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Article

Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates

by
Angelica Daffinà
1,2,
Carlotta Acconito
1,2,* and
Michela Balconi
1,2
1
International Research Center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy
2
Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(6), 2941; https://doi.org/10.3390/app15062941 (registering DOI)
Submission received: 28 January 2025 / Revised: 3 March 2025 / Accepted: 6 March 2025 / Published: 8 March 2025
(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)
Browse Figures
Figure 1
<p>Behavioral results. The bar chart shows significant differences in Strategy, with higher scores in S-ds compared to I-ds and in T-ds compared to I-ds and S-ds. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons.</p> "> Figure 2
<p>EEG results: theta band. The bar chart shows significant differences for the theta band in Electrodes, with higher activity in AF8 compared to TP9. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (on the right) represents the increase in EEG power at specific EEG electrodes.</p> "> Figure 3
<p>EEG results: gamma band. The bar chart shows significant differences for the gamma band in Strategy × Electrodes, with higher activity in T-ds compared to I-ds in AF8. Bars represent ±1 standard error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (below) represents the increase in EEG power at the specific EEG electrode for each strategy.</p> ">
Versions Notes

Abstract

:
Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, and integrated. This study explored the behavioral and electrophysiological (EEG) correlates of individual tendencies to rely on three distinct decisional strategies: Information (I-ds), Situation (S-ds), or Task (T-ds). A total of 51 individuals performed a decision-making task that required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different sources of contextual features to make the best decision and manage the problem. The behavioral data and EEG frequency bands (delta, theta, alpha, beta, and gamma) were collected during the decision-making task. The results evidenced a general predisposition to adopt a T-ds. In addition, EEG findings reported a higher increase in theta band power in the right frontal area (AF8) compared to the left temporoparietal site (TP9). Moreover, for the gamma band, higher activity was found in the T-ds compared to the I-ds in AF8. Overall, responsiveness to the context was closely linked to the assignment’s requirements. Additionally, adopting a T-ds requires high levels of multilevel attention control systems and a significant workload on human performance. Nevertheless, the T-ds remain the most employed type of responsiveness to the context approach, when compared to situational and contextual aspects.

1. Introduction

Decision-making is pivotal in personal and professional life, driven by the conscious and unconscious processing of contextual features from the external environment alongside individual traits. Indeed, decision-making is founded on contextual feature-processing strategies, where different elements are collected, evaluated, and integrated according to the requirements of the task and the decision-maker’s prior knowledge and experience [1,2,3]. Over time, various theoretical approaches have explored the decision-making process, including the DECIDE model [4] (D = define the problem, E = establish the criteria, C = consider all the alternatives, I = identify the best alternative, D = develop and implement a plan of action, and E = evaluate and monitor the solution and provide feedback when necessary) and the Binding and Retrieval in Action Control (BRAC) framework [5]. Despite their differences, these models converge on the fundamental steps involved in the decision-making process and recognize the pivotal role of gathering and processing contextual features within the complex structure of decision-making.
On the one hand, the DECIDE model [4] identifies six distinct steps in the decision-making process (defining the problem, establishing the criteria, considering all alternatives, identifying the best alternative, developing and implementing a plan of action, and evaluating and monitoring the solution, as well as incorporating feedback when necessary) and, specifically, the third step—considering alternatives—is a critical phase that requires an active search for contextual features to identify and assess the available options. On the other hand, the BRAC framework [5] explores how the characteristics of a stimulus, the response to it, and its effect are associated (binding) and retrieved (retrieval) to control behavior. This theoretical model focuses on the cognitive processes that allow individuals to select and maintain control over their actions, highlighting the importance of integrating sensory contextual features and cognitive strategies to make effective decisions and perform complex tasks.
However, the availability of relevant contextual features can be influenced by various factors, including human elements, physical or technological limitations, the extent of corporate authority, and individual values and priorities [4]. Therefore, behaviors and actions arise from a complex decision-making process that involves gathering and evaluating contextual features, assessing alternatives, and expressing preferences.
While the importance of contextual features in decision-making is widely recognized, the role of decisional strategies in shaping individual attitudes toward decision-making, as well as the impact of the type, quality, and actual use of available contextual features during the process, remains a topic of ongoing debate [6]. Recently, it was investigated how diverse decision-makers use three different sources of contextual features in their decision-making, through an assignment based on realistic decision-making scenarios [7]. Specifically, the authors identified three different decision-making levels: the basic level (focusing on specific details relevant to a decision), mid-level (considering the task and focusing on the goals of the decision), and higher level (taking into account situational aspects). Additionally, a recent study by Balconi et al. [8] evidenced how correctly defining the objectives to be achieved is one of the most critical steps in making good and efficient decisions. Moreover, the level of ability to self-represent objectives could be an implicit dominant key guiding daily tasks and duties. Indeed, objectives drive attention and effort toward achievement and effective decision-making.
Despite this attempt, however, prior research focused on investigating specific decisional strategies adopting a self-reported approach. Nevertheless, self-report measures may not be the most suitable approach for achieving a full and deep understanding of decision-making processes, since they focus solely on the explicit and relatively conscious aspects of decision-making styles, which may be influenced by various factors, including social desirability.
Moreover, the brain is influenced by environmental factors, which shape and modulate decision-making processes that determine our preferences, behaviors, actions, and emotions [2,9,10] Indeed, previous research evidenced how different decision-making choices are characterized by activation of the frontal area [8,11,12,13].
Thus, a significantly different approach is represented by the neuroscientific perspective, which, by acknowledging the influence of environmental factors on the brain, permits the exploration of the explicit and implicit components of a decision-making process [8,12,14,15]. To reach this aim, neuroscientific tools, such as electroencephalogram (EEG), offer insights into the brain’s electrical activity, providing the assessment of cognitive load and mental effort involved in decision-making processes, and including the aspects of contextual and emotional processing [16,17,18]. This is achieved through the analysis of the functional significance of various frequency bands—delta, theta, alpha, beta, and gamma—which play complementary and specific roles. At a general level, greater activation in the low-frequency bands (theta and delta) could be associated with emotional processes [17], while the high-frequency bands are indices of cognitive effort, active attention, and engagement [18,19]. Specifically, delta waves represent a pivotal component of information consolidation and implicit monitoring, contributing to the prior evaluation of stimuli [20]; they are also considered a correlate of attentional cognitive processes and explicit and declarative memory formation, in addition to emotional states [21,22]. Theta frequency bands, on the other hand, facilitate working memory and cognitive conflict resolution, which are essential for choices requiring integration and control of action [21,23] and act as a “surprise signal” to adapt behavior [24]. Instead, alpha waves have been shown to regulate selective attention and promote a relaxed state, allowing the individual to focus on the decisive elements of the task and inhibiting conflicting or unnecessary processes [25,26]. Moreover, the beta band has been observed to increase in conjunction with heightened levels of attention and vigilance. This phenomenon is concomitant with the enhancement of strategic thinking and cognitive control, facilitating conscious, goal-based decision-making [27]. Finally, gamma activity has been associated with complex dynamics involving mental fatigue, sustained attention, vigilance, and higher cognitive performance [28,29,30]. Moreover, it has been related to a multilevel attentional control system which is capable of detecting changes in both low-level and high-level cognitive processes [31]. Gamma oscillations also modulate brain activity in synergy with other bands, dynamically adapting to the specific demands of decision-making. Furthermore, the integration of information from different cognitive areas is facilitated by gamma oscillations, which result in a unified and coherent representation of events. This process enables the brain to consolidate and retrieve information effectively, ensuring consistency in the processing and application of cognitive data, even in subsequent contexts [24].
Recognizing the gap in the literature and the added value provided by neuroscience, the present study aimed to explore, through a neuroscientific approach employing EEG, individual decisional strategies—Information-decisional strategy (I-ds), Task-decisional strategy (T-ds), and Situation-decisional strategy (S-ds)—related to a potential work situation (i.e., being late for an important meeting due to a technical malfunction in the rail system). In particular, the three decisional strategies were connotated by the type of contextual features progressively investigated. So, on one hand, the I-ds represents a decisional strategy that considers features related to the details of a decision; people who prefer this strategy focus on intrinsically salient perceptual details and basic, easily processed cues. In this perspective, the I-ds is the simplest and least rational strategy, which may require little time for decision-making and less cognitive effort. On the other hand, the T-ds considers the duty that has to be accomplished and the objectives to be pursued; people who favor this strategy rely mainly on clues considered relevant and regulate endogenous mechanisms for focusing and integrating data according to the demands of the assignment. Indeed, the T-ds requires more cognitive effort and is more demanding than the I-ds but also permits one to make purposeful decisions concerning task demands. Finally, the S-ds subsumes both situational aspects and features of the context; people who use this strategy are guided by contextual features, considering different alternatives to define the best option. Since the S-ds is a comprehensive strategy, it is the most rational decisional strategy that involves more cognitive effort and time resources than the I-ds and the T-ds.
Specifically, after the presentation of each scenario, participants were instructed to express their level of agreement with various proposed solutions to the potential work situation, involving the evaluation of different types of contextual features.
Based on the above-introduced background knowledge and the previous description of the decision-making strategies, this study aimed to explore potential differences in decisional strategies’ preferences. Specifically, to make an effective and efficient decision within a real and unpredictable decision duty, it was expected to observe an increased predisposition of adopting the T-ds, since the correct identification of objectives to be pursued is the dominant key that guides daily duties. Moreover, although the adoption of the S-ds represents the most comprehensive strategy, it is also the one that involves greater cognitive effort and time resources, an aspect often lacking in real-world contexts, characterized by unexpected changes and the need to make quick but effective decisions.
Additionally, regarding the neural correlates of individual decisional strategies, executive functions such as cognitive flexibility and problem-solving are thought to have a central role in organizing contextual features and managing strategies; thus, it is expected to observe greater frontal activation. Moreover, this frontal activation could be mainly expected in the theta band, since the decision-making process requires integration of different contextual features, working memory, and control of action, as well as the ability to resolve possible cognitive conflicts among possible alternatives. Additionally, it could be hypothesized to have greater gamma activation, since this frequency band is associated with cognitive dynamics such as focusing, gathering, and integrating contextual features in complex decision scenarios.

2. Materials and Methods

2.1. Sample

A sample of 51 healthy individuals, aged 18–28 years old (Meanage = 22.02; DSage = 2.21; M = 29), after signing the informed consent form, voluntarily took part in the study without receiving any financial compensation for their participation.
All participants lived in Northern Italy, were right-handed, and had normal-to-corrected vision. An additional inclusion criterion was age >18 years. Moreover, the group of participants was engaged in collaboration with different Italian universities representing a diverse range of academic disciplines, such as psychology, economics, and engineering. This heterogeneity in educational background with the caution of recruiting individuals at different years of study represented crucial steps in reducing potential biases and enhancing the generalizability of the results to a broader population. Furthermore, participants were recruited by meeting the following exclusion criteria: history of neurological or psychiatric disorders, severe depressive episodes, high stress levels, impaired global cognitive functioning, or undergoing therapy with psychoactive drugs. Indeed, the presence of one of these aspects could affect cognitive decision-making processes and have an impact on information processing ability.
This study received the approval of the Ethics Committee of the Department of Psychology, Catholic University of the Sacred Heart of Milan in Italy, and was conducted following the Helsinki Declaration (2013), according to the GDPR-Reg. UE 2016/679 and its ethical guidelines.

2.2. Procedure

The experimental procedure took place in a quiet and dedicated room where participants were comfortably seated in chairs placed 80 cm away from a computer screen, where the task was administered.
After providing their consent to participate in the study by completing and signing the informed consent, participants were introduced to the experimental setting and procedures. Subsequently, the EEG wearable MUSE™ headband (version 2; InteraXon Inc., Toronto, ON, Canada) was placed on the participants’ heads to record 120 s of neurophysiological resting-state baseline activity. Following this step, participants were presented with the decision-making task, during which their neurophysiological activity was continuously monitored and recorded.
The entire procedure lasted approximately 20 min.

2.2.1. Behavioral Data Acquisition

To explore individual tendencies to rely on information of increasing complexity in a decision-making process, the experimental task was administered via a web-based experiment-management platform (Quatrics XM, Qualtrics LLC, Provo, UT, USA).
Specifically, the task was the same developed and adopted in the research by Balconi [1] and required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different strategies to make the best decision and manage the problem. Indeed, participants were presented with a series of realistic decision-making scenarios of increasing complexity, simulating unpredictable but real-life everyday possible scenarios (e.g., “The high-speed train is delayed, and the regional train would make you late for the meeting, albeit by a little. What do you do?”) to facilitate their understanding and self-identification, aspects not always ensured by protocols used in previous studies. After immersing themselves in the scenario, participants were required to express the level of agreement with each proposed option (e.g., “The high-speed is faster than the regional train. The repair of the malfunction may be resolved quickly. If I remain on board, there is still a reasonable chance of arriving on time”) for managing the unexpected event on a five-point scale (1 = total disagreement; 5 = complete agreement). By involving participants in these realistic decision-making scenarios, the study aimed to analyze the complexity of the decision-making process in depth, assessing not only the explicit choice of strategies but also the cognitive effort required to deal with them. Unlike previous research, which considered decision-making as a unified process without distinguishing between strategies [32,33], this approach enabled a more detailed analysis of the factors influencing strategy choice. Furthermore, this study integrated behavioral data with electrophysiological measures, in particular EEG recordings, to investigate the neural dynamics underlying different decision-making strategies. This methodological integration allows a more comprehensive understanding of the cognitive processes involved, providing both explicit responses and implicit neural activity associated with decision complexity and cognitive load.
For the behavioral data, both response scores (the degree of agreement expressed with each phrase) and response time (RTs) were collected for each decision-making scenario and then transcribed offline to create three behavioral indices, the Information-strategy Index (I-i), the Situation-strategy Index (S-i), and the Task-strategy Index (T-i), respectively. Specifically, RTs were collected as behavioral metrics of performance, mirroring the efficacy in responding to the task and information processing, as well as an indirect measure of the workload and cognitive effort required by the decision-making process [34,35].
To calculate the behavioral indices, the following formulae were adopted:
I − i =   I n f o r m a t i o n   s c o r e   i n   d e c i l e s 11 − R T s   I n f o r m a t i o n   i n   d e c i l e s
S − i =   S i t u a t i o n   s c o r e   i n   d e c i l e s 11 − R T s   S i t u a t i o n   i n   d e c i l e s
T − i =   T a s k   s c o r e   i n   d e c i l e s 11 − R T s   T a s k   i n   d e c i l e s
where the score in deciles was represented by the average of the scores obtained from the Likert scales for each of the three scenarios converted into deciles, and the RTs in deciles were obtained by averaging the RTs of each response for each scenario, setting 2 s as the lower limit and 20 s as the upper limit. The decision to use deciles facilitated the achievement of comparable measurements, given that scores and times were measured on different scales.

2.2.2. EEG Data Acquisition

To non-invasively collect neurophysiological data and measure changes in EEG spectral activity between the resting-state baseline and task phase, the Muse™ headband (version 2; InteraXon Inc., Toronto, ON, Canada) was adopted. This wearable device enables the detection of neurophysiological EEG activity through an integrated accelerometer, gyroscope, pulse oximeter, and seven electrodes constructed from conductive materials, specifically silver and silicon rubber. Of these electrodes, three serve as references, while the remaining four detect EEG spectral activity in the frontal and temporoparietal regions, following the international 10–20 system. Specifically, the electrodes were placed in the left and right hemispheres, respectively in AF7 and AF8 for the frontal area and in TP9 and TP10 for the temporoparietal area.
Data was recorded at a sampling rate of 256 Hz and transmitted via Bluetooth to an associated smartphone using the mobile app Mind Monitor, whose software applied a 50-Hz notch filter. Moreover, participants were instructed to minimize movement to reduce artifacts in the EEG signal. After collecting the data, the raw EEG data were visually inspected to eliminate motor artifacts, such as jaw clenching and eye blinks, and processed using a Fast Fourier Transform (FFT) to convert the logarithm of the power spectral density into brain wave frequencies across several bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–44 Hz). Finally, the EEG activity collected during the task was normalized relative to the 120 s of neurophysiological resting-state baseline activity gathered at the beginning of the experimental phase.

2.3. Data Analyses

Concerning data analyses, repeated measures of ANOVA with Strategy (3: I-ds, T-ds, and S-ds) as independent within-subject factors were applied to the behavioral index (I-i, S-i, and T-i).
Moreover, regarding the EEG data, repeated measures of ANOVA with Strategy (3: I-ds, T-ds and S-ds) and Electrodes (4: TP9, AF7, AF8, TP10) as independent within-subject factors were applied to each frequency band (delta, theta, alpha, beta, and gamma) as a dependent variable.
Pairwise comparisons were applied to the data in case of significant effects and Bonferroni correction was used to reduce multiple comparisons potential biases. For all the ANOVA tests, the degrees of freedom were corrected using 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 was estimated by computing partial eta squared (η2) indices. The threshold for statistical significance was set at α = 0.05.

3. Results

3.1. Behavioral Results

The ANOVA performed on behavioral indices reported a significant main effect in Strategy (F (2,100) = 31.0, p < 0.001, η2 = 0.184) (Figure 1). Specifically, pairwise comparison evidenced higher values in S-ds compared to I-ds (p < 0.001), as well as higher average scores in T-ds compared to I-ds (p < 0.001) and compared to S-i (p < 0.001).

3.2. EEG Results

From the Strategy x Electrodes ANOVA, a main effect in Electrodes (F (3,150) = 3.649, p = 0.023, η2 = 0.025) was found. Pairwise comparison reported higher scores in AF8 compared to TP9 (p = 0.014) for theta band (Figure 2).
Moreover, a significant interaction effect Strategy × Electrodes (F(6,300) = 3.978, p = 0.007, η2 = 0.005) was found in the gamma frequency band. Pairwise comparison evidenced higher activity in T-ds compared to the I-ds in AF8 (p = 0.030) (Figure 3).
No other significant differences were observed for the other frequency bands (delta, alpha and beta).

4. Discussion

The purpose of the current research was to adopt, for the first time, a neuroscientific approach to explore individual tendencies to rely on different decision-making strategies during a decision-making task based on complex real-life scenarios in which an unexpected event occurs. Moreover, a neuroscientific approach was exploited to investigate not only the explicit components but also the implicit neurophysiological correlates of individual decisional strategies.
The analysis performed on behavioral data showed (i) higher values in the T-ds than in the S-ds and I-ds, and (ii) in S-ds compared to I-ds. For the neurophysiological data, instead, the analyses reported (iii) greater activation in the right frontal area (AF8) than in the left temporo-parietal one (TP9) for the theta band; and (iv) higher gamma frontal (AF8) activity in the T-ds compared to the I-ds.
Specifically, according to the hypothesis, the behavioral finding revealed that to make an effective and efficient decision in real-life and unpredictable scenarios, the key aspect that drives people is the objective of the task itself. Therefore, in unpredictable and critical decision-making situations, individuals rely on a strategy based on the goal to be achieved and only partially consider the details or the overall contextual elements. This result could be read in line with previous studies [8,36,37] which showed that identifying and understanding goals is one of the most critical steps in making good decisions and, at the same time, affects the ability to control the time spent on each task. Moreover, this decisional strategy, as opposed to the S-ds, which focuses on contextual factors and exhaustively searching for all available information, would ensure more efficient management of cognitive resources and enable faster and more effective decision-making. In this perspective, the T-ds would optimize cognitive load and increase the probability of success even under conditions of uncertainty. Additionally, the reason for individual predisposition to adopt T-ds rather than S-ds may be influenced by the context in which people are used to working and making decisions. Indeed, the need to deal with unforeseen events, make quick decisions, and cope with possible changes while always keeping in mind the different daily activities and personal goals, requires being able to properly divide the cognitive load and time to be devoted to each activity. In this sense, adopting S-ds, although it could represent the most comprehensive strategy, may involve a lot of cognitive effort, which is often unnecessary, and take time away from other tasks. Therefore, the individual predisposition to T-ds could be the most effective and efficient strategy for coping with the complexity of the decision-making environment in which people operate.
Moreover, the second behavioral result supported the previous description of decisional strategies and their level of complexity, highlighting how the S-ds, which is a comprehensive strategy including both situational aspects and features of the context, is more preferred compared to the I-ds, the simplest and least rational strategy, which may require little time for decision-making and less cognitive effort but also a possible ineffective decision.
Regarding the neurophysiological correlates of individual decisional strategies, results provided further evidence on the mechanisms that guide decision-making strategies. On one hand, frontal area activation was previously associated with decision-making processes [8,11,12,13] as well as with the promotion of goal selection [38] and working memory activity [39]. On the other hand, in terms of the EEG cortical oscillations’ functional meaning, the theta band activity in the frontal area can be explained by the role of this frequency in decision-making, which requires the integration of different contextual features, working memory, cognitive flexibility, and the resolution of cognitive conflicts between possible alternatives. These results are consistent with the findings of Beste et al. [24], who emphasized the role of the theta band in monitoring and regulating cognitive processes, as well as in adapting behavior through retrieval processes. Moreover, decision-making processes in complex real-life scenarios in which an unexpected event prompted the presence of a theta band in the frontal area highlighted an important role in cognitive control [40] and are modulated by the uncertainty of the context [41,42]. Finally, the occurrence of the theta band was associated with the encoding of new information in memory and learning associated with task difficulty, as well as with the process of creative insight and integration of new information coupled with an affective orientation towards satisfaction [43,44].
Furthermore, heightened gamma activity in the frontal area in the T-ds, compared to the I-ds, was in line with the behavioral findings. Indeed, the presence of this frequency band in this area evidenced how high-frequency bands, such as gamma, play a crucial role in contextual feature integration during complex decision-making scenarios [24]. Moreover, relevant to the present discussion, frontal gamma activity was previously associated with the known control of mental resources [45] and the dynamic between mental fatigue and sustained attention [28,46], as well as vigilance and cognitive performance [47]. According to these perspectives, the higher gamma activity in the T-ds compared to the I-ds evidenced how the first decisional strategy required more mental fatigue, attention, and cognitive control compared to the second one. Indeed, as previously described in the introduction, the I-ds represents the simplest decisional strategy that considers features related to the details of a decision, while the T-ds is more demanding than the I-ds and permits one to make purposeful decisions considering the duty that has to be accomplished and the objectives to be pursued. Therefore, the increase in gamma activity in the frontal area for the T-ds reflected the executive control necessary for decision-making and for the correct identification of the objectives, through the binding and retrieval of contextual factors, to facilitate the use of rapid and effective context-related decision-making strategies.

Conclusion and Future Perspectives

To conclude, to the best of our knowledge, this research explored the neurophysiological correlates of individual decisional strategies in a complex real-life scenario with unexpected event prompts by adopting, for the first time, a combined behavioral and neurophysiological approach. By integrating EEG data with behavioral analysis, this research provided novel insights into the neural correlates of different decision-making strategies, offering a more comprehensive understanding of both explicit and implicit cognitive processes underlying real-world decision-making. The results showed that people have a predisposition to adopt the T-ds, a decisional strategy focused on the consideration of the duty that has to be accomplished and the objectives to be pursued. Indeed, this decisional strategy permits making an effective decision in real-life and unpredictable scenarios in which people are required to act efficiently and in a short time. Moreover, adopting a decisional strategy, specifically the T-ds, is characterized by a frontal activation of theta and gamma bands, indices of the promotion of goal selection, working memory, mental fatigue, and cognitive flexibility. So, the integration of EEG data reinforces the significance of theta and gamma frequency bands for efficient decision-making processes.
Based on these findings, this study’s novelty lies in the integration of EEG correlates of decision-making strategies with behavioral data, providing a novel opportunity to explore brain activity underlying the decision-making process in complex environmental contexts. This approach enabled a more in-depth exploration of decision-making strategies, overcoming the limitations of previous studies that focused mostly on only behavioral aspects. Compared to existing research, which primarily highlighted the impact of individual variables or decision-making styles on the strategy adopted [37,48], the present study provided a more comprehensive and integrated perspective on the decision-making process, highlighting not only the behavioral aspect but also and especially the electrophysiological activity as an index of effort and cognitive load.
From these results, future research could extend the investigation of decision-making strategies to different domains beyond real-life unpredictable scenarios. For instance, exploring how these strategies operate in professional settings such as emergency response, financial trading, or medical decision-making could provide valuable insights into their applicability in high-stakes environments. Additionally, examining the role of decision-making strategies in group dynamics and collaborative problem-solving may reveal how individuals adapt their approaches when working within teams. Furthermore, the influence of digital technologies, such as artificial intelligence-assisted decision-making, could be explored to understand how human decision-making strategies interact with automated systems. This could be particularly relevant in fields where technology plays a critical role in shaping choices, such as autonomous driving and personalized healthcare.
Despite the fact that the study provided interesting data, it is important to consider some possible limitations. The first limitation regards the sample size, which although moderate, may limit the generalizability of the results to larger populations. Additionally, future studies should recruit samples from different environments, such as multiple organizations, to highlight possible differences in individual decisional strategy tendencies due to different work contexts and operate a comparison between professional and non-professional samples. Furthermore, the integration of complementary neuroimaging techniques could facilitate a more comprehensive investigation of brain activity underlying the process under analysis. In addition to EEG, which provides high temporal resolution for the study of electrical signals in the brain, the use of functional near-infrared spectroscopy (fNIRS) may be useful for monitoring changes in cortical blood oxygenation in a non-invasive manner and in more natural contexts to promote a better understanding of decision making and related strategies. Alongside this neuroimaging tool, moreover, it would also be interesting to investigate any emotional processes related to different decision-making strategies, adopting for example a biofeedback technique. Finally, future studies could also integrate self-report data on preferences for decision-making strategies, such as the General Decision-Making Style Questionnaire (GDMS; [49]), or on the role of individual differences or personality traits, to understand possible effects on decision-making strategies. Additionally, longitudinal studies could examine how exposure to different decision-making contexts over time shapes individuals’ strategic preferences.

Author Contributions

A.D.: formal analysis, resources, software, writing—original draft, and writing—review and editing. C.A.: data curation, formal analysis, investigation, resources, software, writing—original draft, writing—review and editing, and visualization. M.B.: conceptualization, funding acquisition, methodology, software, validation, resources, writing—review and editing, supervision, and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

Università Cattolica del Sacro Cuore contributed to the funding of this research project and its publication (grant D3.2 2023: “Comunicare la scienza. Mediazione e mediatori del sapere scientifico nella società complessa.”—“Communicating science. Mediation and mediators of scientific knowledge in complex society”).

Institutional Review Board Statement

This study involving human participants was conducted in accordance with the Declaration of Helsinki (2013) and reviewed and approved by the Ethics Committee of the Catholic University of the Sacred Heart, Milan, Italy (approval code: 125/24—Valutare il Decision-Making: consapevolezza e metacognizione decisionale; approval date: 23 July 2024).

Informed Consent Statement

The participants provided their written informed consent to participate in this study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to ethical reasons for sensitive personal data protection (requests will be evaluated according to the GDPR-Reg. UE 2016/679 and its ethical guidelines).

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 02941 g001
Figure 1. Behavioral results. The bar chart shows significant differences in Strategy, with higher scores in S-ds compared to I-ds and in T-ds compared to I-ds and S-ds. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons.
Figure 1. Behavioral results. The bar chart shows significant differences in Strategy, with higher scores in S-ds compared to I-ds and in T-ds compared to I-ds and S-ds. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons.
Applsci 15 02941 g001
Applsci 15 02941 g002
Figure 2. EEG results: theta band. The bar chart shows significant differences for the theta band in Electrodes, with higher activity in AF8 compared to TP9. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (on the right) represents the increase in EEG power at specific EEG electrodes.
Figure 2. EEG results: theta band. The bar chart shows significant differences for the theta band in Electrodes, with higher activity in AF8 compared to TP9. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (on the right) represents the increase in EEG power at specific EEG electrodes.
Applsci 15 02941 g002
Applsci 15 02941 g003
Figure 3. EEG results: gamma band. The bar chart shows significant differences for the gamma band in Strategy × Electrodes, with higher activity in T-ds compared to I-ds in AF8. Bars represent ±1 standard error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (below) represents the increase in EEG power at the specific EEG electrode for each strategy.
Figure 3. EEG results: gamma band. The bar chart shows significant differences for the gamma band in Strategy × Electrodes, with higher activity in T-ds compared to I-ds in AF8. Bars represent ±1 standard error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (below) represents the increase in EEG power at the specific EEG electrode for each strategy.
Applsci 15 02941 g003
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MDPI and ACS Style

Daffinà, A.; Acconito, C.; Balconi, M. Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates. Appl. Sci. 2025, 15, 2941. https://doi.org/10.3390/app15062941

AMA Style

Daffinà A, Acconito C, Balconi M. Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates. Applied Sciences. 2025; 15(6):2941. https://doi.org/10.3390/app15062941

Chicago/Turabian Style

Daffinà, Angelica, Carlotta Acconito, and Michela Balconi. 2025. "Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates" Applied Sciences 15, no. 6: 2941. https://doi.org/10.3390/app15062941

APA Style

Daffinà, A., Acconito, C., & Balconi, M. (2025). Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates. Applied Sciences, 15(6), 2941. https://doi.org/10.3390/app15062941

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