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Citation: Balconi, M.; Angioletti, L.;
Acconito, C. Self-Awareness of Goals
Task (SAGT) and Planning Skills: The
Neuroscience of Decision Making.
Brain Sci. 2023,13, 1163. https://
doi.org/10.3390/brainsci13081163
Academic Editor: Youngbin Kwak
Received: 28 June 2023
Revised: 24 July 2023
Accepted: 1 August 2023
Published: 3 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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4.0/).
brain
sciences
Article
Self-Awareness of Goals Task (SAGT) and Planning Skills: The
Neuroscience of Decision Making
Michela Balconi 1,2 , Laura Angioletti 1, 2, * and Carlotta Acconito 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.);
carlotta.acconito1@unicatt.it (C.A.)
2Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the
Sacred Heart, Largo Gemelli 1, 20123 Milan, Italy
*Correspondence: laura.angioletti1@unicatt.it; Tel.: +39-027-234-5929
Abstract:
A goal’s self-awareness and the planning to achieve it drive decision makers. Through a
neuroscientific approach, this study explores the self-awareness of goals by analyzing the explicit
and implicit processes linked to the ability to self-represent goals and sort them via an implicit
dominant key. Thirty-five professionals performed a novel and ecological decision-making task, the
Self-Awareness of Goals Task (SAGT), aimed at exploring the (i) self-representation of the decision-
making goals of a typical working day; (ii) self-representation of how these goals were performed
in order of priority; (iii) temporal sequence; and (iv) in terms of their efficacy. Electrophysiological
(i.e., alpha, beta, and gamma band), autonomic, behavioral, and self-report data (General Decision
Making Style and Big Five Inventory) are collected. Higher self-awareness of goals by time as well
as efficacy and the greater activation of alpha, beta, and gamma bands in the temporoparietal brain
area were found. Correlations reported positive associations between the self-awareness of goals via
a time and dependent decision-making style and a conscientious personality, but also between the
self-awareness of goals via an efficacy and rational decision-making style. The results obtained in this
study suggest that the SAGT could activate recursive thinking in the examinee and grasp individual
differences in self-representation and aware identification of decision-making goals.
Keywords: decision making; self-awareness; goals; behavioral neuroscience; EEG
1. Introduction
Each individual makes decisions in every moment of his daily life, both in his private
life and in professional contexts. Specifically, in organizational settings, professionals and
managers are called to make decisions dynamically and quickly, taking into consideration
multiple sources of information to formulate flexible action plans and balance their multiple
work goals.
To choose an optimal course of action, select and analyze a large amount of information
and multiple alternatives, make strategic adjustments to changes in a situation, and correctly
define the objectives to be achieved, it is crucial for decision makers to develop continuous
awareness about themselves and the context around them [1–4].
An individual’s knowledge about one or more aspects of the self [
4
], as well as the
ability to identify, process, and store information about oneself [
5
], falls under the definition
of the concept of “self-awareness”. Regarding the decision-making process, early research
proposed that states of elevated self-awareness led people to be more aware of their own
goals [6,7], in addition to their thoughts, feelings, and behavior.
Given the importance of these capabilities, various research has been conducted in
different disciplinary fields, especially in organizational psychology [
8
]. Self-awareness of
one’s goals, together with the capacity to predict the possible obstacles to achieving them,
indeed drive a decision maker in his/her decisions [
2
]. Each step of the decision-making
Brain Sci. 2023,13, 1163. https://doi.org/10.3390/brainsci13081163 https://www.mdpi.com/journal/brainsci
Brain Sci. 2023,13, 1163 2 of 16
process, such as (i) problem identification and goal definition, (ii) information gathering,
(iii) elaboration and prediction, (iv) strategic and tactical planning, (v) decision making and
action, and (vi) the evaluation of outcomes with a possible modification of strategy, can be
marked by different levels of self-awareness.
Focusing on the first step, problem identification and goal definition have long been
considered some of the most critical steps in making good decisions [
9
], since these first
steps influence subsequent performance. Objectives, in fact, drive attention and effort
toward activities relevant to the achievement and satisfaction of the goal itself in addition to
affecting the amount of cognitive, physiological, and subjective effort put into the activity.
Goals also influence effort in relation to the ability to control the time spent on each task, but
also indirectly by leading to the arousal, discovery, and/or use of task-relevant knowledge
and strategies [3].
Within this initial stage of the decision-making process, self-awareness plays a primary
role by leading individuals to identify what the goals to be achieved are in relation to
different tasks. In addition, the ability to have a clear and conscious vision of one’s goals
is also responsible for self-regulatory processes that support the achievement of the goals
themselves [
10
]. Specifically, individuals in a more self-aware state more correctly identify
goals and monitor their pursuit more actively, identifying and reducing any discrepancies
between them [7].
Indeed, as a result it is essential for an individual to be proficient at self-representing
his/her own goals (by being able to list them, for instance), as well as sorting them according
to different criteria [11].
The level of ability to self-represent one’s goals could provide insights into the most
relevant factors within the decision-making process and highlight the implicit dominant
key guiding the self-awareness of daily tasks and objectives—i.e., whether an individual is
mainly guided by decision priority, timing, or efficacy when recalling his/her own objec-
tives. The target environment must also be taken into consideration when self-representing
decision-making goals. For instance, making a decision in a complex context, such as a
professional context, could be a situation featuring stressful and unplanned situations,
unexpected changes, and constrained time.
With reference to the first criteria, goal prioritization, it is a key concept for under-
standing the pursuit of different goals and can be defined as the temporary increase in
the importance attributed to one or more goals and the resources directed toward them to
facilitate their execution [
12
]. Specifically, priority could be assigned to goals that have a
high informational value, high affective value, and high expectancy [
12
]. Having a good
awareness and adequate representation of one’s prioritization of goals has previously
been associated with a better translation of goals into actions [
13
], but also with a greater
likelihood of achieving results, especially when deadlines are involved [
14
]. Prioritization
leads to a higher commitment of time and energy to pursuing established goals [
15
] (),
inhibiting those that are not prioritized [12].
Secondly, the ability to prioritize one goal over another in accordance with the time
variable is linked to goal shielding, a phenomenon that, in accordance with Orehek and
Vazeou-Nieuwenhuis’ theory [
16
], is a self-regulation strategy that facilitates the man-
agement of multiple goals. The deadline of a goal, in fact, is another important factor
in deciding how to prioritize an entire set of goals [
17
]. The importance of prioritization
according to time also appears to be more important than the distance of the goal. In accor-
dance with the research of Schmidt and DeShon (2007), in fact, people normally tend to
prioritize the goal furthest from completion, but as the deadline for a given goal approaches
the latter is prioritized more [18].
Thirdly, the ability to order one’s goals by efficacy involves the need to develop an
awareness of one’s effectiveness, identifying goals achieved and those yet to be achieved.
With respect to the efficiency factor, previous studies have shown that the perceived
difficulty attributed to a goal is, in fact, one of the possible drivers of goal prioritization.
Brain Sci. 2023,13, 1163 3 of 16
Specifically, if a goal is perceived as too difficult to reach, it is postponed to the advantage
of a more achievable goal [8,19].
Overall, the ability to represent one’s own goals more or less effectively could also
affect an individual’s decision-making style. Indeed, by investigating an individual’s level
of self-awareness of their own goals, it is possible to understand how often a person adopts
a more reflective or impulsive approach to decision making. Specifically, having a good
self-representation of one’s goals can be understood as an indication of a mostly rational
decision-making style, in that a person requires the careful consideration of all alternatives
and the various related efficacies, but also a conscious prioritization of activities, to have a
good awareness of his/her goals.
From a methodological perspective, some studies have previously tried to define
how people make decisions via the use of questionnaires that allow the defining of a
person’s decision-making style, such as the General Decision Making Style [
20
], the Big
Five Inventory [
21
], or the Maximization Scale [
22
]. However, with this approach it is
possible to focus exclusively on the explicit and relatively conscious parts of decision-
making styles, or what people believe and refer to as their own knowledge, which may be
biased by a variety of factors, such as social desirability.
A significantly different approach, which is that of neuroscience, consists of asking
individuals to perform a task related to their ability to self-represent their decision-making
goals and in the meantime collect the behavioral responses and neuroscientific correlates
of this process. Indeed, the neuroscientific perspective, which enables researchers to fo-
cus not only on the explicit but also on the implicit components of a decision-making
process, has recently started to contribute to understanding decision-making styles in
all their complexity, specifically in the marketing field [
23
,
24
]. Indeed, neuroscientific
tools, such as electroencephalograms [
25
] and autonomic measure recordings [
26
], allow
the measurement of the typical neuro- and psycho-physiological responses of a person
during the decision-making process, highlighting levels of cognitive effort and emotional
engagement [
27
]. Electroencephalograms, in particular, provide information on the electro-
physiological activity of the brain, highlighting the functional meaning of frequency bands
to assess the possible load and cognitive effort required by decision-making processes.
Specifically, greater activation in low-frequency bands (theta and delta) could be associated
with emotional processes [
28
], while alpha and beta bands are indices of cognitive effort,
active attention, and engagement [
29
,
30
]. On the other hand, autonomic measures, such as
electrodermal activity (i.e., skin conductance level and response) and cardiovascular indices
(i.e., heart rate), provide insight into emotional processes such as emotional engagement,
arousal, and stress levels [31–33].
In the professional field, decision making has been explored through a neuroscientific
perspective in different applied contexts [
28
,
31
,
34
–
36
]. Additionally, basic research has
used EEG to investigate specific effort allocation during task prioritization. Although
these studies show that the prioritization task requires cognitive control, evidenced by
low-frequency band and alpha as well as beta band brain activity [
37
,
38
], no previous works
have been conducted to explore the self-representation of decision-making goals as defined
in our study. Furthermore, to the best of our knowledge, no previous neuroscientific studies
have focused on the neurophysiological correlates of self-representing our own decision
goals in a professional context and sorting them for priority, timing, or potential efficacy.
In order to compensate for the gap in the literature, this study aims to explore, with a
neuroscientific approach, how individuals, particularly professionals, are characterized by
different cognitive and emotional reactions connected to the awareness of one’s decision-
making goals.
For the purpose of measuring the ability of a sample of professionals to list decision-
making objectives, to have them evident and contextualized with respect to the present
moment, we designed a novel experimental task to detect the self-awareness of one’s
decision-making goals in individuals, named the self-awareness of goals task (SAGT). This
task is composed of four different steps: (i) the self-awareness of the decision-making
Brain Sci. 2023,13, 1163 4 of 16
goals of the working day before (e.g., goals listing), (ii) the self-awareness of how these
were performed in order of priority (i.e., sorting the goals in order of importance), (iii) the
self-awareness of how these were carried out in a temporal sequence (i.e., sorting the goals
into the order in which they were executed), and (iv) the self-awareness of these in terms of
efficacy (i.e., sorting the goals according to what degree the person was able to accomplish
them). To measure the self-awareness of one’s goals in a complex context, such as the
professional one, a reduced time window was given to provide a behavioral response for
each step.
Throughout the duration of the task, electrophysiological (EEG) and autonomic activ-
ity were detected to complement and enrich observations based on participants’ behaviors
and responses. For each step, specifically, besides the number of decision-making goals in-
dicated by the professional, response times (RTs) were also collected as an indirect measure
of workload to assess the effort employed by participants for each self-representation step.
Indeed, RTs allowed for the evaluation of participants’ responses considering the cognitive
cost of the identification and decision-making processes that underlie the self-representation
of the decision-making goals. In particular, longer RTs reflect greater task-related effort
and, in turn, could suggest greater effort in the self-representation of goals.
In addition to the behavioral measures and to have a comprehensive overview of
the self-awareness of the decision-making goals, the self-report measures of the General
Decision Making Style and 10-item Big Five Inventory were administrated to measure a par-
ticipant’s decision-making style and personality traits. With these self-report instruments, it
is possible to compare the level of decision-making self-representation, investigated mainly
via implicit measures, with the decision-making style detected via validated questionnaires
referring to the most conscious and aware part of one’s decision-making processes.
In terms of specifics, it was expected that the level of self-representation of decision-
making goals in a group of professionals called upon to operate and make decisions in a
complex work environment may be characterized by a style more focused on ordering their
goals by time or by efficacy. This style will be characterized by a higher behavioral score in
the ability to self-represent how goals were achieved in temporal sequence and in terms of
efficacy. At the same time, as an index of less cognitive effort, lower RTs are expected in
these two specific steps of the SAGT compared to the other steps.
Regarding EEG results, it was hypothesized that a general decrease in the alpha band
and an increase in the beta band in the frontal areas were indicators of cognitive effort and
engagement during this novel experimental task [
29
,
30
]. Concerning autonomic measures,
it was estimated that high levels of skin conductance level and heart rate would be found,
especially in the different sorting tasks. High levels of these autonomic indices, in agreement
with the literature, can be interpreted as cues of stress and emotional engagement [
33
,
39
],
which may result from both the difficulty of having a clear representation of the different
variables of priority, efficacy, and time attributable by the different goals as well as from
awareness with respect to the need to complete the SAGT in the shortest RT.
Finally, it was expected that high values in the self-representation of decision-making
goals in terms of timing would correlate with a conscientious (10-item Big Five Inventory
score) decision-making style, since completing different tasks on time requires good organi-
zational skills and planned behavior. Similarly, high values in the self-representation of
decision goals in terms of efficacy would correlate with a more rational decision-making
style (General Decision Making Style score), typical of a person who is aware that a given
choice may lead to outcomes other than the ones predetermined.
In conclusion, this research aimed to explore professionals’ cognitive and affective
responses connected to the self-awareness of one’s decision-making goals by exploiting, for
the first time, a combined behavioral and neurophysiological approach. The decision to
conduct this study on a sample of professionals was motivated by the fact that they are
involved in complex decision-making processes on a daily basis and strive to achieve as
well as maintain optimal performance under changing conditions, emphasizing the need to
Brain Sci. 2023,13, 1163 5 of 16
adapt decisions to real-world situations and the constraints imposed by continuous change
and ambiguity.
2. Materials and Methods
2.1. Sample
The sample consisted of 35 professionals (female = 22; male = 13), with ages ranging
from 24 to 59 years old (mean age = 38.29, standard deviation of age = 9.53), working in
the managerial departments of a large service company in Italy. All of the participants had
been employed in the same job position for about two years at the time of the experiment.
This precaution was introduced to avoid including participants who were more stressed
due to, for instance, job changes or an increase in workload while shifting to new tasks or
duties. Participants belonged to distinct internal departments with different specializations
(e.g., human resource management, training and professional learning, engineering and
maintenance management, service quality monitoring, infrastructure management, and
others), so as to not only focus on one single professional specialty.
The sample was also defined according to the following exclusion criteria: (i) severe
levels of depression, (ii) history of psychiatric or neurology disorders, (iii) abnormal short-
and long-term memory, (iv) low global cognitive functioning, and (v) undergoing a con-
current therapy based on psychoactive drugs that could alter cognitive or decisional skills.
Finally, all participants had normal-to-corrected vision.
All participants signed written informed consent and participated voluntarily without
receiving any compensation. The research protocol has been approved by the Ethics
Committee of the Department of Psychology, Catholic University of the Sacred Heart,
Milan, Italy, and conducted in accordance with the Helsinki Declaration (2013).
2.2. Procedure
The experiment took place in a quiet dedicated room at the participants’ workplace, to
preserve the everyday working context of the participants and, in the meantime, improve
the ecological validity of the data collection.
The participants were asked to sit on a comfortable chair in front of a PC monitor
placed about 80 cm away from them. After signing the written informed consent, a non-
invasive EEG and autonomic measures recording device for collecting EEG and autonomic
responses at rest and during task execution were applied to a participant. A 120 s eyes
open baseline was collected before participants were given the instruction to perform
the experimental task. At the end of the task, the General Decision Making Style and
10-item Big Five Inventory were administrated to collect self-report data. The experimental
procedure had a duration of about 10 min.
2.2.1. Behavioral Measures: Experimental Task and Data Processing
The participants were required to execute a novel ecological decision-making task,
the SAGT, designed to evaluate the ability and modality of the self-representation of
one’s decision-making goals and administered via a web-based survey and experiment
management platform (Qualtrics XM platform; Qualtrics LLC, Provo, UT, USA).
Specifically, the SAGT is composed of four different steps:
(i)
The self-representation of the decision-making goals of the working day before (i.e.,
goal listing).
(ii)
The self-representation of how these were performed in order of priority (i.e., sorting
the goals in order of importance).
(iii)
The self-representation of how these were carried out in temporal sequence (i.e.,
sorting the goals in the order in which they were executed during a day).
(iv) The self-representation of these in terms of efficacy (i.e., sorting the goals according to
how much the person was able to accomplish them).
In the first step (i.e., “goal listing” in Figure 1), after an instruction screen, participants
were asked to refer to the last working day and to list in a text box, as quickly as possible,
Brain Sci. 2023,13, 1163 6 of 16
all of the decision goals they had set for that particular day that involved a decision. To list
the goals there was a time window of 60 s.
Brain Sci. 2023, 13, x FOR PEER REVIEW 6 of 17
(iv) The self-representation of these in terms of efficacy (i.e., sorting the goals according
to how much the person was able to accomplish them).
In the first step (i.e., “goal listing” in Figure 1), after an instruction screen, participants
were asked to refer to the last working day and to list in a text box, as quickly as possible,
all of the decision goals they had set for that particular day that involved a decision. To
list the goals there was a time window of 60 s.
After pressing the “forward” buon, participants were shown a screen asking them
to recall previously wrien decision objectives and reorder them by priority, assigning the
most important to the first position and the least important to the last position (i.e., “pri-
ority” in Figure 1). Then, in the next step, reordering by temporal sequence was required,
where the first position is represented by the goal carried out first in the day and the last
position by the one carried out last (i.e., “time” in Figure 1). Finally, in the last phase of
the task, participants were required to reorder the goals by efficacy: the goal with the best
efficacy is represented in the first position, while the one with the worst efficacy is shown
in the last position (i.e., “efficacy” in Figure 1). For each reordering phase (by priority,
temporal sequence, and efficacy), participants were given a maximum time of 30 s.
The SAGT is administered under time pressure in order to make the decisional pro-
cess cognitively demanding and to make RTs an informative and discriminative measure
of the cognitive load and efficiency of information processing.
Throughout the duration of the task, electrophysiological and autonomic activity
were detected to complement and enrich observations based on participants’ behaviors
and RTs. For a description of the procedure and of the SAGT, see Figure 1.
Figure 1. Experimental procedure and the SAGT. The figure shows the experimental procedure and
the SAGT steps. Throughout the duration of the task, EEG and autonomic activity were detected to
complement and enrich observations based on participants’ behavioral data. SAGT: Self-Awareness
of Goals Task; GDMS: General Decision -Making Style; BFI: Big Five Inventory.
Figure 1.
Experimental procedure and the SAGT. The figure shows the experimental procedure and
the SAGT steps. Throughout the duration of the task, EEG and autonomic activity were detected to
complement and enrich observations based on participants’ behavioral data. SAGT: Self-Awareness
of Goals Task; GDMS: General Decision -Making Style; BFI: Big Five Inventory.
After pressing the “forward” button, participants were shown a screen asking them
to recall previously written decision objectives and reorder them by priority, assigning
the most important to the first position and the least important to the last position (i.e.,
“priority” in Figure 1). Then, in the next step, reordering by temporal sequence was
required, where the first position is represented by the goal carried out first in the day and
the last position by the one carried out last (i.e., “time” in Figure 1). Finally, in the last
phase of the task, participants were required to reorder the goals by efficacy: the goal with
the best efficacy is represented in the first position, while the one with the worst efficacy
is shown in the last position (i.e., “efficacy” in Figure 1). For each reordering phase (by
priority, temporal sequence, and efficacy), participants were given a maximum time of 30 s.
The SAGT is administered under time pressure in order to make the decisional process
cognitively demanding and to make RTs an informative and discriminative measure of the
cognitive load and efficiency of information processing.
Throughout the duration of the task, electrophysiological and autonomic activity were
detected to complement and enrich observations based on participants’ behaviors and RTs.
For a description of the procedure and of the SAGT, see Figure 1.
With regard to behavioral data, for the first step of the task, the number of the listed
goals and the RTs employed to list the goals were considered.
Additionally, for each step of the SAGT, the number of the sorted goals for the specific
criteria (priority, time, and efficacy) and the RTs used to sort the goals were collected.
Brain Sci. 2023,13, 1163 7 of 16
As noted above, besides the actual responses and related scores, RTs were also collected
as an indirect measure of workload to assess the effort imposed on participants by each
decisional step.
Response scores and response times were, then, transcribed offline into a common
metric scale ranging between 1 and 10, and used to compute the self-representation of the
decision-making goals (Self-Repi), the self-representation of how these were performed in
order of priority (Priority-Repi), the self-representation of how these were carried out in
temporal sequence (Temporal-Repi), and the self-representation of these in terms of efficacy
(Efficacy-Repi) indices. These indices, therefore, express measures related to the SAGT and
provide insights with respect to each participant’s level of ability to self-represent his or
her goals and the dominant implicit key that drives self-awareness of daily tasks and goals.
Such indices were calculated through a mathematical algorithm based on the ratio between
response scores and response times, as follows:
Self-Repi = N goalsdec/RTdec
Priority-Repi = ∆prioritydec/RTdec
Temporal-Repi = ∆timedec /RTdec
Efficacy-Repi = ∆efficacydec/RTdec
where N goals
dec
refers to the total number of goals listed by the participants converted into
the decile metric scale,
∆
priority
dec
refers to the difference between the total goals listed
in Step 1 and the total goals sorted by priority,
∆
time
dec
refers to the difference between
the total goals listed in Step 1 and the total goals sorted by time,
∆
efficacy
dec
refers to the
difference between the total goals listed in Step 1 and the total goals sorted by efficacy, and
RTdec refers to participants’ response times converted into the decile metric scale.
Considering these calculations, high scores and low RTs would lead to the highest
Self-Repi, Priority-Repi, Temporal-Repi, and Efficacy-Repi values, mirroring, respectively,
greater self-awareness of the decision-making goals of the working day before and self-
awareness of how these goals were performed in order of priority, in temporal sequence,
and of efficacy.
The basis for this computation is the assumption that shorter RTs would mark lower
task-related effort due to either more effective appraisal and decision-making skills in goal
self-representation or implicit preference for one of the three sorting criteria (i.e., priority,
temporal sequence, or efficacy) when planning one’s goals.
2.2.2. EEG Data Acquisition
A wearable electroencephalogram system with dry sensors (Muse
TM
headband, ver-
sion 2; InteraXon Inc., Toronto, ON, Canada) was employed to record the resting-state and
task-related variations in EEG spectral activity (standard-frequency band power: delta,
theta, alpha, beta, and gamma) in a non-invasive manner. Three electrodes were used
as a reference, and the remaining four were placed in the frontal (AF7 and AF8, left and
right forehead, respectively) and temporoparietal (TP9 and TP10, left and right ear, re-
spectively) regions. The three reference electrodes, which were not used to capture brain
signals, were located between the two input electrodes on the forehead and corresponded
to the electrode position Fpz. The frontal and temporoparietal electrodes’ positioning in
the Muse headband followed the international 10–20 system [
40
] and were made up of
conductive material (silver) and silicon rubber, respectively. The system was equipped
with an accelerometer, gyroscope, and pulse oximetry. Data were collected and transmitted
via Bluetooth to a connected smartphone, using the mobile application Mind Monitor. Data
were sampled at a constant of 256 Hz, and a 50 Hz notch frequency filter was applied. Mind
Monitor automatically processes raw data by applying a fast Fourier transform (Hamming
window: length 10%, 0.5 Hz) to obtain brain waves at different frequency bands, using
Brain Sci. 2023,13, 1163 8 of 16
the logarithm of the power spectral density of the raw EEG data coming from each chan-
nel. Participants were instructed to minimize eye blinks and movements. The following
frequency bands were extracted from each channel of the recorded electrophysiological
signals: delta (
1–4 Hz
), theta (4–8 Hz), alpha (7.5–13 Hz), beta (13–30 Hz), and gamma
(30–44 Hz). All of the EEG power spectral density values collected by Mind Monitor were
typically in the range of
−
1 to +1. Finally, the normalization-applied procedure consisted
of the baseline (as the 120 s resting baseline was recorded at the start of the experiment)
correction of the signal.
The decision to use the Muse
TM
headband as a brain activity detection tool was
based on the fact that it is a portable device that is easy to use in business contexts,
thereby preserving the ecological validity of the study. Additionally, even though this
device only consists of two electrodes placed in the frontal region (AF7 and AF8) and
two in the temporoparietal region (TP9 and TP10), it still permits the investigation of key
regions of interest, such as the bilateral frontal regions, widely involved in decision-making
processes [
41
], and the bilateral temporoparietal brain regions, which are involved in self-
representation mechanisms as well as in stimulus–context integration processes [42–44].
2.2.3. Autonomic Data Acquisition
A Biopac MP 150 system (Biopac Systems Inc., Goleta, CA, USA) was used to collect
resting-state and task-related variations in the autonomic activity.
Electrocardiogram activity was recorded continuously in lead one from two electrodes
attached to the lower wrist, with the positive pole on the left arm and the negative pole
on the right one. The electrocardiogram signal was sampled at 1000 Hz with Biopac
Acknowledge 3.7.1 software (Biopac Systems Inc.). The electrocardiogram was converted
into heart rate. The signal was low-pass filtered at 35 Hz and high-pass filtered at 0.05 Hz
for motor and ocular artifacts.
Electrodermal activity was recorded via the electrodes for the skin conductance re-
sponse and skin conductance level attached to the distal phalanges of the first and fifth
fingers of the left hand. The signal was sampled at 1000 Hz and low-pass filtered at 10 Hz
for motor, ocular, and biological artifacts.
Electromyographic activity was recorded from 2 electrodes attached to the corrugator
muscle with 2 electrodes attached, with the positive pole above the left eyebrow and the
negative pole on the center of the forehead. The signal was sampled at 500 Hz and low-pass
filtered at 10 Hz.
2.2.4. Self-Report Data Acquisition
The General Decision-Making Style [
20
] and 10-item Big Five Inventory scales [
21
]
were adopted to collect self-report data on individuals’ decision-making styles and person-
ality traits.
Specifically, the General Decision-Making Style defines an individual’s decision-
making style according to five different styles: rational, intuitive, dependent, avoidant,
and spontaneous. The rational style, in particular, is characterized by a comprehensive
and exhaustive search for information, considering all alternatives and their consequences;
the intuitive style is defined by a focus on global aspects and a tendency to decide on
hunches; the dependent style identifies a person who prefers to receive suggestions and
advice; the avoidant style defines the person who tends to avoid making decisions; and the
spontaneous style is typical of those who prefer to make a decision as quickly as possible.
On the other hand, the 10-item Big Five Inventory is a ten-item scale designed to assess
the Big Five personality dimensions in a very short amount of time. This tool specifically
provides guidance about five different aspects of personality: extroversion (being enthusi-
astic and extroverted), agreeableness (being likeable and warm), conscientiousness (being
organized and self-disciplined), emotional stability (being calm, stable, and balanced), and
openness (being imaginative and open to new experiences).
Brain Sci. 2023,13, 1163 9 of 16
2.3. Statistical Analyses
A set of repeated measures analyses of variance (ANOVAs) were applied to behavioral,
EEG, and autonomic data, considering the entire sample.
A first ANOVA with Condition (4: goals listing, priority, time, and efficacy) as
the within-subject factor was applied to the behavioral indices (Self-Repi, Priority-Repi,
Temporal-Repi, and Efficacy-Repi).
Secondly, for EEG data, five ANOVAs with Condition (4: goals listing, priority, time,
and efficacy), Localization (2: frontal and temporoparietal), and Lateralization (2: right;
left) as the independent within-subject factors were performed for each frequency band
(delta, theta, alpha, beta, and gamma).
Finally, six ANOVAs with Condition (4: goals listing, priority, time, and efficacy) as
the within-subject factor was applied to each autonomic index (i.e., skin conductance level,
skin conductance response, heart rate, and electromyography).
Pairwise comparisons were applied to the data in cases of significant effects. Simple
effects for significant interactions were further checked via pairwise comparisons, and
the Bonferroni correction was used to reduce potential biases of multiple comparisons.
For all of the ANOVA tests, the degrees of freedom were corrected using the Greenhouse–
Geisser epsilon where appropriate. Furthermore, the normality of the data distribution was
preliminarily assessed by checking kurtosis and asymmetry indices. The size of statistically
significant effects has been estimated by computing partial eta-squared (η2) indices.
Pearson correlations, with Bonferroni corrections for multiple comparisons, were
applied to the behavioral indices (Self-Repi, Priority-Repi, Temporal-Repi, and Efficacy-
Repi) and General Decision-Making Style as well as 10-item Big Five Inventory scores of
the entire sample.
3. Results
3.1. Behavioural Results
As shown by the ANOVA for behavioral data, a significant main effect in the within-
subject factor SAGT step was found (F[
1
,
34
] = 11,990 p
≤
0.05
η2
= 0.261), for which an
increase in Efficacy-Repi (behavioral index referring to the efficacy) compared to Self-Repi
(p= 0.002) and Priority-Repi (p= 0.001) (the behavioral indices referring to goal listing
and priority, respectively) was observed. Additionally, an increase in Temporal-Repi
(the behavioral index referring to time) was found compared to Self-Repi (p= 0.005) and
Priority-Repi (p= 0.008) (Figure 2).
3.2. EEG and Autonomic Results
Regarding the ANOVAs performed on the EEG data, for the alpha band a significant
main effect was found in the within-subject factor Localization (F[
1
,
34
] = 6942 p= 0.025
η2= 0.410
), with an increase in the activity of this band in the temporoparietal compared to
the frontal area (Figure 3A).
Similarly, for the beta band, a significant main effect was found in the within-subject
factor Localization (F[
1
,
34
] = 21,751 p
≤
0.05
η2
= 0.685), with increased beta power in the
temporoparietal compared to the frontal areas (Figure 3B).
Additionally, for the gamma band, a significant main effect was found in the within-
subject factor Localization (F[
1
,
34
] = 25,721 p
≤
0.05
η2
= 0.720), with an increase in the
power of this band in the temporoparietal compared to the frontal locations (Figure 3C).
No other significant differences were found, and no significant differences were found for
the other EEG frequency bands (delta and theta bands).
Additionally, the ANOVAs performed on the autonomic indices (skin conductance
level, skin conductance response, heart rate, and electromyography) did not show any
significant difference.
Brain Sci. 2023,13, 1163 10 of 16
Brain Sci. 2023, 13, x FOR PEER REVIEW 10 of 17
Figure 2. Behavioral results. The bar graph shows statistically significant differences in the behav-
ioral SAGT indices for each SAGT step. Bars represent ± 1 standard error and stars (*) mark statisti-
cally significant comparisons.
3.2. EEG and Autonomic Results
Regarding the ANOVAs performed on the EEG data, for the alpha band a significant
main effect was found in the within-subject factor Localization (F[1,34] = 6942 p = 0.025 η2
= 0.410), with an increase in the activity of this band in the temporoparietal compared to
the frontal area (Figure 3A).
Similarly, for the beta band, a significant main effect was found in the within-subject
factor Localization (F[1,34] = 21,751 p ≤ 0.05 η2 = 0.685), with increased beta power in the
temporoparietal compared to the frontal areas (Figure 3B).
Additionally, for the gamma band, a significant main effect was found in the within-
subject factor Localization (F[1,34] = 25,721 p ≤ 0.05 η2 = 0.720), with an increase in the
power of this band in the temporoparietal compared to the frontal locations (Figure 3C).
No other significant differences were found, and no significant differences were found for
the other EEG frequency bands (delta and theta bands).
Additionally, the ANOVAs performed on the autonomic indices (skin conductance
level, skin conductance response, heart rate, and electromyography) did not show any
significant difference.
Figure 2.
Behavioral results. The bar graph shows statistically significant differences in the behavioral
SAGT indices for each SAGT step. Bars represent
±
1 standard error and stars (*) mark statistically
significant comparisons.
Brain Sci. 2023, 13, x FOR PEER REVIEW 11 of 17
Figure 3. EEG results. (A) The bar graph shows significant differences for the alpha band in Locali-
zation. (B) The bar graph shows significant differences for the beta band in Localization. (C) The bar
graph shows significant differences for the gamma band in Localization. For all graphs, bars repre-
sent ± 1 standard error and stars (*) mark statistically significant comparisons.
3.3. Correlational Results
The Pearson correlation performed between the SAGT behavioral indices (Self-Repi,
Priority-Repi, Temporal-Repi, and Efficacy-Repi) and 10-item Big Five Inventory scores
showed a positive correlation between Temporal-Repi and conscientiousness (r = 0.433, p
= 0.009) (see Figure 4A).
The Pearson correlation performed between the behavioral SAGT indices (Self-Repi,
Priority-Repi, Temporal-Repi, and Efficacy-Repi) and General Decision Making Style
showed a positive correlation between Temporal-Repi and a dependent decision-making
style (r = 0.348, p = 0.047) (see Figure 4B), as well as between Efficacy-Repi and a rational
decision-making style (r = 0.441, p = 0.010) (see Figure 4C).
On the other hand, no other significant correlations were found.
Figure 3.
EEG results. (
A
) The bar graph shows significant differences for the alpha band in
Localization. (
B
) The bar graph shows significant differences for the beta band in Localization.
(
C
) The bar graph shows significant differences for the gamma band in Localization. For all graphs,
bars represent ±1 standard error and stars (*) mark statistically significant comparisons.
Brain Sci. 2023,13, 1163 11 of 16
3.3. Correlational Results
The Pearson correlation performed between the SAGT behavioral indices (Self-Repi,
Priority-Repi, Temporal-Repi, and Efficacy-Repi) and 10-item Big Five Inventory scores
showed a positive correlation between Temporal-Repi and conscientiousness (r = 0.433,
p= 0.009) (see Figure 4A).
Brain Sci. 2023, 13, x FOR PEER REVIEW 12 of 17
Figure 4. Pearson correlations. (A) The scaer plots display a significant positive correlation be-
tween the temporal representation SAGT index and conscientiousness. (B) The scaer plots display
a significant positive correlation between the temporal representation SAGT index and a dependent
decision-making style. (C) The scaer plots display a significant positive correlation between the
efficacy representation SAGT index and a rational decision-making style.
4. Discussion
The purpose of the current research was to adopt, for the first time, a neuroscientific
perspective to explore the different cognitive and affective responses related to the self-
awareness of one’s decision-making goals in a sample of professionals. To achieve this
aim, a novel behavioral and ecological decision-making task (i.e., the SAGT) was de-
signed. A neuroscientific approach was also exploited to deepen not only the explicit at-
tributes (personality traits and decision-making style) but also the implicit neurophysio-
logical correlates of the self-awareness of one’s decision-making goal process.
The analysis performed on behavioral, autonomic, electrophysiological, and self-re-
port data showed the following: (i) higher self-awareness of goals in terms of time and
efficacy than in terms of priority; (ii) greater activation in the temporoparietal brain area
than in the frontal one for the alpha, beta, and gamma bands; and (iii) positive association
between self-awareness of goals by time and dependent decision-making style (at the Gen-
eral Decision Making Style) and conscientiousness (measured through the 10-item Big
Five Inventory), but also between self-awareness of goals by efficacy and a rational deci-
sion-making style (at the General Decision Making Style).
4.1. Behavioural Findings: Efficacy as the Most Diffused Implicit Key
The behavioral results revealed that every participant was able to identify and write
in the first step of the SAGT at least one main decisional goal when thinking on a typical
working day. The data’s variability suggests that the SAGT could effectively activate re-
cursive thinking in an examinee and grasp individual differences in self-representation
and the self-aware identification of goals relevant to the investigated construct. Examples
of decisional goals identified by professionals are as follows: “meetings with collaborators
on the progress of ongoing projects”, “planning the next week’s activities of the entire
Figure 4.
Pearson correlations. (
A
) The scatter plots display a significant positive correlation between
the temporal representation SAGT index and conscientiousness. (
B
) The scatter plots display a
significant positive correlation between the temporal representation SAGT index and a dependent
decision-making style. (
C
) The scatter plots display a significant positive correlation between the
efficacy representation SAGT index and a rational decision-making style.
The Pearson correlation performed between the behavioral SAGT indices (Self-Repi,
Priority-Repi, Temporal-Repi, and Efficacy-Repi) and General Decision Making Style
showed a positive correlation between Temporal-Repi and a dependent decision-making
style (r = 0.348, p= 0.047) (see Figure 4B), as well as between Efficacy-Repi and a rational
decision-making style (r = 0.441, p= 0.010) (see Figure 4C).
On the other hand, no other significant correlations were found.
4. Discussion
The purpose of the current research was to adopt, for the first time, a neuroscientific
perspective to explore the different cognitive and affective responses related to the self-
awareness of one’s decision-making goals in a sample of professionals. To achieve this
aim, a novel behavioral and ecological decision-making task (i.e., the SAGT) was designed.
A neuroscientific approach was also exploited to deepen not only the explicit attributes
(personality traits and decision-making style) but also the implicit neurophysiological
correlates of the self-awareness of one’s decision-making goal process.
The analysis performed on behavioral, autonomic, electrophysiological, and self-report
data showed the following: (i) higher self-awareness of goals in terms of time and efficacy
than in terms of priority; (ii) greater activation in the temporoparietal brain area than in the
frontal one for the alpha, beta, and gamma bands; and (iii) positive association between self-
awareness of goals by time and dependent decision-making style (at the General Decision
Brain Sci. 2023,13, 1163 12 of 16
Making Style) and conscientiousness (measured through the 10-item Big Five Inventory),
but also between self-awareness of goals by efficacy and a rational decision-making style
(at the General Decision Making Style).
4.1. Behavioural Findings: Efficacy as the Most Diffused Implicit Key
The behavioral results revealed that every participant was able to identify and write
in the first step of the SAGT at least one main decisional goal when thinking on a typical
working day. The data’s variability suggests that the SAGT could effectively activate
recursive thinking in an examinee and grasp individual differences in self-representation
and the self-aware identification of goals relevant to the investigated construct. Examples
of decisional goals identified by professionals are as follows: “meetings with collaborators
on the progress of ongoing projects”, “planning the next week’s activities of the entire work
team”, “creating communication content for the next event”, and “reading and responding
to the email received”.
Data analysis, performed by comparing the performance of the sample in the part of
the SAGT concerning the ability to self-represent their own decisional tasks and activities,
also suggested that the most diffused implicit key is their efficacy, followed by the temporal
sequence. These results could be analyzed by referring to the complex environment in
which professionals work every day, which, together with the need to perform adequately
by achieving the required outcomes, could affect the strategies adopted to prioritize one’s
goals. In a dynamic context characterized by the need to cope with continuous requests and
unexpected change, often with little time available, it is essential to adopt a prioritization
of one’s goals in terms of time, prioritizing those referring to activities with an imminent
deadline, but also always keeping in mind the efficacy of the task itself [18].
4.2. EEG and Autonomic Findings: Self-Representation of Decision-Making Goals as a
Cognitivez Process
The second finding of this study concerned the significant presence of alpha, beta, and
gamma bands in the temporoparietal area of the brain during the SAGT execution. First, it
is essential to point out how the presence of significant activity of these frequency bands
led to the consideration that a primarily cognitive, rather than emotional, response was
involved in the processes of the self-representation of decision-making goals. In agreement
with the literature, the EEG delta and theta bands were previously associated with the
stimulus or information emotional processing, also in the decision-making domain [
45
,
46
].
For instance, Balconi and colleagues have shown that higher values in the theta band
are associated with emotionally charged states due to the decision-making process in
a social setting and a sense of belonging [
46
]. On the other hand, the alpha, beta, and
gamma bands can usually be associated with cognitive information processing. Since
the task requires a memory recall of one’s decision-making goals, the significant lower
presence of alpha power in the frontal regions could be interpreted as an index of the
state of internally oriented information processing marked by mental simulation rather
than bottom-up processing driven by the stimulus itself [
42
]. The beta and gamma bands’
activation could also be interpreted as a cognitive index of the activation of the neural
networks involved in memory maintenance and decision making [
43
,
44
]. Indeed, the task
required recalling, maintaining in memory, and selecting a rule for ordering the different
decisional goals. In line with this, Kaiser and colleagues demonstrated how a short-term
spatial memory paradigm activates oscillatory activity in the gamma band as a correlate of
cognitive processes [44].
Another important finding in EEG data concerns the localization of the augmented beta
and gamma power, namely the bilateral temporoparietal brain regions, which, according
to previous studies, are involved in the self-representation mechanism, but also in the
cognitive elaboration processes due to stimulus–context integration [
47
–
49
]. Indeed, for
example, Jackobs and colleagues found a steady increase in brain activity predominantly
Brain Sci. 2023,13, 1163 13 of 16
in the temporoparietal junction in the presence of higher demands for stimulus–context
integration [48].
Furthermore, the lack of results for the autonomic indices can be interpreted in a
twofold way. A first potential explanation relates to the fact that professionals may have
developed adequate abilities to manage and tolerate the stress response linked to any
unexpected request (such as for instance, the request to list their daily goals in a restricted
amount of time). In fact, since job stress has been associated by Jarczok and colleagues
with autonomic nervous system imbalances [
50
], the absence of significant differences in
autonomic measures might indicate that there are not high levels of stress. The second
interpretation supports the results observed in the EEG data. Since autonomic indices
provide insight into emotional processes such as arousal, stress, and anxiety [
31
–
33
], the
lack of significant variations in these indices could suggest that the self-representation of
one’s decision-making goals is mainly a cognitive process that may marginally involve
emotional processing.
4.3. Correlation Findings: Professionals Are Success-Oriented People
Finally, the correlation between behavioral indices and self-report measures revealed
that this novel task, designed to investigate the awareness of decision-making goals, can
be considered a useful way to support the behavioral study of decision-making styles.
Specifically, we found a positive relation between the conscientiousness personality trait
measured in the 10-item Big Five Inventory and the tendency of individuals to self-represent
their own goals by adopting temporal sequence criteria. This result could suggest a
higher tendency of preferring time criteria in individuals that adopt planned behavior and
have good organizational skills, with a system-oriented view of an organization [
21
,
51
].
In addition, we observed a relationship between a rational decision-making style and
the tendency of individuals to self-represent their own goals by adopting the criteria
of efficacy. This relationship could suggest that professionals who organize their goals
according to the implicit key of efficacy are success-oriented people who carefully evaluate
all the alternatives for making a decision and are able to consider the different possible
developments of the choice taken [
20
,
51
]. This evidence paves the way for an in-depth
study of personality traits, decision-making styles, and the tendency to self-represent one’s
goals according to a specific implicit criterion, which translates into distinct evidence at the
behavioral and neurophysiological levels.
5. Conclusions
To conclude, to the best of our knowledge, this research explored professionals’ cog-
nitive and affective responses connected to the self-awareness of one’s decision-making
goals by exploiting, for the first time, a combined behavioral and neurophysiological ap-
proach. The results showed that professionals were able to self-represent their goals, and
the prevalent implicit key used to self-represent them is their efficacy and time. Addi-
tionally, this study highlighted how this novel and ecological decision-making task (the
SAGT), which requires participants to self-represent their decision-making targets and sort
them according to distinct criteria (priority, time, and efficacy), entails cognitive process-
ing supported by frequency bands and the activation of specific neural areas that deal
with memory, attention, and the synthesis of multiple types of information. Finally, this
study highlights the value of integrating behavioral results with neurophysiological and
autonomic evidence as a best practice for capturing valuable information on the cognitive
load and emotional engagement during different phases of the decision-making process in
professional contexts, including the first step of having self-awareness of one’s decision
aims. This information could reflect the strengths and weaknesses of a decision maker and
can be useful for identifying target aspects for tailor-made neurocognitive enhancement
protocols, to even be applied in professional contexts.
Despite the novelty of this research, which attempts to fill an existing gap in the
literature on the study of decision making, some limitations should be considered. The first
Brain Sci. 2023,13, 1163 14 of 16
limitation regards the selected sample: to improve the representativeness and reliability of
the results, future studies should recruit samples from different and multiple organizations
(such as economists, computer scientists, or entrepreneurs) to highlight possible differences
in the ability and method of self-representation of decisional goals due to different work
cultures and contexts. Similarly, it would also be interesting to operate a comparison and
explore decision-making self-representation ability in a non-professional sample, such as
university students. This would also help to disentangle the interpretation of the lack of
significant differences in the autonomic indices. In fact, it might be possible that, compared
to professionals, students or other population categories would display a different pattern
of cognitive and emotional responses during the self-representation of their own decisional
goals. On the other hand, the absence of autonomic variations in the group of students
could also confirm the prevalent cognitive rather than emotional response to this task.
Future research could also consider controlling or exploring the contribution of gender
variables to the self-representation of one’s decisional goals process. Future studies could
use multichannel electrophysiological instruments to collect more comprehensive data on
brain activity, although this choice could have an impact on the ecological validity of the
study itself.
Finally, it might be desirable to develop other studies that use a quali-quantitative ap-
proach and consider other investigative tools, to explore how aspects such as the emotional
component, the impact of cultural factors, or prior experience may impact decision-making
processes. For example, semistructured interviews or other self-report measures, such
as the Maximization Scale, aimed at investigating individual differences in maximizing
one’s choice, could be used to assess a subject’s subjective experience [
52
]. Regarding
the neuroscientific approach, research protocols using multiple neurophysiological tech-
niques, such as EEG integrated with functional near-infrared spectroscopy (fNIRS), could
be implemented to better understand the implicit component of decision making.
Author Contributions:
Conceptualization, M.B.; methodology, M.B., L.A. and C.A.; software, C.A.;
validation, M.B.; formal analysis, C.A.; investigation, L.A. and C.A.; resources, M.B. and C.A.; data
curation, M.B. and C.A.; writing—original draft preparation, L.A. and C.A.; writing—review and
editing, M.B., L.A. and C.A.; visualization, L.A. and C.A.; supervision, M.B.; project administration,
M.B. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
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 (approval code: 2021 TD—for thesis dissertation; approval
date: 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.
Acknowledgments:
We would like to acknowledge all of the professionals that took part in the study.
Conflicts of Interest: The authors declare no conflict of interest.
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