socioeconomically
disadvantaged children are available, a number of

750

K. POON ET AL.

research gaps still need to be addressed. For
instance, most of these empirical studies are
largely limited to understanding and confirming
the association between these two factors. Research
on whether EF could be enhanced through a taskswitching paradigm or whether the training effects
of EF could be transferred to other areas in a
sample of Chinese children with low SES, would
provide valuable insight into the understanding
and development of early interventions. In sum,
the main objectives of the present study were: (1)
to examine group differences in the transfer of
task-switching training to a similar new switching
task; (2) to examine the far-transfer of task-switching
training to other EF tasks including inhibition,
working memory, and academic performance in
socioeconomically disadvantaged children; and (3)
to explore the sustainability of the training effect
(immediate vs. one year after the intervention).

2. Methods
2.1. Participants
Fifty primary school students (Mage = 8.6 years, SD =
0.7) participated in the present study. This sample
size was obtained for a power of .80, a medium
effect size of .25, and an alpha level of .05 using
G*Power (Erdfelder et al., 1996). They were recruited
from primary schools in Hong Kong. All of them had
household incomes below half of the median
household income adjusted according to household
size. They had not been diagnosed with psychological disorders such as attention deficit and hyperactivity disorder, reading disability, and autism. Their
first spoken language was Cantonese, they were of
normal intelligence (≥ 80), with no suspected
brain damage, neurological, sensory, or psychiatric
problems. The present study was approved by the
Human Research Ethics Committee at the first
author’s institution. Prior to the commencement of
data collection, written consents were obtained
from all selected students and their parents.

2.2. Overview of the procedures
The current study adopted a cluster randomised
controlled design, consisting of a screening stage,
an assessment stage and an intervention stage.
During the screening stage, participants were
tested with Raven Standard Progressive Matrices
(Raven et al., 2000) as a proxy of intelligence to

rule out those with low intellectual functioning.
Demographic data including educational level and
family income were collected through questionnaires from their parents. The assessment stage consisted of pre-test, post-test and one year follow-up.
A pre-test measuring three behavioural tasks on EF
(untrained task-switching paradigm, Stroop test,
and Backward Digit Span task) was administered
individually in a pseudo-randomised order two
weeks prior to intervention stage. Participants’ performance on three core subjects (Chinese, English,
and Mathematics) was also collected through teachers’ reports as one of the pre-test measures. The
pretest was followed by four intervention sessions.
During the intervention stage, participants were
randomly assigned to two groups with 24 participants in the app training group and 26 participants
in the inactive control group. The app training
group participated in four 1-hour app training sessions on task-switching once a week over four
weeks. Participants in the inactive control group
did not receive any training during the intervention
period. After the intervention stage, a posttest and a
follow-up test were conducted a week and approximately a year after the completion of the four intervention sessions.

2.3. Training situations
The training regime was based on Karbach and
Kray’s (2009) study, which showed a reduction in
switching and mixing costs after four training sessions in children. A 7.9-inch touch-screen Apple
mini iPad was used for item presentation and
response collection. During the training, participants were asked to perform two simple decision
tasks and to switch between them due to a
specific signal. The task-switching paradigm
included both single-task and mixed-task blocks.
The single-task block contained only one of the
two tasks while in the mixed-task block, trials with
both tasks were included with the task switched
on every other trial. Each training session began
with two mixed-task practice blocks followed by
24 experimental mixed-task blocks with 17 trials
each, with the task switched in every other trial.
Trials began with a fixation cross for 1400 ms followed by the appearance of a target word. The
inter-trial interval was 25ms and feedback on their
accuracy was given after each trial. The procedure
of the untrained task-switching paradigm at the
assessment stage was similar to that of the task-

JOURNAL OF COGNITIVE PSYCHOLOGY

switching test in the pretest, posttest, and follow-up
but with some modifications concerning the objects
presented and the task instructions.

2.4. Transfer situations
Transfer of training was assessed through a pretest,
posttest, and follow-up design. This was defined as
performance improvement in two posttests
(immediate vs. one year after intervention) and
then compared to the baseline performance at
pretest. This pretest baseline measure was conducted two weeks prior to the intervention. The
contents of these tests were identical to the
pretest session and lasted 60–70 min each. All participants in the app training and inactive control
groups completed the assessments individually in
a classroom setting at schools. Participants’ performance on three core subjects (Chinese, English,
and Mathematics) was also collected through teachers’ reports as one of the pre-test measures. All
training and assessments were administered by
well-trained research assistants.

2.5. Measures
2.5.1. Untrained task-switching task
In the pretest, posttest, and follow-up tests, participants were shown one of 16 vegetables and 16
fruits of different sizes (i.e. large vs. small) in
different trials (see Figure 1). They had to categorise
the object shown as either vegetable or fruit in the

751

object categorisation task, and to categorise the
object shown as either large or small in the size categorisation task as quickly and accurately as possible. Overall accuracy rate and reaction time (for
correct trials only) were measured for the taskswitching paradigm. Mixing cost was calculated as
the mean reaction time for the performance
between single-task and mixed-task blocks. Switching cost was calculated as the difference in the
mean reaction time between the switch and nonswitch trials within mixed-task blocks. The first
trials of both single-task and mixed-task blocks
were excluded from analysis.

2.5.2. The task-switching paradigm
During the intervention stage, participants were
asked to perform two simple decision tasks and to
switch between them due to a specific signal. In
task A, they are asked to indicate the object presented was either a car or a plane and then in the
task B, they are asked to indicate the number of
object (i.e. one or two car(s)/plane(s)). Example
trials are shown in Figure 2.
2.5.3. General intellectual ability
The general intellectual ability of the participants
was examined using the Raven’s Progressive
Matrices (Raven et al., 2000). This test contained
60 items in total and each item consisted of a
visual pattern with a missing piece. Participants
had to identify the correct piece to fill in the
missing part and make the pattern intact.

Figure 1. Example trials in the task-switching paradigm in the pretest, posttest, and follow-up tests.

752

K. POON ET AL.

normative data for Chinese populations was validated by the Hong Kong Psychological Society
(2020).

Figure 2. Example trials in the task-switching test in app
training.

Participants who scored below 80 were excluded
from the current study. This criteria was used to
exclude those with poor executive function
because of low intellectual abilities.

2.5.4. Stroop color and word test (Stroop,
1935)
Inhibition plays a key role in task-switching as it is
primarily triggered by task-irrelevant and task-relevant information, in which the participants are
instructed to inhibit or control impulsive responses
(Koch et al., 2010). Inhibition was measured under
the word condition, colour condition, and colourword condition in three separate 1-minute rounds
(Stroop, 1935). In the word condition (W), participants were instructed to read the Chinese words
and name the Chinese names of colours printed in
black ink. In the colour condition (C), participants
were required to name the colours of colour
patches of red, blue, and green inks. Finally, in the
colour-word condition (CW), the name of a colour
(e.g. BLUE) was presented using a congruent
colour (e.g. blue) or an incongruent colour (e.g.
yellow) on paper in Chinese. Participants had to
name the colour, instead of reading the word on
the paper as quickly and accurately as possible. To
obtain the interference score as a measure of inhibition, the colour and word score was subtracted
from the actual number of items correctly named
in the incongruous condition using the formula:
CW–[(C × W)/(C + W)] (Golden, 1987). The word
score (W) represents the number of items (words)
completed. The colour score (C) represents the
number of items (name of colour) completed. This
scoring system referenced the standardised
version of similar test for other populations
(Golden, 1987). The Chinese version of the noncomputerised Stroop Color and Word Test with

2.5.5. Backward digit span
Working memory was measured using the Backward Digit Span subtest of the Wechsler Intelligence
Scale for Children, Third Edition [WISC-III] (Wechsler,
1981). Participants were orally presented with 18
sequences of single-digit numbers with increasing
length from two to nine (two sequences per
length), and they had to repeat the numbers in
backward order. One point was given for each completely recalled sequence, and the test was terminated after unsuccessful recalls of two consecutive
sequences.
2.5.6. Academic subjects
The academic achievement of the students was
measured by teachers’ ratings in three key learning
areas (i.e. Chinese, English, and Mathematics). Teachers chose the most suitable range of performance
level (bottom 20%, 21%−40%, 41%-60%, 61%-80%,
and top 20% in the class) for each participant and
the values were transformed to a scale from 1
(bottom 20%) to 5 (top 20%) for data analysis. This
scale has been adopted in previous research that
measured academic achievement in students in
Hong Kong (Chen, 2005, 2008).
2.6. Data analysis
An independent sample t-test and a chi-square test
were conducted to explore whether the demographic characteristics differed across the two
groups of socioeconomically disadvantaged children. A 2 (app training vs. inactive control) x 3
(pretest vs. posttest vs. follow-up) repeated
measures ANOVA was also carried out, with time
as a within-subjects factor and group as a
between-subjects factor. The dependent variables
were overall accuracy rate, reaction time, mixing
cost, switching cost, inhibition, working memory,
Chinese, English, and Mathematics.

3. Results
Table 1 summarises the demographic background
of the two groups of participants. Results from an
independent sample t-test indicated that there
were no significant group differences in age (p
= .55), intellectual ability (p = .85), paternal

JOURNAL OF COGNITIVE PSYCHOLOGY

Table 1. Demographic characteristics in app and inactive
control groups
App (n = 24)
Age
IQ

Inactive
Control (n = 26)

M

SD

M

SD

t

8.56
110.04

.41
15.46

8.67
109.19

.86
16.31

.62
.19

(Continued)

education level (p = .98), maternal educational level
(p = .06), and family income (p = .90). A chi-square
test was also conducted to examine the group
differences in gender. No significant difference
was found in two groups, χ² (1, 50) = 2.83, p = .09.
In the app training group, there were 10 males
and 14 females. In the inactive control group,
there were 17 males and 9 females.
To prevent baseline differences, participants were
matched based on their pretest performance in accuracy rate, reaction time, mixing cost, and switching
cost. There were no baseline differences between
the two groups in accuracy rate (F(1, 48) = .87, p
= .36; η2 = .02), reaction time (F(1, 48) = .62, p = .43;
η 2 = .01), mixing cost (F(1, 48) = 3.15, p = .08; η2
= .06), and switching cost (F(1, 48) = .65, p = 42; η2
= .01). Table 2 shows the descriptive statistics of the
two groups of participants in pretest, posttest, and
follow-up tests for all outcome measures.

3.1. Training data
3.1.1. Overall accuracy rate
The main effect of time (F(2, 47) = 11.72, p < .001; η 2
= .33), and the interaction effect of time x group on

753

accuracy rate (F(2, 47) = 6.14, p < .01; η 2 = .21) were
significant. The main effect of group was not significant, F(1, 48) = 1.95, p = .17, η 2 = .04. An analysis of
simple effects showed that the main effect of time
was significant for the app training group (F(2, 22)
= 11.81, p < .001; η 2 = .52), but not for the inactive
control group (F(2, 24) = .90, p = .42; η 2 = .07). Posthoc comparisons revealed that the overall accuracy
rates of the students in the app training group did
not differ between the pretest and follow-up test
(p = .08), however they had lower overall accuracy
rate in the posttest compared to the rates in the
pretest and follow-up test (p < .001) (see Figure 3).

3.1.2. Reaction time
There were significant main effect of time (F(2, 47) =
51.45, p < .001; η 2 = .69) and its interaction with
group (F(2, 47) = 7.77, p < .01; η 2 = .25) on reaction
time. No significant main effect of group was
found, F(1, 48) = 1.58, p = .21, η 2 = .03. Simple
effects analysis showed that the main effect of
time was significant for both app training group (F
(2, 22) = 30.75, p < .001; η 2 = .74) and inactive
control group (F(2, 24) = 20.38, p < .001; η 2 = .63).
Post-hoc comparisons showed that students in the
app training group had a significantly shorter reaction time in the follow-up test than in the pretest
(p < .001) and posttest (p < .001). However, their
reaction time in the posttest was longer than that
in the pretest (p < .01). Students in the inactive
control group had significantly shorter reaction
time in the follow-up test than in the pretest and
posttest (p < .001), but there was no significant

Table 2. Descriptive statistics of the app training group and inactive control group of participants in pretest, posttest, and
follow-up for all outcome.
App (n = 24)

Measures
Accuracy rate (%)
Reaction time (all trials)
Mixing cost (ms)
Switching cost (ms)
Inhibition
Working memory
Chinese
English
Mathematics

M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD

Inactive Control (n = 26)

Pretest

Posttest

Follow-up

Pretest

Posttest

Follow-up

92.61
2.78
1519.77
156.65
182.14
101.92
147.85
70.47
9.17
1.99
9.23
2.09
3.04
.91
2.50
.98
2.95
1.17

84.10
10.05
1727.97
307.64
53.82
143.44
52.29
100.59
9.96
1.64
9.67
2.24
2.92
1.02
2.50
1.32
2.95
1.05

94.19
3.41
1267.99
244.35
41.04
107.57
57.71
61.22
11.43
1.93
10.04
2.12
2.83
1.13
2.58
1.38
2.90
1.23

91.72
3.80
1563.89
229.09
132.40
96.19
128.21
98.21
9.20
2.06
9.55
2.63
3.17
1.52
2.83
1.44
3.13
1.45

90.88
5.86
1498.96
217.16
113.37
112.22
65.50
97.11
9.79
1.67
10.48
2.95
3.29
1.33
3.22
1.57
3.38
1.41

92.78
4.82
1258.54
209.19
121.30
81.77
63.98
73.26
10.13
2.11
9.80
3.63
3.29
1.23
3.17
1.47
3.38
1.41

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Figure 3. Accuracy rate in task-switching task between the app training and inactive control groups in pretest, posttest, and
follow-up. Error bars represent standard errors.

difference between the pretest and posttest (p
= .15) (see Figure 4).

3.2. Near-transfer effects
3.2.1. Mixing cost
There were significant main effect of time (F(2, 47) =
7.98, p < .01; η 2 = .25) and interaction effect of time x
group (F(2, 47) = 5.52, p < .01; η 2 = .19) on mixing
cost. The main effect of group was not significant,
F(1, 48) = 2.88, p = .10, η 2 = .06. Simple effects analysis showed that the main effect of time was significant for the app training group (F(2, 22) = 14.08, p
< .001; η 2 = .56), but not for the inactive control
group (F(2, 24) = .22, p > .05; η 2 = .02). Specifically,
students in the app training group showed that
their mixing cost was lower in posttest (p < .01)

and follow-up test (p < .001) compared to pretest.
The difference between posttest and follow-up
test for students in the app training group was not
significant (p = .67) (see Figure 5).

3.2.2. Switching cost
The main effect of time on switching cost was significant, F(2, 47) = 14.64, p < .001; η 2 = .38.
However, time did not interact with group (F(2,
47) = .50, p > .05; η 2 = .02). There was no significant
main effect of group on switching cost, F(1, 48)
= .00, p = .99, η 2 = .00. Post-hoc comparisons
revealed that the switching cost was generally
lower in posttest and follow-up test compared to
pretest (p < .001). The difference between posttest
and follow-up test was not significant (p = .91) (see
Figure 6).

Figure 4. Reaction time in task-switching task between the app training and inactive control groups in pretest, posttest, and
follow-up. Error bars represent standard errors.

JOURNAL OF COGNITIVE PSYCHOLOGY

755

Figure 5. Mixing cost in task-switching task between the app training and inactive control groups in pretest, posttest, and
follow-up. Error bars represent standard errors.

Figure 6. Switching cost in task-switching task between the app training and inactive control groups in pretest, posttest,
and follow-up. Error bars represent standard errors.

3.3. Far-transfer effects
3.3.1. Inhibition
There were significant main effect of time (F(2, 44) =
27.15, p < .001; η 2 = .55), and interaction effect of
time x group (F(2, 44) = 5.00, p < .05; η 2 = .19) on
inhibition. No significant main effect of group was
obtained, F(1, 45) = 1.19, p = .28, η 2 = .03. An analysis
of simple effects revealed that the main effect of
time was significant for both app training (F(2, 21)
= 19.24, p < .001; η 2 = .65) and inactive control
group (F(2, 22) = 7.49, p < .01; η 2 = .41). Specifically,
students in the app training group had higher inhibition in the follow-up test than in the pretest (p
< .001) and posttest (p < .001). Their posttest score
was also higher than pretest score in inhibition (p
< .05). None of the differences across the three
different time points were found for students in
the inactive control group (p = .15) (see Figure 7).

3.3.2. Working memory
The main effects of group (F(1, 47) = .21, p = .65, η 2
= .01) and time (F(2, 46) = 2.37, p = .11; η 2 = .09), as
well as the interaction effect of time x group (F(2,
46) = .90, p = .42; η 2 = .04) on working memory
were not significant (see Figure 8).
3.3.3. Academic performance in Chinese,
English, and Mathematics
The main effects of time on Chinese (F(2, 45) = .09,
p = .92; η 2 = .00), English (F(2, 44) = 1.25, p = .30; η 2
= .05), and Mathematics (F(2, 43) = .75, p = .48; η 2
= .03) subjects were not significant. The interaction
effects of time x group on these three academic
subjects also did not reach statistical significance
(Chinese = F(2, 45) = .91, p = 41; η 2 = .04, English = F
(2, 44) = .87, p = .43; η 2 = .04, and Mathematics = F
(2, 43) = .86, p = .43; η 2 = .04). There were no

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Figure 7. Differences in inhibition between the app training and inactive control groups in pretest, posttest, and follow-up.
Error bars represent standard errors.

Figure 8. Differences in working memory between the app training and inactive control groups in pretest, posttest, and
follow-up. Error bars represent standard errors.

significant main effects of group (Chinese: F(1, 46) =
1.00, p = .32, η 2 = .02; English: F(1, 45) = 2.17,
p = .15, η 2 = .05; Mathematics: F(1, 44) = .95, p = .34,
η 2 = .02).

4. Discussion
The overall aim of this study was to evaluate the
transfer effect of app task-switching training on EF
and academic achievement of socioeconomically
disadvantaged children. The current study not
only examined the transfer effect of the app training
programme but also explored the sustainability of
the training effect (immediate vs. one year after
the intervention).

4.1. Findings from the app task-switching
training group
The present findings confirmed the effectiveness of
app task-switching training for cognitive enhancement of disadvantaged children. There were two
major findings. First, the current study found evidence for a substantial transfer effect of task-switching training to a structurally similar new switching
task. The mixing cost of the app task-switching
group significantly improved at both posttest and
one-year follow-up. This finding is in line with
earlier studies examining other age groups with
switch task training (Karbach & Kray, 2009; Kray
et al., 2012; Soveri et al., 2013; Zhao et al., 2018;
Zinke et al., 2012). Mixing cost represents the

JOURNAL OF COGNITIVE PSYCHOLOGY

difference in mean reaction time between single
task and mixed task blocks. The reduction in
mixing cost signifies that students from the training
group spent less time in reconfiguring the demands
of the task and also in overcoming interference
effects in mixed-task blocks than students from
the inactive control group. There may be two
reasons for the reduction in mixing cost. Previous
studies explained that extensive cognitive training
may make a skill more automatic, and cause
changes in brain activation that are suggestive of
this automaticity (Rogers & Monsell, 1995). It is
also possible that learning across training sessions
would equip students in task-set anticipation,
which results in cognitive load reduction in the
face of task-set conflicts and stimulus ambiguity
(e.g. Jausovec & Jausovec, 2012; Olesen et al., 2004).
On the other hand, switching cost represents the
difference in non-switch and switch trials within the
mixed task blocks (Karbach & Kray, 2009). The fact
that switching cost did not reduce at a follow-up
stage is probably due to high demands made on
cognitive control induced by task uncertainty (i.e.
the absence of task cues). Unlike mixing cost that
involves sustained effects and is thought to reflect
a more global ability to maintain multiple task
sets, switching cost is attributed more to transient
control and depends on carry-over effects (Philipp
et al., 2008). This is because these affect both relevant and irrelevant task sets during the process
of overcoming interference from a previously activated task which is no longer valid. Plus, this also
involves the reconfiguration of a new task set
(Philipp et al., 2008).
Although the overall accuracy rate of the app
task-switching group dropped at the posttest, it
returned to the pretest level at one-year follow-up.
The drop-in accuracy rate at posttest might be
due to the increased cognitive load associated
with the intensive training, which possibly affected
students’ performance over time (e.g. Endres et al.,
2015). Another possible explanation might be a
speed-accuracy trade-off as participants responded
more quickly at posttest after repeated training,
but with more errors (e.g. Samavatyan & Leth-Steensen, 2009).
The second result is that the task-switching training had a beneficial effect on students’ performance
on a structurally dissimilar EF task ⍰ Inhibition. This
training effect was found even at one-year followup and was not attributed to age as no training
effect was seen in the other group. To understand

757

how far-transfer effect took place, some studies
suggested that task-switching training facilitates
attentional control, a prefrontal cortex (PFC)mediated ability that fosters knowledge and the
transfer of skills to novel contexts (Sabah et al.,
2018). Other studies suggested that contentrelated features and the intervals between training
sessions (also known as the spaced training) might
be crucial factors in producing the amount of fartransfer effect. The current study examined a fourweek training paradigm focused on the ability to
alternate between two cognitive tasks (transportation task vs. number task). Similarly, the Stroop
test contained two dimensional stimuli (i.e. name
vs. ink colour) and involved incongruent trials in
which the task-irrelevant feature has to be inhibited
(when the word does not match the ink colour).
Although there are a few important differences
(i.e. switching demand and nature of interference),
both task-switching and the Stroop test required
rapid and flexible processing of visual input,
reinforced activation of relevant task demand and
the inhibition of irrelevant information (Koch et al.,
2010). The current study provides additional
support that training-related improvement may
not be specific to structurally similar task mechanics,
it may generalize to other stimulus-response modalities that share a similar cognitive structure (Kattner
et al., 2019; Miyake et al., 2000; Zhao et al., 2018).
In fact, inhibition is closely linked to classroom
learning. Good inhibition skills make it possible for
students to sit still, pay attention, and follow rules
(Lyons & Zelazo, 2011; Marcovitch et al., 2008; Zimmerman, 2008). Inhibitory control has been
reported to be significantly weaker in poor children
in several studies (Evans & Kim, 2012; Hackman
et al., 2010; Noble et al., 2015). Future research is
needed to determine the factors that enable this
transfer. Since transfer distance is an important
factor for evaluating training programmes, it
seems that this type of task-switching training is
suitable for promoting not only one, but several
executive control abilities.
Consistent with previous research, there were no
beneficial effects on academic performance after
training. Transfer of training to academic abilities
depends on the training regime and the characteristics of the study sample (Titz & Karbach, 2014).
Some studies (e.g. Traverso et al., 2019; Weissheimer
et al., 2019) reported the far-transfer effects of EF
training to academic abilities implemented in
much longer training protocols (e.g. six sessions

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K. POON ET AL.

for two hours). It is also possible that younger children are less likely to develop their own strategy
without being trained in strategy development. In
a meta-analysis by Powers et al. (2013) on the
efficacy of intensive app training, they pointed out
that although there were significant overall effects
for all age groups, the transfer effects for the youngest group was comparatively less. In other words,
young children may require more support on strategy development and application after training
(Holmes et al., 2009; Klingberg et al., 2005). Moreover, it is possible that the training effect on strategy development needs time to take effect or may
vary depending on the level of the strategy developed by the participant (Gibson et al., 2011). Previous research suggests that such changes may
occur later, thereby requiring a long-term followup (Holmes et al., 2009).

4.2. Limitations
The present study has some limitations that must be
addressed to inform future research. Firstly, the academic performance of the students was measured
by teachers’ rating on three key learning areas (i.e.
Chinese, English, and Mathematics) using an interval
scale. Although easier to administer, these interval
scales might be less suitable for detecting subtle
effects or changes, especially in detecting a training-related improvement in academic performance.
Future studies should use standardised achievement tests to examine the training-gained improvement among groups. Secondly, it should be noted
that this present study replicated the EF tasks
based on Karbach and Kray’s (2009) study. Researchers may wish to consider opting for the gold standard of training studies – training that refers to
the most validated and most adaptive task-switching training task available for Chinese children.
Thirdly, researchers should remain cognisant of
the active control vs. inactive control groups for
the game-training studies. While an active control
group is superior to an inactive control group for
examining the effectiveness of the intervention
(Green et al., 2014; Karbach & Kray, 2009; Shawn
Green et al., 2019), the active control group is
suggested to be used when it shares the same
expectation of improvement as the experimental
group so that researchers could attribute differential
improvements to the efficacy of the intervention
(Boot et al., 2013). Mahncke et al. (2006) compared
between an active control group and an inactive

control group using a brain plasticity-based training
programme. They found no difference in memory
function, suggesting that both types of control
groups may produce similar controlling effect.
Nevertheless, researchers should be fully aware of
the design limitations, the expectation effects, and
the placebo effects in future intervention studies.
It is hoped that with better designs, future research
would provide more compelling evidence for the
effectiveness of interventions. Finally, to address
the potential role of training effects that might be
relevant for the amount of transfer produced, replicating the current findings with a larger sample of
participants is also recommended in future research.

5. Conclusions
Childhood poverty not only has negative outcomes
on child development but also leads to an economic
burden on society through reduced productivity
and output and the cost of crime – and it increases
health expenditures. To advocate for a long-term,
effective poverty prevention initiative, the current
study examined the potential of a simple app taskswitching training intervention in influencing the
cognition and academic outcomes of disadvantaged children in Hong Kong. The results of the
current study may have potential theoretical and
practical implications for future research in taskswitching training. On the theoretical side, since
the transfer distance is an important aspect for evaluating the effectiveness of the task-switching training programmes, future studies should examine to
what extent training-based EF skills transfer to
real-world applications such as improved academic
performance. On the practical side, it is recommended that the task-switching training programme has a well-refined design which uses a
larger sample should be tested. If this training programme is proven to be effective, it can be
implemented in educational settings.

Disclosure statement
No potential conflict of interest was reported by the
author(s).

Funding
This research project [project number: 2015.A5.015.15D] is
funded by the Public Policy Research Funding
Scheme from the Policy Innovation and Coordination

JOURNAL OF COGNITIVE PSYCHOLOGY

Office of the Hong Kong Special Administrative Region
Government.

ORCID
Kean Poon
http://orcid.org/0000-0002-6696-3518
Kee-Lee Chou
http://orcid.org/0000-0003-3627-9915

References
Ackerman, D. J., & Friedman-Krauss, A. H. (2017).
Preschoolers’ executive function: Importance, contributors, research needs and assessment options. ETS
Research Report Series, 1(1), 1–24. https://doi.org/10.
1002/ets2.12148
Alloway, T. P., Bibile, V., & Lau, G. (2013). Computerized
working memory training: Can it lead to gains in cognitive skills in students? Computers in Human Behavior, 29
(3), 632–638. https://doi.org/10.1016/j.chb.2012.10.023
Anguera, J. A., Boccanfuso, J., Rintoul, J. L., Al-Hashimi, O.,
Faraji, F., Janowich, J., Kong, E., ..., & Gazzaley, A (2013).
Video game training enhances cognitive control in
older adults. Nature, 501(7465), 97–101. https://doi.
org/10.1038/nature12486
Arán Filippetti, V., & Richaud, M. C. (2017). A structural
equation modeling of executive functions, IQ and
mathematical skills in primary students: Differential
effects on number production, mental calculus
and arithmetical problems. Child Neuropsychology, 23
(7), 864–888. https://doi.org/10.1080/09297049.2016.
1199665
Bennett, S. J., Holmes, J., & Buckley, S. (2013).
Computerized memory training leads to sustained
improvement in visuospatial short-term memory skills
in children with Down syndrome. American Journal on
Intellectual and Developmental Disabilities, 118(3),
179–192. https://doi.org/10.1352/1944-7558-118.3.179
Berryhill, M. E., & Hughes, H. C. (2009). On the minimization of task switch costs following long-term training.
Attention, Perception, & Psychophysics, 71(3), 503–514.
https://doi.org/10.3758/APP.71.3.503
Bherer, L., Kramer, A. F., Peterson, M. S., Colcombe, S.,
Erickson, K., & Becic, E. (2008). Transfer effects in taskset cost and dual-task cost after dual-task training in
older and younger adults: Further evidence for cognitive plasticity in attentional control in late adulthood.
Experimental Aging Research, 34(3), 188–219. https://
doi.org/10.1080/03610730802070068
Birgisdóttir, F., Gestsdóttir, S., & Thorsdóttir, F. (2015). The
role of behavioral self-regulation in learning to read: A
2-year longitudinal study of Icelandic preschool children. Early Education and Development, 26(5-6), 807–
828. https://doi.org/10.1080/10409289.2015.1003505
Blair, C. (2016). Executive function and early childhood
education. Current Opinion in Behavioral Sciences, 10,
102–107. https://doi.org/10.1016/j.cobeha.2016.05.009
Blair, C., Raver, C. C., Granger, D., Mills-Koonce, R., Hibel, L.,
& Family Life Project Key Investigators. (2011). Allostasis
and allostatic load in the context of poverty in early

759

childhood. Development and Psychopathology, 23(3),
845–857. https://doi.org/10.1017/S0954579411000344
Boot, W. R., Simons, D. J., Stothart, C., & Stutts, C. (2013).
The pervasive problem with placebos in psychology:
Why active control groups are not sufficient to rule
out placebo effects. Perspectives on Psychological
Science, 8(4), 445–454. https://doi.org/10.1177/
1745691613491271
Bridgett, D. J., Oddi, K. B., Laake, L. M., Murdock, K. W., &
Bachmann, M. N. (2013). Integrating and differentiating
aspects of self-regulation: Effortful control, executive
functioning, and links to negative affectivity. Emotion,
13(1), 47–63. https://doi.org/10.1037/a0029536
Buitenweg, J. I., Murre, J. M., & Ridderinkhof, K. R. (2012).
Brain training in progress: A review of trainability in
healthy seniors. Frontiers in Human Neuroscience, 6,
183. https://doi.org/10.3389/fnhum.2012.00183
Cantin, R. H., Gnaedinger, E. K., Gallaway, K. C., HessonMcInnis, M. S., & Hund, A. M. (2016). Executive functioning predicts reading, mathematics, and theory of mind
during the elementary years. Journal of Experimental
Child Psychology, 146, 66–78. https://doi.org/10.1016/j.
jecp.2016.01.014
Cepeda, N. J., Kramer, A. F., & Gonzalez de Sather, J. C.M.
(2001). Changes in executive control across the life
span: Examination of task-switching performance.
Developmental Psychology, 37(5), 715–730. https://doi.
org/10.1037/0012-1649.37.5.715
Chen, J. J. L. (2005). Relation of academic support from
parents, teachers, and peers to Hong Kong adolescents’
academic achievement: The mediating role of academic engagement. Genetic, Social, and General
Psychology Monographs, 131(2), 77–127. https://doi.
org/10.3200/MONO.131.2.77-127
Chen, J. J. L. (2008). Grade-level differences: Relations of
parental, teacher and peer support to academic
engagement and achievement among Hong Kong students. School Psychology International, 29(2), 183–198.
https://doi.org/10.1177/0143034308090059
Chen, S. H., Main, A., Zhou, Q., Bunge, S. A., Lau, N., & Chu,
K. (2015). Effortful control and early academic achievement of Chinese American children in immigrant
families. Early Childhood Research Quarterly, 30, 45–56.
https://doi.org/10.1016/j.ecresq.2014.08.004
Chung, K. K. H., Liu, H., McBride, C., Wong, A. M., & Lo,
J. C. M. (2016). How socioeconomic status, executive
functioning and verbal interactions contribute to
early academic achievement in Chinese children.
Educational Psychology, 37, 1–19. https://doi.org/10.
1080/01443410.2016.1179264
Clark, C. A. C., Nelson, J. M., Garza, J., Sheffield, T. D., Wiebe,
S. A., & Espy, K. A. (2014). Gaining control: Changing
relations between executive control and processing
speed and their relevance for mathematics achievement over course of the preschool period. Frontiers in
Psychology, 5, 107. https://doi.org/10.3389/fpsyg.2014.
00107
Clark, C. A., Pritchard, V. E., & Woodward, L. J. (2010).
Preschool executive functioning abilities predict early
mathematics achievement. Developmental Psychology,
46(5), 1176–1191. https://doi.org/10.1037/a0019672

760

K. POON ET AL.

Clark, C. A., Sheffield, T. D., Wiebe, S. A., & Espy, K. A. (2013).
Longitudinal associations between executive control
and developing mathematical competence in preschool boys and girls. Child Development, 84(2), 662–
677. https://doi.org/10.1111/j.1467-8624.2012.01854.x
de Rosa Piccolo, L., Arteche, A. X., Fonseca, R. P., GrassiOliveira, R., & Salles, J. F. (2016). Influence of family
socioeconomic status on IQ, language, memory, and
executive functions of Brazilian children. Psicologia
Reflexão e Crítica, 29(1), 23. https://doi.org/10.1186/
s41155-016-0016-x
Diamond, A. (2012). Activities and programs that improve
children’s executive functions. Current Directions in
Psychological Science, 21(5), 335–341. https://doi.org/
10.1177/0963721412453722
Diamond, A. (2013). Executive functions. Annual Review of
Psychology, 64, 135–168. https://doi.org/10.1146/
annurev-psych-113011-143750
Diamond, A., Barnet, W. S., Thomas, J., & Munro, S. (2007).
Preschool program improves cognitive control. Science,
318(5855), 1387–1388. https://doi.org/10.1126/science.
1151148
Eisenberg, N., Smith, C. L., Sadovsky, A., & Spinrad, T. L.
(2004). Effortful control: Relations with emotion regulation, adjustment, and socialization in childhood. In
R. F. Baumeister, & K. D. Vohs (Eds.), Handbook of selfregulation: Research, theory, and applications (pp. 259–
282). The Guilford Press.
Endres, M., Houpt, J. W., Donkin, C., & Finn, R. R. (2015).
Working memory capacity and redundant information
processing efficiency. Froniters in Psychology, 6, 594.
https://doi.org/10.3389/fpsyg.2015.00594
Erdfelder, E., Faul, F., & Buchner, A. (1996). GPOWER: A
general power analysis program. Behavior Research
Methods, Instruments, & Computers, 28(1), 1–11.
https://doi.org/10.3758/BF03203630
Evans, G. W., & Kim, P. (2012). Childhood poverty, chronic
stress, self-regulation, and coping. Child Development
Perspectives, 7(1), 42–48. https://doi.org/10.1037/
a0024768
Fuhs, M. W., Farran, D. C., & Nesbitt, K. T. (2015).
Prekindergarten children’s executive functioning skills
and achievement gains: The utility of direct assessments and teacher ratings. Journal of Educational
Psychology, 107(1), 207–221. https://doi.org/10.1037/
a0037366
Gerst, E. H., Cirino, P. T., Fletcher, J. M., & Yoshida, H. (2017).
Cognitive and behavioral rating measures of executive
function as predictors of academic outcomes in children. Child Neuropsychology, 23(4), 381–407. https://
doi.org/10.1080/09297049.2015.1120860
Gibson, B. S., Gondoli, D. M., Johnson, A. C., Steeger, C. M.,
Dobrzenski, B. A., & Morrissey, R. A. (2011). Component
analysis of verbal versus spatial working memory training in adolescents with ADHD: A randomized, controlled trial. Child Neuropsychology, 17(6), 546–563.
https://doi.org/10.1080/09297049.2010.551186
Golden, C. J. (1987). Manual for the stroop color and word
test. Stoelting Co.
Green, C. S., Strobach, T., & Schubert, T. (2014). On methodological standards in training and transfer

experiments. Psychological Research, 78(6), 756–772.
https://doi.org/10.1007/s00426-013-0535-3
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010).
Socioeconomic status and the brain: Mechanistic
insights from human and animal research. Nature
Reviews Neuroscience, 11(9), 651–659. https://doi.org/
10.1038/nrn2897
Haft, S. L., & Hoeft, F. (2017). Poverty’s impact on children
executive functions: Global considerations. New
Directions for Child and Adolescent, 158(158), 69–79.
https://doi.org/10.1002/cad.20220
Hebb, D. O. (1949). The organization of behavior: A neurophysiological theory. Wiley.
Holmes, J., Gathercole, S. E., & Dunning, D. L. (2009).
Adaptive training leads to sustained enhancement of
poor working memory in children. Developmental
Science, 12(4), F9–F15. https://doi.org/10.1111/j.14677687.2009.00848.x
Hong Kong Psychological Society. (2020). http://www.
hkps.org.hk/en/.
Hsu, N. S., Novick, J. M., & Jaeggi, S. M. (2014). The development and malleability of executive control abilities.
Frontiers in Behavioral Neuroscience, 8, 221. https://doi.
org/10.3389/fnbeh.2014.00221
Jacob, R., & Parkinson, J. (2015). The potential for schoolbased interventions that target executive function to
improve academic achievement: A review. Review of
Educational Research, 85(4), 512–552. https://doi.org/
10.3102/0034654314561338
Jausovec, N., & Jausovec, K. (2012). Working memory training: Improving intelligence-changing brain activity.
Brain & Cognition, 79(2), 96–106. https://doi.org/10.
1016/j.bandc.2012.02.007
Karbach, J., Könen, T., & Spengler, M. (2017). Who benefits
the most? Individual differences in the transfer of
executive control training across the lifespan. Journal
of Cognitive Enhancement, 1(4), 394–405. https://doi.
org/10.1007/s41465-017-0054-z
Karbach, J., & Kray, J. (2009). How useful is executive
control training? Age differences in near and far transfer of task-switching training. Developmental Science, 12
(6), 978–990. https://doi.org/10.1111/j.1467-7687.2009.
00846.x
Karbach, J., & Schubert, T. (2013). Training-induced cognitive and neural plasticity. Frontiers in Human
Neuroscience, 7, 48. https://doi.org/10.3389/fnhum.
2013.00048
Karbach, J., Strobach, T., & Schubert, T. (2015). Adaptive
working memory training benefits reading, but not
mathematics
in
middle
childhood.
Child
Neuropsychology, 21(3), 285–301. https://doi.org/10.
1080/09297049.2014.899336
Kattner, F., Samaan, L., & Schubert, T. (2019). Cross-modal
transfer after auditory task-switching training. Memory
& Cognition, 1–18. https://doi.org/10.3758/s13421019-00911-x
Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost,
K., Philipp, A. M., & Koch, I. (2010). Control and interference in task switching—A review. Psychological
Bulletin, 136(5), 849–874. https://doi.org/10.1037/
a0019842

JOURNAL OF COGNITIVE PSYCHOLOGY

Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M.,
Gustafsson, P., Dahlström, K., & Westerberg, H. (2005).
Computerized training of working memory in children
with ADHD-a randomized, controlled trial. Journal of
the American Academy of Child & Adolescent
Psychiatry, 44(2), 177–186. https://doi.org/10.1097/
00004583-200502000-00010
Koch, I., Gade, M., Schuch, S., & Philipp, A. M. (2010). The
role of inhibition in task switching: A review.
Psychonomic Bulletin & Review, 17(1), 1–14. https://doi.
org/10.3758/PBR.17.1.1
Kray, J., & Fehér, B. (2017). Age differences in the transfer
and maintenance of practice-induced improvements in
task switching: The impact of working-memory and
inhibition demands. Frontiers in Psychology, 8, 410.
https://doi.org/10.3389/fpsyg.2017.00410
Kray, J., Karbach, J., Haenig, S., & Freitag, C. (2012). Can
task-switching training enhance executive control
functioning in children with attention deficit/-hyperactivity disorder? Frontiers in Human Neuroscience, 5, 1–9.
https://doi.org/10.3389/fnhum.2011.00180
Lawson, G. M., Duda, J. T., Avants, B. B., Wu, J., & Farah, M. J.
(2013). Associations between children’s socioeconomic
status and prefrontal cortical thickness. Developmental
Science, 16(5), 641–652. https://doi.org/10.1111/desc.
12096
Lawson, G. M., Hook, C. J., & Farah, M. J. (2018). A metaanalysis of the relationship between socioeconomic
status and executive function performance among children. Developmental Science, 21(2), e12529. https://doi.
org/10.1111/desc.12529
Li, J., Zhao, Y., Zhou, S., Pu, Y., He, H., & Zhao, M. (2020).
Set-shifting ability is specifically linked to high-school
science and math achievement in Chinese adolescents.
PsyCh Journal, 9(3), 327–338. https://doi.org/10.1002/
pchj.328
Lillard, A., & Else-Quest, N. (2006). The early years:
Evaluating montessori education. Science, 313(5795),
1893–1894. https://doi.org/10.1126/science.1132362
Liu, Q., Zhu, X., Ziegler, A., & Shi, J. (2015). The effects of
inhibitory control training for preschoolers on reasoning ability and neural activity. Scientific Reports, 5(1),
14200. https://doi.org/10.1038/srep14200
Lugo-Gil, J., & Tamis-LeMonda, C. S. (2008). Family
resources and parenting quality: Links to children’s
cognitive development across the first 3 years. Child
Development, 79(4), 1065–1085. https://doi.org/10.
1111/j.1467-8624.2008.01176.x
Lyons, K. E., & Zelazo, P. D. (2011). Monitoring, metacognition, and executive function: Elucidating the
role of self-reflection in the development of selfregulation. Advances in Child Development and
Behavior, 40, 379–412. https://doi.org/10.1016/B978-012-386491-8.00010-4
Mahncke, H. W., Connor, B. B., Appelman, J., Ahsanuddin,
O. N., Hardy, J. L., Wood, R. A., Joyce, N. M., Boniske, T.,
Atkins, S. M., & Merzenich, M. M. (2006). Memory
enhancement in healthy older adults using a brain
plasticity-based training program: A randomized,
controlled study. Proceedings of the National
Academy of Sciences of the United States of America,

761

103(33), 12523–12528. https://doi.org/10.1073/pnas.
0605194103
Marcovitch, S., Jacques, S., Boseovski, J. J., & Zelazo, P. D.
(2008). Self-reflection and the cognitive control of
behavior: Implications for learning. Mind, Brain, and
Education, 2(3), 136–141. https://doi.org/10.1111/j.
1751-228X.2008.00044.x
Melby-Lervåg, M., & Hulme, C. (2016). There is no convincing
evidence that working memory training is effective: A
reply to Au et al. (2014) and Karbach and Verhaeghen
(2014). Psychonomic Bulletin & Review, 23(1), 324–330.
https://doi.org/10.3758/s13423-015-0862-z
Miller, M. R., Müller, U., Giesbrecht, G. F., Carpendale, J. I., &
Kerns, K. A. (2013). The contribution of executive function and social understanding to preschoolers’ letter
and math skills. Cognitive Development, 28(4), 331–
349. https://doi.org/10.1016/j.cogdev.2012.10.005
Minear, M., & Shah, P. (2008). Training and transfer effects
in task switching. Memory & Cognition, 36(8), 1470–
1483. https://doi.org/10.3758/MC.336.8.1470
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H.,
Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to
complex “frontal lobe” tasks: A latent variable analysis.
Cognitive Psychology, 41(1), 49–100. https://doi.org/10.
1006/cogp.1999.0734
Monsell, S. (2003). Task switching. Trends in Cognitive
Sciences, 7(3), 134–140. https://doi.org/10.1016/S13646613(03)00028-7
Nesbitt, K. T., Baker-Ward, L., & Willoughby, M. T. (2013).
Executive function mediates socio-economic and
racial differences in early academic achievement. Early
Childhood Research Quarterly, 28(4), 774–783. https://
doi.org/10.1016/j.ecresq.2013.07.005
Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E.,
Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C.
S., Libiger, O., & Schork, N. J. (2015). Family income, parental education and brain structure in children and
adolescents. Nature Neuroscience, 18(5), 773–778.
https://doi.org/10.1038/nn.3983
O’Connor, T. G., Rutter, M., Beckett, C., Keaveney, L., &
Kreppner, J. M., & English and Romanian Adoptees
Study Team. (2000). The effects of global severe privation on cognitive competence: Extension and longitudinal follow-up. Child Development, 71(2), 376–390.
https://doi.org/10.1111/1467-8624.00151
Olesen, P. J., Westerberg, H., & Klingberg, T. (2004).
Increased prefrontal and parietal activity after training
of working memory. Nature Neuroscience, 7(1), 75–79.
https://doi.org/10.1038/nn1165
Peng, P., Namkung, J., Barnes, M., & Sun, C. (2016). A metaanalysis of mathematics and working memory:
Moderating effects of working memory domain, type
of mathematics skill, and sample characteristics.
Journal of Educational Psychology, 108(4), 455–473.
https://doi.org/10.1037/edu0000079
Peng, P., Wang, C., & Namkung, J. (2018). Understanding the
cognition related to mathematics difficulties: A metaanalysis on the cognitive deficit profiles and the bottleneck theory. Review of Educational Research, 88(3), 434–
476. https://doi.org/10.3102/0034654317753350

762

K. POON ET AL.

Pereg, M., Shahar, N., & Meiran, N. (2013). Task switching
effects are mediated by working-memory management. Intelligence, 41(5), 467–478. https://doi.org/10.
1016/j.intell.2013.06.009
Philipp, A. M., Kalinich, C., Koch, I., & Schubotz, R. I. (2008).
Mixing costs and switch costs when switching stimulus
dimensions in serial predictions. Psychological Research,
72(4), 406–414. https://doi.org/10.1007/s00426-0080150-x
Poon, K., & Ho, C. S. H. (2014). Contrasting deficits on
executive functions in Chinese delinquent adolescents
with attention deficit and hyperactivity disorder symptoms and/or reading disability. Research in
Developmental Disabilities, 35(11), 3046–3056. https://
doi.org/10.1016/j.ridd.2014.07.046
Powers, K. L., Brooks, P. J., Aldrich, N. J., Palladino, M. A., &
Alfieri, L. (2013). Effects of video-game play on information processing: A meta-analytic investigation.
Psychonomic Bulletin & Review, 20(6), 1055–1079.
https://doi.org/10.3758/s13423-013-0418-z
Raven, J., Raven, J. C., & Court, J. H. (2000). Manual for
Raven’s progressive matrices and vocabulary scales.
Section 3: The Standard Progressive Matrices. The
Psychological Corporation.
Rogers, D. R., & Monsell, S. (1995). Costs of predictable
switch between simple cognitive tasks. Journal of
Experimental Psychology: General, 124(2), 207–231.
https://doi.org/10.1037/0096-3445.124.2.207
Rubin, O., & Meiran, N. (2005). On the origins of the task
mixing cost in the cuing task-switching paradigm.
Journal of Experimental Psychology: Learning, Memory,
and Cognition, 31(6), 1477–1491. https://doi.org/10.
1037/0278-7393.31.6.1477
Sabah, K., Dolk, T., Meiran, N., & Dreisbach, G. (2018). When
less is more: Costs and benefits of varied vs. fixed
content and structure in short-term task switching
training. Psychological Research, 83(7), 1531–1542.
https://doi.org/10.1007/s00426-018-1006-7.
Samavatyan, H., & Leth-Steensen, C. (2009). The time
course of task switching: A speed—accuracy trade-off
analysis. Memory & Cognition, 37(7), 1051–1058.
https://doi.org/10.3758/MC.37.7.1051
Sektnan, M., McClelland, M. M., Acock, A., & Morrison, F.
J. (2010). Relations between early family risk, child’en’s behavioral regulation, and academic achievement. Early Childhood Research Quarterly, 25(4),
464–479.
https://doi.org/10.1016/j.ecresq.2010.02.
005
Shawn Green, C., Bavelier, D., Kramer, A. F., Vinogradov,
S., Ansorge, U., & Ball, K.K., ... & Witt, C. (2019).
Improving methodological standards in behavioral
interventions for cognitive enhancement. Journal of
Cognitive Enhancement, 3(1), 2–29. https://doi.org/10.
1007/s41465-018-0115-y.
Soveri, A., Waris, O., & Laine, M. (2013). Set shifting training
with categorization tasks. PlosOne, 8(12), e81693.
https://doi.org/10.1371/journal.pone.0081693
Strobach, T., Liepelt, R., Schubert, T., & Kiesel, A. (2012).
Task switching: Effects of practice on switch and
mixing costs. Psychological Research, 76(1), 74–83.
https://doi.org/10.1007/s00426-011-0323-x

Stroop, J. R. (1935). Studies in interference in serial verbal
reactions. Journal of Experimental Psychology, 18(6),
643–662. https://doi.org/10.1037/h0054651
Titz, C., & Karbach, J. (2014). Working memory and executive functions: Effects of training on academic achievement. Psychological Research, 78(6), 852–868. https://
doi.org/10.1007/s00426-013-0537-1
Traverso, L., Viterbori, P., & Usai, M. C. (2019). Effectiveness
of an executive function training in Italian preschool
educational services and far transfer effects to pre-academic skills. Frontier in Psychology, 10, 2053. doi:doi:10.
3389/fpsyg.2019.02053
Viterbori, P., Usai, M. C., Traverso, L., & De Franchis, V.
(2015). How preschool executive functioning predicts
several aspects of math achievement in Grades 1 and
3: A longitudinal study. Journal of Experimental Child
Psychology, 140, 38–55. https://doi.org/10.1016/j.jecp.
2015.06.014
von Bastian, C. C., & Oberauer, K. (2013). Distinct transfer
effects of training different facets of working memory
capacity. Journal of Memory and Language, 69(1), 36–
58. https://doi.org/10.1016/j.jml.2013.02.002
Wanless, S. B., McClelland, M. M., Lan, X., Son, S. H.,
Cameron, C. E., Morrison, F. J., … Sung, M. (2013).
Gender differences in behavioral regulation in four
societies: The United States, Taiwan, South Korea, and
China. Early Childhood Research Quarterly, 28(3), 621–
633. https://doi.org/10.1016/j.ecresq.2013.04.002
Wanless, S. B., McClelland, M. M., Tominey, S. L., & Acock,
A. C. (2011). The influence of demographic risk factors
on child’en’s behavioral regulation in prekindergarten
and kindergarten. Early Education & Development, 22
(3), 461–488. https://doi.org/10.1080/10409289.2011.
536132
Wass, S. V., Scerif, G., & Johnson, M. H. (2012). Training
attentional control and working memo–y - Is
younger, better? Developmental Review, 32(4), 360–
387. https://doi.org/10.1016/j.dr.2012.07.001
Wechsler, D. (1981). WAIS-R Manual: The Wechsler Adult
intelligence scale-Revised. Harcourt Brace Jovanovich
[for] The Psychological Corporation.
Weissheimer, J., Fuji, R. C., & de Souza, J. G. M. (2019). The
effects of cognitive training on executive functions
and reading in typically developing children with
varied socioeconomic status in Brazil. Ilha Desterro,
72(3), 85–100. https://doi.org/10.5007/2175-8026.
2019v72n3p85
Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E.
(2010). The development of cognitive skills and gains in
academic school readiness for children from lowincome families. Journal of Educational Psychology,
102(1), 43–53. https://doi.org/10.1037/a0016738
White, H. A., & Shah, P. (2006). Training attention-switching ability in adults with ADHD. Journal of Attention
Disorders, 10(1), 44–53. https://doi.org/10.1177/
1087054705286063
Yeniad, N., Malda, M., Mesman, J., van IJzendoorn, M. H., &
Pieper, S. (2013). Shifting ability predicts math and
reading performance in children: A meta-analytical
study. Learning and Individual Differences, 23, 1–9.
https://doi.org/10.1016/j.lindif.2012.10.004

JOURNAL OF COGNITIVE PSYCHOLOGY

Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K.,
Beaumont, J. L., & Weintraub, S. (2013). II. NIH
Toolbox cognition Battery (CB): measuring executive
function and attention. Monographs of the Society for
Research in Child Development, 78(4), 16–33. https://
doi.org/10.1111/mono.12032
Zhang, Q., Wang, C., Zhao, Q., Yang, L., Buschkuehl, M., &
Jaeggi, S. M. (2019). The malleability of executive function in early childhood: Effects of schooling and targeted training. Developmental Science, 22(2), e12748.
https://doi.org/10.1111/desc.12748
Zhao, X., Wang, H., & Maes, J. H. R. (2018). Training and
transfer effects of extensive task-switching training in
students. Psychological Research, 1–15. https://doi.org/
10.1007/s00426-018-1059-7

763

Zimmerman, B. J. (2008). Investigating self-regulation and
motivation: Historical background, methodological
developments, and future prospects. American
Educational Research Journal, 45(1), 166–183. https://
doi.org/10.3102/0002831207312909
Zinke, K., Einert, M., Pfennig, L., & Kliegel, M. (2012). Plasticity
of executive control through task switching training in
adolescents. Frontiers in Human Neuroscience, 6, 41.
https://doi.org/10.3389/fnhum.2012.00041
Zinke, K., Zeintl, M., Rose, N. S., Putzmann, J.,
Pydde, A., & Kliegel, M. (2014). Working memory
training and transfer in older adults: Effects of
age, baseline performance, and training gains.
Developmental Psychology, 50(1), 304–315. doi:10.
1037/a0032982