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Citation: Balconi, M.; Acconito, C.;
Allegretta, R.A.; Angioletti, L.
Neurophysiological and Autonomic
Correlates of Metacognitive Control of
and Resistance to Distractors in
Ecological Setting: A Pilot Study.
Sensors 2024,24, 2171. https://
doi.org/10.3390/s24072171
Academic Editor: Fow-Sen Choa
Received: 12 February 2024
Revised: 21 March 2024
Accepted: 27 March 2024
Published: 28 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
sensors
Article
Neurophysiological and Autonomic Correlates of Metacognitive
Control of and Resistance to Distractors in Ecological Setting:
A Pilot Study
Michela Balconi 1,2 , Carlotta Acconito 1, 2, * , Roberta A. Allegretta 1,2 and Laura Angioletti 1,2
1International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the
Sacred Heart, Largo Gemelli 1, 20123 Milan, Italy; michela.balconi@unicatt.it (M.B.);
robertaantonia.allegretta1@unicatt.it (R.A.A.); laura.angioletti1@unicatt.it (L.A.)
2Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the
Sacred Heart, Largo Gemelli 1, 20123 Milan, Italy
*Correspondence: carlotta.acconito1@unicatt.it; Tel.: +39-0272345929
Abstract: In organisational contexts, professionals are required to decide dynamically and prioritise
unexpected external inputs deriving from multiple sources. In the present study, we applied a
multimethodological neuroscientific approach to investigate the ability to resist and control ecological
distractors during decision-making and to explore whether a specific behavioural, neurophysiological
(i.e., delta, theta, alpha and beta EEG band), or autonomic (i.e., heart rate—HR, and skin conductance
response—SCR) pattern is correlated with specific personality profiles, collected with the 10-item Big
Five Inventory. Twenty-four participants performed a novel Resistance to Ecological Distractors (RED)
task aimed at exploring the ability to resist and control distractors and the level of coherence and
awareness of behaviour (metacognition ability), while neurophysiological and autonomic measures
were collected. The behavioural results highlighted that effectiveness in performance did not require
self-control and metacognition behaviour and that being proficient in metacognition can have an
impact on performance. Moreover, it was shown that the ability to resist ecological distractors
is related to a specific autonomic profile (HR and SCR decrease) and that the neurophysiological
and autonomic activations during task execution correlate with specific personality profiles. The
agreeableness profile was negatively correlated with the EEG theta band and positively with the EEG
beta band, the conscientiousness profile was negatively correlated with the EEG alpha band, and the
extroversion profile was positively correlated with the EEG beta band. Taken together, these findings
describe and disentangle the hidden relationship that lies beneath individuals’ decision to inhibit
or activate intentionally a specific behaviour, such as responding, or not, to an external stimulus, in
ecological conditions.
Keywords: resistance to distractors; metacognition; decision-making; EEG; autonomic measures;
personality profiles
1. Introduction
In organisational contexts, professionals are frequently required to make decisions
rapidly and dynamically, interrupting workflows and, when necessary, prioritising unex-
pected external inputs deriving from multiple sources, a process that can often be effortful,
and it is not always successful [1,2].
Indeed, to efficiently manage a decision and, at the same time, be able to correctly
select the stimuli that deserve immediate attention and inhibit the behaviour towards
distractors, an essential role is played by executive functions (EFs). According to Miller
and Cohen, indeed, EFs are high-level cognitive processes that are crucial for managing
attentional resources, impulsive actions, and promoting behaviour [
3
]. Thanks to EFs, an
Sensors 2024,24, 2171. https://doi.org/10.3390/s24072171 https://www.mdpi.com/journal/sensors
Sensors 2024,24, 2171 2 of 18
individual is indeed able to inhibit automatic responses and impulses, manipulate and
recall information from memory, and respond flexibly to environmental changes [4].
In particular, three partially separable components of EFs are involved in the decision-
making process: (i) updating, understood as the constant monitoring and rapid addition
and/or deletion of working-memory contents; (ii) shifting, in terms of flexible switching
from one task to another or from one mental set to another; and (iii) inhibition, the ability
to deliberately override dominant or predominant responses [
5
,
6
]. Moreover, according
to Friedman et al. (2008), inhibition can be defined as the lowest common denominator in
all these functions [
7
]. Inhibition, in fact, which is responsible for the active maintenance
and goal management of the current task, is included in the EFs of updating and shifting,
which, however, also involve other abilities. For example, in the updating function, there
is not only the deliberate inhibition of certain responses, but also the monitoring of the
current contents of working memory. Similarly, in the shifting function, it is necessary to
implement a process of inhibiting the less salient information from time to time, to move
from one task to another flexibly.
The ability to regulate the inhibition process and the subset of control mechanisms respon-
sible for monitoring cognitive control is called metacontrol [
8
]. The function of metacontrol
consists of adopting different control modalities in response to various tasks, difficulties, or
different possible consequences [9], according to a cost–benefit analysis [10,11].
Specifically, according to a review of the scientific literature, there are two distinct control
systems—differing in flexibility and computational cost—that reflect two decision-making
strategies [
12
,
13
]: model-free decision-making and model-based decision-making [
12
,
14
].
These decision-making strategies represent a distinction between automatic and deliberative
modes of information processing [15].
Model-free decision-making is defined as a reflexive strategy, in which decisions are
made based on previously experienced and learned action–reward associations [
13
]. It is,
therefore, a simple strategy, which can be inaccurate and ineffective if people face new
stimuli/situations that have not been previously experienced and learned. From this
perspective, then, this process can be effective and facilitate decision-making in familiar
and well-known situations, but it can also be more energy-consuming and less effective
in ambiguous, new contexts and in response to distracting elements that may not have
been previously experienced, and to which one is not used to responding. On the other
hand, model-based decision-making is defined as a deliberative and prospective strategy,
in which decisions are made by evaluating different choice options and their respective
consequences. This strategy is usually more accurate and effective, but it also requires
greater cognitive effort. At the same time, however, making decisions based on this strategy
provides higher behavioural flexibility because dynamic changes in the environment can
be accounted for more quickly.
In this theoretical framework, it is important to highlight that cognitive control often
requires a balance between goal persistence and flexibility as a key component of decision-
making [
16
]. Goal persistence can help to focus on relevant information and suppress
irrelevant external stimuli, which, because of their possible salience, may trigger automatic
responses in individuals, distracting them from the cognitive process. In this regard,
it can also increase the likelihood of the cognitive system becoming too inflexible and
insensitive to alternative possibilities and to important external features of the environment.
In turn, adopting a flexible approach facilitates switching between alternative possibilities
and actions; however, it can increase the probability of distraction and possible mistakes
between cognitive representations [16,17].
The Metacontrol State Model (MSM), a model developed by Hommel, can be used to
better understand how this balance works [
8
]. According to the MSM, decision-making is
understood as a process of competition between options based on goals, and an optimal
decision-making process can be described as a balance between persistence and flexibility.
Thus, if two or more goal-related representations compete for consideration, selection
requires one alternative to pass a certain threshold, which, given mutual inhibition, would
Sensors 2024,24, 2171 3 of 18
result in the relative inhibition of the unselected alternative. The individual metacontrol
state, which ranges from persistence to flexibility, determines the extent to which alterna-
tives compete and the extent to which they are affected by current goals. Extreme flexibility
would consist of minimal competition, while extreme persistence would consist of sig-
nificant mutual competition [
8
]. In this sense, cognitive control can be defined not only
as a top-down mechanism [
18
], but also as a bottom-up process triggered by contextual
elements, indicating an automatic aspect of cognitive control [19].
In addition, previous studies [
20
,
21
] identified two different types of metacontrol:
(i) proactive metacontrol, according to which a list of all goal representations is maintained
in memory and can be activated at the beginning of any decision-making process, allowing
individuals to anticipate and prevent the possible interference of distractors [
22
], and
(ii) reactive or transient metacontrol, in which goal representations are activated and
retrieved only after the beginning of a specific decision-making process and following the
detection of a specific interference [22].
To sum up, proactive metacontrol allows behaviours to be continuously modified to
facilitate the achievement of the goal [
23
], but it is effortful, given that it requires a high level
of consumption of cognitive resources [
22
,
23
], while reactive metacontrol is effortless, since
it makes it possible to allocate one’s resources to several tasks simultaneously. However, in
this last type of metacontrol, there is a greater dependence on the eliciting events themselves
because, if they are not sufficiently salient or discriminating, they will not result in the
reactivation of the goal [23].
To study metacontrol and the degree to which the “intentional” and “automatic”
pathways affect decision-making processes, performance tests and tasks derived from the
field of cognitive psychology, such as the Stroop test [
24
,
25
] and the Simon effect [
26
],
were widely used in previous works. However, two main limitations characterise such
works. First, these tasks do not measure specifically and in an ecological way the abil-
ity to resist and control contextual distractors when taking a decision [
27
], as a proxy of
a successful decision-making process in organisational contexts. Secondly, to obtain a
more comprehensive and deep study of the phenomenon, it is possible to adopt a multi-
methodological approach that combines multiple assessment levels (i.e., the behavioural,
neurophysiological, autonomic, and self-report levels) and allows a focus not only on the
explicit components of metacontrol (e.g., the behavioural correlates), but also on the implicit
components, such as the neurophysiological basis of the process. This multimethodological
approach was exploited in previous basic neuroscience research [
28
], as well as in applied
neuroscience studies. The aims were to deepen the influence of and resistance to nudge
decision-making in a group of professionals when performing a behavioural task in eco-
logical conditions [
29
], as well as to explore the neurophysiological correlates of a novel
behavioural task assessing decision-making functions [30].
Indeed, the behavioural level, encompassing behavioural measures of performance
[such as the participant’s response, the response times (RTs)] and the individual’s metacon-
trol ability (in terms of consistency and awareness of his/her behavioural performance), can
be complemented by the neurophysiological and autonomic level, providing information
on the contribution of the central nervous system (CNS) and autonomic nervous system
(ANS) to the process.
Starting from the behavioural level, in addition to the participants’ responses, RTs
can be considered as indirect workload measures to evaluate participants’ cognitive effort
during the performance of a task. For example, collecting RT during a task designed to
explore the ability to resist and control contextual distractors may provide insight into the
cognitive load and effort required to implement an inhibitory process. In particular, longer
RTs reflect greater task-related effort and, in turn, might suggest greater effort in inhibiting
automatic behaviours elicited by salient external cues.
The literature suggests various ways to measure an individual’s metacontrol ability in
terms of the consistency and awareness of behavioural performance [
31
]. The most widely
used method to assess metacontrol is to directly ask participants to label their experience
Sensors 2024,24, 2171 4 of 18
while performing a task. For example, Wenke et al. explore the sense of control and overall
confidence in one’s judgments on a scale ranging from 0 to 100 [
32
]. Questienne et al. (2018),
instead, tested the metacognitive experience of response conflict by asking their participants
to answer the question, “Do you think there was a conflict between the two arrows in this process?”
by choosing from several possible alternatives related to whether the participant believed
there was a conflict [33].
Secondly, regarding the CNS markers, electrophysiological (EEG) frequency bands (i.e.,
delta, theta, alpha, beta, and gamma), their functional meaning and their brain localisation
make it possible to understand the cognitive and attentional effort in the information
processing [
34
] and emotional processing [
28
] associated with decision-making [
35
] and
metacontrol. For instance, Basharpoor et al. (2021) demonstrated a relationship between EFs
and the EEG activity of the theta, beta, and alpha bands in the frontal regions; specifically,
frontal brain activity is associated with EFs and cognitive control [
36
]. Furthermore, the
theta band has been associated with an increased cognitive control mechanism in people
who are less prone to taking risks and changing their decision-making process [
37
,
38
]. The
beta band, instead, can be interpreted as an index of active attention and engagement [
34
].
The activation of this specific band might also be associated with inhibitory control, which,
as mentioned above, could be an essential skill for resisting and controlling contextual
distractors in the decision-making process [
39
]. On the other hand, a greater activation
of the alpha band is an indicator of cognitive effort and engagement during the decision-
making process, as shown by Runnova et al. [
40
]. Additionally, the presence of the alpha
band in the parietal areas has been interpreted as an index of attentional requests from the
environment [41].
Thirdly, focusing on ANS measures, the electrodermal activity (EDA) and cardiac
parameters provide information on the ANS responsiveness in terms of electrodermal (skin
conductance Level—SCL, skin conductance response—SCR) or cardiac activity (i.e., heart
rate—HR, heart rate variability—HRV) through an autonomic measurement recording
tool (e.g., biofeedback) [
42
]. In particular, within the research conducted on decision-
making processes, Dawson et al. (2011) highlighted how the SCR reflects the anticipation
of a potential negative outcome and its absence might instead be associated with the
decision not to take any risks during the decision-making process [
43
]. Regarding the
inhibitory process, a decrease in HR has been shown to be related to intentional action and
inhibition [
44
], which is more pronounced in complex situations [
45
], such as the decision-
making process. Furthermore, ANS indices have been identified as correlates of emotional
processes, such as emotional engagement or stress levels, during moral-decision-making
tasks [46,47].
Fourthly, concerning the self-report level, it should be noted that different personality
profiles have also been associated with the manifestation of specific EEG frequency bands.
For example, Li et al. found associations between the alpha and beta bands over the frontal
area and the agreeableness personality profile, and between the theta and beta activities
in the temporal and parietal area and the conscientiousness personality. Furthermore, the
extraversion profile was shown to correlate with the delta and beta bands in the frontal
and with the theta in the occipital areas [48].
Moreover, there is considerable evidence that personality is associated with human
decision-making performance: in particular, it has been demonstrated that an impulsive
personality is linked to risky decisions in the social, ethical, and gambling domains, whereas
anxiety is linked to risk-averse choices [
49
]. Additionally, the personality attribute of
extraversion is connected to the inhibitory process [
50
] and is found to be central to
metacognitive awareness, as well as the personality profile of openness [
51
]. Finally, a
recent finding suggests the association between a high level of conscientiousness and
agreeableness and the inhibition EF [52].
Therefore, when adopting a multimethodological approach, it proves interesting to
include self-report measures of individuals’ characteristics, such as the 10-item Big Five
Inventory (10-item BFI) [
53
], which allows the highlighting of five specific personality
Sensors 2024,24, 2171 5 of 18
profiles: extroversion, agreeableness, conscientiousness, emotional stability, and open-
mindedness. It is also pertinent to suppose that different personality profiles might be
associated with different levels of ability to resist distractors, as well as with the activation
of specific neurophysiological and autonomic patterns.
Within this theoretical framework, this study applied a multimethodological neuro-
scientific approach to investigate the behavioural, neurophysiological, autonomic, and
self-report correlates of the ability to resist and control ecological distractors during an
ecological-decision-making task. The participants performed a novel Resistance to Ecologi-
cal Distractors (RED) task while neurophysiological and autonomic data were continuously
detected through the recording of EEG and autonomic measures. At the end of the task,
the 10-item BFI [53] was administered to investigate the individuals’ personality profiles.
The goal of this research was to explore whether a specific behavioural, neurophysio-
logical, autonomic pattern activated during the task is correlated with specific personal-
ity profiles.
Specifically, with respect to behavioural data, it was first hypothesised that a correla-
tion would be identified between the participants’ ability to resist and control contextual
distractors (computed in an index considering each participant’s behavioural responses
and RTs) and the coherence and awareness of their behaviour (i.e., metacognitive ability).
Specifically, it was expected that the increased ability to resist and control distractors,
involving the adoption of a persistence approach, would lead the participants to becoming
more aware of what had been done.
With reference to the relationship between behavioural indices of resistance, metacog-
nition and neurophysiological (EEG frequency bands), we expected to observe a positive
correlation between the theta band power in the frontal brain regions, as a marker of
increased cognitive control [
37
,
38
], and the ability to resist and control contextual distractor
stimulus and metacognition. In addition, we expected to find a positive correlation between
the power of the beta band in the frontal areas, as an indicator of attention and involve-
ment [
34
], and the same behavioural correlates (the ability to resist ecological distractors
and metacognition).
Concerning the ANS indices, it is thought that the inability to resist and control
contextual distractor stimulus could be associated with higher levels of SCR and HR,
as an index of emotional and cognitive engagement [
46
,
47
]. Indeed, it is possible to
suppose that a consideration of and elaboration on the distractor stimulus could involve
a more flexible approach and since, with this approach, the individual switches between
different goals, greater cognitive effort and emotional engagement are required, as the
main information-processing process is interrupted to respond to the external stimulus
and resumed later [
16
,
23
]. On the other hand, a decrease in HR may be correlated with the
ability to resist and control distractors, as an index of the inhibition process [45].
Finally, with reference to the relationship between a specific neurophysiological profile
supporting task performance and personality profiles, it can be supposed that a lower level
of agreeableness is associated with greater activation in low-frequency bands (i.e., theta)
during task performance, as an indication of an increased cognitive control mechanism
in people who are less prone to taking risks and to changing their decision-making pro-
cesses [
37
,
38
]. Similarly, a higher level of agreeableness and extroversion could be associ-
ated with higher task-related activity in the beta band, as an index of active attention and
engagement [
34
]. Conscientious people, on the other hand, may have less activation of the
alpha band, as an indicator of cognitive effort and engagement during the decision-making
process [40] in individuals who tend towards precision, accuracy, and success.
Furthermore, regarding the relationship between the autonomic indices supporting
task performance and personality profiles, a higher level of extroversion would be expected
to correlate with a decrease in HR while performing a task, as an extroverted personality is
focused, engaged, and concentrated on gathering as much detail and stimuli as possible
about the task it is performing, inhibiting possible external influences [54].
Sensors 2024,24, 2171 6 of 18
2. Materials and Methods
2.1. Sample
In total, 24 healthy individuals, 11 females and 13 males, with an average age of
35.33 years (Standard Deviation
age
= 11.70) were recruited to participate in this study vol-
untarily and without receiving any compensation for their participation. The sample was
defined according to the following exclusion criteria: history of neurological or psychiatric
disorders, severe depressive episodes, high level of stress, low global cognitive functioning,
and undertaking therapy based on psychoactive drugs that can alter cognitive decision-
making functioning.
In addition, all individuals had normal-to-corrected vision and, to be included in the
experimental sample, they signed their written informed consent. Moreover, the sample
members were right-handed and there were no differences in education level or type
of profession. The experimental study was conducted in accordance with the Helsinki
Declaration (2013) and approved by the Ethics Committee of the Department of Psychology,
Catholic University of the Sacred Heart of Milan in Italy.
2.2. Procedure
The whole experimental procedure lasted approximately 20 min and was conducted
in a dedicated and quiet room where participants sat in a comfortable chair in front of a
computer monitor 80 cm from their faces. After being fully instructed on the experimental
procedure, the enrolled individuals filled in their informed consent. Subsequently, the
EEG wearable MUSE
TM
headband (version 2; InteraXon Inc., Toronto, ON, Canada) was
applied to the participants’ heads and the X-pert2000 portable Biofeedback (Schuhfried
GmbH, Modling, Austria) was placed on the non-dominant hand for recording a total of
120 s of neurophysiological and autonomic resting-state baseline. Participants were then
presented with the RED task and their neurophysiological and autonomic activity was
collected continuously through the task.
Following task completion, participants filled in the 10-item Big Five Inventory ques-
tionnaire [
53
]. Figure 1shows the experimental setup with the EEG wearable MUSE
TM
headband, the X-pert2000 portable Biofeedback, and the computerised task.
Sensors 2024, 24, x FOR PEER REVIEW 6 of 20
personality is focused, engaged, and concentrated on gathering as much detail and stimuli
as possible about the task it is performing, inhibiting possible external influences [54].
2. Materials and Methods
2.1. Sample
In total, 24 healthy individuals, 11 females and 13 males, with an average age of 35.33
years (Standard Deviationage = 11.70) were recruited to participate in this study voluntarily
and without receiving any compensation for their participation. The sample was defined
according to the following exclusion criteria: history of neurological or psychiatric
disorders, severe depressive episodes, high level of stress, low global cognitive
functioning, and undertaking therapy based on psychoactive drugs that can alter
cognitive decision-making functioning.
In addition, all individuals had normal-to-corrected vision and, to be included in the
experimental sample, they signed their wrien informed consent. Moreover, the sample
members were right-handed and there were no differences in education level or type of
profession. The experimental study was conducted in accordance with the Helsinki
Declaration (2013) and approved by the Ethics Commiee of the Department of
Psychology, Catholic University of the Sacred Heart of Milan in Italy.
2.2. Procedure
The whole experimental procedure lasted approximately 20 min and was conducted
in a dedicated and quiet room where participants sat in a comfortable chair in front of a
computer monitor 80 cm from their faces. After being fully instructed on the experimental
procedure, the enrolled individuals filled in their informed consent. Subsequently, the
EEG wearable MUSETM headband (version 2; InteraXon Inc., Toronto, ON, Canada) was
applied to the participants’ heads and the X-pert2000 portable Biofeedback (Schuhfried
GmbH, Modling, Austria) was placed on the non-dominant hand for recording a total of
120 s of neurophysiological and autonomic resting-state baseline. Participants were then
presented with the RED task and their neurophysiological and autonomic activity was
collected continuously through the task.
Following task completion, participants filled in the 10-item Big Five Inventory
questionnaire [53]. Figure 1 shows the experimental setup with the EEG wearable MUSETM
headband, the X-pert2000 portable Biofeedback, and the computerised task.
Figure 1. Experimental procedure. The figure shows the experimental procedure with the EEG
wearable MUSETM headband and the X-pert2000 portable Biofeedback adopted to collect EEG and
autonomic activity during the duration of the RED task. At the end of the task, the 10-item Big Five
Inventory questionnaire was administered.
Figure 1. Experimental procedure. The figure shows the experimental procedure with the EEG
wearable MUSE
TM
headband and the X-pert2000 portable Biofeedback adopted to collect EEG and
autonomic activity during the duration of the RED task. At the end of the task, the 10-item Big Five
Inventory questionnaire was administered.
Sensors 2024,24, 2171 7 of 18
2.2.1. The Resistance to Ecological Distractors Task (RED Task)
The Resistance to Ecological Distractors (RED) task was administered via a web-based
experiment-management platform (PsyToolkit, version 3.4.4) [
55
,
56
] and was designed to
assess individuals’ ability to optimally resist and control distractors ecologically defined by
contextual cues in decision-making conditions. The participants were presented with two
realistic decision-making scenarios, in which they were asked to identify themselves and
make decisions.
Each scenario consisted of listening to a dialogue between two persons. The participant
was asked to understand the dialogue and count the number of times a specific background
sound was presented during the dialogue. In the middle of the dialogue, participants were
presented with a distracting stimulus: in particular, on the screen appeared the notification of
a call coming from a colleague. The participant had to decide whether to accept or reject the
call. After this decision, the dialogue resumed and concluded (see Table 1for the scenarios).
For instance, in the first scenario, participants received the following instructions
and reminder:
“Today is a particularly busy working day. You ate fast in the cafeteria aware that at 2:00
p.m. you have one last update meeting for a project that needs to be finished within two
weeks. Attention: during the meeting, you will listen to, you will have to understand the
dialogue and count how many times you hear a sound similar to a notification”.
“Reminder: Recall as soon as possible Roberto Rossi (project manager of the company).”
At the end of each dialogue, the participants were explicitly asked to (i) indicate
their decision regarding the distractor stimulus (whether they accepted or rejected the
call), (ii) justify their decision by selecting a specific reason for their choice among various
answer options, and (iii) specify the number of times they heard the background sound.
On the basis of their decision regarding the distractor stimulus (whether they accepted
or rejected the call), they were presented with one of the following multiple-choice questions.
If participants chose to respond to the colleague’s call that appeared during the
dialogue, they had to justify that choice by selecting one of the following options:
(a) No doubt it was more important to respond immediately to the call because there was a greater
need to speak with the colleague as soon as possible;
(b) It seemed better to me to solve first the question of the pending call and then dedicate myself to
the meeting;
(c)
Since the reminder had been set at that time, it was important to respect the commitment made;
(d)
I reacted spontaneously, without thinking too much about it;
(e)
I did not make a choice.
Conversely, if the participants decided to decline their colleagues’ call, they had to
justify the choice made by selecting one option from the following alternatives:
(a)
No doubt it was more important to follow the meeting so as not to lose track of the sounds;
(b)
If I had answered, I would have lost important information about the project, which needs to
be finished soon;
(c)
It seemed better to do one thing at a time;
(d)
I reacted spontaneously, without thinking too much about it;
(e)
I did not make a choice.
This last question was administered under time pressure, with 20 s as the maximum
time window to provide a response.
Sensors 2024,24, 2171 8 of 18
Table 1. The realistic decision-making scenarios with related distractor stimulus and metacognition
questions.
Scenario Distractor
Stimulus Metacognition Questions
Today is a particularly busy working day. You
ate quickly in the cafeteria, aware that at
2:00 p.m. you have one last update meeting for a
project that needs to be finished within two
weeks. Attention: during the meeting you will
listen to, you will have to understand the
dialogue and count how many times you hear a
sound similar to a notification.
Reminder: Recall as soon as possible Roberto
Rossi (project manager of the company).
Notification of a call
coming from a colleague
Acceptance of the distractor stimulus
(a)
No doubt it was more important to
respond immediately to the call because
there was a greater need to speak with the
colleague as soon as possible
(b)
It seemed better to me to solve first the
question on the pending call and then
dedicate myself to the meeting
(c)
Since the reminder had been set at that
time, it was important to respect the
commitment made
(d)
I reacted spontaneously, without thinking
too much about it
(e)
I did not make a choice
Resistance to the distractor stimulus
(a)
No doubt it was more important to follow
the meeting so as not to lose track of
the sounds
(b)
If I had answered, I would have lost
important information about the project,
which needs to be finished soon
(c)
It seemed better to do one thing at a time
(d)
I reacted spontaneously, without thinking
too much about it
(e)
I did not make a choice
As in every year, by the end of this year, you
must complete 30 h of training through online
courses. You are completing a training session,
and you are required to listen to the audio
presented carefully, since you will be asked
specific questions at the end. If you fail to
answer these questions, you will have to start the
session over again. Attention: during the
training, you will listen to audio recordings, and
you will be tasked with understanding the
dialogue and counting how many times the
word “training” appears.
Reminder: You are waiting for an important
email about the approval of an investment
project; the deadline for submitting this project
to your supervisor is today, so it is important to
respond to this email as soon as possible after
you receive it.
Notification of the e-mail
about the approval of the
investment project
Acceptance of the distractor stimulus
(a)
No doubt it was more important to
respond immediately to the e-mail given
the close deadline
(b)
It seemed better to me to solve first the
question regarding the pending project’s
approval
(c)
Since my stakeholders were expecting my
response within a day, I responded
immediately to respect the
commitment made
(d)
I reacted spontaneously, without thinking
too much about it
(e)
I did not make a choice
Resistance to the distractor stimulus
(a)
No doubt it was more important to follow
the audio carefully so that I would not
have to repeat the training session later if I
got something wrong on the test
(b)
If I had answered, I would have lost
important information about the
training course
(c)
It seemed better to do one thing at a time
(d)
I reacted spontaneously, without thinking
too much about it
(e)
I did not make a choice
Sensors 2024,24, 2171 9 of 18
2.2.2. Behavioural Data Acquisition
For the behavioural data, both response scores (whether they accepted or rejected
distractor stimulus, [i.e., the call] and the option selected to justify the choice) and RTs were
collected for each type of scenario and then transcribed offline to create two behavioural
indices, the Resistance Index (Res-i) and the Metacognition Index (Met-i), respectively.
The Res-i was calculated through the following steps. First, a score of four points was
assigned if the participant did not respond to the distractor stimulus in either scenarios, two
points if the participant responded in only one scenario, and zero points if the participant
responded in both scenarios. This score was then converted into a common decimal metric
scale and composed the Res-i, an index measuring whether an individual is able to resist and
control distractors ecologically defined by contextual cues in the decision-making process.
The Met-i was computed through the following steps. First, a score ranging from one to
five points was assigned on the basis of the type of option chosen to justify the decision made
on the multiple-choice questions. In this case, five points were given if the chosen justification
reflected a high level of consistency in the actual behavioural performance. For instance, if the
participant chose to respond to the colleague’s call that appeared during the dialogue and
selected option (a), “No doubt it was more important to respond immediately to the call because there
was a greater need to speak with the colleague as soon as possible”, he/she obtained five points.
Similarly, if the participant decided to decline their colleague’s call and selected option (a),
“No doubt it was more important to follow the meeting so as not to lose track of the sounds”,
he/she obtained five points. For both multiple-choice questions, fewer points were attributed
if the chosen alternative represented a lack of reflection on the participant’s behaviour, which
then occurred almost spontaneously (e.g., two points if the participant selected option (d),
“I reacted spontaneously, without thinking too much about it”. In both cases, if participants selected
option (e), “I did not make a choice”, they obtained only one point.
The scores derived from the two scenarios were then averaged and converted to a
decile scale.
The RTs were collected for the answers from the two scenarios, divided by the total
allowed time (20 s), averaged, and converted in a decile scale.
These scores were then used to calculate Met-I, which is a ratio between the response
score and the RTs, both expressed in decile scale. The Met-i measures the level of consistency
between the decision made and the justification given in the management of distractors.
2.2.3. Big Five Inventory: Self-Report Data Acquisition
The Italian version of the 10 item-BFI [
53
] was administered to collect information on
individuals’ personality dimensions. Through a total of 10 items, this inventory makes it
possible to measure five different dimensions of personality: (i) extroversion; (ii) agree-
ableness; (iii) conscientiousness; (iv) emotional stability; and (v) open-mindedness. The
scale consists of an initial statement, “I see myself as a person who
. . .
”, and the participant
is asked to respond to each statement that describes his/her personality. Participants are
asked to respond on a five-point Likert-type scale ranging from 1 (“strongly disagree”) to
5 (“strongly agree”). Higher mean scores on a subscale indicate a greater presence of that
specific personality profile.
2.2.4. The MuseTM Headband for Neurophysiological Data Acquisition
The Muse
TM
headband (version 2; InteraXon Inc., Toronto, ON, Canada) was em-
ployed to collect, in a non-invasive manner, the neurophysiological data and to measure
EEG spectral activity changes, between the resting-state baseline and task phases. This
wearable tool, indeed, permits the detection of EEG neurophysiological activity via an
accelerometer, gyroscope, pulse oximetry, and seven electrodes. Of these seven electrodes,
specifically, three are used as references, while the others detect the EEG spectral activity
in the frontal (AF7 and AF8, left and right forehead, respectively) and temporoparietal
(TP9 and TP10, left and right hemisphere, respectively) areas, according to the interna-
Sensors 2024,24, 2171 10 of 18
tional 10–20 system [
57
]. These electrodes were made of conductive material (silver) and
silicon rubber.
The data were gathered at a sampling rate of 256 Hz via the mobile app Mind Monitor
and transferred via Bluetooth to the associated smartphone. Participants were instructed to
minimise their movements, in order to reduce artifacts in the EEG signal. Mind Monitor
applied a 50-hertz notch frequency filter, and data were then visually inspected to remove
motor artifacts (such as jaw clenching and eyeblinks). Through the logarithm of the power
spectral density of the raw EEG data from each channel, raw data were transformed by
using a Fast Fourier Transform (FFT) into brain waves at various frequency bands: delta
(1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–44 Hz). The
recording of a 120-s baseline took place at the beginning of the experimental phase and,
for each participant, EEG activity during the experimental conditions was weighted over
baseline values.
2.2.5. The X-Pert2000 Biofeedback for Autonomic Data Acquisition
The X-pert2000 portable Biofeedback system with a MULTI radio module (Schuhfried
GmbH, Modling, Austria) was employed to collect, in a non-invasive manner, the auto-
nomic data and to identify possible variations in skin conductance and cardiovascular
parameters, between the resting-state baseline and task phases. This portable tool, indeed,
permits the detection of peripheral parameters, such as skin conductance level (SCL), skin
conductance response (SCR), heart rate (HR), and heart rate variability (HRV), via a sensor
placed on the distal phalanx of the second finger of the non-dominant hand.
The data for skin conductance parameters (SCL and SCR) were gathered with an
electrodermal activity (EDA) gold electrode. On the other hand, the data on cardiovascular
parameters (HR and HRV), measured in beats per minute (bpm), were gathered with
photoplethysmography. To prevent non-dominant-hand movements from interfering
with the recordings, the accelerometer of the transmission unit was used, calibrated in
meters/second squared (m/s2).
2.3. Data Analyses
To explore whether a specific behavioural, neurophysiological, or autonomic pattern
activated during the task was correlated with specific personality profiles, the following
steps in analysis were performed. Before the analyses, the normality of data was tested
with Shapiro–Wilk and the normality of the data was confirmed.
First, a Pearson correlation between the two behavioural indices (Res-i and Meta-i)
was performed, to explore a potential relationship between task performance, in terms of
the ability to resist ecological and contextual distractors, and metacognition, understood
as the level of consistency between the decision made and the motivation given in the
management of distractors.
Secondly, the two behavioural indices (Res-i and Meta-i) were separately correlated
with EEG frequency bands (delta, theta, alpha, beta, and gamma) for the four electrodes
(AF7, AF8, TP9, TP10) and, secondly, with the autonomic indices (SCL, SCR, HR, HRV)
recorded during task performance. This step in analysis was performed to explore whether
a specific neurophysiological or autonomic profile supported task execution or the metacog-
nitive phase.
Finally, to check whether the neurophysiological or autonomic profile identified during
the task execution or the metacognitive phase was related to specific personality profiles,
Pearson correlations were performed between each of the EEG frequency bands (delta,
theta, alpha, beta, gamma) for the four electrodes (AF7, AF8, TP9, and TP10) recorded
during task performance and the 10-item BFI subscales scores, as well as between the
autonomic indices (SCL, SCR, HR, HRV) recorded during task performance and the 10-item
BFI subscales scores.
Sensors 2024,24, 2171 11 of 18
3. Results
For the first step in the analysis, the Pearson correlation performed between Res-i and
Meta-i showed a significant negative correlation (r = −0.694, p≤0.001) (Figure 2).
Sensors 2024, 24, x FOR PEER REVIEW 11 of 20
between the autonomic indices (SCL, SCR, HR, HRV) recorded during task performance
and the 10-item BFI subscales scores.
3. Results
For the first step in the analysis, the Pearson correlation performed between Res-i and
Meta-i showed a significant negative correlation (r = −0.694, p ≤ 0.001) (Figure 2).
Figure 2. Pearson correlations between behavioural indices. The scaer plot displays a significant
negative correlation between Res-i and Meta-i. The straight line represents the global linear trends.
In the second step in the analysis, negative correlations were found between the Res-
i and the mean SCR values (r = −0.483, p = 0.050) and between the Res-i and mean HR
values (r = −0.533, p = 0.016) (Figure 3A,B). No other significant correlations were found
between the Res-i and the autonomic data, nor for the Meta-i. No significant correlations
were found between the behavioural indices (Res-i and Meta-i) and the EEG data.
Figure 3. Pearson correlations between behavioural and autonomic indices. (A) The scaer plot
displays a significant negative correlation between Res-i and mean SCR values. (B) The scaer plot
displays a significant negative correlation between Res-i and mean HR values. The straight lines
represent the global linear trends.
Concerning the third step in the analysis, Pearson correlations between the 10-item
BFI subscales and the four electrodes of the EEG frequency bands’ power showed
significant correlations for the theta, alpha, and beta bands. No significant correlations
were found between the gamma and delta bands. Specifically, for the theta band, a
Figure 2. Pearson correlations between behavioural indices. The scatter plot displays a significant
negative correlation between Res-i and Meta-i. The straight line represents the global linear trends.
In the second step in the analysis, negative correlations were found between the Res-i
and the mean SCR values (r =
−
0.483, p= 0.050) and between the Res-i and mean HR
values (r =
−
0.533, p= 0.016) (Figure 3A,B). No other significant correlations were found
between the Res-i and the autonomic data, nor for the Meta-i. No significant correlations
were found between the behavioural indices (Res-i and Meta-i) and the EEG data.
Sensors 2024, 24, x FOR PEER REVIEW 11 of 20
between the autonomic indices (SCL, SCR, HR, HRV) recorded during task performance
and the 10-item BFI subscales scores.
3. Results
For the first step in the analysis, the Pearson correlation performed between Res-i and
Meta-i showed a significant negative correlation (r = −0.694, p ≤ 0.001) (Figure 2).
Figure 2. Pearson correlations between behavioural indices. The scaer plot displays a significant
negative correlation between Res-i and Meta-i. The straight line represents the global linear trends.
In the second step in the analysis, negative correlations were found between the Res-
i and the mean SCR values (r = −0.483, p = 0.050) and between the Res-i and mean HR
values (r = −0.533, p = 0.016) (Figure 3A,B). No other significant correlations were found
between the Res-i and the autonomic data, nor for the Meta-i. No significant correlations
were found between the behavioural indices (Res-i and Meta-i) and the EEG data.
Figure 3. Pearson correlations between behavioural and autonomic indices. (A) The scaer plot
displays a significant negative correlation between Res-i and mean SCR values. (B) The scaer plot
displays a significant negative correlation between Res-i and mean HR values. The straight lines
represent the global linear trends.
Concerning the third step in the analysis, Pearson correlations between the 10-item
BFI subscales and the four electrodes of the EEG frequency bands’ power showed
significant correlations for the theta, alpha, and beta bands. No significant correlations
were found between the gamma and delta bands. Specifically, for the theta band, a
Figure 3. Pearson correlations between behavioural and autonomic indices. (A) The scatter plot
displays a significant negative correlation between Res-i and mean SCR values. (B) The scatter plot
displays a significant negative correlation between Res-i and mean HR values. The straight lines
represent the global linear trends.
Concerning the third step in the analysis, Pearson correlations between the 10-item BFI
subscales and the four electrodes of the EEG frequency bands’ power showed significant
correlations for the theta, alpha, and beta bands. No significant correlations were found
between the gamma and delta bands. Specifically, for the theta band, a negative correlation
was found between the 10-item BFI subscale of agreeableness and the theta band in AF7
(r =
−
0.489, p= 0.040) (Figure 4A). A negative correlation was also reported between the
10-item BFI subscale of conscientiousness and the alpha band in TP9 (r =
−
0.495, p= 0.043)
(Figure 4B). Finally, the beta band power in AF7 was correlated positively with the 10-
Sensors 2024,24, 2171 12 of 18
item BFI subscale of agreeableness (r = 0.572, p= 0.021) and the 10-item BFI subscale of
extroversion (r = 0.509, p= 0.044) (Figure 4C,D).
Sensors 2024, 24, x FOR PEER REVIEW 12 of 20
negative correlation was found between the 10-item BFI subscale of agreeableness and the
theta band in AF7 (r = −0.489, p = 0.040) (Figure 4A). A negative correlation was also
reported between the 10-item BFI subscale of conscientiousness and the alpha band in TP9
(r = −0.495, p = 0.043) (Figure 4B). Finally, the beta band power in AF7 was correlated
positively with the 10-item BFI subscale of agreeableness (r = 0.572, p = 0.021) and the 10-
item BFI subscale of extroversion (r = 0.509, p = 0.044) (Figure 4C,D).
Lastly, a negative correlation was found between the 10-item BFI subscale of
extroversion and the mean HR values (r = −0.526, p = 0.030) (Figure 5).
Figure 4. Pearson correlations between 10-item BFI scores and EEG indices. (A) The scatter plot
displays a significant negative correlation between theta band power and agreeableness profile. (B)
The scatter plot displays a significant negative correlation between alpha band power and
conscientiousness profile. (C) The scatter plot displays a significant negative correlation between beta
band power and agreeableness profile. (D) The scatter plot displays a significant positive correlation
between beta band power and extroversion profile. The straight lines represent the global linear trends.
Figure 5. Pearson correlations between 10-item BFI scores and autonomic indices. The scaer plot
displays a significant negative correlation between HR and extroversion profile. The straight line
represents the global linear trends.
Figure 4. Pearson correlations between 10-item BFI scores and EEG indices. (A) The scatter plot
displays a significant negative correlation between theta band power and agreeableness profile. (B) The
scatter plot displays a significant negative correlation between alpha band power and conscientiousness
profile. (C) The scatter plot displays a significant negative correlation between beta band power and
agreeableness profile. (D) The scatter plot displays a significant positive correlation between beta band
power and extroversion profile. The straight lines represent the global linear trends.
Lastly, a negative correlation was found between the 10-item BFI subscale of extrover-
sion and the mean HR values (r = −0.526, p= 0.030) (Figure 5).
Sensors 2024, 24, x FOR PEER REVIEW 12 of 20
negative correlation was found between the 10-item BFI subscale of agreeableness and the
theta band in AF7 (r = −0.489, p = 0.040) (Figure 4A). A negative correlation was also
reported between the 10-item BFI subscale of conscientiousness and the alpha band in TP9
(r = −0.495, p = 0.043) (Figure 4B). Finally, the beta band power in AF7 was correlated
positively with the 10-item BFI subscale of agreeableness (r = 0.572, p = 0.021) and the 10-
item BFI subscale of extroversion (r = 0.509, p = 0.044) (Figure 4C,D).
Lastly, a negative correlation was found between the 10-item BFI subscale of
extroversion and the mean HR values (r = −0.526, p = 0.030) (Figure 5).
Figure 4. Pearson correlations between 10-item BFI scores and EEG indices. (A) The scatter plot
displays a significant negative correlation between theta band power and agreeableness profile. (B)
The scatter plot displays a significant negative correlation between alpha band power and
conscientiousness profile. (C) The scatter plot displays a significant negative correlation between beta
band power and agreeableness profile. (D) The scatter plot displays a significant positive correlation
between beta band power and extroversion profile. The straight lines represent the global linear trends.
Figure 5. Pearson correlations between 10-item BFI scores and autonomic indices. The scaer plot
displays a significant negative correlation between HR and extroversion profile. The straight line
represents the global linear trends.
Figure 5. Pearson correlations between 10-item BFI scores and autonomic indices. The scatter plot
displays a significant negative correlation between HR and extroversion profile. The straight line
represents the global linear trends.
4. Discussion
By adopting a multimethodological neuroscientific approach, this study explores
whether a specific behavioural, neurophysiological, and autonomic pattern activated dur-
Sensors 2024,24, 2171 13 of 18
ing an ecological decision-making task (the RED task) is correlated with specific personality
profiles in healthy individuals. In this work, the RED task was designed to assess indi-
viduals’ ability to resist and control ecological distractors defined by contextual cues in
decision-making conditions and to measure the coherence and awareness of individuals’
decisions (i.e., metacognitive ability). The results obtained by analysing the relation be-
tween the behavioural, neurophysiological, autonomic, and self-report data during the task
are discussed below.
First, regarding the behavioural level of this multimethodological approach, a negative
correlation was found between the Res-i and Meta-i indices in relation to task execution and
the metacognitive phase. This result can be interpreted by considering two perspectives.
According to the first perspective, this significant negative correlation could suggest
that the greater ability to resist and control ecological distractors is related to a lower level
of awareness of these distractors. Indeed, it might be plausible that the more a person
is effective in his/her performance (and is able to resist distractors), the less awareness
is necessary for them to have self-control over their own behaviour: in other words, the
person has automated their resistance behaviour effectively and, thus, self-control in the
performance is no longer necessary.
If we read this result from another perspective, a lower ability to resist and control
ecological distractors is related to a higher level of awareness of these distractors. Even
this behavioural situation can be plausible. In fact, it can be argued that displaying
higher levels of awareness and self-control over behaviour while executing a task under
pressure can negatively affect behavioural performance. Monitoring a behaviour that
should be automated and self-monitoring during performance can influence RTs and lead
to distraction from one’s performance.
Both these perspectives feature strengths and weaknesses and, perhaps, adopting
a balanced or flexible approach may represent the best attitude. This balance might be
even more important and valuable when considering the organisational setting, where
professionals are often required to make decisions quickly and dynamically, in some cases
prioritising unexpected external inputs [
1
,
2
]. Indeed, it would be desirable for an individual
(and especially a professional) to be able to isolate himself/herself to ensure maximum
concentration on a given task, but also for the individual to always be ready to seize
salient and useful external stimuli to effectively complete decision-making processes [
58
].
From this perspective, future work could consider replicating this study by adopting a
between-subject approach, perhaps including a sample of managers, to explore potential
group differences in terms of professional background, age, or expertise. In fact, it has been
shown that achieving a balance between persistence and flexibility may depend not only
on the type of task, but also on the individual’s age [59].
Secondly, correlational analyses were performed to explore whether a specific neu-
rophysiological or autonomic profile supports task execution or the metacognitive phase.
Concerning the link between the behavioural indices and the CNS markers, no direct
correlations were found between the behavioural indices and the EEG data. This is because
this report lacked the consideration of specific personality profiles as mediators of this
relationship. Indeed, as demonstrated by the significant correlation results described below,
specific personality profiles were correlated with the EEG data during task performance,
and they could play a key role in mediating the decision-making process.
On the other hand, the analysis performed on the relationship between the behavioural
indices and ANS indices showed two significant results: (i) a negative correlation between
Res-i and the mean SCR values, and (ii) a negative correlation between Res-i and the mean
HRs values during task performance. Focusing on the relation between the decrease in SCR
index and the increased ability to resist ecological distractors, it is possible to explain this
result by considering that the goal-persistence approach [
16
] and the consequent inhibition
of automatic behaviours elicited by salient external stimuli may be due to a desire to avoid
taking risks during decision-making. An SCR response is indeed definable as an index of
the anticipation of significantly adverse outcomes related to decision-making [43,60].
Sensors 2024,24, 2171 14 of 18
Furthermore, the decrease in HR during the task in relation to the ability to resist and
control ecological distractors can be interpreted as a marker of the inhibitory process put in
place towards external stimuli to enable one to keep focused on a goal [
44
]. On the other
hand, if we consider the increase in both these ANS indices in relation to the inability to
resist ecological distractors, they can be interpreted as markers of emotional involvement,
greater effort, and an increase in stress levels [
46
], which may occur as a result of adopting
a flexible approach that allows switching between possibilities and alternative actions [
15
].
Finally, some interesting results were found thanks to the analysis performed to check
whether the neurophysiological or autonomic activity identified during the task execution
or the metacognitive phase was related to specific personality profiles.
Significant correlations were found between the theta, alpha, and beta bands activated
during the task and some personality profiles measured with the subscale of the 10-item BFI.
The negative correlation between the subscale of agreeableness and the theta band
in the left frontal regions (AF7) can be explained by focusing on the characteristics of this
personality profile. Indeed, a person with high levels of agreeableness is characterised
by traits such as cooperativeness and empathy, and has a predisposition toward external
aspects [
61
]. Thus, individuals with high agreeableness, who pay attention to external
stimuli, might show a lower presence of low-frequency bands, such as the theta band, in
frontal brain sites, since they activate cognitive and inhibitory control mechanisms less,
which are instead typically characterised by a high presence of the theta band in the frontal
areas [37,38].
In addition, the negative correlation between the conscientiousness subscale and the
alpha band in the left temporo-parietal regions (TP9) is in line with the existing literature.
In fact, according to Runnova et al. (2021), reduced activation of the alpha band is to be
interpreted as an index of cognitive effort and commitment during the decision-making
process [
40
], which is definitely higher in individuals who tend towards precision, accuracy,
and success, such as people characterised by the conscientiousness profile [62].
Moreover, both the agreeableness and the extroversion subscale were positively cor-
related with beta band presence in the left frontal area (AF7). The association between
beta band power and both agreeableness and extroversion reflect the prediction models
developed by Li et al. [
48
]. In fact, according to the researchers, for the agreeableness
model, the largest contribution was observed in the beta band activation in the frontal area,
among others, and, similarly, a frontal distribution of the beta band was also found among
the profiles in the extroversion models. Additionally, since greater beta band activation is
predictive of active attention and engagement [
34
], it is likely that cooperative, empathic
people who are inclined towards external stimuli, such as those characterised by the profile
of agreeableness [
61
], as well as those who are dynamic, those who seeking emotions, and
focused, as extroverts [62], are more involved and engaged in tasks [63,64].
The presence of positive correlations between specific personality profiles and EEG
parameters exclusively in the left hemisphere (AF7 and TP9) can also be explained by
focusing on the characteristics of the sample, which, as described above, consisted of
right-handed people.
Lastly, a negative correlation was found between the subscale of extroversion and
the mean HR values. As mentioned above, a person with an extroverted personality [
62
]
can be focused and engaged and able to concentrate on an external goal (e.g., the task
that is performed) [63,64] and can tend to inhibit possible external distractors [54]: this in-
hibitory process can also be characterised by a decrease in HR during task performance [
44
].
However, the relationship between personality profiles, EEG, and autonomic profiles
that characterise the RED task must be explored in depth and, possibly, confirmed by
other studies.
5. Conclusions
To conclude, this study exploited a newly designed ecological task to demonstrate
a relationship between the behavioural ability to resist and control ecological distractors
Sensors 2024,24, 2171 15 of 18
and metacognition. Moreover, it was shown that the ability to resist ecological distractors
is related to a specific autonomic profile and how the neurophysiological and autonomic
activations that occur during task execution correlate with specific personality profiles.
Taken together, these findings are presented in order to describe and disentangle the
hidden relationship behind individuals’ decision inhibit or activate a specific behaviour
(such as responding, or not, to an external stimulus), consciously or unconsciously, under
ecological conditions.
Despite the added value of this work in the study of the ability to resist ecological
distractors and metacontrol, some limitations should be considered. Future research should
increase the sample size to improve the representativeness and reliability of the current
results. Furthermore, it would be interesting to compare the current sample with a sample
of professional managers, to explore potential differences in the ability to control and
resist ecological distractors in relation to expertise or professional background. Future
research may also include a long-term study to determine whether a person’s inhibitory
ability to resist ecological distractors can alter over time and in response to environmental
circumstances, or whether it can be trained by specific neurocognitive interventions.
Finally, focusing on the multimethodological approach, it might be desirable to de-
velop studies that include other self-report measures, like the General Decision-Making
Style [
65
,
66
], to profile individuals’ decision-making styles, or additional neuroscientific
tools, such as functional near-infrared spectroscopy (fNIRS), to deepen our understanding
of whether the hemodynamic variations in specific brain areas can be associated with
the ability to resist ecological distractors and metacontrol. Furthermore, future studies
may consider the use of statistical tests that determine a cause–effect link (rather than
simply a correlational link, as in this study) to infer causality or directionality in the ob-
served relationships between personality traits, neurophysiological responses, and decision-
making performance.
Overall, it is important to emphasise that decision-making is one of the most complex
high-order processes, which takes into account several variables and can be characterised
by unpredictability. However, this study attempted to grasp the complexity of the correlates
of the decision-making process by creating an ecologically valid decision-making task that
represents a real-life situation.
Author Contributions: Conceptualisation, M.B.; methodology, M.B., C.A. and L.A.; software, L.A.;
validation, M.B.; formal analysis, R.A.A. and L.A.; investigation, C.A. and R.A.A.; resources, M.B.,
C.A., R.A.A. and L.A.; data curation, M.B. and L.A.; writing—original draft preparation, C.A. and
L.A.; writing—review and editing, M.B., C.A. and L.A.; visualisation, C.A. and L.A.; supervision,
M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the
published version of the manuscript.
Funding: This work was supported by UniversitàCattolica del Sacro Cuore [grant D11 2023, “Nuovi
tool digitali neuroscientifici per il decision-making”].
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of the Department of Psychology, Catholic
University of the Sacred Heart, Milan, Italy.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The datasets generated and analysed during the current study are
available from the corresponding author upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.
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