Successful Emotional Priming in Virtual Reality was Not Modulated by tDCS and Did Not Affect Time Perception

doi:10.21203/rs.3.rs-4923641/v1

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Successful Emotional Priming in Virtual Reality was Not Modulated by tDCS and Did Not Affect Time Perception

https://doi.org/10.21203/rs.3.rs-4923641/v1

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This study investigates the effects of emotional priming in virtual reality (VR) on time perception using a temporal bisection task and the potential effect of transcranial direct current stimulation (tDCS) over the right ventromedial prefrontal cortex (vmPFC) in hindering emotional processing. Fifty-three participants underwent active anodal or sham tDCS on the right vmPFC while exposed to neutral or fear-inducing VR videos. The participants then completed a temporal bisection task. The study measured arousal and valence through self-report questionnaires and psychophysiological measures (heart rate, heart rate variability, electrodermal activity). The results indicate that VR priming was effective in producing changes in arousal and valence, but this had no impact on time perception. Also, tDCS did not modulate the relationship between priming and time perception. These findings show the viability of using VR to generate emotional states, but these may not always produce changes in time perception. tDCS, as applied according to our protocol, also seemed unable to regulate fear processing.

tDCS

emotional priming

virtual reality

time perception

psychophysiology

Some studies have shown that emotions can alter our perception of time (Droit-Volet & Meck, 2007; Özgör et al., 2018). This may be due to an alteration of the speed of the pacemaker component of our internal clock as proposed by the Scalar Expectancy Model of time perception (Gibbon et al., 1984; Wearden, 1991, 2003; Wearden & Ferrara, 1996). This model posits that humans possess a pacemaker that generates pulses at a constant rate that are then stored in an accumulator. During a timing task, a given number of pulses can be compared to the number of pulses stored in reference memory and a decision arises. The pacemaker rhythm is variable and sensitive to emotional states, stimuli and content, and in particular to stimuli arousal, which increases are associated with an increased pacemaker speed (Cui et al., 2023; Volkinburg & Balsam, 2014). The resulting greater number of pulses being generated leads to overestimation of durations compared to neutral conditions.

Although time perception can be studied in several paradigms (Hancock & Block, 2012; Ünver, 2023), an established task that can be used to investigate biases in time perception is the temporal bisection task (Carvalho et al., 2019; Grommet et al., 2019; Kopec & Brody, 2010). In such a task, participants are first presented with reference standard durations, usually by presenting the same stimulus for a short period of time (e.g., 300ms) and for a long period of time (e.g., 1300ms), so that they learn these standards. After successfully learning these, comparison stimuli are presented. These are presented in various lengths between the two learnt standard durations (e.g., 500, 700, 900, and 1100ms). Participants’ task is to determine whether the duration of each stimulus is closest to the short or to the long standard duration. By fitting a psychometric curve to this data, it is possible to determine the bisection point, or point of subjective equivalence (PSE), in which participants are as likely to respond “short” as “long”. A shift to the bisection function to the left shows an overestimation of duration, whereas a shift to the right shows a relative underestimation.

Regarding the elicitation of emotion, various paradigms and tasks have also been used including affective or emotional priming (Avero & Calvo, 2006; Brunet, 2023; Chen et al., 2020; Kang et al., 2021; Klauer, 1998; Lohse & Overgaard, 2019; Schräder et al., 2023) and employing different stimuli modalities such as auditory (Degner, 2011; Lin & Liang, 2023) and visual stimuli (Maureira et al., 2015; Zhang et al., 2006). This was done to such an extent that several databases exist providing researchers with validated stimuli for their own studies, such as the International Affective Picture System or IAPS for pictures (Bradley & Lang, 2007; Branco et al., 2023), the International Affective Digitized Sounds or IADS (Bradley, 1999; Yang et al., 2018) and the Oxford Vocal Sounds database or OxVoc (Parsons et al., 2014) for sounds, the Emotional Movie Database or EMDb for films (Carvalho et al., 2012) and, recently, several virtual reality databases (Dozio et al., 2021; Gnacek et al., 2024; Li et al., 2017; Mancuso et al., 2024). There is some research showing the feasibility of affective priming in virtual reality (Burattini et al., 2023; Faita et al., 2016; Lipp et al., 2021; Somarathna et al., 2023). Although measuring emotions accurately in virtual reality can be challenging (Bastida et al., 2024; Marín-Morales et al., 2020), this can be done with wearable sensors (Marín-Morales et al., 2018; Rahmani et al., 2024) when participants are required to be mobile during the experiment but also with standard laboratory equipment when they are not (Hinkle et al., 2019; Tabbaa et al., 2022). Studies show that virtual reality experiences produce a higher sense of presence or immersion (Lønne et al., 2023; Schöne, Kisker, Lange, et al., 2023; Servotte et al., 2020; Wilkinson et al., 2021) and equivalent (Chirico & Gaggioli, 2019; Rivu et al., 2021) or more pronounced emotional responses (Estupiñán et al., 2014; Hidaka et al., 2017; Higuera-Trujillo et al., 2017; Schöne, Kisker, Sylvester, et al., 2023) especially in the case of fear (Diemer et al., 2015; Liao et al., 2019).

Emotional priming has been paired with time perception tasks (Gros et al., 2016). Findings suggest that priming individuals to positive and negative emotions can alter our perception of time due to the heightened arousal of non-neutral stimuli (Droit-Volet & Gil, 2016; Droit-Volet & Meck, 2007; Lehockey et al., 2018; Ma et al., 2021) leading to time overestimations for emotional stimuli. This seems compatible with an arousal-based timing mechanism in humans rather than an attentional one (Ma et al., 2021), which is explained by the SET model presented above. At any rate, a time-perception model must consider attentional, arousal, and memory components, especially when utilizing biologically relevant stimuli that may capture attention away from competing nonbiologically relevant stimuli with similar arousal levels (Lake, 2016; Lake et al., 2016; Ohman et al., 2001). Studies have shown that aversive stimuli lead to duration overestimation (Dirnberger et al., 2012), especially when stimuli are anger-inducing (Wang et al., 2024) or fear-inducing (Fayolle et al., 2015; Grommet et al., 2011), an effect that increases with arousal despite some variation existing among stimuli with similar levels of arousal (Gil & Droit-Volet, 2012). Other studies have shown that emotional states can lead to a time-drag effect, that is, a perceived slowing down of the flow of time that is opposite to a time-flying effect, the perception of time passing faster (Li & Yuen, 2015), and that other factors such as perceptual complexity can also influence time perception (Folta-Schoofs et al., 2014). Specifically, when a bisection task is used, studies have shown fearful or threatening stimuli (Droit-Volet & Gil, 2016; Fayolle et al., 2015; Grommet et al., 2019; Tipples, 2011, 2019) and anger stimuli (Gil & Droit-Volet, 2011; Tipples, 2008) leading to a shift in the bisection curve to the left with lower bisection points corresponding to an overestimation of duration. Positive emotions produce conflicting results with overestimations and underestimations appearing in the literature (Colonnello et al., 2016; Droit-Volet et al., 2010), and a similar effect can be found for negative stimuli when low arousal and high arousal stimuli are compared (Angrilli et al., 1997; McManus et al., 2024) with low arousal negative stimuli being underestimated and high arousal negative stimuli being overestimated compared to positive stimuli. A recent meta-analysis (Cui et al., 2023) has shown that, in general, stimuli of negative valence and of high arousal tend to result in overestimations compared to positive valence and low arousal. This effect, however, can depend on stimuli modality and temporal paradigm. In addition, while a recent study has found it feasible to employ fear priming in virtual reality for the purposes of studying time perception (Kitajima et al., 2022), it remains unclear what were the effects of fear as invoked in virtual reality on time perception. This is particularly important considering that a different study has shown that a VR experience, compared to a non-VR but similar experience, caused by itself changes in time perception (Bogon et al., 2024). Indeed, while activity in VR may not cause changes in time perception, stimuli’s spatial characteristics may do so (Read et al., 2023).

There are a few that studies that have explored the patterns of brain activity during time perception tasks (Radua et al., 2014; Üstün et al., 2017) and others that have explored the patterns of brain activity after emotional priming (Suslow et al., 2013). However, existing literature that has simultaneously explored activations during time perception tasks after priming is scarce. Some studies have identified critical structures and patterns in emotional processing (Bush et al., 2018) and time perception (Coull et al., 2004; Fontes et al., 2016; Meck, 2005). One such is the dorsolateral prefrontal cortex (DLPFC), a structure that is also known to participate in the regulation of negative emotions, of which fear is a particular case (Sotres-Bayon & Quirk, 2010), together with the left lateral prefrontal, dorsal medial prefrontal, left rostral medial prefrontal, posterior cingulate, and orbital prefrontal cortices (Ochsner et al., 2004). This structure is also involved in time perception both in humans (Smith et al., 2003; Tregellas et al., 2006) and in other primates (Onoe et al., 2001). Some studies have shown that the ventromedial prefrontal cortex (vmPFC) also contributes to emotional regulation by encoding emotional stimuli and by regulating anxiety and fear (Battaglia et al., 2022; Delgado et al., 2008; Diekhof et al., 2011; Gonzalez & Fanselow, 2020; Suzuki & Tanaka, 2021) and processing higher-order reward (Kroker et al., 2022, 2024). Crucially, this area does not seem to be associated with time perception other than mental time travel and future thinking (Bertossi et al., 2016; Ciaramelli et al., 2021).

With non-invasive neuromodulation techniques such as tDCS or transcranial magnetic stimulation (TMS) it is possible to manipulate the neutral activity of target areas to enhance or hinder emotional regulation (Albein-Urios et al., 2023; Choi et al., 2016; Clarke et al., 2020, 2021; De Smet et al., 2023; Qiu et al., 2023; Trémolière et al., 2018) or to alter time perception (Jones et al., 2004; Koch et al., 2004; Méndez et al., 2017; Vicario et al., 2013). Even though the underlying mechanisms of tDCS are still not fully understood, typically, because anodal stimulation depolarizes neurons and thus increases the probability of action potentials to occur and cathodal stimulation does the opposite (Nitsche et al., 2008, 2015; Utz et al., 2010), protocols place the anode over the area they wish to excite or the cathode in the area they wish to inhibit. Recent studies have indeed found some modulating effects of tDCS on vmPFC (Boehme et al., 2024; Roesmann et al., 2022), with anodal stimulation of the right vmPFC showing the most promise in preventing fear from being processed (Abend et al., 2016; Lei et al., 2024).

To our knowledge, no one has explored whether changing neural activity using tDCS impacts the effect of VR emotional priming on time perception in a region mostly associated with emotional processing but not time perception such as the vmPFC. Thus, the aim of the current work is to investigate the impact of VR emotional priming on a specific time perception task, the temporal bisection, and to explore potential modulating effects of tDCS over the vmPFC on the relationship between emotional priming and time perception. Specifically, we expect: a) Participants’ heart-rate, heart-rate variability, electrodermal activity and self-reported arousal will increase during the exposure a fear-inducing VR video while self-reported valence will decrease; b) Conversely, those in the neutral VR condition will show no difference in those measures compared to their baseline; c) Participants’s points of subjective equivalence will be lower in the fear-inducing VR condition compared to the neutral VR condition; d) The effects of priming will be prevented for those participants that were submitted to active tDCS.

Participants

Fifty-three undergraduate and graduate students from an institution of higher education in Lisbon, Portugal (10 men and 43 women, between 18 and 56 years of age (M = 24.25, SD = 8.28)), took part in this study in exchange for course credit. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra, Portugal. Written signed informed consent was obtained from all participants included in the study.

Materials and procedure

After arriving at the lab, the participants were informed about the study and signed an informed consent. They then answered a socio-demographic questionnaire and filled the Discrete Emotions Questionnaire (DEQ: Harmon-Jones et al., 2016) and the Self-Assessment Manikin (SAM: Bradley & Lang, 1994) while undergoing a neurostimulation session through tDCS. During the time remaining to finish the tDCS session, participants were asked to relax while the experimenter placed the electrodes and transducers for psychophysiological data collection (see below under Psychophysiological Measures). At least a baseline of three minutes was recorded prior to the next phase of the experiment. Afterwards, participants viewed two videos in VR, and finally performed a computerised temporal bisection task (Fig. 1).

tDCS Session

Participants were submitted to a single 20-minute offline tDCS session using a battery-powered tDCS device (Brain Premier E1 Plus, Yingchi Technology, Shenzhen, PRC), 11.4 cm² round electrodes and saline-infused sponges. This stimulation was either active (with current flowing during the 20-min session) or sham (with current flowing only during a 60-second period at the beginning of the 20-min period). As a method for blinding, electrode placement was similar for participants in the sham group, however active tDCS was only flowing for one minute (starting with 30s of current ramp-up and ending with 30s of current ramp-down). Stimulation was set at a constant current of 2 mA. Electrodes were placed billateraly over the vmPFC with the anode placed over the right vmPFC (AF4 on the 10/10 system) and the cathode over the left vmPFC (AF3 on the 10/10 system) (Fig. 2). This protocol follows good practices in tDCS stimulation (Thair et al., 2017), a technique that is highly dependent on particular settings and protocol changes.

VR priming

After stimulation, participants were presented with two 360-degree videos in VR using a head-mounted display (Meta Quest 2, Meta Reality Labs, Menlo Park, CA, USA). The first video was a neutral video, and the second video was either a neutral or a fearful video depending on the condition. The three videos were chosen according to validated levels of arousal and valence (Li et al., 2017) and availability in YouTube VR. These were “Spangler Lawn” (M_Valence = 5.09, M_Arousal = 3.27), always shown to participants as Video 1; “Blyde Canyon” (M_Valence = 4.82, M_Arousal = 3.09) as Video 2 to participants in the neutral condition; and “Zombie Apocalypse Horror” (M_Valence = 3.20, M_Arousal = 5.60) as Video 2 to participants in the fear condition. After watching both videos, participants filled once more then DEQ and the SAM reporting to both VR videos.

Temporal Bisection Task

The timing task was programmed using E-Prime 2 for Windows (Psychology Software Tools, Pittsburgh, PA, USA) and presented on a 24-inch monitor with a resolution of 1920x1080 resolution. Participants provided their responses using a standard USB keyboard. The task consisted in three distinct phases, the first being the learning stage, the second the practice stage, and the third the testing stage. In all stages, after a red fixation cross was presented for 500ms the stimulus - a grey rectangle - was shown. Either after a response (practice and testing stages) or an elapsed amount of time (learning stage), a blank white screen was presented for 1000ms, after which a new trial began. In the learning stage this was repeated six times, three in which the stimulus was presented for a short period of time (300ms) and three for a long period of time (1100ms) in a randomised fashion. The participants' task during this phase was to learn the duration of the standard short and standard long stimuli and therefore asked not to provide a behavioural response. In the practice stage, participants were presented with the same stimuli five times for each duration and had to respond, using the keyboard, whether they judged the duration of each stimulus to be short, by pressing the “F” key, or long, by pressing the “J” key. As the stimuli duration was the same as in the previous phase, we have provided feedback to guarantee participants had learned the standard durations. There was no time constraint for participants to provide their response. All participants had a precision of at least 90%. In the testing stage, participants were presented with durations ranging from 100 to 1300ms in 200ms intervals (thus, 100, 300, 500, 700, 900, 1100, and 1300ms) so as to include the standard durations, one duration 200ms shorter than the short standard, one duration 200ms longer than the long duration, and three intermediate durations. For each stimulus, participants had to respond using the keyboard to see whether they considered the duration of the stimulus to be the closest to the short, pressing the “F” key, or closest to the long duration, pressing the “J” key (Fig. 3).

Self-assessment measures

As mentioned above, participants filled both a discrete emotions questionnaire, the Discrete Emotions Questionnaire (DEQ) (Harmon-Jones et al., 2016b), and a dimensional emotions scale, the Self-Assessment Manikin (SAM) (Bradley & Lang, 1994). The DEQ is a self-assessment questionnaire in which participants indicate their emotional state. For each of 32 emotional words - such as wanting, easy-going, or lonely -, participants respond to the extent in which they are experiencing that “emotion” on a Likert-type scale of one, not at all, to seven, an extreme amount. Items are grouped in eight factors corresponding to the emotions of anger, disgust, fear, anxiety, sadness, desire, relaxation, and happiness. The questionnaire was translated and validated in the Portuguese population by our team, and the results will be published elsewhere. Although not widely use, it has some advantages over more established scales such as PANAS (Watson et al., 1988), namely the ability to discriminate between specific discrete emotions (Harmon-Jones et al., 2016a, b). The SAM is a non-verbal pictorial self-assessment tool that measures three dimensions of emotion: valence, arousal, and dominance. Dominance was not used due to its inconsistency (Libkuman et al., 2007) and because valence and arousal are sufficient to describe emotional states, especially when using the Circumplex Model of Affect and Core Affect frameworks (Posner et al., 2005; Russell & Barrett, 1999). Using this tool, participants indicate their emotional state by selecting from a series of five humanoid figures representing different levels of each dimension. Figures in the valence dimension vary from a frowning unhappy face (low valence) to a smiling happy face (high valence). Figures in the arousal dimension range from a calm, sleepy figure (low arousal) to an excited, wide-eyed figure (high arousal). In both cases, intermediate figures show varying levels of valence and arousal between the two extremes, and participants can respond on a 9-point scale by selecting one figure or the interval between two figures. This tool was used in both VR (Burattini et al., 2023; Liao et al., 2020; Ortiz & Elizondo, 2023; Xie et al., 2020) and emotional priming experiments (Lohse & Overgaard, 2019; Zhu & Takeda, 2023) with success.

Psychophysiological measures

We have measured participants’ electrodermal activity (EDA) and both heart rate and heart rate variability through an electrocardiogram (ECG). We used the BIOPAC MP150 system with GSR100C and ECG100C amplifiers to collect these data (BIOPAC Systems, Goleta, California, USA) at a sample rate of 1000 Hz. Acqknowledge from BIOPAC Systems was used to both collect and pre-process data. To collect EDA, we have placed two reusable transducers on the medial phalanges of the middle and index fingers of the nondominant hand of the participants via Velcro bands. To collect cardiac measurements, we placed disposable wet Ag/AgCl electrodes according to a Lead III montage (ground electrode on the right ankle of the participants, positive electrode on their left ankle, and negative electrode under their right clavicle). While physiological data was recorded participants were asked to remain as still as possible and in a relaxed position. While exploring in VR, participants were asked not to use their nondominant hand while a controller was placed on their dominant hand. Prior to processing, EDA data were resampled to 62.5 samples/second by linear interpolation, median smoothed at 62 samples, and a low-pass filter of 1 Hz was used. For ECG data a FIR band-pass filter was used with a low frequency cut-off of 1 Hz and a high frequency cut-off of 35 Hz. A threshold of fixed 2 mV upward peak direction was used to detect cycles with intervals between 50 and 130 bpm, and a baseline window of 25ms was used.

Analysis Strategy

Our main between-subjects independent variables are tDCS condition (sham vs. active) and VR condition (neutral vs. fear) and our main within-subjects independent variable is Moments (baseline, after video 1, after video 2). Because Moments is defined by VR condition (those in the neutral condition are presented with neutral videos in both moments while those in the fear condition are presented first with a neutral video but afterwards are presented with a fear video), main effects of Moments should not be taken into consideration. To simplify, we have named these moments Moment 1 (baseline), Moment 2 (after first video), and Moment 3 (after second video). Our main dependent variables are arousal measured by heart rate variability (in milliseconds, ms), heart rate (in beats per minute, bpm), electrodermal activity (in microsiemens, µS) and self-report (using the SAM); valence (self-reported using the SAM) and the bisection point or point of subjective equality (PSE, in milliseconds) as determined by fitting a psychometric curve to the data. This fit was obtained using PRISMA 10.2.3 for macOS (GraphPad Software, Boston, Massachusetts, USA). A one-way repeated measures ANOVA was conducted to determine group differences and interactions across moments in valence and arousal. A one-way analysis of variance was performed to determine the differences between groups in time perception. These analyses were conducted in SPSS Statistics 29 for macOS.

Arousal and valence

There was a main effect of Moment on heart rate variability, F(1.121, 52.674) = 4.33, p = .04, η² = .08; heart rate, F(1.580, 74.256 = 34.55, p < .001, η² = .42; electrodermal activity, F(1.595, 74.944) = 43.66, p < .001, η² = .48; and self-reported arousal, F(1.598, 75.128 = 20.70, p < .001, η² = .31. There was no main effect of Moment on self-reported valence, F(2, 94) = 0.62, p = .31, η² = .01. Post-hoc tests for the different Moments and measures showed a decrease in heart rate between Moment 1 (M = 83.35, SE = 1.22) and Moment 2 (M = 77.67, SE = 1.22), p < .001; a decrease in heart rate between Moment 1 and Moment 3 (M = 79.56, SD = 1.27), p < .001; and an increase in heart rate between Moment 2 and Moment 3, p = .001. For electrodermal activity, there was an increase between Moment 1 (M = 2.48, SE = 0.20) and Moment 2 (M = 3.01, SE = .21), p < .001; an increase between Moment 1 and Moment 3 (M = 2.83, SD = 0.21), p < .001; and a decrease between Moment 2 and Moment 3, p < .001. Regarding self-reported arousal, there was an increase between Moment 1 (M = 3.70, SE = .20) and Moment 3 (M = 5.11, SE = .28), p < .001; and an increase between Moment 2 (M = 3.38, SE = .22) and Moment 3, p < .001. No other differences were found for self-reported valence, .94 < p < .99., and for heart rate variability, .10 < p < .99.

There was also a main effect of the VR condition but only on self-reported valence, F(1, 47) = 4.21, p = .05, η² = .08. There were no additional main effects of VR condition on other measures, .45 < p < .94; and no main effects of tDCS condition, .09 < p < .94. Post hoc tests showed that regardless of moment, participants in the neutral VR condition reported higher valence (M = 6.31, SE = 0.14) compared to those in the fear VR condition (M = 5.90, SE = .15), p = .05.

There was an interaction effect between Moment and tDCS condition on electrodermal activity only, F(1.595, 74.944) = 4.59, p = .01, η² = .09. No other interaction effects were found, .30 < p < .98. Post-hoc tests revealed no significant differences, only a marginally significant difference for self-reported valence for Moment 3 (M_Sham = 5.60, SE_Sham = 0.29; M_Active = 6.34, SE_Active = 0.27), p = .07.

There were interaction effects between Moment and VR condition on all measures, namely heart rate variability, F(1.121, 52.674) = 3.77, p = .03, η² = .07; heart rate, F(1.580, 74.944) = 3.06, p = .05, η² = .06; electrodermal activity, F(1.595, 74.944) = 7.46, p < .001, η² = .14; self-reported valence, F(2, 94) = 34.98, p < .001, η² = .43; and self-reported arousal, F(1.598, 75.128 = 19.18, p < .001, η² = .29. Post-hoc tests showed a statistically significant difference for self-report valence and arousal in Moment 2 and in Moment 3. In Moment 2, those in the neutral VR condition rated their valence as higher (M = 5.43, SE = 0.25) than those in the fear VR condition (M = 7.06, SE = 0.27), p < .001; the arousal of those in the neutral VR condition was also rated as higher (M = 4.33, SE = 0.31) than of those in the fear VR condition (M = 2.42, SE = 0.33), p < .001. Regarding Moment 3, those in the neutral VR condition rated their valence as higher (M = 7.26, SE = 0.27) than those in the fear VR condition (M = 4.68, SE = 0.29), p < .001; and those in the neutral VR condition rated their arousal as lower (M = 4.29, SE = 0.38) than those in the fear VR condition (M = 5.92, SE = 0.40), p = .01.

Further post hoc tests showed additional differences between moments for each VR condition and each measure. Those in the neutral VR condition showed a decrease in heart rate variability from Moment 1 (M = 9.61, SE = 0.36) to Moment 2 (M = 9.06, SE = 0.32), p = − .02, from Moment 1 to Moment 3 (M = 9.02, SE = 0.30), p = .01, but not from Moment 2 to Moment 3, p = 1.00; those in the fear VR condition showed no change in heart rate variability between moments. Those in the neutral VR condition also showed a decrease in heart rate from Moment 1 (M = 83.68, SE = 1.67) to Moment 2 (M = 78.78, SE = 1.68), p < .001; from Moment 1 to Moment 3 (M = 78.95, SE = 1.75), p < .001; but there was no difference between Moment 2 and Moment 3, p = 1.00. As for those in the fear VR condition there was a decrease in heart rate from Moment 1 (M = 83.03, SE = 1.77) to Moment 2 (M = 76.56, SE = 1.77), p < .001; but an increase from Moment 2 to Moment 3 (M = 80.17, SE = 1.85), p < .001. No differences were found between Moment 1 and Moment 3, p = .06. Regarding electrodermal activity, for those in neutral VR condition there was an increase from Moment 1 (M = 2.59, SE = 0.27) to Moment 2 (M = 2.93, SE = 0.29), p < .001; a decrease from Moment 2 to Moment 3 (M = 2.75, SE = 0.29), p = .01; but no difference between Moment 1 and Moment 3, p = .30. As for those in the fear VR condition, there was an increase from Moment 1 (M = 2.36, SD = 0.29) to Moment 2 (M = 3.09, SD = 0.31), p < .001; a decrease from Moment 2 to Moment 3 (M = 2.92, SD = 0.30), p = .01; and an increase from Moment 1 to Moment 3, p < .001. As for self-reported valence, for those in the neutral VR condition there was no difference between Moment 1 (M = 6.25, SD = 0.20) and Moment 2 (M = 5.43, SD = 0.25), p = .05; but there was an increase between Moment 2 and Moment 3 (M = 7.26, SD = 0.27), p < .001; and also an increase between Moment 1 and Moment 3, p = .01. As for those in the fear VR condition, there was an increase in valence between Moment 1 (M = 5.97, SD = 0.21) and Moment 2 (M = 7.06, SD = 0.27), p = .01, but a decrease between Moment 2 and Moment 3 (M = 4.68, SD = 0.29), p < .001; there was also a decrease between Moment 1 and Moment 3, p < .001. Finally, for self-reported arousal, there were no changes for those in the neutral VR condition; for those in the fear VR condition, there was a decrease from Moment 1 (M = 3.61, SD = 0.29) to Moment 2 (M = 2.42, SD = 0.33), p = .01; an increase from Moment 2 to Moment 3 (M = 5.92, SD = 0.40), p < .001; and also an increase from Moment 1 to Moment 3, p < .001.

Finally, there was a three-way interaction effect between Moment, tDCS condition and VR condition on electrodermal activity, F(1.595, 74.944) = 3.63, p = .03, η² = .07. However, no significant post-hoc tests appeared as significant, .11 < p < .99.

Emotion ratings

Overall, the participants reported high levels of happiness and relaxation and low levels of anger, sadness, disgust, and fear throughout the experiment (Fig. 4). Crucially, it is important to determine changes in those ratings by moment and condition.

There was no main effect of tDCS condition on any emotion across moments, .09 < p < .96. There was a main effect of VR condition on anger, F(1, 49) = 9.48, p < .01, η² = .16, desire, F(1, 49) = 4.88, p < .05, η² = .09, happiness, F(1, 49) = 7.16, p = .01, η² = .13, and fear, F(1, 49) = 4.37, p < .05, η² = .08. There were no interaction effects between tDCS and VR conditions.

Pairwise comparisons showed a significant change in anger ratings for the fear VR condition between baseline (M = 1.12, SD = 0.30) and second video (M = 1.58, SD = 0.26), p < .001, and between the first (M = 1.00, SD = 0.17) and the second video, p < .001. There was also a significant change in desire ratings for the neutral VR condition between baseline (M = 2.52, SD = 1.09) and second video (M = 3.38, SD = 1.26), p < .01, and between the first (M = 2.33, SD = 1.08) and the second videos, p < .001. The happiness scores changed for the neutral VR condition between the baseline (M = 3.62, SD = 1.44) and the second video (M = 4.53, SD = 1.32), p = .01, and between the first (M = 3.02, SD = 1.48) video and the second video, p < .001. These ratings also changed for the fear VR condition between baseline (M = 3.59, SD = 1.07) and second video (M = 2.01, SD = 1.29), p < .001, between first (M = 3.57, SD = 1.23) and second videos, p < .001, and between baseline and second videos, p < .001. Fear ratings also changed for the fear VR condition between the baseline (M = 1.13, SD = 0.41) and the second video (M = 2.50, SD = 1.64), p < .001, and between the first (M = 1.04, SD = 0.12) and second videos, p < .001.

Effects on time perception

First, psychometric curves were fitted to the data across conditions (Fig. 5). Despite differences in average PSE (Sham + Neutral: M = 615.43, SD = 129.47; Sham + Fear: M = 588.12, SD = 155.25; Active + Neutral: M = 617.25, SD = 162.54; and Active + Fear: M = 670.59, SD = 146.23)), no significant differences were found, F(3, 51) = 0.69, p = .57, η² = .04 (Fig. 6).

The experiment revealed changes in both arousal and valence through psychophysiological measures and self-report ratings. Participants experienced a decrease in heart rate from baseline to the first video, followed by a slight increase from the first video to the second video. Electrodermal activity showed an overall increase from baseline to the first video and a slight decrease from the first video to the second video. Self-reported arousal increased progressively throughout the experiment. These changes indicate that participants became more physiologically aroused and their bodily responses increased over time regardless of condition. While this highlights the problem of trying to employ “neutral” stimuli – which is part of a broader discussion on “neutrality” (Silva, 2018) – in experimental studies (Davidovic et al., 2019; Kesler/West et al., 2001; Lee et al., 2008; Potvin et al., 2016; Rohrbeck et al., 2023; Tae et al., 2020), it also hints at the mere effects of VR enviornments in the sense of presence and emotionality (Diemer et al., 2015; Lemmens et al., 2022; Tian et al., 2022) regardless of valence. This seems particularly true for the most common neutral stimuli in video, nature (Browning et al., 2020; Wang et al., 2024). VR condition significantly affected self-reported valence, with participants in the fear VR condition reporting lower valence compared to those in the neutral VR condition. The fearful VR environment appeared to foster a more negative emotional state compared to the neutral environment. Interaction effects between moments and VR conditions were observed across all measures, highlighting how participants' physiological and self-reported responses varied with the VR context over time. In the neutral VR condition, the participants showed a decrease in heart rate variability and heart rate from baseline to the first video, but no significant changes thereafter. Their electrodermal activity increased from baseline to the first video and decreased slightly by the second video. The self-reported valence under neutral conditions did not change significantly from baseline to the first video, but increased with the second video. In contrast, those in the fear VR condition experienced an increase in electrodermal activity and self-reported arousal between baseline and the second video, indicating heightened physiological and emotional responses to the fear-inducing environment. This is expected, considering that VR is a feasible medium to induce emotions (Burattini et al., 2023; Liao et al., 2019; Lipp et al., 2021; Lohse & Overgaard, 2019; Somarathna et al., 2023). In summary, the experiment demonstrated that VR conditions significantly influenced participants' physiological arousal and emotional valence. A neutral VR condition fostered higher self-reported valence and stable arousal, while a fear-inducing VR condition elicited increased arousal and decreased valence.

Regarding emotion scores, participants generally experienced high levels of happiness and relaxation, coupled with low levels of anger, sadness, disgust, and fear. Despite these general trends, the VR conditions significantly influenced these emotional responses, whereas the tDCS conditions did not have a noticeable effect. Throughout the experiment, participants' anger increased specifically when they were exposed to the fear VR condition, suggesting that the fear-inducing environment effectively heightened feelings of anger over time. On the contrary, the neutral VR condition led to an increase in desire, indicating that a more neutral setting could stimulate a sense of longing or interest. Happiness levels also varied with the VR condition. In the neutral VR condition, participants reported increased happiness as the experiment progressed, pointing to the soothing or pleasant nature of this environment. However, in VR fear condition, happiness decreased significantly, reflecting the adverse emotional impact of the fear-inducing scenario. Similarly, fear ratings rose markedly in the fear VR condition, confirming that the environment successfully elicited stronger fear responses as participants continued to engage with it. These changes in emotional states highlight the powerful effect of the VR environments on participants' emotions, illustrating how different virtual settings can distinctly shape emotional experiences. In summary, VR conditions played a crucial role in modulating emotions, with fear-inducing environments heightening anger and fear accompanied by higher arousal and lower valence, while neutral environments fostered increased happiness and desire with average arousal and high valence. Put together, these findings show successful priming to fear. However, it also points to the fact that neutral experiences can also produce changes in emotion ratings, valence, and arousal, especially positive emotions.

tDCS, on the other hand, did not significantly influence any of the measures, either on its own or in interaction with VR condition, except for a marginal effect on electrodermal activity. Although the vmPFC is associated with regulating fear, tDCS montages dramatically change its ability to modulate these regions. In addition, it is possible that tDCS stimulation also modulates participants’ response to the neutral stimuli, in particular because the neutral stimuli in our experiment are relatively pleasant nature videos (Junghofer et al., 2017; Winker et al., 2018).

This experiment also explored the effects of VR environments and tDCS on time perception employing a temporal bisection task. Despite the observed differences in the average Point of Subjective Equality (PSE) across conditions, no significant differences were found. This suggests that although VR environments influenced emotional and physiological responses, these changes did not significantly alter participants' time perception. In other words, the lack of significant differences in PSE values between conditions indicates that increased arousal and altered valence induced by fear or neutral VR environments did not translate into measurable changes in the way participants perceived time. This outcome suggests a dissociation between emotional priming effects on immediate physiological and self-reported emotional responses and their impact on cognitive tasks such as time perception.

Contributions, future directions, and limitations

The research presented here provides important information on the role of VR environments in modulating physiological and emotional responses, demonstrating that both neutral and fear-inducing VR conditions can significantly influence the arousal and valence of participants. The study contributes to the growing body of evidence that VR can be a powerful tool for emotional priming, offering a controlled environment to study the nuanced effects of emotional stimuli on psychophysiological measures. The findings also underscore the challenge of identifying truly "neutral" stimuli, as even seemingly neutral environments, such as nature scenes, can evoke distinct emotional responses.

Exploring future avenues, research could delve into a more extensive array of emotional and virtual reality settings to assess the applicability of these findings across diverse stimuli and participant demographics. Moreover, integrating a richer spectrum of psychophysiological assessments, including facial electromyography and advanced brain imaging methodologies, could unveil a deeper understanding of the intricate mechanisms underpinning these responses. The potential influence of transcranial direct current stimulation (tDCS) on emotional and physiological reactions, in conjunction with virtual reality, also demands further scrutiny. This is especially pertinent when considering the modulation of these effects through varied montage configurations and stimulation parameters, aiming to optimize its efficacy. This is, in fact, the main limitation of our study. The lack of significant effects of tDCS may be due to the specific montage, intensity, or duration used, suggesting the need for more optimized protocols. The experiment's reliance on a single type of neutral stimulus (nature videos) also limits the generalizability of the findings to other neutral contexts. Furthermore, the lack of significant findings in time perception despite emotional and physiological changes indicates that the relationship between emotion and cognitive tasks in VR settings is complex and may require more sensitive measures or different methodological approaches. These limitations point to the need for continued exploration of VR as a research tool, particularly in its capacity to generate and measure emotional and cognitive responses.

Data statement

The data that support the findings of this study are available from the authors upon reasonable request.

Acknowledgements

The author would like to thank the students Afonso Rocha, Cátia Fiuza, Daniela Mendes, Eduardo Carvalho, Filipa Gomes, Filipe Lopes, Helena Silva, Humberto Paiva, Maria Inácio, Mariana Cardoso, and Sofia Miranda for their help during data collection; the team at ISCTE-IUL’s LAPSO laboratory, in particular Sofia Frade, Helena Santos, and Inês Brito; and professors Ana Ganho-Ávila, Luke Jones, Ning Wang, and Nuno de Sá Teixeira.

Funding

This work was financially supported by BIAL Foundation through Grant 318/18.

Disclosure and compliance with ethical standards

The author has no relevant financial or nonfinancial interests to disclose. As this research involves human participants, the broad research project was submitted to an appropriate Ethics Committee as stated in the participants' section and gold standards in research ethics were followed. Participants gave their informed consent, and their data is anonymized, but available for exclusion for the next five years.

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Successful Emotional Priming in Virtual Reality was Not Modulated by tDCS and Did Not Affect Time Perception

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Introduction

Materials and methods

Participants

Materials and procedure

tDCS Session

VR priming

Temporal Bisection Task

Self-assessment measures

Psychophysiological measures

Analysis Strategy

Results

Arousal and valence

Emotion ratings

Effects on time perception

Discussion

Contributions, future directions, and limitations

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References

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