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Citation: Balconi, M.; Acconito, C.;
Angioletti, L. Not Everyone Chooses
Profit (If It Is too Tiring): What
Behavioral and EEG Data Tell Us.
Appl. Sci. 2024,14, 4793. https://
doi.org/10.3390/app14114793
Academic Editor: Alexander
N. Pisarchik
Received: 9 May 2024
Revised: 27 May 2024
Accepted: 30 May 2024
Published: 1 June 2024
Copyright: © 2024 by the authors.
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4.0/).
applied
sciences
Article
Not Everyone Chooses Profit (If It Is too Tiring): What Behavioral
and EEG Data Tell Us
Michela Balconi 1,2 , Carlotta Acconito 1,2 and Laura Angioletti 1, 2, *
1International Research Center for Cognitive Applied Neuroscience (IrcCAN), UniversitàCattolica del Sacro
Cuore, 20123 Milan, Italy; carlotta.acconito1@unicatt.it (C.A.)
2
Research Unit in Affective and Social Neuroscience, Department of Psychology, UniversitàCattolica del Sacro
Cuore, 20123 Milan, Italy
*Correspondence: laura.angioletti1@unicatt.it; Tel.: +39-(0)2-7234-5929
Abstract: Background: A more rewarding choice, even if it requires more effort, is usually preferred
by individuals; yet, in some cases, individuals choose less profitable and less tiring options. This
study explored the behavioral and electrophysiological (EEG) correlates of healthy adults performing
a task, designed to investigate the decision-making process behind the selection of more effortful (but
highly monetarily rewarding) and less effortful (but less monetarily rewarding) options. Methods: A
sample of 20 healthy adults (mean age = 46.40) performed the decision-making task, while behavioral
data and EEG frequency bands (delta, theta, alpha, and beta) were collected. The Maximization Scale
(MS) was administered to evaluate individuals’ differences in the tendency to maximize their choices.
Results: the results showed a general preference for selecting more compared to less effortful options,
while no significant differences were obtained for the response times. Individuals who score higher
on the MS High Standards subscale are more inclined to choose less effortful options; conversely,
those with lower scores are more likely to choose a more effortful and rewarding option. However,
no significant correlations were found between the behavioral data and the alternative search, or
the decision difficulty subscales of the MS. EEG findings reported a significant interaction effect
Choice
×
Electrode in delta, theta, alpha and beta bands. Specifically, the choice of less effortful
options is associated with a higher increase in delta, theta, alpha, and beta band power in the right
temporoparietal area (TP10) and by a lower activation of delta and theta in the contralateral site
(TP9). The delta band decreased in left frontal area (AF7) during the task for the less versus more
effortful options. Conclusions: Overall, despite more effortful and more monetarily rewarding
options seeming to be the most rational ones to choose, less effortful choices are associated with
specific EEG correlates, suggesting that there is a perceived advantage in avoiding automatisms,
delaying gratification, and maximizing future possibilities.
Keywords: EEG frequency bands; effort; reward; boredom; delay gratification; automatism
1. Introduction
In everyday life, depending on the type of decision and the potential reward that can
be obtained, there are situations in which one decides to activate the “autopilot mode”. Yet,
even in the face of greater economic rewards, some people tend to avoid repetitive tasks
and automatic behaviors, perhaps because they can be boring and alienating.
To fully understand if and when it is appropriate or not to use automatisms (i.e.,
to activate automatic responses), it may be useful to think of those situations in which
more effortful behavior must be enacted to complete a given task and achieve a pre-
determined goal.
More effortful behavior could be defined as the result of repetition over time of the
same type of behavior, which, in the long run, is then performed automatically and outside
of awareness [
1
]. The more frequently a person repeats a given task, the easier it is to repeat
Appl. Sci. 2024,14, 4793. https://doi.org/10.3390/app14114793 https://www.mdpi.com/journal/applsci
Appl. Sci. 2024,14, 4793 2 of 13
and complete it, as a routine of execution is developed, and at the same time knowledge
and skills about it increase. Moreover, the more often the task is repeated, the less conscious
deliberation is required for its execution (which is performed non-consciously); with each
repetition, therefore, nonconscious processing becomes more dominant, and task execution
more routinized and automatic [
1
]. However, previous research studies have shown
that this type of action can be linked to boredom [
1
,
2
]. Boredom could be defined as an
unpleasant mental state in which one wants to do something satisfying but is unable to
obtain it [
3
]. In this kind of situation, the term boredom refers to the state of boredom
indicating a transient experience in response to a particular situation [4,5].
On the other hand, less effortful tasks encompass behaviors that require less effort
and less time consumption, and could include behaviors characterized by behavioral or
cognitive variety [
1
]. As opposed to the more effortful tasks previously described, less
effortful tasks might require less effort and could be perceived as less boring.
Completing a more effortful task can be an element of personal gratification, as it
increases one’s sense of efficacy in one’s personal and interpersonal world; in this sense,
effort can increase value through the “strength of engagement” [
6
]. Similarly, many tasks
associated with feelings of mental effort seem to have good outcomes and generate positive
sensations, such as working hard yielding professional success [
7
]. Nonetheless, performing
effortful task could also lead to boredom, while less effortful tasks have the advantage of
being less physically and cost-demanding, and leave room for creativity. Therefore, it might
be plausible that the decision regarding whether to engage in more or less effortful tasks
also depends on the potential advantages of this choice, and on individual differences.
Based on the descriptions of more effortful and less effortful tasks, some questions
arise: Do people prefer a less effortful task to a more effortful one? What are the cortical
effects of choosing a more effortful vs. less effortful choice? Are there any individual
characteristics associated with choosing more effortful or less effortful options?
To answer these questions, it is useful to highlight the fact that decision-making
processes are strictly interrelated with human agency and reward mechanisms [
8
]. Indeed,
decision-making can also be influenced by internal and external aspects, such as the
presence of a specific type of reward (e.g., economic rewards) or the value given to the
reward itself [
9
]. Several studies found that decisions are not only driven by the amount
of the expected reward; other variables such as the probability of the reward occurring,
the expenditure of energy required, and the delay to the reward are involved in the
choice process [
10
–
13
]. The set of these variables has been defined as subjective “decision
value” [9].
Based on the value attributed to a stimulus—which may be derived from the outcome
of a decision—the motivation to select a specific choice can be defined by the processes
that regulate action toward rewarding stimuli [
14
]. To study the role of reward in decision-
making processes, several experimental tasks were developed: the Effort-Expenditure
for Rewards Task (EEfRT) is an objective behavioral measure of the effort-based decision-
making process [
15
]. It consists of a series of repeated trials in which the participant can
choose between a high- or low-effort task to obtain monetary rewards of varying amounts
(low-reward low-effort task and high-reward high-effort task) [15].
Despite the EEfRT representing a highly reliable and objective behavioral measure of
individual differences in reward motivation, previous studies have not only collected behav-
ioral data, but have also integrated the measurement of the neural correlates of EEfRT [
16
],
thus highlighting the importance of an integration of these two levels of measurement. This
evidence highlights how the neuroscientific perspective, which integrates the implicit brain
responses with the individual’s explicit and behavioral ones, has significantly contributed
to the study of decision-making processes [
17
]. In particular, among the neuroscientific
tools, the electroencephalogram (EEG) is an affordable technology for recording the brain’s
electrical impulses through electrodes placed on the scalp. The variations in electrical
potential generated by the activity of cortical neurons are gathered in real time and with a
Appl. Sci. 2024,14, 4793 3 of 13
millisecond-level temporal accuracy [
18
]. Moreover, the EEG is a wearable, non-invasive
technique that can be used in an ecological and naturalistic non-laboratory setting [
19
–
21
].
Additionally, the EEG proves useful for providing insights into the brain’s electrical
activity, and allows the evaluation of the potential load and mental effort needed for
decision-making processes, through the analysis of the functional meaning of the different
frequency bands (delta, theta, alpha, and beta band) [
16
,
22
]. Indeed, changes in the theta
band could be associated with cognitive control and the monitoring of one’s actions [
23
],
as well as in the processing of emotional responses [
24
]. The delta band, instead, is an
index of the arousing power of the stimulus [
25
], but also a correlate of active cognitive
processes [
26
] and emotion states [
24
,
27
]. The beta band, on the other hand, may be
involved in the processing of cognitive effort, since an increase in frontal beta power has
been proposed to reflect stress-related neural activity [
28
]. Finally, variations in the alpha
band are associated with cognitive effort, engagement [
29
], and the processing of task
demands [
30
]. Additionally, variations in the alpha band reflect task complexity and may
indicate increased cortical activation as a result of mental effort [
31
,
32
]. Along with the
functional significance of each band, it is also interesting to map the neural localization
of EEG frequency bands’ significant variations. For example, previous studies showed
how the activation of left frontal activity (LFA) is related to motivation to approach or
pursue reward [
16
,
33
], and that the dorsolateral prefrontal cortex (DLPFC), important for
the representation and encoding of rewards, as well as the anticipation of motivationally
salient events, is primarily responsible for this activity [
34
,
35
]. For instance, Hughes and
colleagues [
16
] found that individuals with greater LFA for the alpha band at rest were
more willing to expend greater effort in the pursuit of larger rewards, particularly when
reward delivery was less likely.
Within this theoretical framework, the present study adopted a multi-methodological
approach to explore and integrate the behavioral and EEG correlates of a novel behavioral
task, created to investigate the decision-making process behind the selection of more
effortful (but highly monetarily rewarding and longer-duration) and less effortful (but less
monetarily rewarding and lower-duration) options. Thus, option preference depends on
the interaction of two different variables: the “cost” and “benefit” associated with each
option. The “cost” refers to the performance of a repetitive and automatic task for different
time intervals, whereby the more effortful option costs more because it includes a greater
waste of time associated with a boring task, while the less effortful option has a lower cost
since it takes less time and is less tiring and boring. Instead, the “benefit” indicates the
chance of gaining a higher or lower economic reward (and thus, a high or low benefit). In
addition, the Maximization Scale (MS) [
36
,
37
] was administered to evaluate individuals’
differences in their tendency to maximize their choices and to explore how individual
characteristics in the decision-making process can be linked to the different propensities
to reinforcement.
Specifically, we have hypothesized that participants prefer to choose the more effortful
compared to the less effortful options, due to the presence of the higher reward (giving
more weight to the “benefit” variable) associated with this kind of option. In fact, according
to the literature, people display a higher attracted to rewards in similar behavioral tasks [
16
].
Also, it is supposed that the selection of the more effortful option, the one preferred by the
participants, will be chosen more quickly compared to the less effortful options.
However, it is interesting to explore the behavioral and EEG correlates of both the
more effortful and less effortful conditions, including the latter (the least popular), in which,
regardless of the higher economic reward, automatic and repetitive tasks are avoided.
Focusing on the EEG correlates, we expect that the selection of less effortful options
(accompanied by a lower attraction to an economic reward) can be marked by a decrease
in the delta activity, since this band is an index of engaging in decision-making tasks
associated with positive aspects (such as the presence of greater economic reward).
On the other hand, concerning the variable “cost”, the choice of less effortful options
could be determined by the decision to avoid repetitive behavior and perform tasks uncon-
Appl. Sci. 2024,14, 4793 4 of 13
sciously and unattentively, and instead to increase the sense of being in control of one’s
own behavior, leading to greater activation in the theta, beta, and alpha bands, which are
associated with cognitive control and the attentional process.
Finally, as regards the self-report data, we expect a potential link between individuals
who prefer less effortful choices and higher scores in the MS (and especially in the MS-
high standards), since it might be possible that individuals who hold high standards for
themselves and things in general do not want to engage in boring and repetitive tasks (i.e.,
effortless choices) regardless of the expected reward.
2. Materials and Methods
2.1. Sample
A sample of 20 healthy adults was recruited through a non-probabilistic convenience
sample method [N males = 11, N females = 9; Mean (M) age = 46.40; Standard Deviation
(SD) age = 11.32]. To determine the minimum required sample size, we performed an a
priori power analysis for repeated measures ANOVA using G*Power 3.1 software (Heinrich-
Heine, Düsseldorf, Germany), and we found that a total sample size of 15 participants
(with alpha error probability = 0.05 and power 0.80) was the minimum required for the
detection of a significant within or between effect. By estimating a subject attrition of 15%,
we added three more subjects, reaching the minimum sample size of 18 participants.
Each participant had normal or corrected-to-normal vision and was right-handed. All
adults were native Italian speakers, had a minimum of 16 years of education and at least
work experience. Additionally, they lived in Northern Italy. Exclusion criteria were severe
physical and chronic diseases, congenital and non-congenital brain injuries, epilepsy, and
neurological, psychiatric, and psychological disorders. Participants voluntarily completed
written informed consent forms; they received no compensation for their time or participa-
tion in the study. The Ethics Committee of the Department of Psychology of the Catholic
University of the Sacred Heart in Milan, Italy, gave its consent to this study according to
the GDPR—Reg. UE 2016/679 and its ethical guidelines. The Declaration of Helsinki’s
guiding principles (2013) were adhered to when conducting the research.
2.2. Procedure
For the experimental procedure, which lasted approximately 10 min, the participants
sat on a comfortable chair in front of a PC monitor placed about 80 cm away from their eyes
in a quiet dedicated room. Each participant completed a consent form and a demographic
data survey.
Next, an EEG resting state baseline of 120 s was collected before the task’s execution.
After receiving brief verbal instructions, participants performed the novel task while
behavioral and EEG data were recorded continuously. At the end of the task, the MS was
administered to collect self-report data to explore individuals’ differences in the tendency
to maximize their choices.
2.3. Experimental Task
The participants were required to execute a novel behavioral task, administered via
a web-based experiment management platform (PsyToolkit, version 3.4.4) [
38
,
39
] and
designed to study the propensity to reinforce under the condition of choosing between
more effortful and less effortful tasks.
This task was composed of ten rounds, each of which required participants to choose
from two different options, a more effortful or less effortful option, to obtain a specific
economic reward.
The more effortful option required the subject to press the space bar for 64 trials. For
the less effortful option, the participant had to press the space bar 17 trials to complete the
task. The reward for the more effortful option was fixed at USD 8.00, while for the less
effortful option it was EUR 4.00.
Appl. Sci. 2024,14, 4793 5 of 13
For all rounds, participants had to press the space bar repeatedly for a variable amount
of time depending on the choice they made (i.e., if they selected the more effortful or less
effortful option). Each press of the space bar increased the level of a virtual “bar” displayed
on the screen, which highlighted where they were in the trial. Participants could win the
money associated with each round if they reached the end of the virtual “bar” within the
predetermined time.
Each round began with a description of a specific goal to be achieved (i.e., to fill a jug
with water, water a flower until it blooms, drive a car to the finish line) and a maximum of
10 s to choose between the more effortful or less effortful option.
After the decision, participants were then shown a 1 s fixation cross, a reminder of the
action to be taken to complete the task for 5 s and a “Ready” screen for 1 s. On completion
of the task, or at the end of the predetermined time, a feedback information screen was
shown to participants for 2 s informing them about whether they had achieved the goal of
the trial. If the participant completed the trial and achieved the goal, a second feedback
screen was displayed for 2 s, showing the phrase “You won” and the amount corresponding
to the type of choice made (more effortful versus less effortful) for that trial (Figure 1).
Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 14
Figure 1. Experimental task flow. The figure shows a schematic representation of a single round of
the task, where the more effortful option is chosen (reward obtained: EUR 8.00).
2.4. Maximization Scale
The Maximization Scale (MS) is a 13-item self-report scale examining people’s pro-
pensity to maximize their options, or, conversely, to choose a solution that meets their
standards for sufficient quality [36,37]. A 7-point Likert-type scale is used for each item
and measures the tendency of decision-makers to: (i) hold high standards for themselves
and things in general (high standard subscale); (ii) search for beer options (alternative
search subscale); and (iii) find it difficult to decide (decision difficulty subscale).
2.5. EEG Data Acquisition and Biosignal Analysis
EEG data were collected during the resting state condition for 120 s and while per-
forming the novel behavioral task through a wearable and non-invasive EEG recorder,
the Muse™ headband (version 2; InteraXon Inc., Toronto, ON, Canada). This EEG system
permits the detection of the spectral activity of each standard frequency band (delta,
theta, alpha, beta and gamma) with seven dry sensors made of conductive material (sil-
ver) and silicon rubber, placed according to the international 10–20 system [40]. Specifi-
cally, of these seven electrodes, three are used as a reference and the remaining four are
placed on the left and the right sides of the forehead, respectively; two in the frontal area
(AF7 and AF8) and two in the temporoparietal area (TP9 and TP10). The data, sampled at
256 Hz and with a 50 Hz notch frequency filter, were detected via a system with an ac-
celerometer, gyroscope, and pulse oximetry and through the Mind Monitor mobile ap-
plication via Bluetooth. For the purpose of limiting the presence of artifacts, participants
were told to keep their eye blinks and movements to a minimum. The removal of artifacts
(such as eye blinks, jaw clenching, and movements) was performed after a visual inspec-
tion of all the data.
The EEG data from each electrode and each frequency band were converted in re-
al-time into Power Spectral Density (PSD) via Fast Fourier Transformation and computed
for each frequency band: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz),
and gamma (30–44 Hz).
The variations in EEG power values during the task were computed by weighting
the EEG power values during the task over the EEG power values in the baseline. For the
analyses, the EEG tracing segments related to the more effortful and less effortful choices
were considered.
For this study, a MuseTM headband was employed as an ecological and easy-to-use
EEG wearable tool. It allows one to map the activation of the bilateral frontal and tem-
Figure 1. Experimental task flow. The figure shows a schematic representation of a single round of
the task, where the more effortful option is chosen (reward obtained: EUR 8.00).
The behavioral data from each subject were collected for every single trial of each
round and permitted us to explore the trends in the type of choice, the response times to
make the choice (RTs), and details of the total choices, in terms of how many times the
more effortful option was chosen and how many times the less effortful option was chosen.
RTs, indeed, represent an indirect measure of the workload required to choose among
alternatives, highlighting the cognitive cost of the decision-making process.
Prior to EEG data recording, the participants became familiarized with the overall
procedure trough a familiarization session. The first four rounds were used to enable
familiarization to the task and the following six rounds were considered in data analysis.
The duration of this session was approximately two minutes, during which every subject
was presented with four rounds with both more and less effortful options. After the
familiarization phase, in which the participants experienced both the more effortful and less
effort choices, the participants were asked how they perceived the task, and on a qualitative
level all the participants reported that the more effortful task was more monotonous and
boring, compared to the less effortful one.
Appl. Sci. 2024,14, 4793 6 of 13
2.4. Maximization Scale
The Maximization Scale (MS) is a 13-item self-report scale examining people’s propen-
sity to maximize their options, or, conversely, to choose a solution that meets their standards
for sufficient quality [
36
,
37
]. A 7-point Likert-type scale is used for each item and measures
the tendency of decision-makers to: (i) hold high standards for themselves and things in
general (high standard subscale); (ii) search for better options (alternative search subscale);
and (iii) find it difficult to decide (decision difficulty subscale).
2.5. EEG Data Acquisition and Biosignal Analysis
EEG data were collected during the resting state condition for 120 s and while per-
forming the novel behavioral task through a wearable and non-invasive EEG recorder, the
Muse™ headband (version 2; InteraXon Inc., Toronto, ON, Canada). This EEG system
permits the detection of the spectral activity of each standard frequency band (delta, theta,
alpha, beta and gamma) with seven dry sensors made of conductive material (silver) and
silicon rubber, placed according to the international 10–20 system [
40
]. Specifically, of these
seven electrodes, three are used as a reference and the remaining four are placed on the left
and the right sides of the forehead, respectively; two in the frontal area (AF7 and AF8) and
two in the temporoparietal area (TP9 and TP10). The data, sampled at 256 Hz and with a
50 Hz notch frequency filter, were detected via a system with an accelerometer, gyroscope,
and pulse oximetry and through the Mind Monitor mobile application via Bluetooth. For
the purpose of limiting the presence of artifacts, participants were told to keep their eye
blinks and movements to a minimum. The removal of artifacts (such as eye blinks, jaw
clenching, and movements) was performed after a visual inspection of all the data.
The EEG data from each electrode and each frequency band were converted in real-
time into Power Spectral Density (PSD) via Fast Fourier Transformation and computed for
each frequency band: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and
gamma (30–44 Hz).
The variations in EEG power values during the task were computed by weighting
the EEG power values during the task over the EEG power values in the baseline. For the
analyses, the EEG tracing segments related to the more effortful and less effortful choices
were considered.
For this study, a Muse
TM
headband was employed as an ecological and easy-to-use EEG
wearable tool. It allows one to map the activation of the bilateral frontal and temporoparietal
regions: two areas that are crucially involved in decision-making processes [41,42].
2.6. Data Analysis
First, for the behavioral data, two one-way ANOVAs were applied on the behavioral
data with Choice (2: more effortful, less effortful) as the independent variable and mean
scores and RTs as dependent variables.
Secondly, for EEG data, four repeated measures analyses of variance (ANOVAs) were
separately applied to the dependent measure of frequency bands (delta, theta, alpha, beta).
Analysis was carried out with the following factors: choice (two: more effortful, less
effortful) and electrodes (four: AF7, AF8, TP9 and TP10). For all ANOVA tests, degrees
of freedom were corrected by the Greenhouse–Geisser epsilon when appropriate. Simple
effects for significant interactions were further checked via pairwise comparisons, and
Bonferroni correction was used to reduce multiple comparison potential biases. The sizes of
statistically significant effects have been estimated by computing eta squared (
η2
) indices.
The threshold for statistical significance was set at
α
= 0.05. For the statistical analysis, IBM
SPSS 29 (IBM Corp., Chicago, IL, USA) was used.
Thanks to a preliminary analysis, potential biases related to gender were checked for
and excluded. No statistically significant main or interaction effects including gender were
observed; this variable was thus not included in the analyses reported below.
Finally, correlational analyses were also applied between the behavioral data collected
during the task and each of the subscale’s scores in the MS.
Appl. Sci. 2024,14, 4793 7 of 13
3. Results
3.1. Behavioural Results
From the first ANOVA, a significant main effect of the choice factor was found
[
F(1, 19) = 19,754
p
≤
0.001,
η2
= 0.510] (Figure 2), for which higher behavioral scores
were detected for the more effortful compared to the less effortful choices.
No significant differences were obtained for the RTs.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 7 of 14
poroparietal regions: two areas that are crucially involved in decision-making processes
[41,42].
2.6. Data Analysis
First, for the behavioral data, two one-way ANOVAs were applied on the behavioral
data with Choice (2: more effortful, less effortful) as the independent variable and mean
scores and RTs as dependent variables.
Secondly, for EEG data, four repeated measures analyses of variance (ANOVAs)
were separately applied to the dependent measure of frequency bands (delta, theta, al-
pha, beta). Analysis was carried out with the following factors: choice (two: more effort-
ful, less effortful) and electrodes (four: AF7, AF8, TP9 and TP10). For all ANOVA tests,
degrees of freedom were corrected by the Greenhouse–Geisser epsilon when appropri-
ate. Simple effects for significant interactions were further checked via pairwise compar-
isons, and Bonferroni correction was used to reduce multiple comparison potential bias-
es. The sizes of statistically significant effects have been estimated by computing eta
squared (η2) indices. The threshold for statistical significance was set at α = 0.05. For the sta-
tistical analysis, IBM SPSS 29 (IBM Corp., Chicago, IL, USA) was used.
Thanks to a preliminary analysis, potential biases related to gender were checked for
and excluded. No statistically significant main or interaction effects including gender
were observed; this variable was thus not included in the analyses reported below.
Finally, correlational analyses were also applied between the behavioral data col-
lected during the task and each of the subscale’s scores in the MS.
3. Results
3.1. Behavioural Results
From the first ANOVA, a significant main effect of the choice factor was found [F(1,
19) = 19,754 p ≤ 0.001, η2 = 0.510] (Figure 2), for which higher behavioral scores were de-
tected for the more effortful compared to the less effortful choices.
No significant differences were obtained for the RTs.
Figure 2. Behavioral data. The bar graph displays the higher behavioral scores for the selection of
more effortful compared to less effortful choices on the six rounds. Bars represent the Standard
Error (SE) of ±1 for all plots; asterisks (*) denote statistically significant differences with p < 0.05.
3.2. EEG Results
3.2.1. Delta Band
For the delta band, a significant interaction effect of choice × electrode was found
[F(3, 56) = 6.54, p ≤ 0.01, η2 = 0.378]. Pairwise comparisons revealed a decrease in delta
band for the less effortful compared to the more effortful choices in TP9 and in AF7. Also,
an increase in the delta band was observed in TP10 for the less effortful compared to the
Figure 2. Behavioral data. The bar graph displays the higher behavioral scores for the selection of
more effortful compared to less effortful choices on the six rounds. Bars represent the Standard Error
(SE) of ±1 for all plots; asterisks (*) denote statistically significant differences with p< 0.05.
3.2. EEG Results
3.2.1. Delta Band
For the delta band, a significant interaction effect of choice
×
electrode was found
[
F(3, 56) = 6.54
,p
≤
0.01,
η2
= 0.378]. Pairwise comparisons revealed a decrease in delta band
for the less effortful compared to the more effortful choices in TP9 and in AF7. Also, an increase
in the delta band was observed in TP10 for the less effortful compared to the more effortful
choices (all comparisons p≤0.05) (Figure 3A). No other significant effects were found.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 8 of 14
more effortful choices (all comparisons p ≤ 0.05) (Figure 3A). No other significant effects
were found.
Figure 3. (A–D) EEG data. (A) The bar graph displays a decrease in delta band power in TP9 and in
AF7, as well as an increase in delta band power in TP10, for the less effortful compared to the more
effortful choices. (B) The bar chart shows a decrease in theta band power in TP9, as well as an in-
crease in theta band power in TP10, for the less effortful compared to the more effortful choices. (C)
The graph shows an increase in alpha band power in TP10 for the more effortful compared to the
less effortful choices. (D) The bar chart represents an increase in beta band power in TP10 for the
less effortful compared to the more effortful choices. Bars represent the Standard Error (SE) of ±1
for all plots; asterisks (*) denote statistically significant differences with p < 0.05.
3.2.2. Theta Band
Regarding theta band, a significant interaction effect of choice × electrode was found
[F(3, 56) = 8.12, p ≤ 0.01, η
2
= 0.402]. Pairwise comparisons revealed a decrease in theta
band for the less effortful compared to the more effortful choices in TP9. Conversely, an
increase in theta band was observed in TP10 for the less effortful compared to the more
effortful choices (all comparisons p ≤ 0.05) (Figure 3B). No other significant effects were
found.
3.2.3. Alpha Band
For the alpha band, the analysis revealed a significant interaction effect of choice ×
electrode [F(3, 56) = 6.09, p ≤ 0.01, η
2
= 0.369]. Pairwise comparisons showed an increase in
alpha band for the more effortful compared to the less effortful choices in TP10 (Figure
3C). No other significant effects were found.
3.2.4. Beta Band
Finally, a significant interaction effect of choice × electrode was observed for beta
band [F(3, 56) = 7.75, p ≤ 0.01, η
2
= 0.398]. Pairwise comparisons showed an increase in beta
band for the less effortful compared to the more effortful choices in TP10 (Figure 3D). No
other significant effects were found.
3.3. Correlation between Behavioral Data and MS Subscales Scores
A negative correlation was found between the more effortful choices’ mean score
and the MS High Standards subscale score (r = −0.696 p ≤ 0.001) (Figure 4A). Also, a posi-
Figure 3. (A–D) EEG data. (A) The bar graph displays a decrease in delta band power in TP9 and
in AF7, as well as an increase in delta band power in TP10, for the less effortful compared to the
more effortful choices. (B) The bar chart shows a decrease in theta band power in TP9, as well as an
increase in theta band power in TP10, for the less effortful compared to the more effortful choices.
(C) The graph shows an increase in alpha band power in TP10 for the more effortful compared to the
Appl. Sci. 2024,14, 4793 8 of 13
less effortful choices. (D) The bar chart represents an increase in beta band power in TP10 for the less
effortful compared to the more effortful choices. Bars represent the Standard Error (SE) of
±
1 for all
plots; asterisks (*) denote statistically significant differences with p< 0.05.
3.2.2. Theta Band
Regarding theta band, a significant interaction effect of choice
×
electrode was found
[F(3, 56) = 8.12, p
≤
0.01,
η2
= 0.402]. Pairwise comparisons revealed a decrease in theta band
for the less effortful compared to the more effortful choices in TP9. Conversely, an increase
in theta band was observed in TP10 for the less effortful compared to the more effortful
choices (all comparisons p≤0.05) (Figure 3B). No other significant effects were found.
3.2.3. Alpha Band
For the alpha band, the analysis revealed a significant interaction effect of
choice ×electrode
[F(3, 56) = 6.09, p
≤
0.01,
η2
= 0.369]. Pairwise comparisons showed an increase in alpha band for
the more effortful compared to the less effortful choices in TP10 (Figure 3C). No other significant
effects were found.
3.2.4. Beta Band
Finally, a significant interaction effect of choice
×
electrode was observed for beta
band [F(3, 56) = 7.75, p
≤
0.01,
η2
= 0.398]. Pairwise comparisons showed an increase in
beta band for the less effortful compared to the more effortful choices in TP10 (Figure 3D).
No other significant effects were found.
3.3. Correlation between Behavioral Data and MS Subscales Scores
A negative correlation was found between the more effortful choices’ mean score and
the MS High Standards subscale score (r =
−
0.696 p
≤
0.001) (Figure 4A). Also, a positive
correlation was observed between the less effortful choices’ mean score and the MS High
Standards subscale score (r = 0.702 p
≤
0.001) (Figure 4B). No other significant correlations
were observed.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 9 of 14
tive correlation was observed between the less effortful choices’ mean score and the MS
High Standards subscale score (r = 0.702 p ≤ 0.001) (Figure 4B). No other significant cor-
relations were observed.
Figure 4. (A,B) Correlation between behavioral and psychometric (MS) data. The scaer plots dis-
play (A) a negative correlation between the more effortful choices’ means score and the MS High
Standards subscale, (B) a positive correlation was observed between the less effortful choices’ mean
score and the MS High Standards subscale score.
4. Discussion
The current work examined the behavioral and EEG correlates of a novel behavioral
task concerning the decision between selecting more effortful (but highly rewarding) and
less effortful (but less rewarding) options. The results derived from the analysis of a
sample of healthy adults showed a significant tendency towards selecting more effortful
options compared to less effortful ones. Interestingly, individuals who scored higher on
the MS High Standards subscale were more inclined to choose less effortful options;
conversely, those who scored lower on this subscale were more likely to choose a more
effortful, yet more rewarding, option. Within this framework, the EEG findings suggest
that the choice of a less effortful option is associated with a greater increase in delta,
theta, alpha and beta band power in the right temporoparietal area, and a lower activa-
tion of the delta and theta bands in the contralateral site. Furthermore, the delta band
decreased in the left frontal area during the less effortful task compared to the more
effortful option. These main findings will be discussed below.
Firstly, the analysis of the behavioral data confirms our prediction and shows that
participants tended to prefer the more effortful compared to the less effortful option,
given the inner probability of the more effortful option to yield a higher reward. In line
with the scientific literature on the EEfRT, individuals tend to display a greater willing-
ness to expend effort for rewards [15,16]. Our results are in line with this evidence and
demonstrate that this is true even though the task may be more effortful, and therefore
boring and alienating. Thus, it seems that most people logically tend to choose the “most
advantageous” option (with the greatest expected reward) even if “it can be boring and
tiring”.
Yet, our data show that even less effortful choices (options with lower monotony and
reward) are selected. Indeed, as a second result, it was found that less effortful options
are more commonly selected by those who tend to obtain higher scores on the High
Standards subscale of the MS; and in turn, individuals who display lower scores on this
MS subscale are likely to prefer a boring, even if more rewarding, task.
According to the literature, “Maximizing” means that one is willing to invest re-
sources to find a solution that is even marginally superior to the greatest one that has
been achieved so far. Specifically, high standards have been linked with perfectionism,
remorse, and the desire for cognition, rather than pleasure, optimism, and life satisfaction
[36]. So, the current results suggest that in the “meta-process” in which people strive to
Figure 4. (A,B) Correlation between behavioral and psychometric (MS) data. The scatter plots display
(A) a negative correlation between the more effortful choices’ means score and the MS High Standards
subscale, (B) a positive correlation was observed between the less effortful choices’ mean score and
the MS High Standards subscale score.
4. Discussion
The current work examined the behavioral and EEG correlates of a novel behavioral
task concerning the decision between selecting more effortful (but highly rewarding) and
less effortful (but less rewarding) options. The results derived from the analysis of a sample
of healthy adults showed a significant tendency towards selecting more effortful options
compared to less effortful ones. Interestingly, individuals who scored higher on the MS
High Standards subscale were more inclined to choose less effortful options; conversely,
Appl. Sci. 2024,14, 4793 9 of 13
those who scored lower on this subscale were more likely to choose a more effortful, yet
more rewarding, option. Within this framework, the EEG findings suggest that the choice
of a less effortful option is associated with a greater increase in delta, theta, alpha and beta
band power in the right temporoparietal area, and a lower activation of the delta and theta
bands in the contralateral site. Furthermore, the delta band decreased in the left frontal area
during the less effortful task compared to the more effortful option. These main findings
will be discussed below.
Firstly, the analysis of the behavioral data confirms our prediction and shows that
participants tended to prefer the more effortful compared to the less effortful option, given
the inner probability of the more effortful option to yield a higher reward. In line with the
scientific literature on the EEfRT, individuals tend to display a greater willingness to expend
effort for rewards [
15
,
16
]. Our results are in line with this evidence and demonstrate that
this is true even though the task may be more effortful, and therefore boring and alienating.
Thus, it seems that most people logically tend to choose the “most advantageous” option
(with the greatest expected reward) even if “it can be boring and tiring”.
Yet, our data show that even less effortful choices (options with lower monotony and
reward) are selected. Indeed, as a second result, it was found that less effortful options are
more commonly selected by those who tend to obtain higher scores on the High Standards
subscale of the MS; and in turn, individuals who display lower scores on this MS subscale
are likely to prefer a boring, even if more rewarding, task.
According to the literature, “Maximizing” means that one is willing to invest resources
to find a solution that is even marginally superior to the greatest one that has been achieved
so far. Specifically, high standards have been linked with perfectionism, remorse, and the
desire for cognition, rather than pleasure, optimism, and life satisfaction [
36
]. So, the current
results suggest that in the “meta-process” in which people strive to balance the decisional
costs and the utility of the chosen option, individuals with higher High Standards scores
seem to devalue the reward, and place greater weight on a low decisional cost (i.e., they
value more highly the importance of carrying out a less boring task). Alternatively, perhaps
these individuals do not feel that they do not want to carry out the more effortful task that
leads to an unsatisfactory repetition (albeit with a greater gain), and are driven by wanting
to minimize the possibility of getting bored (in a certain sense, at any cost). In future
studies, it would be interesting to understand if an individual characteristic such as the
one measured by the MS High Standards is also accompanied by a personality profile or a
decision-making style that leads individuals to make less effortful choices more frequently
in everyday life.
So, which implicit processes underline the selection of less effortful and apparently
less advantageous choices? In this neuroscientific study, we preliminarily explored the
neurophysiological (EEG) correlates associated with the choice of more effortful and less
effortful options. In this way, the behavioral results showing a preference for more effortful
over less effortful options can be integrated by observing another level of analysis, which
deepens the neural activations related to the participants’ effortful or effortless choices.
Thus, as a third interesting effect, we observed a higher activation of the delta, theta,
alpha and beta bands in TP10 during the less effortful compared to more effortful choice.
With reference to the localization of this significant neural effect in the right temporoparietal
area, the right Temporo Parietal Junction (rTPJ) has been consistently shown to be significant
when inferring and comprehending the mental states of others in several fMRI studies,
highlighting its notable relevance for ToM processing [
43
,
44
]. However, the right TPJ also
plays a role in other cognitive processes, and is shown to be involved in interactional
mechanisms in which awareness permits the control of attention [
45
] and decision-making
processes. Previous studies have shown that during more effortful tasks that induce
boredom, levels of commission errors increase, and there is a reduction in two Event-Related
Potentials (ERP, namely, P3 and error-related negativity) related to attention, suggesting
an inadequate engagement of attentional resources [
46
]. Thus, this increase in all EEG
frequency bands in the rTPJ may be due to the presence of greater awareness during less
Appl. Sci. 2024,14, 4793 10 of 13
effortful choices, compared to more effortful choices, which instead involve more automatic
behavioral patterns.
In addition, through the use of functional neuroimaging in conjunction with con-
tinuous theta-burst stimulation, Soutscheck and collagues (2021) showed how the rTPJ
contributes to the delay of gratification and promotes the processing of future events [
47
].
Thus, a possible alternative explanation for this wide increase in EEG activity in rTPJ
could be linked to the role of this region in delayed gratification. So, it might be that the
immediate gratification for selecting less effortful options is related to not having wasted
time performing a boring task and still getting a reward. Indeed, the choice of the less
effortful option still provides a reward (50% lower than the more effortful choices), so it is
possible to state that in this case, it is the “cost” variable rather than the “benefit” variable
(i.e., reward) that weighs more.
On the other hand, a decrease in delta and theta bands was observed in the controlat-
eral site (left TPJ, lTPJ) for the less effortful compared to the more effortful option. Theta
oscillations in the prefrontal cortex were found during the stimulus-processing period [
48
],
while delta oscillations were found in the prefrontal cortex during the decision-making
period [
49
]. Also, the delta band is an index of the arousing power of the stimulus [
25
], and,
under our hypothesis, we expected a decrease in this marker in relation to the expectation
of a lower reward for the less effortful options. This result may appear counterintuitive;
however, it must be considered that, as seen in the previous effect, the delta band increases
significantly in the rTPJ for less effortful options (which include an amount of reward).
Thus, it is possible to read the current result considering the increase in delta band power
in rTPJ and its lower activation in lTPJ for the less effortful option. The suppression of the
delta and theta bands during the less effortful options can be interpreted via consideration
of the localization site, which is the lTPJ.
Further studies will have to clarify the role of low-frequency waves in posterior sites
in relation to the phenomenon in question. Nonetheless, these effects confirm the presence
of EEG frequency bands in brain posterior areas during the processing of reward-related
options with different degrees of effort, and suggest that less effortful choices are more
marked by right temporoparietal EEG activation than left.
Finally, the delta band decreased in the left PFC during the less effortful task compared
to the more effortful option. In previous studies, delta oscillations in the prefrontal cortex
were found to couple with beta oscillations in the premotor/motor cortex and guide
decision-making [
49
,
50
]. Moreover, a former work showed that greater LFA (relative to
the alpha band) was associated with increased willingness to choose the hard task and
expend greater effort for a larger potential reward [
16
]. Perhaps in this context, the increase
in delta band in the left frontal areas during more effortful tasks is a marker of the decision
to engage in a task considered more rewarding, while this correlate decreases significantly
for less rewarding (less effortful) options.
Overall, this study revealed a significant tendency to select more effortful options
over less effortful ones in this sample of healthy participants. This behavior is closely tied
to individuals’ scores on the MS High Standards subscale: those with higher scores tend
to choose less effortful options, while those with lower scores prefer more effortful, yet
potentially more rewarding, choices.
Crucially, EEG findings unveil the neural correlates of these behavioral tendencies.
When individuals choose less effortful compared to more effortful options, there is a
significant increase in delta, theta, alpha, and beta band power in the right temporoparietal
area, alongside a decrease in delta and theta activation in the contralateral (left) site.
Additionally, delta band power decreases in the left frontal area during the less effortful
task compared to the more effortful option. These EEG patterns underscore the distinct
neural activities that underpin the cognitive and motivational factors driving the choice
between effort levels.
The present study is not exempt from limitations. First, it would benefit from a repli-
cation with a larger sample size to increase the validity of its findings. Furthermore, in this
Appl. Sci. 2024,14, 4793 11 of 13
study, there was no gender-specific hypothesis, and no gender differences were identified
in our analysis. However, other research has previously highlighted gender differences
in reward-based decision-making [
51
,
52
]; consequently, it is worthwhile to investigate
gender-related effects in larger cohorts. At the methodological level, Muse
TM
EEG has
been proven to be a valid EEG tool for mapping phenomena related to decision-making
in several ecological contexts [
19
–
21
]; however, it does not guarantee complete coverage
of the scalp for EEG data collection. Future studies could replicate the present study,
employing high-density EEG to explore this phenomenon more thoroughly, considering
the neuroanatomical object and possible developments related to neurofunctional mapping.
Also, future research could deepen the explicit self-reported reasons for choosing more
effortful versus less effortful alternatives.
Finally, reward-based decisions can also depend on the decision-making style of
the decision-maker, on his personality traits, on the levels of impulsivity or even on the
sensitivity to reward; therefore, in future studies it will be appropriate to consider the
integration of self-report scales assessing such individual characteristics.
To conclude, this research discussed the behavioral and implicit EEG correlates associ-
ated with the choice of a more effortful (but more rewarding) or a less effortful (and less
rewarding) option. We were aware that the more effortful and more rewarding options
seem to be the most logical choice to select, and so the current work underlines the EEG
correlates of less frequently chosen, and apparently more irrational, less effortful choices,
which are not selected for their rewarding value, but are more related to the tendency to
want to avoid monotony and automatisms and maximize future possibilities. The current
results show that one does not always choose profit if it is too tiring.
Author Contributions: M.B.: conceptualization, methodology, project administration, supervision,
validation, writing—review and editing. C.A.: data curation, investigation, formal analysis, valida-
tion, writing—original draft, writing—review and editing. L.A.: conceptualization, methodology,
investigation, validation, writing—original draft, writing—review and editing. All authors have read
and agreed to the published version of the manuscript.
Funding: This research was funded by the D1—2023 funding of the Catholic University of the Sacred
Heart, Milan, Italy.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki (2013) and approved by the Ethics Committee of the Department of Psychology, Catholic
University of the Sacred Heart, Milan, Italy (approval code—2021 TD for thesis dissertation; approval
date—1 December 2021).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author due to ethical reasons related to sensitive personal data protection (requests
will be evaluated according to the GDPR—Reg. UE 2016/679 and its ethical guidelines).
Conflicts of Interest: The authors declare no conflicts of interest.
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