Abstract
Aggression is a complex behavior that is difficult to capture using traditional methods, such as questionnaires and lab-based aggression tasks. These methods present challenges due to social desirability bias and limitations in translating findings into real-world situations. In this review, we discuss how emerging technologies, including virtual reality, video games, hyperscanning, biosignal recording, ecological momentary assessment and social media analysis, offer improved construct and ecological validity and can contribute to the refinement of integrative theoretical models of aggression. We comprehensively address advantages (e.g., immersion, realistic simulation, real-time and context-sensitive data collection and interpersonal dynamics) and limitations of each technology compared to traditional methods and highlight remaining gaps in aggression research. Additionally, we examine aggressive behavior related to the emergence of new technologies in digital spaces, focusing particularly on cyberbullying and the metaverse. We also review machine learning approaches for detecting cyber-aggression on social media platforms. We propose shifting from static, individual-level assessments to dynamic, context-sensitive frameworks that capture aggression in real time, in more ecological settings and digital environments. This shift in operationalization holds the potential to advance theoretical understanding, guide future research and inform clinical and forensic interventions.
Keywords
Introduction
Aggression is a multifaceted psychological phenomenon with profound implications for individual well-being and societal functioning. Aggression is usually defined as any behavior directed toward another person with the intention to cause harm, where the perpetrator intents to harm the target and that the target wishes to avoid it (Anderson & Bushman, 2002). Human aggression is a complex behavior that can appear in different forms. Research often differentiates reactive (i.e., provoked, hostile, impulsive) and proactive (i.e., instrumental, planned) acts (Raine et al., 2006). The burden that aggression, especially extreme forms such as violence, places on victims and the socioeconomic system in general underscores the need to understand and ultimately prevent aggression. However, despite decades of theoretical development, empirical research on aggression continues to be constrained by limitations in the measurement and operationalization of the construct. How aggression is measured is critical to accurately understanding and investigating the causes and factors that influence this behavior. Ecological validity (i.e., the extent to which the findings generalize to real-world settings) and construct validity (i.e., the accuracy with which aggression is conceptualized and measured) are particularly important in the field of aggression research because aggression is difficult to measure in laboratory settings and is often socially disapproved. The dominant reliance on self-report questionnaires and lab-based paradigms has led to ongoing concerns about ecological validity, social desirability bias, and disconnection from real-world aggression as it unfolds in dynamic and complex contexts. New technologies and innovative measures for assessing aggression are essential for advancing the field.
The field of aggression research has undergone a significant evolution concerning the assessment of aggression and its precursors over the past few decades, transitioning from naturalistic observational studies and self-report measures, as well as simplistic laboratory tasks (Krahé, 2025), to more sophisticated and immersive techniques. Instruments to measure aggression have been established, each coming with advantages and drawbacks to measure aggression and its precursors. For children and adolescents, the Reactive-Proactive Questionnaire (Raine et al., 2006) is one of the most used self-report instruments. In adults, the Buss-Perry Aggression Questionnaire (Buss & Perry, 1992) may be the gold standard for measuring anger and aggression (Gerevich et al., 2007), but as many other self-report instruments, it has been criticized for a social desirability bias (Vigil-Colet et al., 2012). Additionally, individuals may lack insight into their own aggression levels, highlighting the need for lab-based aggression tasks. A common task is the Taylor Aggression Paradigm (TAP; Taylor, 1967), where participants can administer shocks/aversive noise blasts/subtract points to the opponent after the other lost a competitive task (for different versions of the TAP, see Buades-Rotger et al., 2016; Koch et al., 2024; Konzok et al., 2020; Taylor, 1967; Weidler et al., 2019), see Figure 1(D) for a representation of the task. In the Point Subtraction Aggression Paradigm (Cherek, 1981), participants can reduce points from the opponent in a game and in the Hot Sauce Paradigm, participants give spicy sauce to someone who dislikes it. In contrast to self-report measures that rely on hypothetical or retrospective scenarios, participants are exposed to aggression-triggering situations, but there is not a real but a fictional opponent as the target of aggression. As noted in reviews on typical lab-based aggression paradigms (McCarthy & Elson, 2018; Ritter & Eslea, 2005; Tedeschi & Quigley, 1996), there are limitations concerning credibility, generalization to real-world behavior (ecological validity), intention/motivation assessment, and varying operationalizations of aggressive behavior (construct validity). Examples of static (traditional methods) versus dynamic stimuli (representing novel technologies to investigate aggression). (A) static angry facial expression (Qu et al., 2023); (B) representation of morphed faces with different proportions of fearful and angry faces (Stein et al., 2024); (C) stimuli representing social interactions to investigate hostile bias and aggression response to hypothetical situations (Van Dijk et al., 2019); (D) example of the TAP showing a 2-s video of the opponent (confederate) showing angry or neutral faces while supposedly choosing the punishment level (Buades-Rotger et al., 2016); (E) and (F) represent videos depicting social aggression interactions from first-person perspective (E; Taubner et al., 2021) or third-person perspective (F; Hernandez Pena et al., 2024b); (G) example of a video game designed to measure hostility (Buades-Rotger et al., 2023); (H) and (I) exemplify virtual reality applications; (H) representation of a provoking avatar (Klein Tuente et al., 2018); (I) screenshot from 360° video representing a real-world conflict situation (Van Gelder, 2023), for video see 360° Virtual Scenario Method; (J) and (K) represent hyperscanning settings. (J) represents a face-to-face TAP version where participants see their opponents and play real interactions (McCurry et al., 2024); (K) hyperscanning using two connected scanners recording simultaneous brain activity while participants play against each other (Hernandez‐Pena et al., 2024a). (F) reused with permission from the authors of the original study (Hernandez Pena et al., 2024b). Figures (A, B, C, D, E, G, H, I, J, and K) are reused under the terms of the Creative Commons CC BY license.
We argue that new operationalizations of aggression are both timely and necessary, taking into account recent technological advances in how aggression can be elicited, observed, and understood. Tools such as virtual reality (VR), interactive video games, hyperscanning, ecological momentary assessment (EMA), and social media analysis with machine learning enable the study of aggression in immersive, interpersonal, and ecologically valid contexts. We propose a shift in the operationalization of aggression research, grounded in a technology-integrated framework that emphasizes three major shifts: (1) from isolated individuals to interactive dynamics, aligning with recent second-person approaches (Redcay & Schilbach, 2019); (2) from controlled laboratory proxies to ecologically valid and real-world settings; and (3) from static measures to temporally and contextually embedded behaviors and cognitions.
These technologies have the potential to test and refine existing theoretical models (Koch et al., 2025), such as the General Aggression Model (GAM; Anderson & Bushman, 2002). The GAM proposes an integrative framework considering aggression as the interaction between situational and individual factors, influenced by distal biological and environmental modifiers. Emerging technologies particularly can offer a notable improvement in our ability to capture and manipulate situational factors (e.g., social rejection, provocation, or perceived anonymity) in settings that closely mirror real-world experiences or in real life contexts. These tools are also useful for investigating the routes through which person and situational factors can change an individual’s affect, cognition, and arousal. These states can be more accurately assessed in simulated or real-life situations than through self-report measures alone. In combination with multimodal approaches, these tools have the potential to improve the quality of data on distal factors, including biological and environmental data and their interactions. This integrated approach enables researchers to test the whole model rather than focusing on isolated components.
Summary of advantages, disadvantages and ideal research contexts of emerging technologies in aggression research
Given the broad scope of the topic, we conducted a narrative review rather than a systematic one. We searched Google Scholar, PubMed and Embase for English-language articles published up to January 2025. Search terms combined aggression-related keywords (e.g., “aggression,” “aggressive behavior,” “hostility,” “violence,” “cyber-aggression,” “cyberbullying”) with technology-related terms (e.g., “technology,” “virtual reality,” “video game,” “naturalistic video,” “hyperscanning,” “two person approach,” “ecological momentary assessment,” “experience sampling method,” “wearable devices,” “biosensor technology,” “social media,” “metaverse,” “machine learning,” “natural language processing,” “virtual world,” “digital platforms”). Additional relevant articles were identified through cross-referencing. We included studies investigating aggression using emerging technologies in the domains of virtual reality, video games, naturalistic stimuli, hyperscanning, biosignal recording, ecological momentary assessment or social virtual environments (e.g., social media analysis and the metaverse). We excluded studies focusing primarily on biological correlates (e.g., genetic, neuroimaging, or network analyses), as these represent distinct methodological domains beyond the scope of this review.
Virtual Reality (VR), Video Games and Naturalistic Stimuli
This section discusses studies using VR, video games or naturalistic stimuli to create controlled and highly realistic environments. These settings enable the study of aggressive behaviors in a more ecological and immersive manner.
Naturalistic Stimuli of Aggressive Interactions
Rather than directly measuring interpersonal aggressive behavior, some researchers have investigated how people process and evaluate stimuli depicting aggressive interactions by assessing emotional responses, cognitive evaluations, and attentional biases related to aggression. Historically, aggression and anger research has relied on static facial images (e.g., Blair et al., 1999), especially processing of angry morphed faces (see, e.g., Penton-Voak et al., 2013; Stein et al., 2024; Wilkowski & Robinson, 2012), as well as hypothetical stories (Crick, 1995) and static, simple depictions of social scenarios (such as drawings) as social stimuli (Van Dijk et al., 2019); see Figures 1(A)−(C) for examples. This approach could limit participants’ perspective-taking abilities and provide representations that are far removed from reality. Static angry faces, compared to dynamic angry faces, are processed earlier and may lead to attentional bias (Qu et al., 2023). In contrast, dynamic stimuli enhance arousal and are more ecologically valid by simulating real-life scenarios in a more naturalistic and realistic way (Fehr et al., 2014) and better reflect emotional states (Qu et al., 2023). New technologies have facilitated recent advances in this field by enabling the implementation of more realistic and emotionally engaging stimuli that depict violent conflicts or aggressive interactions (e.g., Hernandez Pena et al., 2024b; Nummenmaa et al., 2008; Van Den Stock et al., 2015; Van Dongen et al., 2018); see examples in Figures 1(E) and (F). The majority of these studies have investigated the processing of violent scenes. Many studies thereby investigated passive processing of stimuli depicting aggressive interactions (e.g., strangling, punching, or threatening scenes) versus neutral social interactions (Nummenmaa et al., 2021; Rantanen et al., 2021; Van Dongen et al., 2018). Others explored reactions toward video clips shown from the aggressor’s first-person perspective (Fehr et al., 2014). In some studies, the victim or the perpetrator perspective was compared; see Figure 1(F) (Hernandez Pena et al., 2024b; Nummenmaa et al., 2008; Taubner et al., 2021; Van Den Stock et al., 2015). More directly investigating aggressive reactions, video clips portraying unfair situations, such as bullying, mocking, or accounts of child abuse, have been used to induce anger in adult participants (Seok & Cheong, 2020). Adapted versions have been developed to study peer victimization in children featuring child actors (Troop-Gordon et al., 2018).
Video Games as Measures of Aggression
Numerous studies, reviews, and meta-analyses have examined the potential association between violent video games and aggressive behavior (e.g., see Burkhardt & Lenhard, 2022; Prescott et al., 2018). More recently, video games have also been included to measure aggressive behavior. The use of video games to measure aggression probably started in the early 2000s, when Carnagey and Anderson (2005) employed a race-car video game to assess both rewarded and punished aggression. Subsequent developments have used first-person shooter games (e.g., Chen et al., 2023; Gentile et al., 2016; Kätsyri et al., 2013; Mathiak & Weber, 2006) and driving cars running over pedestrians (e.g., Klasen et al., 2020; Wolf et al., 2018; Zvyagintsev et al., 2016). These virtual violence scenarios allow for semi-natural behavior (Mathiak & Weber, 2006) and may be particularly relevant for measuring appetitive aggression, that is, positive feelings following aggressive behavior (Elbert et al., 2017). Higher immersion and realism in these video games increase aggressive behavior and intentions (Chen et al., 2023; Farrar et al., 2006), likely due to enhanced aggressive affect and cognition and physiological arousal (Chen et al., 2023).
Additionally, there are also some adaptations of existing laboratory tasks becoming more immersive with adding video game elements used in a more laboratory-controlled format. For example, Buades-Rotger et al. (2023) have proposed an adaptation of a Go-Nogo shooting task to measure hostile expectations learning in a more naturalistic, yet controlled setting (see Figure 1(G)). In recent years, there has also been progress in adapting standard game theory paradigms. For example, a novel version of the Chicken Game task has been developed to assess competition, dominance and aggression in a more immersive and dynamic environment by adopting the first-person perspective of a car driving toward another car (Hernandez‐Pena et al., 2024a); see Figure 1(K). In addition, Ahmed et al. (2023) created a video game environment where participants can interact with avatars and experience game theory paradigms (e.g., Prisoner´s dilemma or Dictator game) in a narrative and interactive manner. There are also some improvements in the virtual environment of the well-established Cyberball paradigm to investigate ostracism increasing engagement and customization (Long et al., 2023).
Using video games as lab-based aggression measures, particularly in decision-making scenarios, offers several advantages including enhanced immersion, intuitiveness, and engagement value (Allen et al., 2024). This technology can be successfully combined with real-time neurofeedback. Baqapuri et al. (2021) designed an immersive shooter video game and trained participants to regulate the motor area. Future research could explore the regulation of different target regions (e.g., prefrontal cortex usually associated with self-regulation and cognitive control; Friedman & Robbins, 2022) and whether this reduces aggression in an active and engaging environment.
Video games, however, have also notable disadvantages to consider. For instance, participants are aware that the avatars or other players are not being harmed for real, which fails to meet the common definition of aggression. Some participants might feel guilty while engaging in unjust or immoral behaviors in violent video games; however, this feeling decreases after repeated exposure (Grizzard et al., 2017). Additionally, engaging in “aggressive” behaviors—such as shooting others, running over pedestrians or crashing—can be entertaining and engaging (Gentile et al., 2016); thus, participants may act aggressively not with the intent to cause harm, but for enjoyment or competitiveness. Indeed, some research has shown that competitiveness, rather than violence, may be the factor driving aggressive behavior in violent video games (Adachi & Willoughby, 2011a). Furthermore, individuals with prior experience playing violent video games may introduce a confounding factor that influences both the construct and external validity of these tasks.
Importantly, only a few studies have investigated the relationship between violent video games and aggressive behavior as measured by the TAP or the Hot Sauce Paradigm, with findings indicating no significant relationship (Adachi & Willoughby, 2011a; Dowsett & Jackson, 2019; Kneer et al., 2016). Given these considerations, questioning the validity of the aggression measures employed seems prudent. Are laboratory tasks and more natural observations of video playing capturing different types of aggression? Are observations of aggressive behavior in video games truly measuring “real life” aggressive tendencies at all?
One potential avenue for bringing tasks that use video games intended to measure aggression closer to real-life interpersonal aggression is to develop video game environments in which participants can freely interact with each other (similar to Second Life or Roblox, see Section ‘Virtual Reality' and Section ‘Metaverse' for more details on virtual worlds), but in a controlled laboratory setting. This approach would provide opportunities for aggressive behavior while ensuring that participants’ actions result in real (and potentially harmful) consequences administered by the experimenter during or after the task, as in standard lab-based aggression tasks. Such an immersive setting could increase realism and participant engagement, combined with tangible repercussions of aggressive behavior simulating situations similar to those experienced in real life. Another suggestion is to consistently include an optimal control condition with a non-violent video game that matches the violent version in terms of competitiveness, difficulty and pace of action (Adachi & Willoughby, 2011b). This would help confirm whether the observed aggressive behavior in violent video games differs from that in non-violent versions and clarify whether the aggression is due to the violence itself rather than simply increased competitiveness, a greater desire to win, or frustration due to varying levels of difficulty.
Virtual Reality
Virtual Reality (VR; immersive, interactive virtual environment that simulates realistic scenes using a three dimensional display) integrates several features by recording real-time data while, most likely, adopting the first-person perspective of an avatar during social interactions, along with other advantages. There are already well-detailed and recent reviews on the implementation of VR in criminology research (Van Gelder, 2023) and particularly in combination with neuroimaging techniques and the study of body perception and expression in relation to aggression (De Gelder et al., 2023), as well as the assessment and treatment of aggression among forensic and clinical populations (Dellazizzo et al., 2019; Sygel & Wallinius, 2021), specifically concerning posttraumatic stress disorder (Rizzo et al., 2017), intimate partner violence (Johnston et al., 2023) or child abusers (Fromberger et al., 2018).
In this chapter, we want to examine the potential of VR as a tool for research on aggression. Interestingly, as highlighted by the focus of other reviews, most research on VR has been oriented toward applied and therapeutic studies, rather than fundamental aggression research. Consequently, the majority of the reviewed studies employed VR primarily for therapeutic applications. We will primarily focus on VR as an assessment tool for aggression while highlighting its advantages in ecological validity and realism (hereafter referred as mimicking real-life scenarios, ability to evoke emotions, immersion, incorporating interaction) over traditional applications. A major advance in research on criminology and aggression-related topics has been made by the group led by Van Gelder. These authors developed several highly realistic 360° video technologies to construct an immersive VR environment; see Figure 1(I) and 360° Virtual Scenario Method for an example. These videos simulate real-world conflict situations designed to elicit emotions such as anger or sexual arousal within controlled and flexible settings (Herman et al., 2024; Van Gelder, 2023). In an anger condition, represented by a provocative male in a bar, participants reported significantly higher feelings of disgust, anger, and annoyance compared to the control condition with large effect sizes (Herman et al., 2024). In a previous study, comparing written scenarios with VR representations of another bar fight situation, the authors discovered that VR significantly increased realism, subjective feelings related to aggression, such as anger, guilt, fear, and the intention to aggress, providing support for a state-trait model of aggression (Van Gelder et al., 2019, 2022) and physiological changes (Van Gelder et al., 2017). These findings highlight the potential of VR technology to create engaging and impactful simulations that can enhance our understanding of criminal and aggressive behavior. However, further testing is needed to determine whether these measures are related to real-life aggression.
Only a few studies have aimed to directly decrease aggressive behavior, measuring aggression as their primary outcome. The most noteworthy attempt was made by Klein Tuente et al. (2018), who developed a VR-based treatment (VRAPT) in forensic psychiatry to target reactive aggression by simulating interpersonal scenarios. The training sessions focused on emotion recognition, identifying and de-escalating aggressive behavior of others (i.e., avatars), regulating arousal and engaging in interactive virtual role-plays (see Figure 1(H)) (Klein Tuente et al., 2018). The treatment was applied to 128 forensic inpatients (Klein Tuente et al., 2020), with therapists controlling the environments, tailoring experiences using voice morphing and providing real-time physiological feedback. While there was no change in staff-reported or self-reported aggression, self-reported anger, hostility, and impulsivity temporarily decreased, with effects dissipating after 3 months. Most participants were willing to engage in VR therapy, recognizing its benefits alongside other therapeutic approaches (Klein Tuente et al., 2020). Another pilot study has used the VRAPT with prisoners finding a decrease in self-reported aggression and staff-rated observational aggressive behavior (Woicik et al., 2023). Overall, the VRAPT represents a valuable initiative for advancing future research aimed at the study and treatment of aggression, given its high acceptance among participants and patients, adaptability to individual characteristics (e.g., voice morphing) and interactive virtual role-playing which improves immersion.
Similar to the original VRAPT, Verhoef et al. (2021) focused on aggressive social information processing, but applied to children and adolescents. A virtual classroom was designed where participants could interact in VR with virtual peers in both proactive and reactive aggressive scenarios. In this pilot study, VR demonstrated good convergent validity, showing moderate correlations with the vignette assessments, while exhibiting greater measurement sensitivity (i.e., being more sensitive to capturing individual differences). Notably, discriminant validity was observed only in the provocative (reactive) but not in the instrumental (proactive) contexts (Verhoef et al., 2021). In a larger sample, the VR assessment elicited increased level of aggressive processing and responses exclusively in provocation scenarios (Verhoef et al., 2022). Interestingly, VR scenarios enhanced predictive validity, explaining an additional 2%−12% of the variance in children´s real-life aggressive behavior and motives beyond what was captured by the vignette assessment in both contexts.
Lobbestael and Cima (2021) developed a VR tool to differentially measure reactive and proactive aggression in young adults. In the reactive scenario, participants were provoked by being hit, with aggression measured by hits against the provoking avatar. The proactive version involved aggression toward an avatar blocking a goal without provocation. While reactive aggression correlated with self-reported aggression, the proactive scenario had limitations, as forced aggression (i.e., hitting the avatar was the only way to achieve the goal) led all participants to hit the avatar. In the same study, the authors tested a revised version using a cheating dartboard game, where participants could choose non-aggressive or aggressive responses (challenging the avatar, starting a fistfight or hitting them with a bottle). Around 80% of participants did not display aggressive behavior in either condition, supporting its ecological validity. Again, behavior in the reactive aggression condition significantly correlated with self-reported aggression and psychopathy traits, but not in the proactive scenario (Lobbestael & Cima, 2021). This revised version appears more suitable because it includes conditions that allow participants to refrain from aggression, mirroring real-life situations more accurately. In a study by Terbeck et al. (2022), participants examined responses to pro-social and provocative interactions with an avatar in VR. Participants were instructed to respond either congruently or incongruently (with an unfriendly punch or a friendly handshake) showing faster responses to provoking avatars.
Another protocol presented the implementation of VR videos depicting experiences of victims of bullying from a first-person perspective, showcasing scenarios such as being insulted, having personal belongings stolen, or experiencing exclusion (Barreda-Ángeles et al., 2021). This pilot study demonstrated the effectiveness of VR in eliciting realistic negative emotional responses to acts of bullying and increasing empathy. When comparing VR to traditional screen presentations, participants reported a greater sense of presence in the VR condition; however, no significant differences were observed in self-reported valence and arousal. Notably, physiological responses were greater in the VR condition compared to the screen version, suggesting increased emotional arousal.
An additional advantage of VR is the option to apply the scenarios as treatment training platform equivalent to exposure therapy. This has been conducted in different settings and populations such as patients with posttraumatic stress disorder (Smeijers et al., 2021; Zinzow et al., 2018). Also, VR has been successfully used to reduce aggressive behavior in children with conduct problems (Alsem et al., 2021), as well as increase empathy in real-life bullying situations (Gu et al., 2023; Ingram et al., 2019).
A recent study on VR and intergroup conflict (Hasler et al., 2021) found that participants in VR perceived harmful actions as less moral and justified, driven by hostile emotions toward in-group actors, compared to those who viewed the same scene on a screen. VR’s ability to enhance immersion and evoke stronger emotions highlights its potential for intergroup violence and its mechanisms.
Some researchers have examined the moderating effect of VR on violent video games. The findings have been mixed, with some studies reporting that VR increases feelings of aggression, hostility and anger compared to screen presentations (Lull & Bushman, 2016; Persky & Blascovich, 2007), while others report a decrease in aggressive behavior and hostile states (Arriaga et al., 2008).
VR as a measurement tool for aggressive behavior offers numerous advantages (see Table 1 for a summary). VR can create artificial environments that closely mimic high-risk real-life situations (Fromberger et al., 2018; Klein Tuente et al., 2018), establishing plausible research settings that are difficult or even impossible to realize in the real world related to aggression and violence (Van Gelder, 2023). This technology provides a visual and auditory perceptual experience comparable to real-world contexts, demonstrating high ecological validity while maintaining a controlled and replicable setting (Rizzo et al., 2017; Van Gelder, 2023). Scenarios are designed in such a way that cover stories are unnecessary, thereby reducing dropout rates or response bias from disbelieving participants (Lobbestael & Cima, 2021). VR can even elicit aggressive responses without endangering others (Fromberger et al., 2018), greatly mitigating ethical and safety concerns (Dellazizzo et al., 2019; Johnston et al., 2023; Van Gelder, 2023); however, this may reduce validity compared to real-life situations where aggressive behavior has consequences. Still, participants tend to respond realistically to virtual representations of real-life events (De Gelder et al., 2023; Dellazizzo et al., 2019) and are less self-conscious about their behavior due to immersive nature of VR (Van Gelder, 2023), which is especially important in aggression research due to issues of social desirability bias.
VR technology, compared to other tools, has consistently been reported to enhance feelings of presence and immersion while effectively triggering emotions like anger (Fromberger et al., 2018; Van Gelder, 2023). Written scenarios are limited in the ability to capture or elicit emotionally laden and visceral aspects due to constraints on contextual information and nonverbal behavior exhibited by characters in the vignettes (Van Gelder et al., 2022). Virtual embodiment through immersive VR facilitates perspective-taking (Gonzalez-Liencres et al., 2020; Johnston et al., 2023; Ventura et al., 2021), which could be especially beneficial for investigating aggression in some psychiatric populations with theory of mind deficits. Also, this technology presents fewer barriers compared to imagination techniques, written scenarios or lab-aggression measures (Fromberger et al., 2018), or self-reported data requiring meta-cognition and retrospection, relying less on participants’ abilities to imagine themselves within those situation effectively (Van Gelder et al., 2022). Specifically, VR can successfully manipulate targeted emotional experiences within environments that represent potential criminal opportunities, facilitating the assessment of decision-making processes while in an emotionally charged state (Herman et al., 2024).
With VR behavioral data is collected in real time, revealing precise information underlying violent and aggression dynamics (Rizzo et al., 2017) and allowing for the identification of event chains linking predictors to outcomes in detail—contrary to standard survey methods (Van Gelder, 2023), facilitating testing of prevalent aggression theories. Moreover, this technology can be combined with physiological data recording and even biofeedback. Advanced VR googles can capture ancillary movements such as eye-tracking, pupil dilatation or reaction times (Herman et al., 2024). This multidata approach can improve validation of integrative aggression theories, such as the General Aggression Model (Anderson & Bushman, 2002) or I3 Model (Finkel & Hall, 2018), because it combines personal characteristics cognitive, emotional and biological responses with situational variables. Another advantage is the possibility to conduct VR experiments within prisons, forensic and clinical institutions, reaching populations that are often difficult to access—such as incarcerated offenders (Van Gelder, 2023). In most studies, participants report high levels of retention rates, motivation, engagement and acceptability when using VR technology to measure aggression (Klein Tuente et al., 2020; Verhoef et al., 2021, 2022; Woicik et al., 2023).
There are also some disadvantages associated with VR technology, consult Table 1 for a summary. Due to the higher levels of immersion, absorption and emotional involvement, participants might experience intense negative emotional responses, which have been linked to negative rumination, anxiety and distress (Lavoie et al., 2021; Lundin et al., 2023), raising concerns about lasting harm to subjects that experimenters must consider carefully (Fromberger et al., 2018). Another limitation is that these experiments often rely on measuring behavioral intentions rather than actual behavior;participants typically report what they would do in specific situations rather than demonstrating those actions directly (Van Gelder, 2023). Thus, one main limitation specifically in aggression research is that participants are aware there are no real-world consequences, which significantly restricts the assumption that they believe that their behavior might cause harm to others or that the others want to avoid such potential harm.
Although it can easily be combined with physiological and eye-tracking recordings, VR has limitations regarding its implementation with neuroimaging techniques. Previous studies indicate difficulties integrating VR within magnetic resonance imaging (MRI) systems due to physical motion, suggesting the use of non-intrusive eye-tracking system as a solution (Qian et al., 2021). Elevated motion can also pose challenges for other neuroimaging techniques such as electroencephalography (EEG) or functional near-infrared spectroscopy (fNRIs), restricting the investigation of neural correlates of aggression using VR. In relation to this head motion, participants may experience “cybersickness”—symptoms like dizziness and nausea—which limits its suitability for all individuals and patients (González Moraga et al., 2022; Lavoie et al., 2021; Lundin et al., 2023; Smeijers et al., 2021; Van Gelder, 2023; Woicik et al., 2023).
How can VR studies be adapted to investigate aggression?
VR technology today is limited in its realism concerning the complexity of real-life interactions (Van Gelder, 2023). Most studies used pre-programmed tasks (e.g., Lobbestael & Cima, 2021; Van Gelder et al., 2019), while only a few have incorporated interactions with real humans (i.e., therapists) (Klein Tuente et al., 2018, 2020). Expanding the use of real interactions within VR environments could enhance ecological validity by involving actual people interacting in VR scenarios with limited potential harmful consequences in a laboratory setting. Even without real consequences, participants might still experience emotional distress during social virtual interactions if they know that they are playing against other real individuals. This research is particularly relevant given the exponential growth of virtual world technologies, such as “the metaverse”; see section ‘Social Virtual World' for more details on social media, metaverse and cyberbullying (Cheng, 2023).
As previously described, a few studies have used VR to investigate reactive and proactive aggression (Lobbestael & Cima, 2021; Verhoef et al., 2021, 2022). All have found support for measuring reactive aggression but not proactive aggression. There is a gap in the literature regarding improving VR assessments for measuring proactive aggression by incorporating non-provoking conditions alongside incentives. One potential approach could involve realistic and engaging settings (e.g., a competitive card or resource-based games) in which participants play against other individuals represented by avatars, while introducing an unfair rule allowing them to earn more points or money by interfering with others’ performance; driven by instrumental motivation (Zhu et al., 2019). Another potential approach could be the use of high monetary incentives (real or fictional) to elicit proactive aggression; however, this raises substantial ethical concerns. As discussed in the ethical considerations section below, participant well-being must always take precedence over experimental aims. Some individuals might experience distress or guilt after realizing that they acted aggressively for personal gain instead of in response to provocation, as in reactive aggression.
Hyperscanning: Real Interactions
This section describes an emerging field in which aggression can be studied through actual real interactions, using tasks that are not manipulated or predefined, unlike most previous aggression research. This approach allows aggression research to benefit from enhanced ecological and construct validities by measuring dynamics of interpersonal aggression.
Most forms of aggression, as well as commonly used definitions, inherently involve interactions between at least two individuals. Nevertheless, research on aggression and irritability has traditionally focused on individual differences, often neglecting the impact of dyadic dynamics (Quiñones-Camacho et al., 2020). More robust experimental evidence is needed using dyadic designs, where two people engage in an aggression task together (McCurry et al., 2024). In our opinion, a major criticism of aggression behavioral research can be the frequent use of fake opponents or confederates in tasks like the TAP and the Point Subtraction Aggression Paradigm. Most studies used “sham” dyadic designs where participants are falsely told they will play a competition task against another person, who is supposedly in a separate room. In previous studies, participants are often required to imagine their opponent (Boccadoro et al., 2021; Repple et al., 2017), or interact with a confederate who is not genuinely competing against them; instead, all responses are manipulated (Konzok et al., 2020; Wagels et al., 2018; Weidler et al., 2019). People behave differently when they are in a social interaction, being observed, or interacting with a trained confederate, so research on real-world social behavior requires placing two or more naïve participants in a real interaction (Hakim et al., 2023).
The “hyperscanning” technique offers a novel approach for investigating neural, physiological, and behavioral correlates of real-time interactions, marking a shift from traditional single-person methods to more comprehensive second-person approaches in naturalistic settings (Redcay & Schilbach, 2019) (see Figures 1(J) and (K)). Hyperscanning is characterized by the simultaneous collection of data from more than one participant and the exchange of information between these participants (Hakim et al., 2023; Redcay & Schilbach, 2019). Few studies have used commonly applied aggression tasks like the TAP in real interactions. Anderson et al. (2008) were the first ones to employ a dyadic version of the TAP to explore reciprocal aggression in college student dyads, who were unknown to each other and never actually met (placed in separate rooms). This study demonstrated that, during real interactions, individuals with high aggression traits not only behaved more aggressively but also elicited more aggression from the other. They also found a reciprocal effect on aggression over time, highlighting the importance of including a second person in these tasks, as this reciprocal influence could not be evident in single-participant versions.
Going a step further, recent studies used the TAP with interactive sibling pairs (Koch et al., 2024) and romantic couples (McCurry et al., 2024). An interactive version of the TAP has been developed to systematically explore the reciprocal dynamics of aggression in dyadic interactions in a more ecological way (Koch et al., 2024). In the study by McCurry et al. (2024), couples interacted during an adapted version of the TAP, competing face-to-face in multiple rounds. Interestingly, facial expressions were recorded throughout the task, making it possible to examine changes in emotional expression and mimicry over the course of interaction. Unlike Anderson et al. (2008), this study found no evidence of escalation over time but did find evidence of immediate retaliation, suggesting that partners tended to match each other’s overall aggression levels. This highlights that emotional co-regulation is a key feature of intimate partners (McCurry et al., 2024) and siblings (Koch et al., 2024) conflicts. Thus, hyperscanning allows the investigation of violence escalation and de-escalation patterns improving our understanding of interpersonal aggression, while maintaining some experimental control. Both Koch et al. (2024) and McCurry et al. (2024) found diverse pair dynamics, with some pairs matching each other’s punishment levels while others showed one participant consistently choosing higher or lower punishments than their partner. These dynamics are crucial for understanding aggressive interactions, which cannot be adequately explored when only one real participant is involved. We are not suggesting that the “fake” TAP is an invalid tool for measuring aggression but rather that it has limitations in capturing the full spectrum and dynamics of interpersonal aggressive behavior.
Moreover, Koch and colleagues (2024) findings revealed that the behavioral patterns of interactions often do not align with the typical interaction patterns seen when using a fictitious and manipulated partner. For example, we observed subgroups of pairs that showed consistently low or high aggression levels over time, unlike the generally predetermined strategies by researchers that fluctuate between low and high provocation conditions. This is crucial because, although researchers often measure the credibility of the cover story, many times they do not report these findings. For example, Beyer et al. (2015) reported that 12% of the participants suspected the cover story, Konzok et al. (2020) reported 24% and Boccadoro et al. (2021) reported 54%. Mixed results have emerged regarding the cover story, with some studies suggesting that suspicion does not significantly influence behavior (Konzok et al., 2020; Weidler et al., 2019), but others reporting that it can reduce levels of proactive aggression (Boccadoro et al., 2021).
The level of suspicion may be higher than typically reported if participants feel compelled to conform to the study’s hypothesis, leading them to report no suspicion (McCarthy & Elson, 2018). Some efforts have been made to improve credibility in the fake version of the TAP; for instance, Beyer et al. (2015), Buades-Rotger et al. (2016), and Lotze et al. (2007) included pre-recorded video clips of the confederate (see Figure 1(D) for an example). In the study of Koch and colleagues 2024) using real pairs, none of the participants suspected manipulation of game outcomes or questioned the authenticity of playing against their partner. While real interactions may reduce direct experimental manipulation and challenge the analysis of data from less controlled experiments, they provide a more ecological assessment of behavior (Hakim et al., 2023). See Table 1 for an overview of main advantages, disadvantages and potential applications of hyperscanning.
In conclusion, we propose an expansion of the field of research on aggression using real interactions between two or more naïve individuals to capture genuine responses over time. Further replication and investigation of dyadic dynamics in diverse samples is needed, as well as exploration of differences between real and fake interactions in the same setting. To date, this method is limited to behaviors that can be captured with lab-based aggression tasks, but it can be adapted to measure behavior in a more naturalistic way.
Real-Time Biosignal Recordings and Ecological Momentary Assessment (EMA)
This section aims to describe aggression research in more ecological settings, particularly focusing on the daily lives of participants and patients, through remote and continuous monitoring of physiological data, as well as emotional and behavioral insights gathered through EMA. This type of data can provide further evidence on the precursors and maintenance of aggressive behavior that is more difficult to capture in artificial laboratory settings.
Audiovisual Recordings and Wearable Devices
A significant discrepancy between lab-based aggression measures and real-life aggressive behavior is evident. Consequently, aggression research can benefit from being examined in authentic, real-world contexts. The challenge of capturing genuine instances of aggression in natural settings is well-known; however, this approach may facilitate a more comprehensive understanding of the precursors and dynamics of aggression. The use of audiovisual recording for the study of children’s aggressive interactions is a well-established practice (e.g., see Pepler & Craig, 1995). However, the use of fixed cameras has been criticized due to the fact that aggression occurs primarily in settings that are not easily accessible, thereby necessitating the use of mobility devices (Wettstein & Jakob, 2010). One approach to approximating real-world aggression scenarios (Wettstein & Jakob, 2010) and the environmental conditions (Wettstein & Scherzinger, 2015) is the use of camera-glasses. Despite its potential as a novel assessment technique and the authors’ assertion of low reactivity and high compliance, further applications of this technique have been limited probably due to legal and ethical considerations and change of behavior because of lack of intimacy (Wettstein & Scherzinger, 2015).
Mobile devices can also assess other parameters. Proactive and reactive types of aggression differ in physiological correlates including heart rate, skin conductance and respiratory sinus arrhythmia (for a specific review, see Fanti et al., 2024). Consequently, some research in the field of aggression has concentrated on using wearable devices (such as sensor wristbands) to monitor physiological data (including heart rate and electrodermal activity) and physical activity (such as motion using an accelerometer). Biosensor technology can be used to anticipate aggression episodes. Previous studies have demonstrated efficacy of predicting naturalistic aggressive behavior in challenging populations, including children and youth with autism spectrum disorder (Goodwin et al., 2019; Imbiriba et al., 2023; Ozdenizci et al., 2018) and patients in psychiatric mental health institutions (De Looff et al., 2019). The analysis of biosignals prior to the onset of aggression has the potential to mitigate the occurrence and impact of aggression in clinical populations. Further research should focus on identifying risk factors, as well as providing more evidence on how reliable this data is in predicting aggression.
Ter Harmsel et al. (2023) used the Sense-IT app for intervention with forensic outpatients with high levels of aggression, finding decreased in self-report aggression, increased interceptive awareness and physiological signals of tension. Thus, this app can not only be used for intervention but also holds promise for acquiring relevant research data on the daily-life associations between physiological data, anger and incidents of aggression measured in combination with EMA.
Ecological Momentary Assessment
As mentioned before, aggression research is mainly investigated to date using lab-based tasks or survey methods. However, this is restricted to the measurement time and context. Ecological Momentary Assessment (EMA), also known as experience sampling method, allows for real-time data collection that captures individuals’ behaviors, thoughts and emotions in their natural environments (Shiffman et al., 2008). This methodology involves repeated assessments of participants’ experiences as they unfold in daily life, thereby minimizing recall bias, enhancing ecological validity, capturing day-to-day variation and improving reliability by collecting multiple data points per item (Borah et al., 2021; Murray et al., 2022; Shiffman et al., 2008). EMA aims to document micro-processes and contingencies between the behavioral, cognitive and emotional dynamics in real-world contexts (Russell & Gajos, 2020; Shiffman et al., 2008) using various technologies, including smartphones and electronic diaries (Borah et al., 2021; Shiffman et al., 2008). The behavioral, emotional, and experience data collected by EMA can be effectively combined with other data simultaneously acquired with wearable sensors, such as physiological data, geographic or motion data (Borah et al., 2021; Russell & Gajos, 2020). By integrating these different data sources, researchers can build a more comprehensive picture of the factors influencing aggressive behavior in real time, which facilitates testing theories combining biological and environmental factors like the General Aggression Model (Anderson & Bushman, 2002).
The application of EMA in aggression research offers unique opportunities while posing specific challenges (see Table 1 for an overview). Aggression trait measures were not originally designed to capture momentary experiences, and there is no guarantee that these measures work as intended with EMA aimed at capturing “real-time” processes, despite adaptations (Borah et al., 2021). Some adaptations may include rewording items to reflect state-like rather than trait-like experiences and adjusting the focus to capture momentary incidents, as serious aggressive events are often too infrequent to be assessed within the short time frames typical of EMA studies (Murray et al., 2022). There is a critical need for psychometrically validated measures specifically designed to assess aggression within this sampling method. The most notable attempt has been made by Borah et al. (2021) and Murray et al. (2022) by creating and validating the Aggression-ES scale, which is tailored to measure aggression in momentary assessments. This scale includes 12 items targeting both proactive and reactive aggression, as well as physical and social/indirect forms of aggression and focuses on common “everyday” manifestations of this behavior due to the lack of probability of serious incidents within the EMA timeframe and the goal of greatest universal applicability (Borah et al., 2021).
Disadvantages of EMA include the potential burden on participants (Borah et al., 2021), which is especially relevant when working with at-risk or under-resourced youth (Russell & Gajos, 2020). Consequently, brief scales are sometimes preferred (Murray et al., 2022) and some studies may benefit from tailoring the assessment protocols to better align with participants’ daily lives to enhance compliance (Russell & Gajos, 2020). Interestingly, using the Aggression-ES scale along with other emotion scales, Murray et al. (2023) found that higher levels of stress and overall negative affect (particularly feelings of being upset) at a given notification predicted a greater likelihood that participants would miss the subsequent prompt. In other words, the emotional state of the participant might influence the likelihood of answering a prompt and, therefore, affecting the data quality. This finding is very relevant for future studies, as it highlights the importance of considering non-random compliance in EMA research. Understanding these dynamics can inform strategies to improve engagement in smartphone-based data collection applications, ultimately improving the quality and reliability of EMA findings. The use of event-contingent, instead of prompt-contingent sampling, in which participants report only when they have engaged in aggressive behavior, might reduce unnecessary data collection and reduce the burden of many EMA assessments. However, this method also presents two major challenges: the need for clear definitions of the aggressive events and the risk that participants may underreport the instances or precursors of aggressive behavior (Murray et al., 2022; Shiffman et al., 2008). Although EMA minimizes issues associated with retrospective recall, it does not guarantee that participants can accurately report their behaviors, emotions, and experiences (Borah et al., 2021). Participants may still be susceptible to response bias, or may struggle to accurately assess their emotions. This is particularly relevant for individuals with alexithymia, who may have difficulty identifying and articulating their emotions. Therefore, researchers must take these factors into account to ensure the validity of data collected in EMA studies.
Future research should focus on examining risk factors such as emotional lability (i.e., variability in emotional states over time) or the co-occurrence of precursors (e.g., provocation and anger) that cannot be examined with traditional survey data (Borah et al., 2021; Russell & Gajos, 2020). Different precursors (e.g., intoxication, lapses in self-control, or hostile states) acquired in natural settings and in the flow of daily life, rather than in artificial environments, may have a stronger predictive effect on aggressive behavior. This research also has the potential to investigate how daily events, behaviors, cognitions, and emotions converge to influence long-term changes in traits (Borah et al., 2021). Using EMA, researchers can investigate how within-person aggressive behavior varies both between individuals and across contexts, such as work, school, home or leisure time (Russell & Gajos, 2020). A multimodal approach combining physiological, location, behavioral and emotional monitoring holds great promise for investigating the precursors and maintenance of aggression and better testing integrative and comprehensive aggression models (Anderson & Bushman, 2002; Baskin-Sommers et al., 2024; Finkel & Hall, 2018).
Social Virtual World
This final section examines how new technologies (i.e., social media and the Internet) have given rise to a new field of research on aggression, with a particular focus on cyberbullying behavior. The first part describes how cyberbullying is typically measured and outlines future research directions, followed by efforts to detect this behavior within large databases of social media data. The final part introduces the metaverse and virtual worlds, exploring the types of aggressive incidents that can occur in these digital spaces and suggesting new avenues for research in this rapidly expanding field.
Cyberbullying
Access to social media and its widespread use have facilitated new avenues for online aggression, with frequency and problematic usage of social platforms strongly associated with cyberbullying (Craig et al., 2020). Cyberbullying is usually defined as “an aggressive, intentional act carried out by a group or an individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself” (Smith et al., 2008). Instances of cyberbullying include sending mean messages or sharing inappropriate images of others (Giumetti & Kowalski, 2022) (see Figures 2(A) and (C) for examples). The nature of social media increases the likelihood of this harmful behavior due to its greater accessibility to potential targets, the ability to edit or delete posts and the expanded anonymity and popularity it provides (Giumetti & Kowalski, 2022). Compared to traditional bullying, which is limited by time and location, cyberbullying is distinguished by its ability to occur anytime and anywhere, the potential for content to be shared widely via bystanders without the aggressor needing to repeat the behavior and the complexities of power dynamics that can arise from anonymity and superior technological skills (Corcoran et al., 2015). (A) Example of a task to investigate cyberbullying (left) compared to neutral (right) interactions in social media; figure modified from McLoughlin et al. (2020) and reused under the terms of the Creative Commons CC BY license. Figures (B), (C) and (D) are examples of an experimental paradigm using simulated social networks, modified from DiFranzo et al. (2018). (B) The picture shows the notification page displaying likes and replies to participants’ own posts. (C) The picture represents an example of cyberbullying post. (D) The picture depicts how task can be manipulated in different factors such as view/no view notifications and audience size; modified from DiFranzo et al. (2018). Figures reused with permission from the authors of the original study (DiFranzo et al., 2018).
Much of the existing research on cyberbullying relies on non-experimental methods such as interviews and primarily self-reported data (Chun et al., 2020; Zhang et al., 2022), which raises concerns about social desirability bias (Nagar & Talwar, 2021). Experimental research, however, can measure actual bullying behavior in controlled environments (Giumetti & Kowalski, 2022; Nagar & Talwar, 2021; Pieschl et al., 2013). Only a limited number of studies have used experimental settings to investigate cyberbullying. The most common method is experimental vignettes controlling for variables related to cyberbullying (Nagar & Talwar, 2021). For example, Jungert et al. (2021) used a written vignette task to depict either direct bullying or cyberbullying, exploring bystanders’ roles. Similarly, Pieschl et al. (2013) used vignettes prompting participants to imagine themselves in various cyberbullying scenarios involving harassment or outing, differing by media type (text vs. video), with distinct coping strategies based on the type of cyberbullying and media. The described studies allow for exploration of individual responses to the same situations; something not achievable with self-reported retrospective data alone. Hypothetical vignettes are a popular method in social science, but more innovative methods observing actual behaviors reflecting cyberbullying are still limited.
Some of these experimental advances include the use of communication platforms like simulated social media websites as experimental paradigms to facilitate and measure cyberbullying behavior and changes in cyber-bystander responses within a controlled virtual environment; a method that remains underutilized (Nagar & Talwar, 2021). Some studies have successfully employed tools within a controlled virtual setting such as Social Network Software Systems to directly measure behaviors or facilitate behavior change. For a comprehensive systematic review on 17 articles utilizing this tool or computer games, please refer to Nagar and Talwar (2021). In short, simulated social networks—mimicking platforms like Facebook—allow researchers to measure behaviors such as forwarding offensive messages, flagging, liking/disliking or deleting them, replying to posts, block or report users (e.g., see Figures 2(B) and (C)) and observing interactions in group chats. Researchers maintain full control over platform features and paradigms, manipulating different variables such as audience (DiFranzo et al., 2018) (see Figure 2(D)), while ensuring participant safety; avatars can be created instead of using real photos on simulated accounts, promoting a sense of integrity while still simulating interactions with others (Nagar & Talwar, 2021). Lastly, a recent study by McLoughlin et al. (2020) explored the neural underpinnings of cyberbullying by examining brain activation in bystanders witnessing cyberbullying behavior (see an example in Figure 2(A)). To illustrate power imbalances, the cyberbullying comment received more “likes” than the post itself.
Future studies could concentrate on cyber-aggression (including a wider behavioral range) rather than focusing on only cyberbullying behaviors to broaden our understanding of harmful online behaviors that can occur without the need for repetition and power imbalance, unlike traditional bullying definitions, while still recognizing that even a single incident can have significant psychological effects on the victim (Corcoran et al., 2015; Zych et al., 2016). Cyber-aggression is defined as “intentional harm delivered by the use of electronic means to a person or a group of people irrespective of their age, who perceive(s) such acts as offensive, derogatory, harmful or unwanted” (Grigg, 2010), including behaviors such as bullying, harassment, abuse or hostility on the Internet. This approach will also broaden the scope by not only examining cyber-aggressive behaviors within peer groups but also addressing targeting of celebrities, vulnerable individuals or groups, and school staff (Corcoran et al., 2015).
Machine Learning Techniques for Identifying Written Aggression on Social Media
Advances in technology have greatly increased the availability of rich datasets that integrate different types of data, requiring the development of novel analytical methods to effectively process and interpret these data. The use of new methods is also becoming increasingly common in the study of aggression and violence on social media and the internet. There is a growing and urgent need for effective approaches to cyberbullying detection. Particularly in social media environments characterized by anonymity, harmful consequences of online aggression are observed (Al-Harigy et al., 2022). The field of cyberbullying and abusive language detection has witnessed considerable advancement through the integration of artificial intelligence and machine learning techniques, including natural language processing and deep learning. Several comprehensive reviews have compared various machine learning classifiers and algorithms (Al-Garadi et al., 2019; Al-Harigy et al., 2022; Han et al., 2024; Muneer & Fati, 2020; Vidgen & Derczynski, 2020) and others have addressed the challenges and limitations associated with automated detection and non-supervised techniques (Elsafoury et al., 2021; Farag et al., 2019; Rosa et al., 2019).
In summary, natural language processing enables researchers to analyze large volumes of user-generated content to identifying instances of aggressive language through techniques with pre-defined rules such as sentiment analysis and topic classification (Al-Harigy et al., 2022). This approach enables the examination of patterns of online aggression across a range of social media platforms, including Twitter, Facebook, Instagram, Formspring and Reddit. The majority of research efforts in the field of cyberbullying detection have concentrated on the analysis of individual social media posts, such as a single tweet or a blog response from a single user, or the aggregation of multiple texts from different users within a conversation. While this approach has proven effective in identifying instances of aggressive language, it often fails to consider the broader context and dynamics that characterize online interactions (Cheng et al., 2021). Meanwhile, deep learning models can handle diverse data types simultaneously, including text, images and metadata. These models can recognize complex patterns considering the relationship between the elements (Al-Harigy et al., 2022), rather than relying solely on predefined lists. This approach enhances detection accuracy by capturing subtle communication forms and contextual features, including emotional cues (Al-Harigy et al., 2022), that traditional approaches may overlook.
Despite these advancements, several challenges remain in this field (see Table 1 for an overview). The lack of consensus on a definition of cyber-aggression hinders both the detection of such incidents and the generalization of models across different studies (Elsafoury et al., 2021; Rosa et al., 2019; Vidgen & Derczynski, 2020). The inconsistent use of terms such as “cyberbullying,” “cyber-aggression,” or “cyber-grooming” across the literature underscores the need for clearer, consensus-driven definitions and standardized operational frameworks (Mladenović et al., 2022). Establishing such definitions within digital aggression contexts would enhance construct validity and conceptual clarity, improve measurement precision and automatic detection, as well as facilitate comparability and replication across studies. Manual data labeling practices frequently exhibit low inter-annotator agreement scores along with class imbalances and insufficient linguistic diversity, which can lead to overfitting and unreliable models (Elsafoury et al., 2021; Farag et al., 2019; Vidgen & Derczynski, 2020). For a repository containing training datasets related to abusive language, refer to https://hatespeechdata.com (Vidgen & Derczynski, 2020). The primary issue is that violent social media data is considerably more intricate than it is often represented in these models. The data on aggression dynamics generated on social media is frequently produced in real time and often comprises interactions among multiple users. Such content often comprises slang expressions (Elsafoury et al., 2021), abbreviations (Al-Garadi et al., 2019), neologisms, emotional expressions and emojis (Al-Harigy et al., 2022), with many posts being informal and lacking grammatical accuracy (Cheng et al., 2021). By focusing solely on individual messages rather than the broader context of conversational flows and sessions, traditional methods may overlook critical patterns related to repetition and power dynamics that are inherent to cyber-aggressive behavior (Cheng et al., 2021). A further limitation to date is the inability of current methods to detect instances of cyber-aggression occurring in real time on different online platforms and social media networks, which is essential for prevention strategies (Han et al., 2024; Muneer & Fati, 2020; Rosa et al., 2019).
Future directions focusing on understanding how aggression propagates through social media sessions require a more comprehensive analysis that considers not just isolated texts but also multimodal data, including images, timestamps and user interactions over time and more complex and dynamic structures (Cheng et al., 2021; Vidgen & Derczynski, 2020), including emojis that may help to determine the context of the sentence and to investigate the sentiment analysis, not merely the words (Al-Harigy et al., 2022). In addition, future research could explore language and conversational parameters such as melody, speed, or content in social media videos on platforms such as TikTok or Instagram to further understand violent interactions online.
Future studies should explore how connections between users change over time, thereby providing a more realistic insight into aggressive user interactions. In a recent study, Terizi et al. (2021) proposed a novel framework for evaluating the propagation of aggression in social networks. This framework uses opinion dynamics models to simulate how aggressive behavior spreads among users. This shift toward session-based detection (i.e., modeling the hierarchical structure of user interaction networks and temporal patterns of social media sessions) has the potential to facilitate more effective identification of power dynamics and repetition of cyberbullying instances, as well as the development of prevention strategies (Cheng et al., 2021). Furthermore, future research should examine pre-trained models like “Bidirectional Encoder Representations from Transformers” on datasets containing slang or informal language to potentially improve its performance in detecting nuanced forms of aggression (Al-Harigy et al., 2022; Elsafoury et al., 2021). Integrating these advancements into practical tools, such as real-time monitoring systems or third-party anti-bullying applications, could greatly enhance efforts to combat cyber-aggression successfully across diverse online environments (Cheng et al., 2021; Farag et al., 2019; Muneer & Fati, 2020; Rosa et al., 2019). For example, Raj et al. (2022) proposed a deep learning model designed to detect cyberbullying in real-time tweets across multiple languages with an impressive accuracy of 98%. This research could expand the scope beyond text-based detection to images and emojis and other social media platforms.
Metaverse
As social virtual environments become increasingly prominent, there is a critical need for further research in the field of cyber-aggression. The Metaverse is an emerging virtual space that integrates various technologies, creating a shared environment where users can interact through avatars, transcending physical limitations of time and space (Wang et al., 2023). Virtual environments and immersive games, including platforms like Second Life, Habbo, Roblox and VRChat, serve as precursors to the Metaverse, offering some insights into potential issues (Dwivedi et al., 2022). Aggressive behaviors in the Metaverse include online abuse, sexual harassment, cyberbullying, hate speech and discrimination (Dwivedi et al., 2022, 2023). The unique characteristics of this virtual world, particularly online disinhibition, can lead individuals to engage in deviant actions more freely than they might in face-to-face interactions (XinYing et al., 2024). A crucial question is how the Metaverse facilitates aggressive and deviant behavior (Dwivedi et al., 2022). XinYing et al. (2024) investigated the influence of various features on deviant behaviors among users of Metaverse platforms. The authors found that technical features, such as immersive experiences and invisibility, alongside social factors like homophily, social ties and social identity significantly impact users’ deviant behavior. In other words, deviant behavior among users can be contagious and tends to increase under conditions of anonymity, while adopting a broad social identity can reduce such behaviors. As previously discussed in the VR section, the immersive nature of the Metaverse may intensify the psychological impact of these aggressive acts on victims, making them feel real despite occurring in a virtual context (Dwivedi et al., 2023).
One critical area for future investigation is the comparison between virtual aggression and real-life aggression. Researchers should explore whether the triggers, manifestations and impacts of aggressive behaviors are consistent across these contexts. Do real-life and virtual aggression share similar risk factors? How does anonymity afforded by avatars exacerbate antisocial behaviors like cyber-bullying and harassment? How do immersive experiences influence users’ emotional responses and behavioral choices within the Metaverse? Gaining further knowledge about these influencing factors can inform effective prevention strategies aimed at reducing cyber-aggressive behavior in emerging digital spaces such as Meta.
Discussion, Future Directions and Ethical Considerations
This review highlights the notable progress in aggression research, particularly through the use of innovative methodologies such as VR, video games, naturalistic stimuli, hyperscanning and EMA; see Table 1 for an overview of main advantages, limitations and feasibility of each method. These technologies improve the ecological validity of investigations into aggressive behaviors by creating immersive environments that closely mimic real-life scenarios, measuring real interactions or instances of aggressive behavior in natural settings, including in digital worlds. Beyond tools for improved measurement, these technologies can overcome traditional theoretical boundaries. They compel us to reconsider what counts as aggression, how it is operationalized and how it unfolds dynamically over time and across contexts.
We argue that aggression should not be conceptualized primarily through the lens of static individual differences or isolated behavioral acts. Instead, the presented evidence suggests that aggression is a complex psychological process, embedded in social and digital environments. Emerging technologies not only enable the refinement of integrative models of aggression, such as the GAM (Allen et al., 2018; Anderson & Bushman, 2002), but also enhance the quality and ecological validity of the data used to evaluate them. These tools are especially promising for improving the measurement of situational factors, affective and cognitive states and arousal in ecological contexts, as well as the interplay between distal and proximate factors within an integrative model. For example, VR and EMA facilitate the investigation of cognition (e.g., intentions, motives and priming), affect (e.g., hostility and anger) and arousal within well-simulated or real-life contexts. When individuals are assessed in their everyday context (e.g., at home), EMA can more precisely measure how input variables (e.g., alcohol or a partner) influence these affective and cognitive states. Moreover, situational factors can be measured more accurately using technologies such as realistic stimuli, video games, VR and social media platforms because they can more effectively simulate and systematically investigate social stress, rejection or provocation, even the presence of fear-inducing stimuli such as weapons, than traditional lab-based paradigms. Similarly, technologies such as hyperscanning allow for the study of reciprocal aggression dynamics, revealing patterns such as escalation, mimicry and emotional co-regulation during social encounters.
As measures of aggression become more contextually grounded and multidimensional, it becomes crucial to apply advanced analytical methods capable of handling large-scale datasets such as machine learning algorithms. These approaches are important for capturing the dynamic interplay within and between individuals involved in aggressive behavior and they further allow for better predictive modeling by considering multiple factors simultaneously. A multilevel approach integrating diverse types of data (behavioral, emotional, physiological, neural and genetic) can reveal biomarkers associated with specific forms of aggression (see Figure 3). Representation of new technologies and their associated advancements in aggression research. The circle illustrates various innovative methods for investigating aggression discussed in this review, while the surrounding elements highlight some key improvements in ecological and construct validities and relevant data that enhance our understanding of aggression. Figure created with biorender.com.
We envision a shift toward new operationalizations within a technology-integrated framework for aggression research that (a) leverages emerging tools and technologies to more accurately measure aggressive behavior (improvements in both ecology and construct validity); (b) integrates multimodal data sources (including biological, behavioral, emotional, cognitive and environmental inputs); (c) maps aggression across multiple temporal scales (immediate, daily, longitudinal); and (d) emphasizes the dynamic interplay between individual and context, including social (i.e., school/work, family, peer groups) and digital spaces such as social media and the metaverse. Such models must be flexible enough to capture emerging forms of aggression, such as cyber-aggression and violence in virtual words. This shift in operationalization toward more ecologically valid, dynamic and technologically enriched paradigms represents an opportunity not only for methodological innovation but also for theoretical advancement, as exemplified with the GAM.
In this review, we have highlighted the significant improvement in ecological validity. Most new technologies primarily enhance realism and participant engagement. However, a critical evaluation of this literature reveals that direct empirical comparisons of these emerging technologies with traditional methods remain scarce. In the field of VR, it is a common practice to compare immersive VR setups with classic 2D screen presentations (e.g., Barreda-Ángeles et al., 2021; Lull & Bushman, 2016; Van Gelder, 2023; Verhoef et al., 2021). However, for other emerging methods, comparisons with established stimuli, self-report or lab-based tasks in the same experiments are absent (e.g., static vs. dynamic stimuli; real interactions during hyperscanning vs. simulated interactions; video game vs. traditional lab-based tasks; EMA vs. single-time self-report data collection; lab-based simulated tasks vs. virtual world/social media analysis). For example, Koch et al., (2024) included some simulated trials, but a more systematic and direct comparison between simulated and real interaction blocks remains lacking. This lack of direct methodological testing likely reflects the high resource demands and time required to implement these comparison studies, as well as the limited incentives to conduct “methods” studies. Yet, such systematic comparisons are essential to confirm whether new technologies truly enhance ecological and construct validity beyond traditional paradigms. It is also essential, as is done in VR research, to ask participants if they perceive these “improvements,” such as increases in realism, engagement or authenticity. Many assumptions about the superiority of these methods made by researchers remain untested, underscoring the need to routinely include subjective measures (e.g., presence, emotional impact or perceived realism) to ensure that future research efforts are appropriately directed. These self-reported experiences can help interpret behavioral results (e.g., whether participants truly believe they are interacting with an opponent or feel fully immersed in the scenario), verify whether experimental manipulations elicit the intended psychological states and can facilitate meaningful comparisons between emerging and traditional experimental approaches.
Most results from aggression research employing these new technologies align with previous studies using traditional paradigms, raising the question of whether further improvements in ecological validity translate into better theoretical precision or if future research should focus on other aspects, such as predictive validity, convergent validity and discriminant validity. There is a need to evaluate whether these measures are truly targeting aggression. Further research should also quantify the effect sizes and sensitivity to individual differences of the described emerging technologies measuring aggression, by comparing highly aggressive individuals (e.g., criminals with antisocial personality disorder and/or psychopathy) with a group of nonviolent controls. Is there a substantial benefit in assessing this population? Can we associate these outcomes with real-life aggressive behavior and predict future recidivism? Given their higher ecological validity, these new technologies may ultimately also demonstrate greater predictive validity than traditional approaches; however, this assumption requires empirical verification through longitudinal and follow-up studies, particularly in high-risk populations, which would also enhance the practical relevance of these approaches (e.g., prediction of recidivism).
Investigating aggressive behavior presents unique challenges, particularly due to social desirability bias inherent in questionnaire methods and the limitations of laboratory aggression tasks. Lab-based paradigms often fail to elicit genuine aggressive responses and raise questions about participants’ intentions to harm others (Ritter & Eslea, 2005). The primary issue with these laboratory tasks is that they focus on the outcomes of specific behaviors rather than their underlying motivations. Understanding the social intentions and motives participants aim to achieve is crucial for accurately interpreting “aggressive behavior” in the tasks (McCarthy & Elson, 2018; Ritter & Eslea, 2005; Tedeschi & Quigley, 1996). Despite multiple and repeated critiques regarding the failure to capture intentions and motives in aggression research, progress has been limited. While some studies include questions about motives post-task completion, this approach does not effectively capture trial-by-trial intentions behind actions. One possible solution may be integrating scales during the tasks allowing participants to adjust their levels of anger, frustration and intentions to harm (reactive intention), influence the other’s behavior (proactive intention) or compete (competitive motives). Another promising avenue involves recording and analyzing facial expressions as proxies for emotions during task, as demonstrated by McCurry et al. (2024), which could provide valuable insights into participants’ emotional states throughout the experiment. Integrating measures of emotional and motivational states including real-time self-report measures, emotional facial expression analysis or physiological indicators, alongside traditional behavioral outcomes, may help disentangle competitive, reactive and proactive aggression, thereby addressing a longstanding limitation of traditional lab-based aggression tasks.
A significant concern remains regarding cover stories used in aggression research, which can lead to serious confounds by altering “aggressive” behavior into prosocial or competitive actions (Ritter & Eslea, 2005). Additionally, the use of supposed targets or victims—often distant or lacking realism—may not convincingly simulate that the participants are inflicting real harm. Effective paradigms should involve direct contact with a real confederate or another participant, as done in hyperscanning research. Another concern is that most aggressive responses in current tasks are assessed in response to specific situations, such as a provocation in social (virtual) interactions where participants can gain something by harming the other, or in response to specific provocations (e.g., TAP). Researchers should include more scenarios and conditions to improve statistical power and generalization to different situations. Furthermore, it is essential to incorporate a range of available responses, including prosocial, communicative and non-aggressive behaviors. If aggression is perceived as the only mode of interaction, our understanding of why individuals choose to act aggressively in real-world contexts remains limited (Ritter & Eslea, 2005). Tasks employing more naturalistic stimuli, such as actual violent interactions videos, video games or VR and settings like hyperscanning improve ecological validity, credibility and immersion for the participant(s). This improvement could facilitate capturing genuine behaviors that reflect real-life aggressive interactions. However, there is not enough specific scientific evidence to date.
Assessing aggressive behavior within participants’ daily lives can yield insights unattainable in laboratory settings by providing unique information on precursors and maintenance factors related to aggression. Much current work identifies risk factors in isolation despite recognizing that aggression develops through complex interactions among various risk and protective factors over time, with a transactional interplay between neurocognitive development and context (Baskin-Sommers et al., 2024). A multilevel approach integrating behavioral, emotional, cognitive data, environmental cues (cultural, geographic, societal) and biological measures, including physiological, neuroimaging, endocrine and genetic data, can offer a comprehensive view of individual differences in aggression manifestation. Like other fields of psychology, aggression research can benefit from examining contextual and within-individual variability in behavior. By analyzing large databases encompassing multiple data types, researchers can focus on individual differences in various levels rather than seeking a “universal” pattern of aggressive profile. Using a multivariate approach with large cohorts with existing phenotypic, neuroimaging, endocrine, immune and genetic data can more effectively address the heterogeneity of aggression and help identity biomarkers associated with specific behaviors (Trofimova et al., 2022) and biocognitive fingerprints (i.e., characterization of different subtypes of antisocial individuals) (Brazil et al., 2018). The ENIGMA-Antisocial Behavior Working Group (https://enigma.ini.usc.edu/ongoing/enigma-antisocial-behavior/) represents a unique consortium for investigating aggression and antisocial behavior using a large-scale dataset (e.g., Gao et al., 2024), facilitating high-powered meta-analyses of the association between clinical, brain and genetic data (Brazil et al., 2018). Conceptualizing aggressive behavior as multidimensional while employing a transdiagnostic approach may also be critical for identifying biomarkers of aggression and violent behavior (Trofimova et al., 2022; Wagels et al., 2022).
Further advancements in computational modeling are crucial for analyzing these complex data sets. Computational modeling can translate theories about aggressive behavior into computational terms that elucidate precise neurocognitive mechanisms underlying aggressive responses (Buades-Rotger et al., 2023; Smeijers et al., 2019). These mathematical models execute specific tasks in a way that resembles the functioning of our brain or cognitive processes, allowing the modeling of aspects of cognition of brain function that are typically difficult to observe, refer to as latent variables (Brazil et al., 2018). Thus, interdisciplinary work of psychologists, computer scientists, neuroscientists and sociologists is urgently needed to move the field forward.
As previously noted, current advancements have improved reactive aggression assessments yet proactive measures remain inadequate (Boccadoro et al., 2021; Lobbestael & Cima, 2021; Verhoef et al., 2022). There is a need to further explore into tasks designed specifically elicit proactive aggressive behavior with no provocation and by using incentives (Raine et al., 2006). A recent attempt by Zhu et al. (2019) by designing and validating the Reward-Interference Task using an unfair rule, which elicited instrumental motivation and moral disengagement to win.
Lastly, it is important to note that assessments of aggression in children currently rely heavily on observational methodologies and self-reports, as well as reports from teachers, peers and parents. However, there remains a notable lack of experimental task-oriented approaches. Developing tools that can effectively measure various forms of aggression across children, adolescents and adults would facilitate comparable studies. Such tools would need adaptations to ensure they are accessible and comprehensible for different age groups, especially for young people, thereby enhancing participant engagement and ecological validity. An attempt to implement the TAP within a social rejection context was recently made by Quarmley et al. (2023). A virtual school environment was used to quantify in-the-moment aggression in response to specific instances of rejection, allowing participants to respond aggressively to peers who provided varying feedback (i.e., accepting, rejecting and unpredictable peers). This innovative approach enhances ecological validity and offers a more effective means of studying bullying behaviors among children.
There are ongoing strong efforts on building large datasets incorporating clinical, behavioral, neuroimaging, genetic and cultural data. These datasets will allow for a more integrative approach to investigating individual differences and enhancing our understanding of the precursors and maintenance factors associated with aggression. Integrating multimodal data in aggression research requires not only conceptual frameworks but also methodological approaches capable of handling complex, high-dimensional datasets. Promising strategies include multilevel modeling to capture variability across individuals and contexts and machine learning techniques to identify latent patterns across modalities, provided they are applied carefully to avoid overfitting and ensure interpretability. Concrete examples illustrate the potential of multimodal integration in aggression research. Combining immersive VR with traditional brain imaging techniques can be challenging due to signal interference and movement constraints, but recent work has demonstrated successful integration of functional near infrared spectroscopy (fNIRS) with VR. For a comprehensive review of this approach, including suggested devices, paradigms and data analysis strategies, see Peng et al. (2024). This combination allows researchers to simultaneously capture neural activity and behavioral responses in dynamic, ecologically valid scenarios. Another promising example is multimodal hyperscanning, which integrates dual-brain recordings with behavioral data (e.g., speech, nodding and eye gaze), physiological data (e.g., breathing and heart rate) and eye-tracking data. The cross-brain general linear model (Hamilton, 2021) can be used to explore specific relationships between behavior and brain activity beyond effects driven by observable behavior. Multimodal diffusion maps can serve as powerful tools for data fusion, integrating heterogeneous modalities and their representation into interpretable latent patterns (Gashri et al., 2025). To identify relevant features, LASSO-regularized generalized linear models are recommended, as they effectively handle high-dimensional, correlated predictors and enhance generalizability by reducing overfitting, making them preferable to traditional stepwise approaches (De Felice et al., 2025). The described emerging technologies can also be combined: for instance, VR with fNIRS hyperscanning creates shared, immersive environments in which multiple participants can interact naturally while their neural activity is monitored simultaneously. This is a particularly promising avenue for studying social interactions in virtual environments (Barde et al., 2020).
Ethical Considerations
The generalizability of aggression research is inherently limited due largely to ethical constraints limiting harmful behavioral operationalization. As result, the majority of the studies include low-harm scenarios where participants perceive their actions only minimally harmful, which may lead them to believe that others might similarly be low motivated to avoid these “harmful” actions. Consequently, behaviors exhibited in lab-based aggression paradigms appear restricted and do not adequately represent the multi-dimensional nature of aggression. To partly address this limitation, it is necessary to investigate manifestations of aggression in daily life across various contexts, including home, work/school and digital environments. VR also offers a promising avenue for simulating different scenarios where participants can exhibit a higher range of aggressive behaviors (or at least show their intentions in that context). However, researchers must remain mindful that lab-based findings under-represent the broader spectrum of aggressive behaviors and should exercise caution when generalizing these results to real-world situations.
The integration of these emerging technologies into aggression research raises important ethical considerations that must be explicitly addressed in both study design and implementation. Highly immersive technologies such as VR, naturalistic stimuli and interactive video games can evoke strong temporary emotions (e.g., anger, fear, distress). While this emotional engagement is valuable for ecological validity, it also increases the risk of short- or long-term discomfort, particularly in vulnerable or clinical populations. Participants may experience lingering rumination, guilt or anxiety after exposure to realistic aggressive scenarios (Lavoie et al., 2021). Historical precedents in social psychology, such as the Milgram obedience experiments (Milgram, 1963), have shown that participants can experience lasting distress when they realize their own capacity for potential harm, even in a controlled research setting (Perry, 2013). Aggression researchers must therefore balance the pursuit of ecological realism with the paramount responsibility to protect participants’ psychological well-being. This is particularly relevant for proactive-aggression paradigms, in which harm may be inflicted for instrumental reasons, which could increase post-experimental rumination or guilt. Hyperscanning also presents unique ethical challenges, as researchers cannot fully control participants’ spontaneous behavior. One participant may act more aggressively than expected, which could cause distress to the other participant(s), or even put a strain on their relationship if they know each other.
To safeguard participants, researchers should provide transparent information about potential adverse effects, ensure that consent is fully informed and understood, allow participants to skip tasks or questions, and make it clear that they can withdraw at any time without penalty. Continuous monitoring is recommended during immersive tasks, particularly for high-arousal tasks or vulnerable groups. After participation, structured debriefing procedures should be used to help individuals process their reactions, understand the experimental context and prevent lingering guilt or distress. When appropriate, participants should have access to psychological support or follow-up contact.
Social-media and digital-platform research presents further ethical challenges due to the blurring of boundaries between public and private spaces. Users may not perceive their online content as public, even when it is technically accessible. Thus, researchers must prioritize transparency, user privacy and harm minimization when analyzing or disseminating online behavioral data. Ethical guidelines should include robust safeguards such as anonymization, encryption and data minimization, in line with data protection frameworks like the General Data Protection Regulation (GDPR; European Commission, 2016) and HIPAA (U.S. Department of Health and Human Services, 2013).
Finally, studies involving vulnerable populations, such as forensic or clinical populations or minor participants, require particular caution. These groups face an increased risk of distress, coercion and data misuse. In such contexts, independent ethical oversight, careful risk-benefit evaluation and comprehensive debriefing are essential to upholding participant well-being.
Conclusion
Effective assessment of aggression and violence remains one of the major challenges in the field. Advances in the operationalization and measurement of aggressive behavior are critical to improve our understanding and validation of existing theories and to inform prevention strategies and targeted treatments for aggressive individuals in forensic and clinical populations. These advanced assessment methods are essential for obtaining more accurate data, particularly from violent or high-risk patients, who are often difficult to reach in aggression research. By improving our measures and translating findings into real-world situations, we can more effectively predict and address aggressive behavior in real life.
We propose a shift in operationalization toward technology-integrated approaches that conceptualize aggression as a dynamic, context-dependent process. These tools aim to improve ecological and construct validity, with support from the integration of multimodal and multitemporal data and addressing emerging forms of aggression. Future research should focus specifically on digital environments, such as social media and the metaverse, where new forms of technology-mediated interpersonal aggression, such as cyber-aggression, may arise and existing theories may not apply adequately.
Footnotes
Acknowledgments
Thanks to the Brain Imaging Facility of the Interdisciplinary Center for Clinical Research (IZKF) Aachen within the Faculty of Medicine at the RWTH Aachen University. The authors would like to thank Prof. Natalie Barazova for providing permission to reuse Figures 2 (B)−(D). The authors would like to thank the anonymous reviewers, whose feedback significantly improved the content and direction of our review.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the International Research Group (IRTG 2150) “The Neuroscience of Modulating Aggression and Impulsivity in Psychopathology” of the Deutsche Forschungsgemeinschaft (DFG – Project number 269953372/GRK2150), by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-TRR 379– 512007073 and DFG Project number WA 4395/4-1.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
This manuscript has no associated data, and therefore, no data is available for sharing.