The Cognitive and Neural Mechanisms of Implicit Emotion Regulation

Kexiang Gao¹, Yuyao Tang¹, Yueyao Zhang¹, Dandan Zhang¹
¹Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China

Abstract: Implicit emotion regulation refers to the process by which individuals modify their emotions without conscious monitoring or explicit intention to regulate. Compared to explicit emotion regulation, implicit emotion regulation relies less on the prefrontal executive control system. Building upon existing dual-classification frameworks, this paper proposes a novel tripartite theoretical framework that categorizes implicit emotion regulation into three types: automatic, task-incidental, and implicit goal-driven regulation. Automatic implicit regulation, exemplified by fear extinction, depends on the ventromedial prefrontal cortex (VMPFC) directly modulating the amygdala. Task-incidental regulation occurs during tasks such as affect labeling and emotional Stroop, where lateral prefrontal regions incidentally regulate emotions through cognitive control systems during task execution. Implicit goal-driven regulation activates implicit emotion regulation goal pursuit through priming or implicit training, which can either utilize VMPFC for automatic regulation or recruit lateral prefrontal cognitive control functions under certain conditions to achieve controlled emotion regulation. Neuromodulation studies confirm that VMPFC is a key causal brain region for implicit emotion regulation, and enhancing its function holds promise for improving implicit emotion regulation capacity in patients with depression and anxiety. The tripartite framework proposed in this paper highlights the diverse mechanisms of implicit emotion regulation, expands the dynamic understanding of emotion regulation theory, and provides promising new avenues for clinical intervention in mood disorders.

Keywords: emotion regulation, implicit emotion regulation, depression, anxiety, neuromodulation
Classification Codes: B842; B845

Emotion regulation refers to the process by which individuals influence which emotions they have, when they have them, and how they experience and express these emotions (Gross, 2015). Successful emotion regulation forms the foundation for maintaining mental health and healthy interpersonal relationships (Aldao et al., 2010; Gross & John, 2003). In clinical practice, persistent negative emotional distress and inability to effectively regulate emotions are considered primary characteristics of patients with mood disorders such as depression and anxiety (Joormann & Stanton, 2016; Liu & Thompson, 2017). Most previous research has focused on explicit emotion regulation (explicit emotion regulation; Gao et al., 2022; Konrad et al., 2025; Lincoln et al., 2022)—the process of emotion regulation resulting from explicit goals to change emotions. In real life, emotions persist and fluctuate continuously, but both consciously initiating and actively maintaining emotion regulation rely on top-down cognitive control mechanisms, which consume substantial cognitive resources (Ferri et al., 2016; Li et al., 2025; Li et al., 2023, 2024; Pruessner et al., 2020; Tang et al., 2025), leading to fatigue. In fact, in most situations, emotional change is not guided by subjective will and effort but occurs in a more adaptive manner, which reflects the important role of implicit emotion regulation (implicit emotion regulation). Unlike explicit emotion regulation, implicit emotion regulation encompasses a broad category of emotion-changing methods without explicit emotion regulation goals; its occurrence is not easily detected, its execution process is covert, and its cognitive and neural mechanisms remain unclear.

Numerous studies have confirmed that implicit emotion regulation can effectively modulate negative emotional experiences and related physiological responses and behavioral manifestations, playing an important role in clinical interventions for mood disorders (see meta-analysis Dalton et al., 2025). Compared to explicit, effortful emotion regulation that requires conscious monitoring and consumes cognitive resources, implicit emotion regulation avoids adverse effects from conscious monitoring and cognitive resource depletion, thus offering advantages in certain contexts. For example, implicit cognitive reappraisal shows better effects in individuals unaccustomed to cognitive reappraisal (Gao et al., 2024; Williams et al., 2009). When individuals experience anger, they often pursue the occurrence and expression of the emotion, which conflicts with the goal of down-regulating it; implicit emotion regulation can avoid this conflict between emotion regulation goals and subjective will, making regulation execution easier and achieving the goal of reducing anger (Mauss et al., 2007b). In complex tasks such as risk decision-making and mathematical learning, emotion regulation requiring cognitive monitoring affects ongoing task performance and produces inappropriate behavioral consequences, whereas implicit emotion regulation effectively regulates emotions while minimally impacting the ongoing task (Yang et al., 2015; Yuan et al., 2019; Zhu et al., 2022). When regulating high-intensity negative emotions, implicit emotion regulation can more effectively down-regulate subjective negative emotion reports and related EEG indices compared to explicit emotion regulation (Y. Zhang et al., 2023). For patients with psychiatric disorders such as depression and anxiety, impaired cognitive control resources limit the implementation of explicit emotion regulation, particularly cognitive reappraisal, but implicit emotion regulation functions remain largely intact (Dalton et al., 2025; Li et al., 2023; Yuan et al., 2022; Mo et al., 2021; Zhang & Li, 2024). For instance, implicitly primed cognitive reappraisal strategies can help depressed patients reduce the late positive potential (LPP) associated with negative emotions (Yuan et al., 2022) and make anxiety patients' emotion ratings more positive (Gao et al., 2024).

In summary, investigating the cognitive and neural mechanisms of implicit emotion regulation can deepen emotion regulation theory and provide guidance for treating clinical mood disorders. This paper first proposes a tripartite classification of implicit emotion regulation, then uses this framework to introduce findings from brain imaging and neuromodulation studies on implicit emotion regulation, and finally elaborates on the clinical application prospects of implicit emotion regulation for mood disorders.

2. A Tripartite Classification of Implicit Emotion Regulation

From a cognitive mechanism perspective, implicit emotion regulation involves various unconscious cognitive processes, including both fully automatic regulation methods and those achieved through top-down cognitive control. Gyurak et al. (2011) proposed the "dual-process framework," which first divided emotion regulation into two categories (explicit and implicit), suggesting that implicit emotion regulation is automatically initiated by emotional stimuli without conscious monitoring and its execution process is not consciously perceived, emphasizing that implicit emotion regulation proceeds automatically compared to explicit emotion regulation. Braunstein et al. (2017) added an "emotion regulation goal" dimension on this basis, proposing a "multi-level framework" that views emotion regulation as a process of achieving emotion regulation goals. Therefore, the "explicit" versus "implicit" nature of emotion regulation goals and the "controlled" versus "automatic" nature of emotion regulation execution processes are two orthogonal dimensions, with the main difference between implicit and explicit emotion regulation being the absence of explicit regulation goals in the former. Additionally, some literature has discussed related classification frameworks (Koole et al., 2011, 2015; Mauss et al., 2007a). The multi-level framework first subdivided implicit emotion regulation into "implicit automatic" and "implicit controlled" subtypes. Implicit automatic emotion regulation does not involve active control, and emotional change occurs alongside emotional learning/value updating processes, with fear extinction being a classic example. Implicit controlled emotion regulation requires active control participation, with emotional Stroop being a classic example—the task itself does not require individuals to regulate emotions, but because emotionally informative materials are used in the task, task execution can incidentally change emotions.

Although the multi-level framework (Braunstein et al., 2017) has deepened our understanding of emotion regulation, particularly implicit emotion regulation, its classification of emotion regulation is static and lacks consideration of dynamic changes. In reality, both goal and process dimensions exhibit dynamic changes. In the goal dimension, emotion regulation goals can shift from explicit to implicit through priming. In the process dimension, the degree of cognitive control involvement changes dynamically with situational changes and individuals' active adjustments. For example, implementation intentions—a form of emotion regulation where individuals pre-plan "when" and "how" to achieve an emotion regulation goal and form an "if...then..." execution pattern that automatically executes when conditions are met to achieve the emotion regulation goal (Gallo et al., 2007, 2009; Webb et al., 2012)—represent a transition from controlled to automatic processes. Additionally, changes in external feedback or situational cues can cause certain habitually executed automatic processes to shift to controlled processes (Hikosaka & Isoda, 2010).

Therefore, we believe it is necessary to separately categorize implicit emotion regulation with dynamic change characteristics in the execution process dimension based on the multi-level framework's dual classification of implicit emotion regulation. This paper terms this "implicit goal-driven emotion regulation," with the most typical example being implicit goal pursuit, which often implicitly induces emotion regulation goals through priming.

As shown in Figure 1 [FIGURE:1]B, this paper classifies implicit emotion regulation into three types based on the degree of controlled processing: automatic implicit emotion regulation (primarily automatic), task-incidental implicit emotion regulation (primarily controlled), and implicit goal-driven emotion regulation (which can change dynamically).

Figure 1. Classification of implicit emotion regulation. (A) This figure is adapted from the multi-level framework of emotion regulation proposed by Gross, a renowned scholar in the field, which divides implicit emotion regulation into two categories: "implicit automatic emotion regulation" and "implicit controlled emotion regulation" (Braunstein et al., 2017). (B) The tripartite framework of implicit emotion regulation proposed in this paper, including three categories: "automatic implicit emotion regulation," "task-incidental implicit emotion regulation," and "implicit goal-driven emotion regulation."

3. Brain Imaging Findings on Implicit Emotion Regulation

Multiple neural models of emotion regulation (Dixon et al., 2017; Etkin et al., 2015; Ochsner et al., 2012; Phillips et al., 2008; Rive et al., 2013; Silvers & Guassi Moreira, 2019; Smith & Lane, 2015) indicate that emotion regulation is achieved by modulating emotion-generating brain regions (such as the amygdala) through regulatory brain regions. Regulatory brain regions consist of cognitive control brain regions and automatic regulation brain regions. Cognitive control brain regions include the supplementary motor area, pre-supplementary motor area, and frontoparietal control network, which includes the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), and parietal cortex. Automatic regulation brain regions include the ventral anterior cingulate cortex, ventromedial prefrontal cortex (VMPFC), hippocampus, and parahippocampal gyrus. Different implicit emotion regulation processes show varying degrees of dependence on cognitive control (Braunstein et al., 2017), suggesting diversity in their regulatory mechanisms and neural pathways. This section explores the cognitive and neural mechanisms of different types of implicit emotion regulation in conjunction with brain imaging research.

3.1 Automatic Implicit Emotion Regulation

Automatic implicit emotion regulation does not rely on explicit regulation intention initiation or involve active processing; emotional change typically occurs during experiential learning and value updating processes (Braunstein et al., 2017; Etkin et al., 2015). Its characteristics and advantages lie in acting on emotional processes without consuming cognitive resources, without requiring attention and prefrontal cognitive control resources, thereby reducing potential conflicts with objective situations or subjective will, making emotion regulation occur more "naturally" with high adaptability and stability, and producing lasting regulatory effects. However, the disadvantage is the lack of conscious monitoring, making external manipulation difficult; when such implicit emotion regulation becomes dysfunctional, it is hard to identify and intervene.

The most typical example is fear extinction, which refers to the gradual weakening or elimination of original conditioned fear responses through repeated presentation of a conditioned fear stimulus (CS) without accompanying unconditioned stimuli (US) such as electric shocks or noise (Dunsmoor et al., 2015; Maren & Holmes, 2016; Zabik et al., 2023). During this process, individuals do not consciously manipulate their emotions, yet their emotional responses to the conditioned stimulus are changed (Velasco et al., 2019). The cognitive mechanism is that when individuals repeatedly encounter previously fear-inducing cues in a safe environment without any harm, the initially learned "fear" association gradually weakens, replaced by new safety memory formation, resulting in significantly reduced emotional responses when encountering the same cues subsequently. Fear extinction has typical inhibitory learning characteristics. Rather than directly eliminating original memory traces, extinction is more considered to form a new memory that competes with the initial fear memory, with individuals learning that the previously threat-predicting cue no longer accompanies danger in the current context. Therefore, the post-extinction CS-"safety" association coexists with the original CS-US fear association and can suppress fear response expression under appropriate conditions (Kalisch et al., 2006). This inhibitory process operates at an implicit level without conscious participation.

In addition to fear extinction, reinforcer revaluation also belongs to automatic implicit emotion regulation. This process indirectly changes emotional responses by altering the value of outcomes associated with stimuli. Common experimental paradigms include reinforcer devaluation and reinforcer inflation. In devaluation experiments, individuals first establish stimulus-outcome associations (e.g., CS-food), then reduce the affective value of the outcome (e.g., inducing satiety or aversion) to weaken responses to the original stimulus. In inflation, outcome salience is increased (e.g., stronger electric shocks, larger rewards) to enhance emotional responses. Although individuals do not actively regulate their emotions, their responses to stimuli change due to outcome value updating (Bouton, 2024; Morrison & Salzman, 2010). Therefore, fear extinction and reinforcer revaluation, as two typical examples of automatic implicit emotion regulation, represent "deconditioning" of negative emotional responses and "recoding" of emotional value, respectively. This regulatory mechanism of habituation/adaptation to emotional stimuli through repeated exposure exists across species, is highly adaptive, and forms the core principle of clinical interventions such as exposure therapy (Herrmann et al., 2017).

Brain imaging research shows that automatic implicit emotion regulation mechanisms involve coordinated action of multiple key brain regions, including the amygdala, hippocampus, and VMPFC. First, the amygdala plays a key role in storing emotional representations and emotional expression, such as storing CS-US associations during initial fear and reinforcer evaluation and driving physiological responses through projections to the brainstem (Braunstein et al., 2017). During fear extinction, the amygdala also participates in learning new safety associations (Li et al., 2011). VMPFC is responsible for regulating amygdala activity and updating stimulus emotional value (Motzkin et al., 2015; Roy et al., 2012). Many studies have found that VMPFC activity increases during fear extinction (Cremers et al., 2021; Wik et al., 1997; Gottfried & Dolan, 2004; Phelps et al., 2004). For example, VMPFC plays an important role in consolidating and retrieving fear extinction memory; after successful extinction, when individuals encounter previously threatening cues again, VMPFC activation facilitates retrieval of extinction memory (i.e., safety memory), thereby inhibiting amygdala fear responses, reducing fear experience, and VMPFC activation level negatively correlates with amygdala response intensity (Bukalo et al., 2015; Sotres-Bayon & Quirk, 2010). Unlike the amygdala, the hippocampus primarily provides context-related information to regulate emotional responses. For example, during fear extinction, it encodes the environmental context where extinction occurs, making retrieval of safety memory context-dependent (Lonsdorf et al., 2014; Goode & Maren, 2019; Brown et al., 2025). When individuals are in the same context as extinction learning, hippocampal activation facilitates VMPFC recruitment of the safety memory network, successfully inhibiting amygdala-initiated fear responses; but if the context changes, hippocampal encoding of the original context no longer matches, extinction memory retrieval is blocked, and amygdala expression of fear memory dominates again, leading to fear response recovery (Kalisch et al., 2006). Empirical studies support this interactive model among brain regions: during extinction recall tests, VMPFC and hippocampus typically show positively correlated synchronous activation, and higher network activation levels correlate with better fear inhibition effects. Conversely, during fear recovery, enhanced amygdala and hippocampus activity is observed accompanied by decreased VMPFC activity (Zabik et al., 2023).

In summary, automatic implicit emotion regulation is an emotion regulation process that changes emotional responses spontaneously through repeated environmental exposure without conscious intervention or reliance on cognitive control resources. During this process, individuals do not actively regulate their emotions, yet their emotional responses are effectively suppressed and maintained long-term, highlighting this regulation form's advantages in adaptability, stability, and energy efficiency. From an evolutionary perspective, this mechanism represents the emotion system's fine-tuning capability driven by experience at an unconscious level. Therefore, automatic implicit emotion regulation is not only a low-cost regulation method but may also represent the fundamental form of the emotion system's endogenous regulatory capacity, holding theoretical value for understanding the evolutionary basis of emotion regulation and designing clinical interventions.

3.2 Task-Incidental Implicit Emotion Regulation

In contrast to automatic regulation, task-incidental implicit emotion regulation depends on cognitive control, where emotion regulation implementation is not motivated by specific regulation goals but rather unconsciously produces changes in emotional responses through activation of top-down cognitive control during execution of other tasks (such as cognitive inhibition, conflict monitoring, or judgment operations). This "incidental emotion regulation" process is not primarily intended to regulate emotions; instead, emotion regulation emerges as a byproduct of task execution, showing clear task-dependency and functional orientation characteristics (Lieberman et al., 2007).

Typical task-incidental implicit emotion regulation appears in affect labeling, emotional Stroop, and emotional Go/No-go tasks. In these paradigms, individuals' goals and tasks are not to actively regulate emotions but to complete cognitive tasks such as rapid responses, semantic judgments, or inhibiting irrelevant stimuli. For example, affect labeling tasks require participants to select a more appropriate word from two options to label the content of currently presented negative pictures—essentially putting a label on the currently experienced emotion. In these paradigms, emotional responses carried or accompanied by tasks are irrelevant to task goals but interfere with task performance (e.g., interfering with semantic processing or inhibitory control). The top-down prefrontal cognitive control system activated by the task then incidentally regulates these responses. This regulation is neither motivated by conscious regulation intention nor directly aimed at changing emotions but rather, while ensuring smooth task completion, automatically suppresses or blocks interfering emotional information, achieving an adaptive regulation process of resource optimization. From an adaptability perspective, when emotional stimuli interfere with cognitive tasks, the regulation process is passively activated as an implicit resource management strategy, enabling rapid filtering and functional suppression of emotional input. Its essence lies in maintaining cognitive system stability and efficiency rather than directly changing emotional states.

Brain imaging studies have found that in paradigms such as emotional Stroop and emotional Go/No-go, individuals must focus on the task itself (e.g., ignoring emotional words and naming facial expressions, or making button responses when seeing fearful faces). Although task goals do not involve emotion regulation intention, they activate cognitive control-related brain regions such as DLPFC and VLPFC during task execution, which incidentally regulate emotional response brain regions while completing cognitive tasks (Ochsner & Gross, 2005). Additionally, similar implicit emotion regulation effects are observed in semantic judgment paradigms. For example, affect labeling tasks require individuals to select appropriate semantic labels (e.g., "fear") when facing emotional faces. Although this task does not involve explicit emotion regulation goals, the label selection process itself activates cognitive control brain regions such as VLPFC, effectively inhibiting emotional responses, reducing amygdala activity, and decreasing subjective negative emotional experiences (Burklund et al., 2015; Cohen & Lieberman, 2010; Kerns et al., 2004; Payer et al., 2012; Townsend et al., 2013). Therefore, lateral prefrontal cortex plays an important role in this type of implicit emotion regulation.

In summary, when cognitive tasks contain emotional interference information irrelevant to task goals, the prefrontal cognitive control network activated by the task itself incidentally regulates emotional responses. This process ensures smooth task completion while effectively changing emotional responses by inhibiting activity in emotion-generating brain regions such as the amygdala. This implicit regulation mechanism emphasizes the resource optimization and functional suppression characteristics exhibited by the emotion system when serving higher-order cognitive goals.

3.3 Implicit Goal-Driven Emotion Regulation

Unlike the clearly automatic or controlled emotion regulation described above, implicit goal-driven emotion regulation demonstrates the potential for dynamic switching between reliance on cognitive control and automatic execution. The involved cognitive mechanism is primarily the implementation process of implicit goals. When emotion regulation goals are implicitly implanted/activated, they can unconsciously influence subsequent processing of emotional stimuli, prompting emotional responses to shift in a goal-consistent direction—a phenomenon known as implicit goal pursuit. In the goal dimension, implicit goal-driven emotion regulation differs from explicit controlled emotion regulation in that regulation goals shift from explicit to implicit (Gyurak et al., 2011; Mauss et al., 2007b). In the process dimension, the initiation and execution of goal-driven implicit emotion regulation do not rely on immediate conscious decision-making or continuous cognitive resource allocation, thus generating less cognitive load, while still being manipulable through control of context, distinguishing it from fully automatic processes (Braunstein et al., 2017).

The most common method of implicit goal-driven regulation is activating emotion regulation goals through priming to induce implicit goal pursuit processes. For example, through sentence unscrambling tasks or word matching tasks, participants are required to rearrange scrambled words into coherent sentences or select a synonym for a target word from two alternatives. During these processes, individuals passively contact and process semantic cues related to regulation (e.g., "stay calm" or "face positively") without explicit regulation intention, automatically activating corresponding regulation goals that induce subsequent emotion regulation. Additionally, implicit goal-driven emotion regulation can function early in emotion generation. For instance, briefly priming regulation-related cues before emotional stimulus presentation leads to altered neural responses during early emotion processing stages, such as significantly enhanced N170 component amplitude related to facial emotion processing (Liu et al., 2018) or reduced P1 component amplitude related to early attention (Gao et al., 2023). These evidences indicate that implicit goal priming begins the regulation process before emotional responses are fully formed.

Implicit goal-driven emotion regulation can adjust emotional responses in a low cognitive load manner according to primed regulation goals without explicit intention. Series of brain imaging studies show that this process involves both broad inhibition of emotion-processing brain regions and selective activation of regulation-related brain regions. For example, Zhang et al. (2020) used an emotion regulation semantic priming paradigm to examine socially acquired fear and found that under implicit regulation conditions, activation in emotion-processing brain regions such as the amygdala significantly decreased, while executive control brain regions such as DLPFC and dorsal anterior cingulate cortex showed no obvious activation enhancement, indicating that implicit regulation at this time does not need to rely on high cognitive-cost executive control. Additionally, Xie et al. (2019) used a subliminal presentation paradigm of emotion regulation words and found that even with implicit priming conditions of only 33 or 50 ms, emotion suppression words could reduce individuals' emotional responses to negative stimuli, with DLPFC and VLPFC activation significantly lower than in explicit regulation conditions, indicating that implicit regulation can achieve effective regulation without significantly consuming cognitive resources. Wyczesany et al. (2021) found that when individuals unconsciously induced self-control or reappraisal goals, activation in emotion-processing brain regions such as visual cortex and amygdala triggered by negative emotional stimuli significantly decreased, indicating effective emotional response inhibition, while activity in cognitive control regions such as DLPFC increased, suggesting that implicit goals can also achieve emotion down-regulation through cognitive control pathways. Similarly, Wang et al. (2017) used a pre-description paradigm where a sentence reinterpreting the content of each negative picture from a positive perspective was presented before the picture, implicitly inducing cognitive reappraisal of upcoming negative information without explicit regulation instructions. Results similarly found that compared to negative description conditions, implicit emotion regulation conditions activated regulatory brain regions such as DLPFC, accompanied by weakened amygdala activation and enhanced negative functional connectivity between prefrontal and limbic systems, confirming that implicit goals can also mobilize top-down regulatory pathways to achieve emotion regulation. Similarly, Zhang et al. (2021) used forward and backward masking techniques to present emotion regulation goal words for only 20 ms. Results found enhanced DLPFC activation that positively correlated with regulation effects, and after acute exercise enhanced DLPFC activation, repeating the above task further enhanced DLPFC activation. This phenomenon reflects that implicit regulation mechanisms can flexibly adjust regulatory strategies according to dynamic changes in external environment and cognitive resources, thereby achieving optimal emotion regulation effects at minimum cost across different contexts.

We propose that implicit goal-driven emotion regulation represents an emotion regulation mode between the classic binary division of "cognitive control" and "automatic response" (Braunstein et al., 2017). This mechanism reflects an adaptive balance optimization achieved by the emotion regulation system during evolution—under conditions of limited resources, it can both maintain flexible responses to emotional stimuli and avoid high costs from continuous cognitive resource occupation, while being highly sensitive to changes in external context and internal resource states. This dynamic operation mode that combines "purposefulness" and "adaptability" not only expands our traditional understanding of emotion regulation theory but also reveals the core evolutionary advantage of the human emotion system in adapting to pressure environments and rapidly and effectively managing emotional responses in complex ecological contexts.

This section systematically reviews the cognitive and neural mechanisms of implicit emotion regulation, identifying key brain regions and neural pathways involved in automatic, task-incidental, and goal-driven implicit regulation. At the extreme, automatic implicit emotion regulation directly modulates the amygdala by VMPFC, almost without relying on lateral prefrontal cognitive control functions. Conversely, task-incidental emotion regulation is directly regulated by lateral prefrontal cortex (Etkin et al., 2015). Between these two types, implicit goal-driven emotion regulation may involve both regulatory mechanisms and may have coordination or switching mechanisms yet to be discovered. A recent explicit emotion regulation study showed that controlled emotion regulation first activates DLPFC and VLPFC, which then recruit VMPFC, which subsequently modulates amygdala and other emotion brain region activity (He et al., 2023). Therefore, we believe VMPFC may be a key brain region for implicit emotion regulation, participating in the above three types of implicit emotion regulation and explicit emotion regulation through different cognitive processes and neural pathways. In addition to the brain imaging evidence mentioned in this section, neuromodulation evidence also supports our view.

4. Neuromodulation Findings on Implicit Emotion Regulation

Although brain imaging research has deeply elucidated the activity patterns of brain networks accompanying implicit emotion regulation, how can we reveal which brain regions play causal roles in implicit emotion regulation? How can we effectively utilize these mechanisms in clinical practice, such as improving patients' emotional symptoms by modulating activity in relevant brain regions? These are urgently needed research directions.

4.1 Causal Brain Regions for Implicit Emotion Regulation

Automatic implicit emotion regulation domain: Studies have used non-invasive neuromodulation techniques such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) to investigate the core role of VMPFC in automatic implicit emotion regulation. For example, researchers combined fMRI and tDCS and found that after activating VMPFC, the tDCS group showed significantly lower negative emotion intensity when watching negative videos compared to the tDCS control group (Abend et al., 2019). Similarly, another study using the ultimatum game to induce anger found that after VMPFC activation, activity in brain regions such as anterior insula significantly decreased, while acceptance rates of unfair proposals increased and subjective anger and aggressive behavior decreased (Gilam et al., 2018). Additionally, activating VMPFC with anodal tDCS or high-frequency repetitive TMS can significantly facilitate fear extinction (Lei et al., 2024; Marković et al., 2021). For instance, Van't Wout et al. (2016, 2017) and Vicario et al. (2020) used anodal tDCS to activate VMPFC and observed enhanced fear extinction in both healthy participants and PTSD patients. Herrmann et al. (2017) applied high-frequency repetitive TMS (10 Hz) to VMPFC to activate this region, then used virtual reality technology with exposure therapy to treat acrophobia. Results showed that compared to the TMS control group, the TMS group showed significantly improved exposure therapy effects. Rodent studies similarly indicate that activating the functional homolog of VMPFC (infralimbic cortex) can facilitate fear extinction (Marković et al., 2021). In addition to targeting VMPFC directly as a site for implicit emotion regulation, some studies have attempted to modulate lateral cortical regions functionally connected to VMPFC, thereby producing connection-based indirect modulation effects on VMPFC and subsequently regulating emotional responses in the amygdala. For example, Raij et al. (2018) used 300 ms online TMS (20 Hz) to activate specific regions of the left frontal cortex that had been shown in previous research to have significant functional connectivity with VMPFC during conditioned fear extinction. Results showed enhanced fear extinction effects in the TMS group compared to the control group.

Task-incidental implicit emotion regulation domain: Neuromodulation studies have found that even without explicit emotion regulation goals, activation of cognitive control brain regions can causally improve emotion regulation effects. For example, Cao et al. (2018) used continuous theta-burst stimulation (cTBS) to activate participants' right prefrontal cortex and found that alpha power in response to positive emotional faces significantly decreased in an emotional Go/No-go task. Since alpha frequency is typically an indicator of cortical inhibition, this result suggests enhanced positive emotions. Bermpohl et al. (2005, 2006) used low-frequency repetitive TMS to inhibit DLPFC and found this operation interfered with behavioral control performance in emotional Go/No-go tasks, particularly under conditions requiring rapid switching between emotional information and task goals. Recently, Lapate et al. (2024) applied cTBS to lateral prefrontal cortex to inhibit its cognitive control function and found that task-incidental implicit emotion regulation effects weakened, with interference from negative emotional cues on No-go tasks significantly enhanced.

Implicit goal-driven emotion regulation domain: Neuromodulation studies have focused on both automatic emotion regulation brain regions and cognitive control brain regions. Hua et al. (2020) found that after inhibiting left orbitofrontal cortex with cathodal tDCS, the subsequent attentional avoidance effect caused by visual masked priming (priming words presented for 20 ms) of emotion control in a dot-probe task significantly weakened, indicating that orbitofrontal cortex has a causal role in attention allocation for this process. Another study used tDCS to activate VMPFC and found that implicit reappraisal effects induced by sentence unscrambling tasks significantly improved, with negative emotional responses clearly decreased (Gao et al., 2023; Gao et al., 2024). Similarly, Q. Zhang et al. (2023) used tDCS to activate right DLPFC and VLPFC and found that implicit goal-driven emotion regulation induced by word matching tasks significantly enhanced regulation of negative emotions (subjective ratings and electrophysiological indices).

Based on existing neuromodulation research, VMPFC is at least a causal brain region for two types of implicit emotion regulation: in automatic implicit regulation, it can down-regulate amygdala emotional responses; in implicit goal-driven regulation, enhanced VMPFC excitability can significantly improve implicit reappraisal effects. Although no neuromodulation studies have directly examined VMPFC's role in task-incidental implicit regulation, existing evidence shows that stimulating lateral prefrontal cortex can indirectly modulate VMPFC through functional connectivity (Lynch et al., 2022; Oathes et al., 2021; Raij et al., 2018; Sydnor et al., 2022), and VMPFC also plays an important role in explicit emotion regulation (which similarly relies heavily on cognitive control) (Diekhof et al., 2011). Therefore, future research can further examine VMPFC's role in controlled implicit emotion regulation.

4.2 Challenges for Neuromodulation Techniques

Although we have found that VMPFC plays a core role in implicit emotion regulation, using VMPFC as a treatment target for non-invasive brain stimulation presents certain anatomical and technical limitations. First, VMPFC's anatomical location poses challenges for effective modulation (Lopez-Persem et al., 2019; Mackey & Petrides, 2014). VMPFC is located deep in the brain. Due to current attenuation effects in brain tissue, the modulation effects of non-invasive brain stimulation techniques on this region are generally weak (Drakaki et al., 2022; Saturnino et al., 2021). Additionally, since it is difficult to avoid applying (even stronger) electromagnetic stimulation to superficial lateral brain regions when delivering non-invasive brain stimulation, this also creates issues with regional targeting focus, reducing the strength of causal inferences about VMPFC from experimental findings. Multi-channel direct current stimulation technology and temporal interference technology may be promising methods. The former improves stimulation focus while increasing safety by shunting current intensity across each electrode pair pathway. For example, the previously mentioned Q. Zhang et al. (2023) used multi-channel tDCS technology to activate right DLPFC and VLPFC regions, finding enhanced implicit goal-driven emotion regulation effects. Meanwhile, the aforementioned Gao et al. (2024) also used focality-optimized multi-channel tDCS to activate VMPFC in high trait anxiety individuals and improved implicit emotion regulation effects. Additionally, Sergiou et al. (2022) used small ring electrodes for high-precision tDCS activation of VMPFC, placing the anode electrode on VMPFC with five return electrodes arranged in a circle around it. Results showed this operation could significantly reduce violent behavior in violent offenders with substance dependence. No applications of temporal interference technology in emotion regulation research have been found yet.

Overall, neuromodulation techniques show considerable application prospects in intervention research on implicit emotion regulation. However, existing neuromodulation techniques mainly target lateral prefrontal cortex and other superficial cortical regions (Qiu et al., 2023). Future research can optimize stimulation depth and targeting precision (in addition to electrical stimulation, ultrasound stimulation is also an emerging effective technique) to further clarify the cognitive and neural mechanisms of implicit emotion regulation and provide effective treatment options for clinical practice.

5. Implications of Implicit Emotion Regulation for Treating Depression and Anxiety

Depression and anxiety, as the two most common mood disorders, are characterized by abnormal emotion regulation (Gross & Jazaieri, 2014; Zilverstand et al., 2017). Depressed patients are immersed in persistent negative emotions, showing anhedonia (Joormann & Stanton, 2016; Liu & Thompson, 2017). Anxiety patients are in a state of high negative emotional arousal long-term and show excessive sensitivity to stress (Elwood et al., 2012). Patients with depression and anxiety have difficulty down-regulating negative emotional experiences through explicit emotion regulation, particularly cognitive reappraisal (Heller et al., 2009; Urry et al., 2009). The main reasons for these patients' difficulties with explicit emotion regulation are that they not only have impaired cognitive resources and executive control functions but also damaged emotion regulation neural circuits and abnormal functional connectivity in emotion regulation brain regions (Park et al., 2019).

Therefore, we believe the three types of implicit emotion regulation proposed in this paper may have advantages for depression and anxiety populations. First, automatic implicit regulation processes can reduce emotional responses without conscious participation, which is very beneficial for alleviating excessive fear and high physiological arousal in anxiety patients. Similarly, such automatic processes can alleviate negative emotions in depressed patients without occupying prefrontal cognitive resources. Second, task-incidental implicit regulation occurs incidentally while individuals perform other cognitive tasks, which is suitable for depressed individuals with limited cognitive resources and insufficient regulation motivation. We can design reasonable task contexts to indirectly activate emotion regulation networks, achieving emotional improvement under non-intentional conditions. Finally, we can unconsciously activate positive emotion regulation goals (e.g., subtly priming "positive reappraisal"), enabling emotion regulation strategies that are difficult to execute actively to occur implicitly.

5.1 Implications for Depression

Depressive symptoms (particularly persistent negative thinking) occupy substantial cognitive resources, impairing patients' executive functions, memory, and attention (Joormann & Quinn, 2014; Quinn et al., 2018; Rock et al., 2014; Snyder, 2013; Vilgis et al., 2015). Depressed patients show insufficient lateral prefrontal activation and excessive amygdala activation during explicit down-regulation of negative emotions (Joormann & Stanton, 2016; Liu & Thompson, 2017). Reduced lateral prefrontal activation indicates deficits in cognitive control capacity, while excessive amygdala activation reflects failure of explicit emotion regulation (Zilverstand et al., 2017). Notably, studies examining both implicit and explicit emotion regulation in depressed patients facing frustration found that compared to healthy controls, patients' ability to improve negative emotions using explicit cognitive reappraisal (instructed to interpret current situations from more positive perspectives) significantly decreased. However, when using word matching tasks to induce implicit cognitive reappraisal, this implicit goal-driven emotion regulation effect showed no significant difference from healthy controls (Yuan et al., 2022). Therefore, implicit emotion regulation provides new insights for depression treatment: implicit emotion regulation types that consume fewer cognitive control resources and are less dependent on cognitive control functions (Mauss et al., 2007a; Yang et al., 2015; Yuan et al., 2019) may play important roles in depressed patients with limited cognitive resources.

5.2 Implications for Anxiety

Chronic hypervigilance in anxious individuals leads to impaired top-down control capacity and insufficient cognitive resources. Control capacity deficits make it difficult for patients to successfully implement explicit emotion regulation strategies such as cognitive reappraisal that consume cognitive resources (Calhoon & Tye, 2015; Ironside et al., 2019; Kenwood et al., 2022; Pruessner et al., 2020; Troy et al., 2018). Meanwhile, anxiety disorder patients show dual characteristics of hyper-sensitive limbic systems related to emotional responses and weakened prefrontal regulatory brain region functions (Brandl et al., 2022; Brändle et al., 2020; Calhoon & Tye, 2015; Hiser & Koenigs, 2018). High trait anxiety individuals show slow emotional state recovery when performing emotional Go/No-go tasks, indicating that anxiety may weaken cognitive control's inhibitory and implicit regulation of emotions (Liu et al., 2018). Wang et al. (2021) found that panic disorder patients, unlike healthy controls, could not effectively reduce subjective negative emotions and amygdala activity when receiving positive suggestions before viewing negative pictures (implanting implicit emotion regulation goals). This failure of implicit emotion regulation positively correlated with insufficient activation of DLPFC and VLPFC. Similarly, generalized anxiety patients also showed reduced activation in prefrontal regions such as orbitofrontal cortex during implicit goal pursuit induced by the same paradigm (Wang et al., 2024). These results suggest that anxiety disorders affect the normal functioning of executive control brain regions, making it difficult for patients to regulate emotional responses even at the implicit level.

We believe this evidence does not deny the value of implicit emotion regulation but rather emphasizes that anxiety treatment should further explore and refine strategies based on patients' neural and functional impairments. In most cases, implicit emotion regulation functions in anxiety patients can still play a role. For example, gradually exposing patients unconsciously to fear stimuli through implicit exposure (e.g., presenting relevant cues without triggering strong subjective fear) can reduce patients' avoidance behavior, achieving effects similar to desensitization exposure therapy (Oyarzún et al., 2018). Similarly, the aforementioned Gao et al. (2024) study found that in implicit cognitive reappraisal goal pursuit initiated by sentence unscrambling tasks, high trait anxiety individuals could effectively use this implicit emotion regulation method to reduce negative emotions, and this process relied less on prefrontal cognitive control systems. Therefore, future research should focus on the specific mechanisms of implicit emotion regulation function impairment in different anxiety disorders and develop targeted intervention strategies accordingly.

In summary, implicit emotion regulation, due to its low cognitive resource dependence and high adaptability, shows great potential in treating resource-limited populations such as depression and anxiety. On one hand, neuromodulation techniques can directly target key brain regions for implicit regulation (such as VMPFC) for targeted intervention, improving regulatory brain regions' capacity to modulate emotion-generating brain regions like the amygdala. On the other hand, we can use implicit training paradigms to internalize emotion regulation goals and strengthen patients' implicit regulation skills (Hopp et al., 2011; Y. Zhang et al., 2023). It is foreseeable that as exploration of implicit emotion regulation mechanisms and their clinical applications continues, intervention strategies oriented toward implicit emotion regulation will provide novel insights and opportunities for clinical treatment of depression and anxiety.

6. Summary and Future Directions

In summary, as an important form of emotion regulation, implicit emotion regulation exhibits diverse cognitive and neural mechanisms and holds potential important value for treating mood disorders such as depression and anxiety. Compared to explicit emotion regulation with obvious regulation intentions and requiring active effort, implicit emotion regulation has higher automaticity and lower cognitive load. We believe implicit emotion regulation represents an adaptive optimization of the emotion system, reflecting "natural and fluent" emotion management capacity formed during human evolution. Even without conscious intervention, emotional responses can be appropriately adjusted, thereby avoiding high energy consumption from continuous high-intensity cognitive control and maintaining emotional stability and functional efficiency at all times. The main theoretical contribution of this paper is the proposal of a tripartite classification framework for implicit emotion regulation, which for the first time defines the category of implicit goal-driven emotion regulation. This implicit emotion regulation type has more flexible dependence on cognitive control, reflecting the dynamic adjustment capacity of emotion regulation processes. It should be noted that implicit emotion regulation also has limitations: due to its lack of conscious participation, individuals find it difficult to actively detect and adjust implicit processes in real-time. When implicit regulation mechanisms themselves have defects or biases (e.g., automatically negative-biased thinking), correction and intervention are relatively difficult. Therefore, during use or training, we should weigh the advantages and limitations of implicit emotion regulation and view it as a beneficial supplement to explicit emotion regulation, jointly helping us improve emotion regulation capacity and maintain mental health.

Future research in implicit emotion regulation has several directions worthy of in-depth exploration. First, at the theoretical level, we need to further reveal the dynamic change patterns of emotion regulation in both regulation goal and execution process dimensions. For example, if we control one dimension while changing the other, how will the cognitive and neural mechanisms of emotion regulation change? Can this generate new clinical intervention methods? Is there competition or synergy between extreme types of regulation within the same dimension? How does switching between different regulation types within the same dimension occur, and can it be manipulated? Do the same brain regions play the same roles across different emotion regulation types? For example, does lateral prefrontal cortex play the same role in explicit controlled and task-incidental implicit emotion regulation?

Second, given that implicit emotion regulation processes are rapid and involve multiple brain regions, using multimodal brain imaging approaches will be an important direction. For example, combining fMRI with simultaneous EEG recording can obtain data with both high spatial and high temporal resolution, thereby dynamically mapping the neural mechanisms of implicit regulation. Additionally, more neuromodulation techniques should be integrated into research paradigms to selectively change the excitability of specific brain regions (e.g., enhancing VMPFC or inhibiting DLPFC activity) and observe effects on implicit regulation outcomes, which can directly test the causal roles of these regions in implicit emotion regulation.

Third, exploring the applicability of implicit emotion regulation from a developmental perspective is needed. Existing research has focused mainly on healthy adults, while the characteristics of implicit emotion regulation in different populations and special groups remain unclear. Therefore, future research should examine the role of implicit emotion regulation in broader populations. For example, investigating the role of implicit regulation in adolescent emotional fluctuations during puberty to reveal how brains at different developmental stages use implicit emotion regulation to improve emotions; or exploring whether implicit emotion regulation in elderly populations can serve as a compensation for cognitive aging, helping and guiding older adults to maintain positive emotions.

Finally, developing simpler, more feasible emotion regulation treatment plans suitable for different patients for clinical populations is needed. Future research can explore personalized neuromodulation treatment plans, such as targeting and modulating specific brain regions based on patients' brain imaging characteristics (e.g., implementing personalized brain modulation to enhance implicit emotion regulation capacity in patients with low VMPFC function). Meanwhile, developing specialized implicit emotion regulation training paradigms is also a worthwhile direction. How can certain emotion regulation strategies be efficiently internalized into automatic responses through short-term practice? Additionally, clinical research should focus on broader populations beyond the depression and anxiety mentioned in this review, including post-traumatic stress disorder, bipolar disorder, autism spectrum disorder, etc., to explore whether patients' implicit emotion regulation functions have special patterns or deficits and thereby contribute to optimizing clinical treatment.

References

Gao, K., Zhang, Y., Li, S., Yuan, J., Li, H., & Zhang, D. (2023). The causal role of ventromedial prefrontal cortex in implicit cognitive reappraisal. Acta Psychologica Sinica, 55(2), 210–223.

Hua, Y., Li, M., Wang, Q., Feng, C., & Zhang, J. (2020). The role of left orbitofrontal cortex in attentional selection under automatic emotion regulation: Evidence from transcranial direct current stimulation. Acta Psychologica Sinica, 52(9), 1048–1056.

Mo, L., Li, Y., & Zhang, D. (2024a). Cognitive and neural mechanisms of social pain emotion regulation: Current status and prospects. Applied Psychology, 30(1), 33–43.

Mo, L., Li, S., & Zhang, D. (2024b). Neural mechanisms of social pain emotion regulation. Psychological Science, 47(3), 530–537.

Mo, L., Guo, T., Zhang, Y., Xu, F., & Zhang, D. (2021). Activating right ventrolateral prefrontal cortex improves emotion regulation capacity for social pain in depressed patients: A TMS study. Acta Psychologica Sinica, 53(5), 494–504.

Zhang, D., & Li, S. (2024). Neural circuits of emotion regulation and their manifestations in mood disorder populations. Journal of Sichuan Normal University (Natural Science Edition), 47(3), 285–293.

Abend, R., Sar-el, R., Gonen, T., Jalon, I., Vaisvaser, S., Bar-Haim, Y., & Hendler, T. (2019). Modulating emotional experience using electrical stimulation of the medial-prefrontal cortex: A preliminary tDCS-fMRI study. Neuromodulation, 22(8), 884–893. https://doi.org/10.1111/ner.12787

Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004

Bermpohl, F., Fregni, F., Boggio, P. S., Thut, G., Northoff, G., Otachi, P. T., Rigonatti, S. P., Marcolin, M. A., & Pascual-Leone, A. (2005). Left prefrontal repetitive transcranial magnetic stimulation impairs performance in affective go/no-go task. Neuroreport, 16(6), 615–619. https://doi.org/10.1097/00001756-200504250-00020

Bermpohl, F., Fregni, F., Boggio, P. S., Thut, G., Northoff, G., Otachi, P. T. M., Rigonatti, S. P., Marcolin, M. A., & Pascual-Leone, A. (2006). Effect of low-frequency transcranial magnetic stimulation on an affective go/no-go task in patients with major depression: Role of stimulation site and depression severity. Psychiatry Research, 141(1), 1–13. https://doi.org/10.1016/j.psychres.2005.07.018

Bouton, M. E. (2024). Habit and persistence. Journal of the Experimental Analysis of Behavior, 121(1), 88–96. https://doi.org/10.1002/jeab.1101

Brandl, F., Weise, B., Mulej Bratec, S., Jassim, N., Hoffmann Ayala, D., Bertram, T., Ploner, M., & Sorg, C. (2022). Common and specific large-scale brain changes in major depressive disorder, anxiety disorders, and chronic pain: A transdiagnostic multimodal meta-analysis of structural and functional MRI studies. Neuropsychopharmacology, 47(5), 1071–1080. https://doi.org/10.1038/s41386-022-01271-y

Brändle, F., Wu, C. M., & Schulz, E. (2020). What are we curious about? Trends in Cognitive Sciences, 24(9), 685–687. https://doi.org/10.1016/j.tics.2020.05.010

Braunstein, L. M., Gross, J. J., & Ochsner, K. N. (2017). Explicit and implicit emotion regulation: A multi-level framework. Social Cognitive and Affective Neuroscience, 12(10), https://doi.org/10.1093/scan/nsx096

Brown, E. C., Artigas, S. O., Elsner, S., Liu, L., & Park, S. Q. (2025). Utilizing rewards to dampen fear and its recovery. Scientific Reports, 15(1), 17671. https://doi.org/10.1038/s41598-025-99758-3

Bukalo, O., Pinard, C. R., Silverstein, S., Brehm, C., Hartley, N. D., Whittle, N., Colacicco, G., Busch, E., Patel, S., Singewald, N., & Holmes, A. (2015). Prefrontal inputs to the amygdala instruct fear extinction memory formation. Science Advances, 1(6), e1500251. https://doi.org/10.1126/sciadv.1500251

Burklund, L. J., Craske, M. G., Taylor, S. E., & Lieberman, M. D. (2015). Altered emotion regulation capacity in social phobia as a function of comorbidity. Social Cognitive and Affective Neuroscience, 10(2), 199–208. https://doi.org/10.1093/scan/nsu058

Calhoon, G. G., & Tye, K. M. (2015). Resolving the neural circuits of anxiety. Nature Neuroscience, 18(10), 1394–1404. https://doi.org/10.1038/nn.4101

Cao, D., Li, Y., Niznikiewicz, M. A., Tang, Y., & Wang, J. (2018). The theta burst transcranial magnetic stimulation over the right PFC affects electroencephalogram oscillation during emotional processing. Progress in Neuro-Psychopharmacology and Biological Psychiatry, https://doi.org/10.1016/j.pnpbp.2017.12.005

Cohen, J. R., & Lieberman, M. D. (2010). The common neural basis of exerting self-control in multiple domains. In R. Hassin, K. Ochsner, & Y. Trope (Eds.), Self Control in Society, Mind, and Brain (pp. 196–209). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195391381.003.0008

Cremers, H., Keedy, S., & Coccaro, E. (2021). The development of an fMRI protocol to investigate vmPFC network functioning underlying generalization and behavioral control. Psychiatry Research: Neuroimaging, 307, 111197. https://doi.org/10.1016/j.pscychresns.2020.111197

Custers, R., & Aarts, H. (2010). The unconscious will: How the pursuit of goals operates outside of conscious awareness. Science, 329(5987), 47–50. https://doi.org/10.1126/science.1188595

Dalton, S. D. P., Cooper, H., Jennings, B., & Cheeta, S. (2025). The empirical status of implicit emotion regulation in mood and anxiety disorders: A meta-analytic review. Journal of Affective Disorders, 380, 256–269. Advance online publication. https://doi.org/10.1016/j.jad.2025.03.118

Diekhof, E. K., Geier, K., Falkai, P., & Gruber, O. (2011). Fear is only as deep as the mind allows: A coordinate-based meta-analysis of neuroimaging studies on the regulation of negative affect. NeuroImage, 58(1), 275–285. https://doi.org/10.1016/j.neuroimage.2011.05.073

Dixon, M. L., Thiruchselvam, R., Todd, R., & Christoff, K. (2017). Emotion and the prefrontal cortex: An integrative review. Psychological Bulletin, 143(10), 1033–1081. https://doi.org/10.1037/bul0000096

Drakaki, M., Mathiesen, C., Siebner, H. R., Madsen, K., & Thielscher, A. (2022). Database of 25 validated coil models for electric field simulations in brain stimulation. Brain Stimulation, 15(3), https://doi.org/10.1016/j.brs.2022.04.017

Dunsmoor, J. E., Niv, Y., Daw, N., & Phelps, E. A. (2015). Rethinking extinction. Neuron, 88(1), 47–63. https://doi.org/10.1016/j.neuron.2015.09.028

Elwood, L. S., Wolitzky-Taylor, K., & Olatunji, B. O. (2012). Measurement of anxious traits: A contemporary review and synthesis. Anxiety, Stress, & Coping, 25(6), https://doi.org/10.1080/10615806.2011.582949

Etkin, A., Büchel, C., & Gross, J. J. (2015). The neural bases of emotion regulation. Nature Reviews Neuroscience, 16(11), 693–700. https://doi.org/10.1038/nrn4044

Ferri, J., Schmidt, J., Hajcak, G., & Canli, T. (2016). Emotion regulation and amygdala-precuneus connectivity: Focusing on attentional deployment. Cognitive, Affective, & Behavioral Neuroscience, 16(6), 991–1002. https://doi.org/10.3758/s13415-016-0447-y

Gallo, I. S., & Gollwitzer, P. M. (2007). Implementation intentions: Control of fear despite cognitive load. Psicothema, 19(2), 280–285.

Gallo, I. S., Keil, A., McCulloch, K. C., Rockstroh, B., & Gollwitzer, P. M. (2009). Strategic automation of emotion regulation. Journal of Personality and Social Psychology, 96(1), 11–31. https://doi.org/10.1037/a0014577

Gao, K., Wong, A. B., Li, S., Zhang, Y., & Zhang, D. (2024). The ventromedial prefrontal cortex plays an important role in implicit emotion regulation: A focality-optimized multichannel tDCS study in anxiety individuals. Human Brain Mapping, 45(13), e26812. https://doi.org/10.1002/hbm.26812

Gao, W., Yan, X., & Yuan, J. (2022). Neural correlations between cognitive deficits and emotion regulation strategies: Understanding emotion dysregulation in depression from the perspective of cognitive control and cognitive biases. Psychoradiology, 2(3), 86–99. https://doi.org/10.1093/psyrad/kkac014

Gilam, G., Abend, R., Gurevitch, G., Erdman, A., Baker, H., Ben-Zion, Z., & Hendler, T. (2018). Attenuating anger and aggression with neuromodulation of the vmPFC: A simultaneous tDCS-fMRI study. Cortex, 109, 156–170. https://doi.org/10.1016/j.cortex.2018.09.010

Goode, T. D., & Maren, S. (2019). Common neurocircuitry mediating drug and fear relapse in preclinical models. Psychopharmacology, 236(1), 415–437. https://doi.org/10.1007/s00213-018-5024-3

Gottfried, J. A., & Dolan, R. J. (2004). Human orbitofrontal cortex mediates extinction learning while accessing conditioned representations of value. Nature Neuroscience, 7(10), 1144–1152. https://doi.org/10.1038/nn1314

Gross, J. J. (2015). The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130–137. https://doi.org/10.1080/1047840X.2015.989751

Gross, J. J., & Jazaieri, H. (2014). Emotion, emotion regulation, and psychopathology: An affective science perspective. Clinical Psychological Science, 2(4), 387–401. https://doi.org/10.1177/2167702614536164

Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362. https://doi.org/10.1037/0022-3514.85.2.348

Gyurak, A., Gross, J. J., & Etkin, A. (2011). Explicit and implicit emotion regulation: A dual-process framework. Cognition and Emotion, 25(3), 400–412. https://doi.org/10.1080/02699931003782614

He, Z., Li, S., Mo, L., Zheng, Z., Li, Y., Li, H., & Zhang, D. (2023). The VLPFC-engaged voluntary emotion regulation: Combined TMS-fMRI evidence for the neural circuit of cognitive reappraisal. Journal of Neuroscience, 43(34), 6046–6060. https://doi.org/10.1523/JNEUROSCI.1337-22.2023

Heller, A. S., Johnstone, T., Shackman, A. J., Light, S. N., Peterson, M. J., Kolden, G. G., Kalin, N. H., & Davidson, R. J. (2009). Reduced capacity to sustain positive emotion in major depression reflects diminished maintenance of fronto-striatal brain activation. Proceedings of the National Academy of Sciences of the United States of America, 106(52), 22445–22450. https://doi.org/10.1073/pnas.0910651106

Herrmann, M. J., Katzorke, A., Busch, Y., Gromer, D., Polak, T., Pauli, P., & Deckert, J. (2017). Medial prefrontal cortex stimulation accelerates therapy response of exposure therapy in acrophobia. Brain Stimulation, 10(2), 291–297. https://doi.org/10.1016/j.brs.2016.11.007

Hikosaka, O., & Isoda, M. (2010). Switching from automatic to controlled behavior: Cortico-basal ganglia mechanisms. Trends in Cognitive Sciences, 14(4), 154–161. https://doi.org/10.1016/j.tics.2010.01.006

Hiser, J., & Koenigs, M. (2018). The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biological Psychiatry, 83(8), 638–647. https://doi.org/10.1016/j.biopsych.2017.10.030

Hopp, H., Troy, A. S., & Mauss, I. B. (2011). The unconscious pursuit of emotion regulation: Implications for psychological health. Cognition and Emotion, 25(3), 532–545. https://doi.org/10.1080/02699931.2010.532606

Ironside, M., Browning, M., Ansari, T. L., Harvey, C. J., Sekyi-Djan, M. N., Bishop, S. J., Harmer, C. J., & O’Shea, J. (2019). Effect of prefrontal cortex stimulation on regulation of amygdala response to threat in individuals with trait anxiety: A randomized clinical trial. JAMA Psychiatry, 76(1), https://doi.org/10.1001/jamapsychiatry.2018.2172

Joormann, J., & Quinn, M. E. (2014). Cognitive processes and emotion regulation in depression. Depression and Anxiety, 31(4), 308–315. https://doi.org/10.1002/da.22264

Joormann, J., & Stanton, C. H. (2016). Examining emotion regulation in depression: A review and future directions. Behaviour Research and Therapy, 86, 35–49. https://doi.org/10.1016/j.brat.2016.07.007

Konrad, A. C., Miu, A. C., Trautmann, S., & Kanske, P. (2025). Neural correlates and plasticity of explicit emotion regulation following the experience of trauma. Frontiers in Behavioral Neuroscience, 19, 1523035. https://doi.org/10.3389/fnbeh.2025.1523035

Kalisch, R., Korenfeld, E., Stephan, K. E., Weiskopf, N., Seymour, B., & Dolan, R. J. (2006). Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network. Journal of Neuroscience, 26(37), 9503–9511. https://doi.org/10.1523/JNEUROSCI.2021-06.2006

Kenwood, M. M., Kalin, N. H., & Barbas, H. (2022). The prefrontal cortex, pathological anxiety, and anxiety disorders. Neuropsychopharmacology, 47(1), 260–275. https://doi.org/10.1038/s41386-021-01109-z

Kerns, J. G., Cohen, J. D., MacDonald III, A. W., Cho, R. Y., Stenger, V. A., & Carter, C. S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 1023–1026. https://doi.org/10.1126/science.1089910

Koole, S. L., & Rothermund, K. (2011). "I feel better but I don't know why": The psychology of implicit emotion regulation. Cognition and Emotion, 25(3), 389–399. https://doi.org/10.1080/02699931.2010.550505

Koole, S. L., Webb, T. L., & Sheeran, P. L. (2015). Implicit emotion regulation: Feeling better without knowing why. Current Opinion in Psychology, 3, 6–10. https://doi.org/10.1016/j.copsyc.2014.12.027

Lapate, R. C., Heckner, M. K., Phan, A. T., Tambini, A., & D’Esposito, M. (2024). Information-based TMS to mid-lateral prefrontal cortex disrupts action goals during emotional processing. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-48015-8

Lei, L., Lai, C. S., Lee, T. M., & Lam, C. L. (2024). The effect of transcranial direct current and magnetic stimulation on fear extinction and return of fear: A meta-analysis and systematic review. Journal of Affective Disorders, 362, 263–286. https://doi.org/10.1016/j.jad.2024.06.060

Li, Y., Li, S., Li, H., Tang, Y., & Zhang, D. (2025). fNIRS neurofeedback facilitates emotion regulation: Exploring individual differences in ventrolateral prefrontal cortex. NeuroImage, 121079. https://doi.org/10.1016/j.neuroimage.2025.121079

Li, S., Chen, J., Gao, K., Xu, F., & Zhang, D. (2023). Excitatory brain stimulation over the left dorsolateral prefrontal cortex enhances voluntary distraction in depressed patients. Psychological Medicine, 53(14), 6646–6655. https://doi.org/10.1017/S0033291723000028

Li, S., Cao, X., Li, Y., Tang, Y., Cheng, S., & Zhang, D. (2024). Enhancing ventrolateral prefrontal cortex activation mitigates social pain and modifies subsequent social attitudes: Insights from TMS and fMRI. NeuroImage, 120620. https://doi.org/10.1016/j.neuroimage.2024.120620

Li, J., Schiller, D., Schoenbaum, G., Phelps, E. A., & Daw, N. D. (2011). Differential roles of human striatum and amygdala in associative learning. Nature Neuroscience, 14(10), 1250–1252. https://doi.org/10.1038/nn.2904

Lieberman, M. D., Eisenberger, N. I., Crockett, M. J., Tom, S. M., Pfeifer, J. H., & Way, B. M. (2007). Putting feelings into words: Affect labeling disrupts amygdala activity in response to affective stimuli. Psychological Science, 18(5), 421–428. https://doi.org/10.1111/j.1467-9280.2007.01916.x

Lincoln, T. M., Schulze, L., & Renneberg, B. (2022). The role of emotion regulation in the characterization, development, and treatment of psychopathology. Nature Reviews Psychology, 1(5), https://doi.org/10.1038/s44159-022-00040-4

Liu, B., Wang, Y., & Li, X. (2018). Implicit emotion regulation deficits in trait anxiety: An ERP study. Frontiers in Human Neuroscience, 12, 382. https://doi.org/10.3389/fnhum.2018.00382

Liu, D. Y., & Thompson, R. J. (2017). Selection and implementation of emotion regulation strategies in major depressive disorder: An integrative review. Clinical Psychology Review, 57, 1–13. https://doi.org/10.1016/j.cpr.2017.07.004

Lonsdorf, T. B., Haaker, J., & Kalisch, R. (2014). Long-term expression of human contextual fear and extinction memories involves amygdala, hippocampus and ventromedial prefrontal cortex: A reinstatement study in two independent samples. Social Cognitive and Affective Neuroscience, 9(12), 1970–1978. https://doi.org/10.1093/scan/nsu018

Lopez-Persem, A., Verhagen, L., Amiez, C., Petrides, M., & Sallet, J. (2019). The human ventromedial prefrontal cortex: Sulcal morphology and its influence on functional organization. Journal of Neuroscience, 39(19), https://doi.org/10.1523/JNEUROSCI.2060-18.2019

Lynch, C. J., Elbau, I. G., Ng, T. H., Wolk, D., Zhu, S., Ayaz, A., Power, J. D., Zebley, B., Gunning, F. M., & Liston, C. (2022). Automated optimization of TMS coil placement for personalized functional network engagement. Neuron, 110(20), 3263–3277.e4. https://doi.org/10.1016/j.neuron.2022.08.012

Mackey, S., & Petrides, M. (2014). Architecture and morphology of the human ventromedial prefrontal cortex. European Journal of Neuroscience, 40(5), 2777–2796. https://doi.org/10.1111/ejn.12654

Marković, V., Vicario, C. M., Yavari, F., Salehinejad, M. A., & Nitsche, M. A. (2021). A systematic review on the effect of transcranial direct current and magnetic stimulation on fear memory and extinction. Frontiers in Human Neuroscience, 15, 655947. https://doi.org/10.3389/fnhum.2021.655947

Maren, S., & Holmes, A. (2016). Stress and fear extinction. Neuropsychopharmacology, 41(1), 58–79. https://doi.org/10.1038/npp.2015.180

Mauss, I. B., Bunge, S. A., & Gross, J. J. (2007a). Automatic emotion regulation. Social and Personality Psychology Compass, 1(1), 146–167. https://doi.org/10.1111/j.1751-9004.2007.00015.x

Mauss, I. B., Cook, C. L., & Gross, J. J. (2007b). Automatic emotion regulation during anger provocation. Journal of Experimental Social Psychology, 43(5), 698–711. https://doi.org/10.1016/j.jesp.2006.07.003

Morrison, S. E., & Salzman, C. D. (2010). Re-valuing the amygdala. Current Opinion in Neurobiology, 20(2), 221–230. https://doi.org/10.1016/j.conb.2010.03.004

Motzkin, J. C., Philippi, C. L., Wolf, R. C., Baskaya, M. K., & Koenigs, M. (2015). Ventromedial prefrontal cortex is critical for the regulation of amygdala activity in humans. Biological Psychiatry, 77(3), 276–284. https://doi.org/10.1016/j.biopsych.2014.02.014

Oathes, D. J., Zimmerman, J. P., Duprat, R., Japp, S. S., Scully, M., Rosenberg, B. M., Flounders, M. W., Long, H., Deluisi, J. A., Elliott, M., Shandler, G., Shinohara, R. T., & Linn, K. A. (2021). Resting fMRI-guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRI. Experimental Brain Research, 239(4), 1165–1178. https://doi.org/10.1007/s00221-021-06036-5

Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9(5), 242–249. https://doi.org/10.1016/j.tics.2005.03.010

Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251(1), E1–E24. https://doi.org/10.1111/j.1749-6632.2012.06751.x

Oyarzún, J. P., Càmara, E., Kouider, S., Fuentemilla, L., & de Diego-Balaguer, R. (2019). Implicit but not explicit extinction to threat-conditioned stimulus prevents spontaneous recovery of threat-potentiated startle responses in humans. Brain and Behavior, 9(1), e01157. https://doi.org/10.1002/brb3.1157

Park, C., Rosenblat, J. D., Lee, Y., Pan, Z., Cao, B., Iacobucci, M., & McIntyre, R. S. (2019). The neural systems of emotion regulation and abnormalities in major depressive disorder. Behavioural Brain Research, 367, 181–188. https://doi.org/10.1016/j.bbr.2019.04.002

Payer, D. E., Baicy, K., Lieberman, M. D., & London, E. D. (2012). Overlapping neural substrates between intentional and incidental down-regulation of negative emotions. Emotion, 12(2), 229–235. https://doi.org/10.1037/a0027421

Phelps, E. A., Delgado, M. R., Nearing, K. I., & LeDoux, J. E. (2004). Extinction learning in humans: Role of the amygdala and vmPFC. Neuron, 43(6), 897–905. https://doi.org/10.1016/j.neuron.2004.08.031

Phillips, M. L., Ladouceur, C. D., & Drevets, W. C. (2008). A neural model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Molecular Psychiatry, 13(9), 829–857. https://doi.org/10.1038/mp.2008.65

Pruessner, L., Barnow, S., Holt, D. V., Joormann, J., & Schulze, K. (2020). A cognitive control framework for understanding emotion regulation flexibility. Emotion, 20(1), 21–29. https://doi.org/10.1037/emo0000658

Qiu, X., He, Z., Cao, X., & Zhang, D. (2023). Transcranial magnetic stimulation and transcranial direct current stimulation affect explicit but not implicit emotion regulation: A meta-analysis. Behavioral and Brain Functions, 19(1), 15. https://doi.org/10.1186/s12993-023-00217-8

Quinn, M. E., Stange, J. P., Jenkins, L. M., Corwin, S., DelDonno, S. R., Bessette, K. L., Welsh, R. C., & Langenecker, S. A. (2018). Cognitive control and network disruption in remitted depression: A correlate of childhood adversity. Social Cognitive and Affective Neuroscience, 13(10), https://doi.org/10.1093/scan/nsy077

Raij, T., Nummenmaa, A., Marin, M. F., Porter, D., Furtak, S., Setsompop, K., & Milad, M. R. (2018). Prefrontal cortex stimulation enhances fear extinction memory in humans. Biological Psychiatry, 84(2), 129–137. https://doi.org/10.1016/j.biopsych.2017.10.022

Rive, M. M., Van Rooijen, G., Veltman, D. J., Phillips, M. L., Schene, A. H., & Ruhé, H. G. (2013). Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 37(10), 2529–2553. https://doi.org/10.1016/j.neubiorev.2013.07.018

Rock, P. L., Roiser, J. P., Riedel, W. J., & Blackwell, A. D. (2014). Cognitive impairment in depression: A systematic review and meta-analysis. Psychological Medicine, 44(10), 2029–2040. https://doi.org/10.1017/S0033291713002535

Roy, M., Shohamy, D., & Wager, T. D. (2012). Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends in Cognitive Sciences, 16(3), 147–156. https://doi.org/10.1016/j.tics.2012.01.005

Saturnino, G. B., Madsen, K. H., & Thielscher, A. (2021). Optimizing the electric field strength in multiple targets for multichannel transcranial electric stimulation. Journal of Neural Engineering, 18(1). https://doi.org/10.1088/1741-2552/abca15

Sergiou, C. S., Santarnecchi, E., Romanella, S. M., Wieser, M. J., Franken, I. H. A., Rassin, E. G. C., & van Dongen, J. D. M. (2022). Transcranial direct current stimulation targeting the ventromedial prefrontal cortex reduces reactive aggression and modulates electrophysiological responses in a forensic population. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(1), https://doi.org/10.1016/j.bpsc.2021.05.007

Silvers, J. A., & Moreira, J. F. G. (2019). Capacity and tendency: A neuroscientific framework for the study of emotion regulation. Neuroscience Letters, 693, 35–39. https://doi.org/10.1016/j.neulet.2017.09.017

Smith, R., & Lane, R. D. (2015). The neural basis of one's own conscious and unconscious emotional states. Neuroscience and Biobehavioral Reviews, 57, 1–29. https://doi.org/10.1016/j.neubiorev.2015.08.003

Snyder, H. R. (2013). Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: A meta-analysis and review. Psychological Bulletin, 139(1), 81–132. https://doi.org/10.1037/a0028727

Sotres-Bayon, F., & Quirk, G. J. (2010). Prefrontal control of fear: More than just extinction. Current Opinion in Neurobiology, 20(2), 231–235. https://doi.org/10.1016/j.conb.2010.02.005

Sydnor, V. J., Cieslak, M., Duprat, R., Deluisi, J., Flounders, M. W., Long, H., Scully, M., Balderston, N. L., Sheline, Y. I., Bassett, D. S., Satterthwaite, T. D., & Oathes, D. J. (2022). Cortical-subcortical structural connections support transcranial magnetic stimulation engagement of the amygdala. Science Advances, 8(25), eabn5803. https://doi.org/10.1126/sciadv.abn5803

Tang, Y., Mo, L., Peng, Z., Li, Y., & Zhang, D. (2025). Causal enhancement of cognitive reappraisal through synchronized dorsolateral and ventrolateral prefrontal cortex activity. Emotion. Advance online publication. https://doi.org/10.1037/emo0001507

Townsend, J. D., Torrisi, S. J., Lieberman, M. D., Sugar, C. A., Bookheimer, S. Y., & Altshuler, L. L. (2013). Frontal-amygdala connectivity alterations during emotion downregulation in bipolar disorder. Biological Psychiatry, 73(2), 127–135. https://doi.org/10.1016/j.biopsych.2012.06.030

Troy, A. S., Shallcross, A. J., Brunner, A., Friedman, R., & Jones, M. C. (2018). Cognitive reappraisal and acceptance: Effects on emotion, physiology, and perceived cognitive costs. Emotion, 18(1), 58–74. https://doi.org/10.1037/emo0000371

Urry, H. L., van Reekum, C. M., Johnstone, T., & Davidson, R. J. (2009). Individual differences in some (but not all) medial prefrontal regions reflect cognitive demand while regulating unpleasant emotion. NeuroImage, 47(3), 852–863. https://doi.org/10.1016/j.neuroimage.2009.05.069

Van't Wout, M., Mariano, T. Y., Garnaat, S. L., Reddy, M. K., Rasmussen, S. A., & Greenberg, B. D. (2016). Can transcranial direct current stimulation augment extinction of conditioned fear? Brain Stimulation, 9(4), 529–536. https://doi.org/10.1016/j.brs.2016.03.004

Van't Wout, M., Longo, S. M., Reddy, M. K., Philip, N. S., Bowker, M. T., & Greenberg, B. D. (2017). Transcranial direct current stimulation may modulate extinction memory in posttraumatic stress disorder. Brain and Behavior, 7(5), e00681. https://doi.org/10.1002/brb3.681

Velasco, E. R., Florido, A., Milad, M. R., & Andero, R. (2019). Sex differences in fear extinction. Neuroscience and Biobehavioral Reviews, 103, 81–108. https://doi.org/10.1016/j.neubiorev.2019.05.020

Vicario, C. M., Salehinejad, M. A., Avenanti, A., & Nitsche, M. A. (2020). Transcranial direct current stimulation (tDCS) in anxiety disorders. In Non-Invasive Brain Stimulation in Psychiatry and Clinical Neurosciences (pp. 421–438). Springer.

Vilgis, V., Silk, T. J., & Vance, A. (2015). Executive function and attention in children and adolescents with depressive disorders: A systematic review. European Child & Adolescent Psychiatry, 24(4), 365–384. https://doi.org/10.1007/s00787-015-0675-7

Wang, H. Y., Xu, G. Q., Ni, M. F., Zhang, C. H., Sun, X. P., Chang, Y., & Zhang, B. W. (2017). Neural mechanisms of implicit cognitive reappraisal: Preceding descriptions alter emotional response to unpleasant images. Neuroscience, 347, 65–75. https://doi.org/10.1016/j.neuroscience.2017.01.047

Wang, H. Y., Xu, G. Q., Ni, M. F., Zhang, C. H., Sun, X. P., Chang, Y., & Zhang, B. W. (2021). Neural basis of implicit cognitive reappraisal in panic disorder: An event-related study. Journal of Translational Medicine, 19(1), https://doi.org/10.1186/s12967-021-02968-2

Wang, H. Y., You, H. L., Song, C. L., Zhou, L., Wang, S. Y., Li, X. L., Liang, Z. H., & Zhang, B. W. (2024). Shared and distinct prefrontal cortex alterations of implicit emotion regulation in depression and anxiety: An fNIRS investigation. Journal of Affective Disorders, 354, 126–135. https://doi.org/10.1016/j.jad.2024.03.032

Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological Bulletin, 138(4), 775–808. https://doi.org/10.1037/a0027600

Wik, G., Elbert, T., Fredrikson, M., Hoke, M., & Ross, B. (1997). Magnetic brain imaging of extinction processes in human classical conditioning. Neuroreport, 8(9–10), 2245–2249. https://doi.org/10.1097/00001756-199705060-00044

Williams, L. E., Bargh, J. A., Nocera, C. C., & Gray, J. R. (2009). The unconscious regulation of emotion: Nonconscious reappraisal goals modulate emotional reactivity. Emotion, 9(6), 847–854. https://doi.org/10.1037/a0017745

Wyczesany, M., Adamczyk, A. K., Ligeza, T. S., Bereś, A., & Marchewka, A. (2021). Implicit induction of emotional control—A comparative fMRI investigation of self-control and reappraisal goal pursuit. Emotion, 21(7), 1379–1391. https://doi.org/10.1037/emo0000852

Xie, Y., Hu, Z., Ma, W., Sang, B., & Wang, M. (2019). Different neural correlates of automatic emotion regulation at implicit and explicit perceptual level: A functional magnetic resonance imaging study. I-Perception, 10(1). https://doi.org/10.1177/2041669519831028

Yang, Q., Tang, P., Gu, R., Luo, W., & Luo, Y. (2015). Implicit emotion regulation affects outcome evaluation. Social Cognitive and Affective Neuroscience, 10(6), 824–831. https://doi.org/10.1093/scan/nsu124

Yuan, J., Long, Q., Li, X., Deng, Z., Ma, B., Chen, S., & Yang, J. (2019). Regulatory effect of implicit acceptance during outcome evaluation: The temporal dynamics in an event-related potential study. International Journal of Psychophysiology, 141, 37–44. https://doi.org/10.1016/j.ijpsycho.2019.05.003

Yuan, J., Zhang, Y., Zhao, Y., Gao, K., Tan, S., & Zhang, D. (2022). The emotion-regulation benefits of implicit reappraisal in clinical depression: Behavioral and electrophysiological evidence. Neuroscience Bulletin. https://doi.org/10.1007/s12264-022-00973-z

Zabik, N. L., Peters, C., Iadipaolo, A., Marusak, H. A., & Rabinak, C. A. (2023). Comparison of behavioral and brain indices of fear renewal during a standard vs. novel immersive reality Pavlovian fear extinction paradigm in healthy adults. Behavioural Brain Research, 437, 114154. https://doi.org/10.1016/j.bbr.2022.114154

Zhang, Q., Chen, T., Liu, S., Liu, X., Zhang, Y., Yu, F., Ji, G. J., Li, X., & Zhu, C. (2023). Effects of high-definition transcranial direct current stimulation on implicit emotion regulation of social pain in healthy individuals. Journal of Affective Disorders, 338, 74–82. https://doi.org/10.1016/j.jad.2023.05.075

Zhang, Y., Chen, S., Deng, Z., Yang, J., & Yuan, J. (2020). Benefits of implicit regulation of instructed fear: Evidence from neuroimaging and functional connectivity. Frontiers in Neuroscience, 14, 201. https://doi.org/10.3389/fnins.2020.00201

Zhang, Y., Li, S., Gao, K., Li, Y., Yuan, J., & Zhang, D. (2023). Implicit, but not explicit, emotion regulation relieves unpleasant neural responses evoked by high-intensity negative images. Neuroscience Bulletin, 39(8), 1278–1288. https://doi.org/10.1007/s12264-023-01036-7

Zhang, Y., Shi, W., Wang, H., Liu, M., & Tang, D. (2021). The impact of acute exercise on implicit cognitive reappraisal in association with left dorsolateral prefrontal activation: A fNIRS study. Behavioural Brain Research, 406, 113233. https://doi.org/10.1016/j.bbr.2021.113233

Zhu, C., Li, P., Li, Y., Jiang, Y., Liu, D., & Luo, W. (2022). Implicit emotion regulation improves arithmetic performance: An ERP study. Cognitive, Affective, & Behavioral Neuroscience, 22(3), 574–585. https://doi.org/10.3758/s13415-021-00979-6

Zilverstand, A., Parvaz, M. A., & Goldstein, R. Z. (2017). Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation: A systematic review. NeuroImage, 151, 105–116. https://doi.org/10.1016/j.neuroimage.2016.06.009