Sensors 2020, 20, 3917; doi:10.3390/s20143917 www.mdpi.com/journal/sensors
Dynamics of the Prefrontal Cortex during Chess-
Based Problem-Solving Tasks in Competition-
Experienced Chess Players: An fNIR Study
Telmo Pereira 1, Maria António Castro 1,2, Santos Villafaina 3,*, António Carvalho Santos 1
and Juan Pedro Fuentes-García 3
1 Polytechnic Institute of Coimbra, Coimbra Health School, 3046-854 Coimbra, Portugal;
email@example.com (T.P.); firstname.lastname@example.org (M.A.C.); email@example.com (A.C.S.)
2 Centre for Mechanical and Engineering Materials and Processes, University of Coimbra,
3030-788 Coimbra, Portugal
3 Faculty of Sport Science, University of Extremadura. Avda: Universidad S/N, 10003 Cáceres, Spain;
* Correspondence: firstname.lastname@example.org
Received: 7 June 2020; Accepted: 12 July 2020; Published: 14 July 2020
Abstract: This study aimed to compare the dynamics of the prefrontal cortex (PFC), between adult
and adolescent chess players, during chess-based problem-solving tasks of increasing level of
difficulty, relying on the identification of changes in oxygenated hemoglobin (HbO2) and
hemoglobin (HHb) through the functional near-infrared spectroscopy (fNIRS) method. Thirty male
federated chess players (mean age: 24.15 ± 12.84 years), divided into adults and adolescents,
participated in this cross-sectional study. Participants were asked to solve three chess problems with
different difficulties (low, medium, and high) while changes in HbO2 and HHb were measured over
the PFC in real-time with an fNIRS system. Results indicated that the left prefrontal cortex (L-PFC)
increased its activation with the difficulty of the task in both adolescents and adults. Interestingly,
differences in the PFC dynamics but not in the overall performance were found between adults and
adolescents. Our findings contributed to a better understanding of the PFC resources mobilized
during complex tasks in both adults and adolescents.
Keywords: chess; prefrontal cortex; functional near-infrared spectroscopy
The prefrontal cortex (PFC) has been thoroughly described as the center of cognitive function,
being involved in executive functions that include decision-making and problem-solving, also taking
part in attention, memory, planning, motor control, and cognitive flexibility [1–3]. The PFC, also
called the frontal associative cortex and the Magister of the mind , is heavily interconnected with
other brain regions, receiving quite diverse sensory and cognitive inputs based on which overall
coordination of behavior is implemented. Thus, this brain region, particularly the dorsolateral part,
is responsible for the temporal organization of behavior, language, and reasoning , and the
definition and coordination of plans for action  entailing its conceptualization and flexibility to the
environmental demands . Furthermore, emotional and inhibitory control processes have been
associated with the orbitofrontal region of the PFC, while the medial region has implications in
motivation and behavioral drive .
Several studies have demonstrated the importance of the frontal lobe, and particularly the PFC,
in problem-solving tasks [8,9] including playing chess games . Playing Chess is a particular and
challenging activity that requires the orchestration of diverse cognitive resources such as memory,
attention, and perceptual grouping . It also involves the recognition of complex spatial
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relationships as determined by the game rules, and thus, the need to simultaneously handle multiple
objects under such rule constraints . In addition, motor timing, movement selection, and gait
control are also enrolled in the multi-componential processes involved in chess-playing, all facets
under PFC control . Other studies have looked into the overall pattern of brain activation as a
function of the level of expertise, identifying differences in the cortical resources engaged during
chess-playing activities, with the experts manifesting significant activation of areas related to object
perception or expertise-related pattern recognition [14,15], as well as recruitment in brain areas
involved in knowledge storage and retrieval and memory, whilst the novice players activate
predominantly brain areas involved in learning and retrieving of new information . Higher
activation of brain regions involved in attention and problem-solving was also demonstrated in
expert chess players engaging a chess-based problem-solving task , highlighting the existence of
significant differences in brain dynamics, and underlying cognitive operations in chess players.
The PFC is also of great interest in adolescence due to its relation to cognitive control and
emotion processing . Differences between adult and adolescence PFC have been reported,
identifying a reduction in the gray matter between adolescence and adulthood . This indicates
that during adolescence, the prefrontal regions are still developing . In this regard, the lateral
regions of the PFC are the latest developing areas involved in executive regions . A recent study
examined the brain electrical pattern of adolescent chess players during problem-solving tasks .
However, this study did not compare the brain processing of adolescent and adult chess players.
Much of the available evidence concerning brain activation during chess playing tasks have been
based on fMRI and electrophysiological methods, but to the best of our knowledge, no studies have
previously addressed the PFC activation associated with chess playing tasks with a functional near-
infrared spectroscopy (fNIRS) method. This method provides information on hemodynamic changes
associated with cortical activation by noninvasively measuring changes in the relative ratios of
deoxygenated hemoglobin (HHb) and oxygenated hemoglobin (HbO2) . Comparatively to other
non-invasive neuroimaging methodologies, fNIR is more tolerant to motion artifacts and provides a
balance between spatial and temporal resolution, thus being a good method for tasks involving motor
Previous studies have compared the dynamics of the PFC between adults and adolescents using
emotional tasks [25–27]. However, to the best of our knowledge, these comparisons have not been
performed using high cognitive demand tasks such as chess. This would be relevant as it would allow
reporting whether the differences in the PFC between adolescents and adults  have any impact
on the dynamics of the PFC or in the task performance during high demand cognitive tasks. Hence,
we sought to compare the dynamics of the PFC activation during three chess-based problem-solving
tasks of increasing level of difficulty in both competitive adult and adolescent chess players, relying
on the identification of changes in HbO2 and HHb. To the best of our knowledge, this is the first
experimental approach of the PFC activation in such particular challenging chess tasks monitored
with fNIR, and, therefore, the results could contribute to a better understanding of the PFC resources
mobilized during the handling of complex problems associated with chess playing in both adults and
2. Materials and Methods
2.1. Study Design and Participants
Federated players from official Portuguese chess clubs were invited to participate in a cross-
sectional study. The playing level for chess was determined by the ELO rating system, developed by
Arpad Elo and introduced by the World Chess Federation (FIDE) as a ranking system . It is a
method for calculating the relative skill levels of players in competitor-versus-competitor games .
All the participants were classified according to the ranking system of the FIDE. Exclusion criteria
included 1) inability to perform the tasks with the computer, 2) diseases that affect the autonomic
and central nervous system, 3) not being on medication, and 4) not being classified by the
International Chess Federation with ELO. A total of 31 players was selected to participate (30 males;
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1 female) and screened for suitability based on clinical history, behavioral profile, and chess practice
characterization. The female player was excluded to avoid gender bias. All the remaining volunteers
met the research requirements and were included in the study, thus, a total of 30 male chess players
(24.15 ± 12.84 years) were enrolled, with more than 4 years of continuous competitive chess playing
experience (participation in chess competition on average: 10.79 ± 7.73 years). Half of the study
population were adults (age > 19 years) and half were adolescents. The participants all had normal
or corrected-to-normal vision. After a detailed description of the objective and research
methodology, all participants signed an informed consent. The study was conducted according to
the guidelines of the Declaration of Helsinki. Anonymity and confidentiality of the collected data
were assured, and the study was developed for scientific purposes only, free of any financial or
economic interests. All procedures were approved by the University Research Ethics committee
(approval number: 85/2015).
Data collection was made in an appropriate room, with adequate temperature and humidity, in
a dimmed environment where light would not contaminate the collected information, and silent, so
the participant’s concentration was not disturbed during the tests. Before the tests, a structured
questionnaire was filled with sociodemographic information and details regarding the chess playing
history, including years of practice, years of competitive playing, hours of practice per day and days
of practice per week, and habit of playing in digital chess platforms and solving problems. The
individual ELO score was calculated [28,29]. The participants were questioned about their baseline
level of motivation towards the task and the degree of tiredness, providing such information on a 10-
The participants were then instructed on how to perform the tasks and all the requirements to
ensure the quality of the physiological information and the ecological approach of the chess-based
problem-solving tasks. The participants were seated in a comfortable chair in front of a computer
screen that ran the chess problems and were monitored with a 16-channel fNIRS stand-alone
functional brain imaging system (fNIR100A-2, Biopac System Inc., CA, USA), adjusted on the
forehead and insulated using a dark light-proof tape. The fNIRS acquisition was performed with a
dedicated computer running the COBI Studio program . Real-time monitoring of HbO2 and HHb
in the prefrontal cortex were performed while the participant solved each one of the three chess
problems randomly presented. After each problem-solving task, the participants were inquired how
they perceived the task in terms of complexity, difficulty, and level of engagement stress.
2.3. Chess Problems
Before starting the experimental task, procedures and protocol requirements were explained to
the participants. Moreover, all participants underwent a familiarization period with the computer
and the equipment required for testing. Participants conducted a total of three chess-based problem-
solving tasks. The problem-solving tasks were selected from Total Chess Training CT-ART 3.0
(Convekta, Moscow, Russia) by a FIDE master (ELO rating of 2300 or more). These chess problems
consisted of three levels of difficulty intended for chess players with an ELO rating of 1600–2400
raised by Blokh , with 1 being the lowest and 10 the highest level of difficulty: low-level (1),
medium-level (5), and high-level (10) chess problems. Participants had two and a half minutes to
solve each problem. Two moves for each problem were required (see Figure 1).
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Figure 1. Representation of the three chess-based problem-solving tasks by level of complexity: (a) panel —
low level problem (L); (b) panel—medium level problem (M); (c) panel—high level problem (H).
The Fritz 15 software, using Stockfish 6, 64 BIT, for Windows was used as chess engine . It is
one of the strongest chess engines in the world and it is open source (GPL license). In addition, chess
engines are a useful tool for chess training, being similar to the tactical responses given by humans
. A laptop was employed (Intel Core i7-6500U, (Intel, Santa Clara, USA) 1 TB, 8 GB memory
DDR3L-SDRAM, (Dell, Round Rock, USA)). In order to simulate a real chess environment, the Fritz
software automatically responded to moves with the best move previously computed by the research
group, simulating a real chess environment. The research technician selected the Fritz level according
to the ELO level of each player. This methodology was used in previous studies [34–36].
2.4. Functional Brain Imaging—fNIRS
The measurement of prefrontal cortex activity was performed with an fNIRS system (fNIR100A-
2, Biopac System Inc., CA, USA), as previously stated, which detects changes in HbO2 and HHb (both
in μmol/L) resulting from brain activation [22–24]. Signal acquisition was performed using a sensor
pad containing 4 light sources (LED) and 10 light detectors with a fixed source-detector distance of
2.5 cm and a depth of light penetration of approximately 1.5 cm beneath the scalp, generating an
array of 16 measurement sites (voxels or channels) per wavelength [22–24]. The sensor array is
embedded in a flexible pad and is placed over the forehead during signal acquisition. The light
sources emit two infrared light wavelengths (730 nm and 850 nm) for every 16 channels. As the light
penetrates the scalp, part of it is absorbed by the hemoglobin and the remaining light reaches the
detectors in a banana-shaped path. Thus, concentrations of hemoglobin are calculated by the ratio of
light absorbed at different wavelengths, considering that HbO2 and HHb have different absorption
coefficients. The sampling rate of the system was 2 Hz and LED current and detector gain was
adjusted prior to the acquisition to prevent signal saturation. The fNIRS acquisition was made for
each problem-solving task, resulting in one signal file for each problem. The acquisition started with
an initial 10-second baseline recording, after which the task was initiated, and was managed with a
dedicated laptop running the Cognitive Optical Brain Imaging (COBI) Studio program  (Biopac
system Inc., CA, USA). Figure 2 represents an example of the mean changes in HbO2 and HHb
recorded for one participant during the time course of the three experimental tasks.
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Figure 2. Example of the relative changes in oxygenated hemoglobin and deoxygenated hemoglobin
in one participant during the three experimental tasks. Bin 0 marks the baseline and Bin 11 the end of
each task. The relative changes were computed as the mean change in the overall optodes.
(a) panel —low level problem (L); (b) panel—medium level problem (M); (c) panel—high level
2.5. Data Processing
After visual inspection and elimination of low-quality channels and motion artifacts, the raw
files were filtered with a 20-order low-pass finite impulse response (FIR) filter (0.02–0.40 Hz) and a
cutoff frequency set at 0.1 Hz to remove long-term drift , high-frequency noise, and cardiac and
respiratory cycle effects [23,24]. After this process, a sliding-window motion artifact rejection
algorithm was used to filter out spikes and to improve signal quality . Relative changes in the
concentration of HbO2 (ΔHbO2) and HHb (ΔHHb) were calculated based on the modified Beer–
Lambert law [23,24,37], with a 10-second baseline recorded at the beginning of each task. Blood
oxygenation (Δoxy) was calculated as the difference of ΔHbO2 – ΔHHb. Blood volume changes
(ΔHbT) were calculated as ΔHbO2 + ΔHHb.
All aspects of data processing were managed with the fNIR software [23,24,30], version 4.5
(Biopac system Inc., USA). The absolute values obtained after data processing were Z-scored and
outliers were removed . Data per channel was averaged for each condition and four regions of
interest (ROI) for the prefrontal cortex were created, as previously proposed , by grouping
anatomically congruent channels. The generated ROIs were the left prefrontal cortex (L-PFC), the
right prefrontal cortex (R-PFC), the left medial prefrontal cortex (LM-PFC), and the right medial
prefrontal cortex (RM-PFC). For studying laterality effects, we further considered the mean left
hemisphere prefrontal cortex (LH-PFC) and the mean right hemisphere prefrontal cortex (RH-PFC)
by grouping channels accordingly.
2.6. Statistical Analysis
Data was gathered in Excel 2016 (Microsoft Office, Redmond, WA, USA), and then imported to
SPSS Statistics version 24 (IBM, Armonk, NY, USA) for statistical analysis.
Categorical variables were reported as frequencies and percentages, and χ2 or Fisher exact tests
were used when appropriate. The Shapiro–Wilks test was used to confirm the normal distribution of
all continuous variables, expressed as mean and standard deviation. As stated before, the fNIRS
variables were Z-scored. Other continuous variables with a non-normal distribution were log-
transformed. Student’s t-test was applied for group comparisons of descriptive variables only.
Individual variables were checked for homogeneity of variance via Levene’s test. A repeated-
measures ANOVA was used to evaluate modifications of the variables between the three problem-
solving tasks, in the whole population, in each, and between groups. Factors included in the ANOVA
were task (three increasing levels of difficulty) and ROI (four prefrontal cortex locations, as described
previously). Group was also entered (two levels: adults and adolescents) to test for interactions. The
Greenhouse–Geisser correction was used when sphericity was violated, and the Bonferroni
adjustment was adopted for multiple comparisons designed to locate the significant effects of a factor.
A correlational analysis, using Pearson correlation coefficient, was performed to test for age or years
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of practice effects on the fNIRS parameters. Significant correlations would be included as covariates
in an ANCOVA analysis for the functional biomarkers. A two-tailed p < 0.05 was considered
significant. The magnitude of the effects was also checked with the ηp2 value.
The main characterization of the study population is summarized in Table 1. The 30 male chess
players enrolled in the study and had a mean age of 24.15 ± 12.84 years, ranging from 13 to 55 years.
All participants were clinically healthy, with three participants reporting the use of medication
(mainly anti-histaminic drugs). Mean chess practice was 14.00 ± 10.30 years (range: 6–43 years) and
mean chess competition participation time was 10.92 ± 7.85 years (range: 4–40 years). The mean ELO
was 1677 ± 332, and all participants referred to regular chess practicing habits as depicted in Table 1.
The majority of the participants attributed the beginning of their chess playing either to school
activities (46.7%) or family influence (36.6%). The population was divided according to age into a
group of adults (age above 19 years) and adolescents (age between 13 and 19 years). As expected, the
adults had a significantly longer chess playing background, although no significant differences were
observed concerning chess practicing habits. Similar patterns of playing with digital chess platforms
and problem-solving training routines were observed in adults and adolescents.
Regarding the initial levels of motivation towards the tasks of the study, a mean score of 7.67 ±
2.26 was obtained for the entire population, with similar results in the adults (mean score: 7.80 ± 2.54)
and the adolescents (mean score: 7.53 ± 2.03; p = .753). The degree of initial tiredness showed a similar
trend, with a mean initial score of 2.30 ± 2.07 in the whole population, and no differences comparing
adults and adolescents (p = .467).
Table 1. Characterization of the participants according to age, chess practicing habits, and individual
(n = 30)
(n = 15)
(n = 15)
Age (years) 24.2 ± 12.8 32.7 ± 13.5 15.6 ± 1.7 <0.001
Chess playing (years) 14.0 ± 10.3 19.6 ± 12.2 08.4 ± 2.0 0.003
Chess competition (years) 10.9 ± 7.8 14.7 ± 9.6 07.2 ± 2.2 0.010
Chess practicing habits
2.5 ± 2.2
1.7 ± 1.3
5.2 ± 6.3
2.5 ± 2.3
1.8 ± 1.3
6.1 ± 7.3
2.5 ± 2.2
1.6 ± 1.4
4.4 ± 5.1
ELO 1677 ± 332 1825 ± 249 1529 ± 347 0.012
Number of problem-solving
chess tasks solved
Low difficulty 22 (73.3%) 12 (80%) 10 (66.7%) 0.409
Medium difficulty 15 (50%) 7 (46.7%) 8 (53.3%) 0.715
High difficulty 0 (0%) 0 (0%) 0 (0%) 1.000
The majority of the players (n = 22; 73.3%) were able to solve the low difficulty problem,
independently of their age (adults: n = 12; adolescents: n = 10; p = .409). Regarding the medium
difficulty problem, half of the participants were able to solve them, with no significant differences
between the adults and the adolescents (p = .715). None of the participants were able to find the
solution for the high difficulty problem.
Table 2 summarizes the main findings for the functional biomarkers in the whole study
population. An overall increase in PFC oxygenation with task difficulty was observed, with a mean
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Δoxy of 0.78 ± 0.21 μmol/L in the low difficulty task, increasing to 0.91 ± 0.26 μmol/L in the medium
difficulty task and 0.98 ± 0.25 μmol/L in the high difficulty task (F = 8.782; p < 0.001; ηp2 = 0.232).
Considering the four defined ROIs, significant changes in all four biomarkers were observed only
over the L-PFC, showing an increase in ∆HbO2, Δoxy, and ΔHbT with increasing task complexity.
These changes were followed by a significant decrease in ∆HHb, indicating a higher activation in the
L-PFC as a function of the difficulty of the problem-solving tasks. The aforementioned results explain
the significant changes observed when comparing hemispheric contributions according to the task
difficulty, with significant changes observed for the left hemisphere region of the PFC (L-PFC and
LM-PFC; Δoxy F = 10.896; p < 0.001; ηp2 = 0.273) but not for the right hemisphere region (R-PFC and
RM-PFC; Δoxy F = 1.637; p = .203; ηp2 = 0.053).
Table 2. Prefrontal cortex dynamics according to the variation of the functional biomarkers in the
three problem-solving chess tasks.
F P ηp2
L-PFC 0.33 ± 0.19 0.50 ± 0.22 0.77 ± 0.28 71.656 <0.001 0.712
R-PFC 0.36 ± 0.26 0.50 ± 0.34 0.41 ± 0.34 1.547 0.221 0.051
0.34 ± 0.22 0.38 ± 0.29 0.41 ± 0.29 0.753 0.476 0.025
0.41 ± 0.27 0.43 ± 0.34 0.49 ± 0.37 0.563 0.572 0.019
L-PFC –0.51 ± 0.35 –0.48 ± 0.25 –0.69 ± 0.32 3.901 0.026 0.119
R-PFC –0.39 ± 0.25 –0.39 ± 0.24 –0.33 ± 0.24 1.049 0.357 0.035
–0.43 ± 0.25 –0.48 ± 0.29 0.34 ± 0.27 2.409 0.099 0.077
–0.37 ± 0.33 –0.45 ± 0.34 –0.38 ± 0.31 3723 0.490 0.024
L-PFC –0.19 ± 0.42 0.01 ± 0.26 0.08 ± 0.35 5.865 0.005 0.168
R-PFC –0.3 ± 0.35 0.11 ± 0.27 0.09 ± 039 1.325 0.274 0.044
–0.10 ± 0.29 –0.10 ± 0.32 0.07 ± 0.36 2.727 0.074 0.086
0.04 ± 0.42 –0.02 ± 0.40 0.11 ± 0.39 0.939 0.397 0.031
L-PFC 0.84 ± 0.38 0.98 ± 0.39 1.50 ± 0.48 23.777 <0.001 0.451
R-PFC 0.74 ± 0.38 0.90 ± 0.53 0.74 ± 0.45 1.150 0.229 0.049
0.77 ± 0.39 0.87 ± 0.49 0.75 ± 0.43 0.898 0.413 0.030
0.78 ± 0.43 0.87 ± 0.56 0.87 ± 0.56 0.438 0.648 0.015
∆HbO2—variation in oxyhemoglobin; ∆HHb—variation in deoxyhemoglobin; ∆HbT—variation in
total hemoglobin; ∆oxy—difference between oxyhemoglobin and deoxyhemoglobin; L-PFC—left
dorsolateral prefrontal cortex; R-PFC—right dorsolateral prefrontal cortex; LM-PFC—left medial
prefrontal cortex; RM-PFC—right medial prefrontal cortex.
Considering the main findings for the functional biomarkers in the adolescents and the adults,
different patterns of oxygenation were depicted as demonstrated in Figure 3. A significant Task
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difficulty*ROI*Group interaction was observed regarding the ∆HbO2 (Finteraction = 2.580; p = 0.020; ηp2
= 0.084) and the ∆oxy (Finteraction = 2.345; p = 0.034; ηp2 = 0.077), but not for the ∆HHb and the ∆HbT. In
both groups, significant changes were observed in the L-PFC, with an increase in ∆HbO2, Δoxy, and
ΔHbT and a decrease in ∆HHb with increasing task complexity. A significant effect was also observed
for the ∆HbO2 in the medium level chess problem, with the adolescents presenting significant greater
relative change over the R-PFC when compared with the adults (F = 4.808; p = 0.004; ηp2 = 0.147). No
significant changes were observed in the low-level chess problem, but differences emerged with
increasing complexity of the task, mainly located in the R-PFC and the dorsolateral regions (both LM-
and RM-PFC) for the medium level chess problem, and in the L-PFC for the high-level chess problem.
In the medium level chess problem, the adolescents presented higher relative changes in Δoxy over
the R-PFC and smaller relative changes in Δoxy over the dorsolateral regions of the PFC. In the higher
complexity task, the L-PFC responded more in the adults, leading to greater relative change in the
Δoxy as compared with the adolescents. No significant differences were observed between groups in
what concerns the overall PFC oxygenation levels in the three experimental conditions. To test for
age or years of practice effects, we performed a correlational analysis of the fNIRS parameters and
these two covariates. A significant (weak to moderate) correlation was found only between age and
the LM-PFC changes in HbO2 (r = 0.371; p = 0.044), HHb (r = −0.378; p = 0.039), and Oxy (r = .449; p =
0.013). Consequently, we performed an ANCOVA analysis for the functional biomarkers, with age
as covariate, and no substantive changes were observed in the factorial results regarding the ∆HbO2
(Finteraction = 2.286; p = 0.038; ηp2 = 0.078) and the ∆oxy (Finteraction = 3.421; p = 0.003; ηp2 = 0.112), although a
significant task difficulty*ROI*Group interaction was depicted for the ∆HHb (Finteraction = 2.590; p =
0.020; ηp2 = 0.088) when age was included as covariate in the model.
Figure 3. Relative changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HHb)
in the adolescents group ((a) panel) and in the adults group ((b) panel), according to the level of
difficulty of the chess-based problem-solving tasks. L-PFC—left prefrontal cortex; R-PFC—right
prefrontal cortex; LM-PFC—left medial prefrontal cortex; RM-PFC—right medial prefrontal cortex.
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The aim of this study was to evaluate and compare the dynamics of the PFC activation during
three chess-based problem-solving tasks of increasing level of difficulty in competitive adult and
adolescent chess players. Several main findings emerged. First, the activation of the PFC, measured
by fNIR spectroscopy, was increased with more demanding chess problems in both adolescent and
adult chess players. Second, differences in the patterns of PFC activation were found between adult
and adolescent chess players. Adult chess players showed greater relative changes in blood
oxygenation over the dorsolateral regions of the PFC and greater activation of the L-PFC during the
high difficulty problem, whereas the adolescent chess players showed a greater relative change in the
∆oxy in the R-PFC in the medium level task. However, an alternative explanation could emerge. In
this regard, since the performance is the same in the two groups, it could be that younger participants
exhibited lower mental effort in the high level condition. Moreover, adults could be more engaged in
the high level problem-solving task than young chess players (less motivated than adult chess
players). In this line, a lower PFC activation when an individual drops the task has been found in
fNIRs studies [38,39].
Considering the factorial profiles of changes in oxyhemoglobin and deoxyhemoglobin, a more
efficient management of cognitive resources is apparent in adult chess players, regardless of the
overall level of PFC activation. Interestingly, these differences in PFC activation had no consequences
in the overall performance. These results have a significant relevance in the field of chess since fNIR
spectroscopy could be used to determine the cognitive load of a task as well as to test if a training
could change the efficiency of the PFC. In addition, future studies should use this technology in older
adults or special populations to evaluate the effects of therapies in the PFC activation.
In order to study the PFC activation, we employed fNIR spectroscopy to measure the relative
changes in HbO2 and HHb during the chess tasks. Changes in HbO2 have been considered as the
most sensitive parameter to measure activity-dependent changes in regional cerebral blood flow [40–
42] and is also particularly sensitive to mental workload variations [37,42]. Our main findings are
consistent with the involvement of the PFC during the experimental tasks, increasing its activation
following the increase in the complexity of the experimental tasks. Furthermore, the defined ROIs in
the PFC showed different contributions as a function of the difficulty of the chess problems,
particularly in the L-PFC in which a progressively higher activation was identified through the four
considered biomarkers with increasing level of difficulty. The increased activation of the PFC for
increasingly more demanding chess problems was observed both in adults and adolescents, although
the level of activation in the considered ROIs was different particularly in the medium and high
difficulty level. The adult chess players showed greater relative changes in blood oxygenation over
the dorsolateral regions of the PFC and greater activation of the L-PFC during the high difficulty
problem as compared with the adolescents, which showed a greater relative change in the ∆oxy in
the R-PFC in the medium level task. Notwithstanding, these between-group differences in the PFC
activation had no consequence in the overall performance during the tasks, since no differences were
observed between the groups when comparing the level of success solving either experimental
The enrollment of the PFC in complex tasks has been widely demonstrated [1–7,9] and was also
observed in our study. Furthermore, the dorsolateral region of the PFC has been shown to play a
crucial role in the overall coordination of several cognitive resources which are necessary during
problem-solving tasks, such as the temporal organization of behavior, language, and reasoning ,
the definition and coordination of plans for action , and the flexibility to the environmental
demands . This PFC region was mostly active in the chess players during the experimental tasks,
and particularly the L-PFC was quite sensitive to changes in the cognitive load as expressed by the
difficulty of the problem, with increasing activation following the increase in the complexity of the
The differences in the patterns of PFC activation in adult and adolescent players could express
the interaction of both brain maturation and level of expertise or experience. In fact, previous research
identified the recruitment of different psychological functions and the activation of different brain
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areas or different magnitudes of activation of the same brain areas during chess-related activities in
expertise versus novice players [10,14–16]. This is in line with our findings which highlight the
existence of significant differences in the PFC dynamics during chess-based problem-solving tasks
comparing the adult with adolescent chess players. This could confirm that the differences in the PFC
detected by previous studies  also affect the functioning of the PFC. Curiously, the different
patterns of PFC activation were not followed by differences in terms of the overall performance
during the task, and, therefore, the differences in brain dynamics over the PFC could merely translate
into greater underlying efficiency in the adult players. This could be connected with the chunking
theory and its augmented theory, the template theory . Chase and Simon  proposed that
Masters access information in long-term memory rapidly by recognizing familiar constellations of
pieces on the board, the patterns acting as cues that trigger access to the chunks. This could be the
reason why cognitive processes are more efficient in adults than in adolescent chess players.
However, future studies should investigate this hypothesis.
This study has several limitations that should be considered. The small number of participants
is a significant aspect, particularly on the between-group comparison, even though the results were
consistent in the most relevant outcomes considered in the study. We did not analyze the temporal
changes in PFC oxygenation during the tasks, and, therefore, the time course of hemodynamic
changes during the problem-solving tasks was not considered for the analysis. Considering that the
fNIRS system used in this research does not integrate short channels-based technology, we cannot
exclude the possibility of picking extra-cerebral oxygenation signals. Such limitation could have been
obviated through the use of task replications, and, therefore, further studies should include
replications in the experimental design. The difference in the ELO score and overall chess experience
between groups could also explain some of the observed differences at the group level and should
be addressed in future research. Due to the heterogeneity of our sample, future studies should
explore the PFC activation during problem-solving tasks in different levels of expertise (novice vs.
expert paradigm) in age-matched groups.
To the best of our knowledge, this is the first study addressing the dynamics of the PFC
activation during chess-based problem-solving tasks with fNIR spectroscopy. The PFC dynamics
differ in adults and adolescents, corresponding to a more efficient cortical organization in the adult
players for the same overall level of performance. Furthermore, we demonstrated the participation
of the PFC during complex chess problem tasks, with the L-PFC responding with increasing
activation to the increasing level of difficulty of the tasks and corresponding cognitive load in both
adults and adolescents.
Our findings contributed to a better understanding of the PFC resources mobilized during the
handling of complex problems associated with chess-playing, also adding evidence to the
understanding of the neural substrate underlying overall human problem-solving mechanisms.
Moreover, revealed differences in the PFC functioning between adults and adolescents during high
cognitive demand tasks should be investigated in future studies. Further studies should also include
the continuous measurement of fNIRS-based biomarkers to allow for the examination of spontaneous
hemodynamic fluctuations (as in Verdière et al.'s  study), as well as the adoption of more
sophisticated NIRS technologies, such as high-definition near-infrared spectroscopy or diffuse optical
tomography. Studying the functional correlates of unsuccessful moves during chess playing could
also add novel insights, and connectivity metrics could also support the study of hemispheric
interplay during complex chess problem tasks, contributing to a better understanding of cortical
dynamics in chess playing.
Author Contributions: Conceptualization, T.P. and S.V.; data curation, T.P., M.-A., C., S.V., and A., C.-S.; formal
analysis, T.P.; funding acquisition, J.P., F.-G.; investigation, T.P., M.-A., C., and S.V.; methodology, M.-A., C. and
S.V.; project administration, J.P., F.-G.; resources, A., C.-S. and J.P., F.-G.; software, T.P. and A., C.-S.; validation, M.-
A., C. and J.P., F.-G.; visualization, A., C.-S. and J.P., F.-G.; writing—original draft, T.P.; writing—review & editing,
M.-A., C., S.V., A., C.-S., and J.P., F.-G.. All authors have read and agreed to the published version of the manuscript.
Sensors 2020, 20, 3917 11 of 12
Funding: This study has been conducted thanks to the contribution of the Ministry of Economy and
Infrastructure of the Junta de Extremadura through the European Regional Development Fund. A way to make
Europe. (GR18129). Furthermore, S.V. was supported by a grant from the regional department of economy and
infrastructure of the Government of Extremadura and the European Social Fund (PD16008) and by a research
mobility grant of the AUIP—Asociación Universitaria Iberoamericana de Postgrado. The funders played no role
in the study design, the data collection and analysis, the decision to publish, or the preparation of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
1. Chayer, C.; Freedman, M. Frontal lobe functions. Curr. Neurol. Neurosci. 2001, 1, 547–552.
2. Goel, V.; Grafman, J. Are the frontal lobes implicated in “planning” functions? Interpreting data from the
Tower of Hanoi. Neuropsychologia 1995, 33, 623–642.
3. Koenraadt, K.L.M.; Roelofsen, E.G.J.; Duysens, J.; Keijsers, N.L.W. Cortical control of normal gait and
precision stepping: an fNIRS study. NeuroImage 2014, 85, 415–422.
4. Goldberg, E. The executive brain: Frontal lobes and the civilized mind; Oxford University Press: New York, NY,
5. Fuster, J.n.M. The prefrontal cortex—an update: time is of the essence. Neuron 2001, 30, 319–333.
6. Robbins, T.W. Dissociating executive functions of the prefrontal cortex. Philos. T. R. Soc. B. 1996, 351, 1463–1471.
7. Damasio, A.R. The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos. T.
R. Soc. B. 1996, 351, 1413–1420.
8. Newman, S.D.; Carpenter, P.A.; Varma, S.; Just, M.A. Frontal and parietal participation in problem solving
in the Tower of London: fMRI and computational modeling of planning and high-level perception.
Neuropsychologia 2003, 41, 1668–1682.
9. Delis, D.C.; Squire, L.R.; Bihrle, A.; Massman, P. Componential analysis of problem-solving ability:
Performance of patients with frontal lobe damage and amnesic patients on a new sorting test.
Neuropsychologia 1992, 30, 683–697.
10. Amidzic, O.; Riehle, H.J.; Fehr, T.; Wienbruch, C.; Elbert, T. Pattern of focal γ-bursts in chess players. Nature
2001, 412, 603.
11. Bilalić, M.; McLeod, P.; Gobet, F. Specialization effect and its influence on memory and problem solving in
expert chess players. Cogn. Sci. 2009, 33, 1117–1143.
12. Gobet, F.; Waters, A.J. The role of constraints in expert memory. J. Exp. Psychol. Learn. 2003, 29, 1082.
13. Liang, L.-Y.; Shewokis, P.A.; Getchell, N. Brain activation in the prefrontal cortex during motor and
cognitive tasks in adults. J. Behav. Brain. Sci. 2016, 6, 12, 463–474.
14. Bilalić, M.; Langner, R.; Erb, M.; Grodd, W. Mechanisms and neural basis of object and pattern recognition:
a study with chess experts. J. Exp. Psychol. Gen. 2010, 139, 728.
15. Bilalić, M.; Langner, R.; Ulrich, R.; Grodd, W. Many faces of expertise: fusiform face area in chess experts
and novices. J. Neurosci. 2011, 31, 10206–10214.
16. Duan, X.; Liao, W.; Liang, D.; Qiu, L.; Gao, Q.; Liu, C.; Gong, Q.; Chen, H. Large-scale brain networks in board
game experts: insights from a domain-related task and task-free resting state. PloS One 2012, 7, e32532.
17. Caballero, A.; Granberg, R.; Tseng, K.Y. Mechanisms contributing to prefrontal cortex maturation during
adolescence. Neurosci. Biobehav. R. 2016, 70, 4–12, doi:10.1016/j.neubiorev.2016.05.013.
18. Sowell, E.R.; Thompson, P.M.; Holmes, C.J.; Jernigan, T.L.; Toga, A.W. In vivo evidence for post-adolescent
brain maturation in frontal and striatal regions. Nat. Neurosci. 1999, 2, 859–861, doi:10.1038/13154.
19. Kanwal, J.; Jung, Y.; Zhang, M. Brain plasticity during adolescence: effects of stress, sleep, sex and sounds
on decision making. Anatomy & Physiology: Current Research 2016, 6, e135.
20. Fuster, J.M. Frontal lobe and cognitive development. J. Neurocytol. 2002, 31, 373–385.
21. Fuentes-Garcia, J.P.; Pereira, T.; Castro, M.A.; Carvalho Santos, A.; Villafaina, S. Psychophysiological stress
response of adolescent chess players during problem-solving tasks. Physiol. Behav. 2019, 209, 112609,
22. Ayaz, H.; Shewokis, P.A.; Curtin, A.; Izzetoglu, M.; Izzetoglu, K.; Onaral, B. Using MazeSuite and
functional near infrared spectroscopy to study learning in spatial navigation. J. Vis. Exp. 2011, 56, 3443.
23. Ayaz, H.; Izzetoglu, M.; Shewokis, P.A.; Onaral, B. Sliding-window motion artifact rejection for functional
near-infrared spectroscopy. In Proceedings of 2010 Annual International Conference of the IEEE
Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August–4 September 2010; IEEE: New
York City, NY, USA, 2010.
24. Izzetoglu, M.; Chitrapu, P.; Bunce, S.; Onaral, B. Motion artifact cancellation in NIR spectroscopy using
discrete Kalman filtering. Biomed. Eng. Online 2010, 9, 16.
Sensors 2020, 20, 3917 12 of 12
25. Mueller, S.C.; Cromheeke, S.; Siugzdaite, R.; Nicolas Boehler, C. Evidence for the triadic model of
adolescent brain development: Cognitive load and task-relevance of emotion differentially affect
adolescents and adults. Dev. Cogn. Neurosci. 2017, 26, 91–100, doi:10.1016/j.dcn.2017.06.004.
26. Silvers, J.A.; Shu, J.; Hubbard, A.D.; Weber, J.; Ochsner, K.N. Concurrent and lasting effects of emotion
regulation on amygdala response in adolescence and young adulthood. Dev. Sci. 2015, 18, 771–784,
27. Hare, T.A.; Tottenham, N.; Galvan, A.; Voss, H.U.; Glover, G.H.; Casey, B.J. Biological substrates of
emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biol. Psychiatry 2008,
63, 927–934, doi:10.1016/j.biopsych.2008.03.015.
28. Elo, A.E. The Rating of Chessplayers, Past and Present. Ishi Press: New York City, NY, USA, 2008.
29. Di Fatta, G.; Haworth, G.M.; Regan, K.W.; Ieee. Skill Rating by Bayesian Inference. In Proceedings of 2009
Ieee Symposium on Computational Intelligence and Data Mining, Nashville, TN, USA, 30 March–2 April 2009;
IEEE: New York City, NY, USA, 2009.
30. Ayaz, H.; Onaral, B. Analytical software and stimulus-presentation platform to utilize, visualize and analyze
near-infrared spectroscopy measures.Master Thesis, Drexel University, Philadelphia, PA, USA, 2005.
31. Blokh, M. Combinational Motifs 1
ed; Hannaco Enterprises: Moscow, Russia, 2003.
32. Regan, K.W.; Biswas, T.; Zhou, J. Human and computer preferences at chess. In Proceedings of Workshops
at the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, QC, Canada, 27–31 July 2014;
AAAI Press: Palo Alto, CA, USA, 2014.
33. Laureano-Cruces, A.L.; Hernández-González, D.E.; Mora-Torres, M.; Ramírez-Rodríguez, J. Application of
a cognitive model of emotional appraisal to the board evaluation function of a program that plays chess.
Revista de Matemática Teoría y Aplicaciones 2012, 19, 211–237.
34. Fuentes, J.P.; Villafaina, S.; Collado-Mateo, D.; de la Vega, R.; Gusi, N.; Clemente-Suarez, V.J. Use of
Biotechnological Devices in the Quantification of Psychophysiological Workload of Professional Chess
Players. J. Med. Syst. 2018, 42, 40, doi:10.1007/s10916-018-0890-0.
35. Fuentes, J.P.; Villafaina, S.; Collado-Mateo, D.; de la Vega, R.; Olivares, P.R.; Clemente-Suárez, V.J.
Differences between high vs low performance chess players in heart rate variability during chess problems.
Front. Psychol. 2019, 10, 409.
36. Villafaina, S.; Collado-Mateo, D.; Cano-Plasencia, R.; Gusi, N.; Fuentes, J.P. Electroencephalographic
response of chess players in decision-making processes under time pressure. Physiol. Behav. 2019, 198, 140–
37. Gateau, T.; Durantin, G.; Lancelot, F.; Scannella, S.; Dehais, F. Real-time state estimation in a flight simulator
using fNIRS. PloS One 2015, 10, e0121279.
38. Durantin, G.; Gagnon, J.F.; Tremblay, S.; Dehais, F. Using near infrared spectroscopy and heart rate
variability to detect mental overload. Behav. Brain Res. 2014, 259, 16–23.
39. Izzetoglu, M.; Bunce, S.C.; Izzetoglu, K.; Onaral, B.; Pourrezaei, K. Functional brain imaging using near-
infrared technology. IEEE Eng. Med. Biol. 2007, 26, 38.
40. Maidan, I.; Bernad-Elazari, H.; Gazit, E.; Giladi, N.; Hausdorff, J.M.; Mirelman, A. Changes in oxygenated
hemoglobin link freezing of gait to frontal activation in patients with Parkinson disease: an fNIRS study of
transient motor-cognitive failures. J. Neurol. 2015, 262, 899–908.
41. Miyai, I.; Tanabe, H.C.; Sase, I.; Eda, H.; Oda, I.; Konishi, I.; Tsunazawa, Y.; Suzuki, T.; Yanagida, T.; Kubota,
K. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. 2001,
42. Hoshi, Y.; Kobayashi, N.; Tamura, M. Interpretation of near-infrared spectroscopy signals: a study with a
newly developed perfused rat brain model. J. Appl. Physiol. 2001, 90, 1657–1662.
43. Gobet, F.; Simon, H.A. Expert chess memory: revisiting the chunking hypothesis. Memory 1998, 6, 225–255,
44. Chase, W.G.; Simon, H.A. Perception in chess. Cogn. Psychol. 1973, 4, 55–81, doi: 10.1016/0010-
45. Verdière, K.J.; Roy, R.N.; Dehais, F. Detecting pilot’s engagement using fNIRS connectivity features in an
automated vs. manual landing scenario. Front. Hum. Neurosci. 2018, 12, 6.
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