Immersion in nature enhances neural indices of executive attention Amy S. McDonnell amy.mcdonnell@utah.edu David L. Strayer 2024 16 1 2024 13 9 2023

There is conjecture that our modern urban environments place high demand on our attentional resources, which can become depleted over time and cause mental fatigue. Natural environments, on the other hand, are thought to provide relief from this demand and allow our resources to be replenished. While these claims have been assessed with self-report and behavioral measures, there is limited understanding of the neural mechanisms underlying these attentional bene昀؀ts. The present randomized controlled trial 昀؀lls this gap in the literature by using electroencephalography to explore three aspects of attention-alerting, orienting, and executive control-from a behavioral and neural perspective. Participants (N = 92) completed the Attention Network Task before and after either a 40-min walk in nature or a 40-min walk in a control, urban environment. Participants that walked in nature reported their walk to be more restorative than those that walked in the urban environment. Furthermore, the nature group showed an enhanced error-related negativity after their walk, an event-related brain component that indexes executive control capacity, whereas the urban group did not. These 昀؀ndings demonstrate that a 40-min nature walk enhances executive control at a neural level, providing a potential neural mechanism for attention restoration in nature.

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Our world is rapidly urbanizing. As of 2010, more people live in cities than in rural areas. e United Nations projects that by 2050, 70% of the world’s population will reside in urban centers1. While urbanization provides exposure to other cultures and access to education and health care, some characteristics of these environments (e.g., pollution, arti cial light, stress, and overstimulation) negatively impact our health and cognition2. From an epidemiological standpoint, urban living is associated with higher rates of mood disorders3, aggression4, schizophrenia5, depression6, anxiety7, and posttraumatic stress disorder8. Furthermore, the modern human operates at an unsustainable pace. We are sensorily and attentionally overstimulated on a day-to-day basis, forced to shi our attention from one thing to the next with little time for recovery. Our workplaces and home environments are riddled with technology, as are the vehicles that transport us between the two. Text message pings, news alerts, and social media updates are a signi cant part of every day. Excessive technology use has been acknowledged by the World Health Organization as an area of public health concern9, as it is associated with negative moods10, stress11, and depletion of memory capacity12. e growing prevalence of smartphones and wearable devices mean that we are connected to technology at every given moment, making it di cult to concentrate on a single task or engage in meaningful face-to-face interactions. Importantly, this persistent overstimulation and task-switching that is characteristic of our modern environments can deplete our attention and lead to mental fatigue13,14.

Exposure to nature is thought to bu er against the attentional detriments associated with modern living. In line with the above, Stephen Kaplan’s Attention Restoration eory (ART) posits that urban environments bombard our senses, placing constant demand on our attentional systems by forcing us to select elements that are useful and ignore those that are not15. is requires e ortful, directed attention that is limited in capacity16 and can be depleted over time17, leading to impairments in executive functioning and self-regulation18. Kaplan postulates that spending time in nature allows the brain to rest and replenish directed attention. He argues that speci c characteristics unique to nature—such as clouds, trees, water, and vast landscapes—can be restorative by recruiting more e ortless, “involuntary” attention19. In other words, nature allows the mechanisms supporting e ortful, directed attention to rest and recover as we e ortlessly attend to the environment around us.

While many activities can be considered restorative (e.g., meditation, sleep, vacation), Kaplan outlines four quali cations that must be met for an environment to be particularly so. First, the environment must be compatible with the goals of the individual experiencing it. For example, an individual that is afraid of the dark may not nd restoration on a camping trip. Second, the environment must allow for the sense of being away, in terms Scienti昀؀c Reports | (2024) 14:1845 of either a physical or mental removal from the situation that demands attention and creates stress. ird, the environment must have extent, or a scope that allows for prolonged exploration both physically and conceptually. Lastly, there must be some level of engagement, which Kaplan calls “so fascination”, that stimulates the senses in a bottom-up, sensory-driven fashion. For example, characteristics of nature like slow-moving clouds, sunsets, and trickling creeks engage the senses e ortlessly. ese four quali cations for restoration are characteristics of natural environments, thus allowing nature to serve as a place to rest and replenish our attentional resources15.

A large body of literature has accrued over the last three decades testing the claims set forth by Attention Restoration eory20,21, with some studies supporting behavioral improvements in attention-related tasks a er exposure to nature and others failing22 to do so. One reason for such mixed evidence is that there is conceptual ambiguity regarding which aspects of attention are most sensitive to depletion and subsequent restoration. Attention is multifaceted and thus there is inconsistency in the way researchers in the literature operationalize it. Attention is thought to be comprised of three anatomical networks that are responsible for di erent functions—alerting, orienting, and executive control23,24. Each of these functions serves a unique role in the way an individual attends to their environment. e alerting system helps achieve and maintain an aroused state over time, allowing us to stay awake, alert, and ready to respond to incoming stimuli25. is implicates neural regions related to arousal such as the locus coeruleus, thalamus, and frontal and parietal cortices26. e orienting system relates to visuospatial attention and a ords the ability to select and prioritize important sensory information from speci c locations in the environment. Orienting attention is associated with frontal eye elds27 and parietal cortices (such as the superior parietal lobe) that make up the dorsal attention network28. Lastly, the executive control system is responsible for higher-level cognitive functions, such as working memory and cognitive control. It helps in switching between di erent tasks and managing competing demands for attention, playing a role in resolving con ict and overriding pre-potent responses23. Executive control implicates the prefrontal cortex, the anterior cingulate cortex, and their associated circuitry29,30. Many have argued that executive control is most re ective of the concept of directed attention described in Attention Restoration eory18,21 because “directed attention” and “executive control” both refer to the ability to focus attention while ignoring distractions31.

e present study explores which, if any, aspects of attention—alerting, orienting, or executive control—are most in uenced by immersion in nature within a single task called the Attention Network Task (ANT). e ANT is designed to assess each aspect of attention independently32. It is a combination of the Posner spatial cueing task33 and the Flanker interference paradigm34 such that target ankers are either temporally or spatially cued.

is task has high utility in the literature because not only does it provide a performance indicator of executive control (which is thought to closely capture the idea of directed attention), but it also provides performance indicators of non-executive control (alerting and orienting). is can help elucidate whether the attentional e ects of exposure to nature are speci c to a particular aspect of attention or if they encompass all three dimensions in a domain-general manner.

is is not the rst study to measure ANT performance before and a er exposure to nature (see Table 1); however, it does seek to overcome several limitations of the existing literature. Prior studies exhibit wide variability in results due to di erences in sample sizes (in which they range from 12 to 60 participants), populations measured (children versus adults), whether participants had a clinical diagnosis (e.g., attention-de cit/hyperactivity disorder), experimental designs (within- versus between-subjects), type of nature (simulated nature versus real nature) and other methodological decisions that may in uence results (e.g., whether participants were experimentally depleted before the walk and whether participants walked alone, with a group, or with a researcher). erefore, the present experiment seeks to overcome this variation with a substantially larger sample size (92 participants), in a highly-controlled, randomized trial with both within- and between-subject components, comprised of a healthy adult sample. Additionally, participants were experimentally depleted at the start of testing to conceptually recreate Kaplan’s idea of the depleted cognitive state from which individuals may seek restoration. Furthermore, participants walked in real nature, enhancing the ecological validity of a literature that o en relies on simulated nature. Lastly, participants in this study walked alone to allow for maximum restoration potential, as walking with other participants or researchers may be distracting or stressful, thus interfering with full cognitive immersion in the environment.

Most importantly, the present study enhances existing work by co-registering behavioral and neurophysiological metrics generated from the ANT to assess all three aspects of attention from both a behavioral and neural level. Electroencephalography (EEG) is a neurophysiological method that records the electrical activity of the brain from electrodes placed on the scalp. It is non-invasive and allows for temporally precise, direct measurement of neural activity (on the order of milliseconds) in response to the demands of the environment. While some initial theorists proposed using EEG to explore nature’s in uence on the brain42, there is still a relatively small literature that utilizes EEG compared to self-report, behavioral, and other neuroimaging methods such as functional magnetic resonance imaging43,44. Of the many ways to extract meaningful information from the EEG signal, researchers in this eld quantify either continuous oscillatory activity in the frequency domain (e.g., alpha band activity from 8 to 12 Hz) or changes in electrical activity in response to discrete events in the environment in the time domain (i.e., event-related potentials).

Event-related potentials (ERPs) are valanced de ections in the EEG waveform that peak following the presentation of a stimulus (stimulus-locked ERPs) or following an individual’s response to a stimulus (response-locked ERPs). Importantly, they provide a direct, millisecond-resolution measure of brain activity and can provide insight into the neurophysiological correlates of both sensory and cognitive processes45. Compared to measuring power in certain frequency bands, ERP components have the advantage of high temporal precision and accuracy because they require minimal data processing or temporal lters46. Additionally, there is extensive literature surrounding the very speci c antecedent conditions that elicit certain ERP components, making the interpretation of results relatively straightforward. e present study harnesses the high temporal precision of EEG and the strengths of the ERP technique to quantify neural metrics of alerting, orienting, and executive control generated Authors from the Attention Network Task before and a er exposure to nature compared to a control, urban environment. Co-registration of behavioral and neural metrics allows not only for information regarding task performance, but also the potential to identify neural mechanisms underlying changes in performance—insight that is largely absent from the Attention Restoration eory literature.

Ninety-two adults took part in the study. Each participant was experimentally depleted at the start of testing with a counting backwards depletion task14,47. e intention behind a depletion manipulation was three-fold: ( 1 ) to conceptually recreate Kaplan’s idea of the depleted cognitive state from which individuals may seek restoration, ( 2 ) to prime participants for maximal restoration potential, and ( 3 ) to ensure that all participants entered their walk in a comparably depleted state. A er cognitive depletion, participants completed the ANT while their behavioral performance and EEG signal were recorded. Participants were then randomly assigned to a 40-min walk in either a natural environment (46 participants) or an urban environment (46 participants) of comparable distance, elevation change, and environmental characteristics such as temperature, humidity, and wind speed. A er the walk, each participant completed the ANT again, followed by a self-report measure of how restorative their walk was. We hypothesized that immersion in nature would be perceived as more restorative and would improve executive control metrics on the ANT, given executive control is thought to align closest with the construct of directed attention described in Attention Restoration eory. We predicted that there would be no changes in alerting or orienting metrics.

Methods and materials

is research complied with the American Psychological Association Code of Ethics and was approved by the Institutional Review Board at the University of Utah (IRB_00153144). All methods and procedures were performed in accordance with relevant guidelines and regulations of this institution. Informed consent was obtained from each participant.

Participants

Participants (N = 92; 71 female, 20 male, 1 non-binary) between the ages of 18 and 57 (M = 29.43, Mdn = 26, SD = 10.52) were recruited via yers, word of mouth, and the university participant pool. University-a liated participants were an undergraduate sample of convenience, so participants from the community were also recruited to increase the generalizability of the results. 80% of participants identi ed as White, 10% as Asian, 7% as Latinx/Hispanic, and 3% as Black. Community participants were paid $50 USD for participating in the study and undergraduate students from the participant pool were granted 3 research credits for their participation.

Sample size was determined by an a priori power analysis conducted in PANGEA v0.248, which indicated that 43 participants in each environmental condition (86 participants total) would provide su cient power at the recommended 0.80 level to detect within-subject by between-group interactions based on a medium e ect size (Cohen’s d = 0.50)49,50. is e ect size was informed by prior research in the eld that found medium-sized e ects of nature exposure on indices of attention36,51. Data were collected from 92 participants to ensure that the sample size would not drop below the required 86 participants a er anticipated data loss.

Design

is randomized controlled trial employed a mixed design with both a within-subjects factor and a betweensubjects factor. All participants completed the Attention Network Task before and a er a 40-min walk; this repeated measure served as the within-subjects factor. Participants were randomized to walk in either a natural or an urban environment, serving as the between-subjects factor, while making sure that each gender was equally represented in each group.

Procedure

All data collection and EEG recordings took place indoors in the Kay and Zeke Dumke, Jr. Horticulture Building at Red Butte Garden at the University of Utah, and both the nature and the urban walking routes le from this location. e procedural steps of the study can be visualized in Fig. 1. Upon arrival to the lab, participants completed a demographics survey while the researcher set up the EEG cap and electrodes. Once setup was complete, participants completed 10-min of a counting backwards task meant to deplete their cognitive resources. A er depletion, participants completed the ANT for the rst time. Participants were then randomly assigned to either a 40-min nature walk in an arboretum or a 40-min urban walk on an adjacent medical campus (see Fig. 2 for route information, Fig. 3 for participant walking setup, and Table 2 for additional information about the two groups). A er the walk, participants returned to the lab and completed the ANT again, followed by the Perceived Restorativeness Scale to rate how restorative their walk was from a self-report standpoint. On average, the research procedure took 3 h to complete.

Walking routes and environmental characteristics

In both walk conditions, participants followed predetermined routes mapped by the research team. e two walking routes were of comparable distance (~ 2 miles) and elevation change (~ 60 to 90 feet). Participants were instructed to leave their cellphones and cellular data-connected watches in the research lab to prevent technological distraction and encourage engagement with their environment. While each participant was on their walk, the researcher recorded the current environmental characteristics (i.e., temperature, weather report, humidity, wind speed) to track some of the uncontrollable variables that may in uence the walking experience. To control for any potential di erences due to time of testing, nature and urban participants were equally distributed between morning and a ernoon testing sessions such that 33 participants in each group testing in the morning and 13 participants in each group tested in the a ernoon. For descriptive statistics of environmental conditions and metrics generated from the GPS watch as a function of walk condition, see Table 2.

In the nature walk condition, participants walked three laps around the route, and in the urban condition, participants walked two laps along the route. e “loop” approach was taken to reduce the cognitive load that may be attributed to navigating in a new environment, with the hope that a er the rst lap of route nding, participants would be able to enjoy their environment for the rest of the walk and not worry about navigating.

e research team con rmed that each participant walked the correct route via the Garmin watch GPS data and GoPro video data.

Depletion task

A counting backwards task known to induce high cognitive load was used to experimentally deplete participants and thus prime them for restoration14,47. For this task, participants were instructed to count backward from 1000 to 0 by 7’s for a total of ten minutes, and to do so out loud so that the researcher could ensure they were doing the task properly.

Depletion manipulation check

Four manipulation check questions related to fatigue, e ort, pleasantness, and frustration were administered immediately following the depletion task52. ese manipulation check questions ensured that participants felt depleted a er counting backwards and that reported levels of depletion were comparable across groups. See Supplemental Materials for the full set of questions.

Perceived Restorativeness Scale

e 11-item version of the Perceived Restorativeness Scale (PRS-11) was administered at the end of the experimental protocol53. is scale assessed how restorative the participant perceived the environment they walked in to be. It includes eleven, 11-point Likert-scale ratings of statements such as “to stop thinking about the things that I must get done I like to go to places like this” and “to get away from things that usually demand my attention I like to go to places like this”. e scale is scored by calculating the average of the eleven items for each participant. A higher score represents greater perceived restoration. See Supplemental Materials for the full scale.

Attention network task

Behavioral and neural metrics of alerting, orienting, and executive control were derived from the Attention Network Task (Fig. 4)32.

e ANT combines temporal and spatial cueing to assess the alerting and orienting attention networks33 with a anker congruency paradigm to assess executive control34. Reaction times (RTs) to the target stimuli are used to quantify the processing e ciency of each network. For example, the di erence in RT to the target stimuli following no cue compared to the double cue indicates the processing e ciency of the alerting network, the di erence in RT to the target stimuli following the center cue compared to the spatial cue indicates the processing e ciency of the orienting network, and the di erence in RT to the incongruent compared to the congruent targets indicates the processing e ciency of the executive control network32.

In addition to behavioral metrics, we make a novel contribution to the Attention Restoration eory literature by time-locking event-related potentials (ERPs) to the ANT, providing an additional level of analysis of alerting, orienting, and executive control. Consistent with prior work in other elds54,55, neural metrics of alerting and orienting can be assessed by quantifying P300 amplitude generated by the di erent cue trials of the ANT. e P300 is a positive de ection in the brain’s electrical activity that typically occurs around 300 ms a er stimulus presentation. e P300 re ects the brain’s response to unexpected or infrequent stimuli, illustrating its role in alerting and orienting the individual to novel or salient events in their environment and facilitating their ability to shi and maintain attention accordingly56. e P300 is thought to be generated by a network of neural structures throughout the brain that changes depending on the task at hand. For relevance to this study, the P300 is maximal over posterior parietal cortices when an individual completes a task that engages alerting and orienting of attention57. is cortical region plays a crucial role in maintaining an alert state, directing attention to spatial locations, and integrating sensory information with motor information to guide behavior58.

e executive control function of attention can be assessed by quantifying error-related negativity (ERN) amplitude generated by correct and incorrect responses to each target. e ERN is a negative de ection in the brain’s electrical activity that occurs shortly a er an individual commits an error on a task. us, the ERN re ects the brain’s ability to detect and evaluate errors in real-time, which is a crucial aspect of cognitive control and selfregulation. is component plays a role in the executive control process by signaling the need for adjustments in attention and behavior to optimize task performance and minimize errors59. Source-localization studies have consistently demonstrated that the ERN is generated in the anterior cingulate cortex60–63, an integrative hub in the brain that is highly involved in executive control64.

Like the RT formulas, ERP metrics were calculated by comparing target responses under the following cue conditions: double cue minus no cue (alerting), spatial cue minus center cue (orienting), and incorrect minus correct responses (executive control). ese cue-network relationships and the outcome measures used to assess them are summarized in Table 3.

Stimulus presentation

Self-report surveys (i.e., demographic questionnaire, depletion manipulation check, and the Perceived Restorativeness Scale) were administered via Qualtrics (Qualtrics, Provo, UT) on a 5th generation iPad mini. e Attention Network Task was programmed in PsychoPy v3.1.3 and presented to participants on a monitor positioned 18 inches from their head. At each time point (pre-walk and post-walk), participants completed three blocks of 192 trials of the ANT, with self-paced breaks between each block. e task took about 18 min to complete.

All target stimuli were presented in black ink on a gray background and the target stimuli that participants responded to subtended 2.60 degrees of visual angle. In each trial, a xation cross (400–1600 ms randomized ISI) was followed by one of four cue conditions (no cue, central cue, double cue, spatial cue) that appeared on the screen for 100 ms. A er cue presentation, the xation cross appeared on the screen again for 400 ms followed by one of three target conditions (congruent, incongruent, or neutral) consistent with the anker congruency paradigm34. e target condition remained on the screen until the participant responded, or until 1700 ms had passed (see Fig. 4). Participants responded to the direction of the arrow in the middle of the target stimulus using the arrow keys on the keyboard and their right hand.

EEG recording and processing

EEG data were collected with a 32-channel, active electrode actiCap manufactured by BrainVision (BrainVision Systems, Morrisville, NC, USA). Channel locations were pre-determined according to the International 10–20 system65 and all impedances were kept below 25 kOhms. e signal was ampli ed with the BrainVision actiCHamp Plus ampli er with an online sampling rate of 500 Hz and acquired by BrainVision Recorder (Version 1.20.0601). e online and o ine reference channel was the right mastoid (TP10). Two auxiliary electrodes were placed above and below the right eye to record blinks and eye movements for later processing.

EEG data were processed in MATLAB using the EEGLab66 and ERPLab67 toolboxes. Raw data were downsampled to 250 Hz and band pass ltered from 0.1 to 30 Hz. Artifacts created by blinks and eye movements were corrected for using Gratton’s Eye Movement Correction Procedure (EMCP)68. To account for any artifacts le uncorrected by EMCP, additional artifact rejection was performed using a moving window to reject sections of data containing atlines or peak-to-peak activity greater than 200 μV67.

P300

e P300 component was used to quantify alerting and orienting on a neural level56. To quantify the stimuluslocked P300, continuous data were epoched from 700 ms before target presentation to 700 ms a er target presentation, with the average activity from – 700 to − 500 ms pre-target presentation (i.e., − 300 to − 100 ms pre-cue presentation) serving as the baseline55. A measurement window from 250 to 450 ms a er target onset was used for the alerting and orienting analysis54.

Attention network

Level of analysis Alerting Orienting Executive control

Behavioral Neural Behavioral Neural Behavioral Neural

Dependent variable RT P300 amplitude RT P300 amplitude RT ERN amplitude

Factor Cue condition (no cue-double) Cue condition (double-no cue) Cue condition (center-spatial) Cue condition (spatial-center) Target condition (incongruent—congruent)

Response Type (incorrect–correct)

To assess the alerting and orienting facets of attention, the stimulus-locked P300 was binned and averaged separately by cue type (no cue, double cue, center cue, spatial cue) and target type (congruent, incongruent, and neutral). Consistent with Fan et al. (2002)’s formulae32, we subtracted the no cue P300 waveform from the double cue P300 waveform (Double Cue-No Cue) to create the P300 for the alerting network. To create the P300 for the orienting network, we subtracted the center cue P300 waveform from the spatial cue P300 waveform (Spatial Cue–Center Cue). We then quanti ed these P300 di erence waveforms by extracting the mean amplitude within the 250–450 ms measurement window for each facet of attention using the ERP Measurement Tool in the ERPLab toolbox67. e P300 was analyzed at electrode Pz, as this is where it was maximally seen in this study.

e average percent of epochs lost in the P300 analysis a er artifact handling was 3.33% (Nature Pre-walk ANT: 2.50%, Nature Post-walk ANT: 4.94%, Urban Pre-walk ANT: 1.32%, Urban Post-walk ANT: 4.60%). Error-related negativity

e ERN component was used to quantify executive control on a neural level59. Consistent with all ERN research that utilizes a anker congruency paradigm, the ERN was time-locked to the participants’ response to the anker arrow presentations34. To quantify the ERN, continuous data were epoched from − 400 ms pre-response to 400 ms a er response. e average activity in the -400 to -200 ms pre-response window served as the baseline to avoid baselining into any pre-motor response brain activity. A measurement window from 7 to 57 ms a er response was determined using the collapsed localizer technique69. is technique involves creating the grand average waveform (collapsed across all participants, timepoints, cue types, and target types) and using this aggregate waveform to identify the peak latency of the overall ERN di erence wave (Incorrect–Correct). e collapsed localizer technique revealed an average ERN peak latency of 32 ms. e measurement window for the ERN was ± 25 ms around the peak (i.e., 7–57 ms).

To assess the executive control facet of attention, the response-locked ERN was binned and averaged separately by response type (incorrect and correct) and the processing e ciency of the executive control network was calculated by creating a di erence wave by subtracting the correct waveform from the incorrect waveform (Incorrect–Correct). We quanti ed the ERN waveforms by extracting the mean amplitude from 7 to 57 ms postresponse for each participant at each timepoint using the ERP Measurement Tool in the ERPLab toolbox67. e ERN was quanti ed at electrode Cz, as this is where it was maximally seen in this study.

Consistent with prior ERN research, a conservative error cuto was employed to ensure reliable ERN amplitudes, therefore participants that made 7 or fewer errors on the ANT a er artifact rejection were not included in the ERN analysis70, which resulted in the exclusion of 18 out of the 184 ANT les (8 nature pre-walk les, 6 nature post-walk les, 3 urban pre-walk le, and 1 urban post-walk le). e average percent of epochs lost in the ERN analysis a er artifact handling was 1.60% (Nature Pre-walk: 1.56%, Nature Post-walk: 2.23%, Urban Pre-walk: 0.74%, Urban Post- walk: 1.95%).

Statistical analyses

All analyses were conducted in R version 4.0.271. To compare depletion manipulation check and Perceived Restorativeness Scale results between the two walking groups, independent samples t-tests were run using the ‘t.test’ function in R. For all ANT results, linear mixed e ect models using the ‘lmer’ function in R’s ‘lme4’ package72 were utilized to control for sources of non-independence in the data (i.e., repeated-measures within an individual) and allow for missing data (e.g., if a participant was missing data from a single time point). All models included the behavioral or neural ANT metric as the dependent variable, Participant as a random intercept, and the interaction between Time (Pre-walk versus Post-walk) and Condition (Nature versus Urban) as a xed e ect predictor to determine if any pre- to post-walk changes di ered between the nature and the urban group. To test the signi cance of all xed e ects we ran likelihood ratio tests using the ‘anova’ function in the ‘stats’ package.

ese tests generated a chi-squared statistic comparing the model with the variable of interest (Time, Condition, and the Time by Condition interaction) entered as a xed e ect and Participant entered as a random intercept, to a model with the xed e ect of interest removed. We calculated Cohen’s d e ect sizes for all signi cant e ects.

Results

One nature participant’s post-walk data were not included in any analyses because their testing session was cut short due to rain and they were unable to complete their walk.

Depletion manipulation check

Four manipulation check questions related to fatigue, e ort, pleasantness, and frustration were administered immediately following the depletion task. ese manipulation check questions ensured that participants felt depleted a er counting backwards and that reported levels of depletion were comparable across groups. Results of the manipulation check can be visualized in Fig. 5. To ensure the integrity of the counting backwards depletion task, we con rmed that, on average, participants rated the pleasantness of the task as below the median rating (below a 3.5 on the 7-point Likert-scale) and their levels of fatigue, e ort, and frustration above the median rating (above a 3.5 on the 7-point Likert-scale). To ensure that each group was comparably depleted before their walk, we compared group means for each question on the depletion manipulation check survey using independent samples t-tests. ere were no signi cant group di erences in self-reported fatigue (t(89.99) = -0.39, p = 0.700), mental e ort (t(88.80) = 0.57, p = 0.571), pleasantness (t(86.44) = 1.43, p = 0.157), or frustration (t(89.30) = -0.32, p = 0.750).

Perceived Restorativeness Scale (PRS-11)

At the end of testing, participants rated how restorative they perceived the environment they walked in to be. Results from the 11-item scale can be visualized in Fig. 5. As expected, the natural environment (M = 9.14, SD = 1.27) was perceived as signi cantly more restorative than the urban environment (M = 6.21, SD = 1.74; t(80.51) = 9.11, p < 0.001, 95% CI [2.29 3.57], Cohen’s d = 1.92).

Alerting

e alerting facet of attention was quanti ed behaviorally (RT) and neurophysiologically (P300). Descriptive statistics of these two alerting indices as a function of Time and Condition can be seen in Supplemental Materials and pre- to post-walk di erence scores can be visualized in Fig. 6. Increases in RT and P300 alerting indices indicate an improvement in alerting.

Behaviorally, linear mixed models revealed a main e ect of Time on the RT alerting index (χ2( 1 ) = 31.81, p < 0.001) such that there was a signi cant increase in the RT alerting index from pre-walk to post-walk (β = 12.39, SE = 2.00, df = 88.48, t = 6.19, p < 0.001, 95% CI [8.43 16.31], Cohen’s d = 0.64). ere was no signi cant main e ect of Condition on the RT alerting index (χ2( 1 ) = 1.57, p = 0.210), nor was there a signi cant Time by Condition interaction (χ2( 1 ) = 1.30, p = 0.254). Neurophysiologically, there was a signi cant main e ect of Time on P300 alerting index (χ2( 1 ) = 6.18, p = 0.0129) such that mean amplitude increased from pre-walk to post-walk (β = 0.62, SE = 0.25, df = 90.75, t = 2.51, p = 0.0138, 95% CI [0.13 1.10], Cohen’s d = 0.28). ere was no signi cant main e ect of Condition on P300 alerting index (χ2( 1 ) = 0.01, p = 0.917), nor was there a signi cant Time by Condition interaction (χ2( 1 ) = 2.23, p = 0.137). ere was no signi cant correlation between the RT alerting index and the P300 alerting index (Pearson’s r = 0.12, p = 0.270).

Orienting

e e ciency of the orienting network was quanti ed behaviorally (RT) and neurophysiologically (P300). Descriptive statistics of these two orienting indices as a function of Time and Condition can be seen in Supplemental Materials and pre- to post-walk di erence scores can be visualized in Fig. 6.

Behaviorally, linear mixed models revealed no signi cant main e ect of Time (χ2( 1 ) = 2.94, p = 0.087), no signi cant main e ect of Condition (χ2( 1 ) = 0.67, p = 0.414), and no signi cant Time by Condition interaction (χ2( 1 ) = 0.57, p = 0.449) on the RT orienting index. Neurophysiologically, models show similar null results of Time (χ2( 1 ) = 1.67, p = 0.196), Condition (χ2( 1 ) = 0.40, p = 0.528), and Time by Condition interaction (χ2( 1 ) = 0.055, p = 0.816) on the P300 orienting index. ere was no signi cant correlation between the RT orienting index and the P300 orienting index (Pearson’s r = 0.14, p = 0.197).

Executive control

e e ciency of the executive control network was quanti ed behaviorally (RT) and neurophysiologically (ERN). Descriptive statistics of these two executive control indices as a function of Time and Condition can be seen in Supplemental Materials and pre- to post-walk di erence scores can be visualized in Fig. 6. Decreases in RT and ERN executive control indices indicate an improvement in executive control.

Behaviorally, linear mixed e ects models revealed a main e ect of Time on the RT executive control index (χ2( 1 ) = 5.90, p = 0.015) such that there was a signi cant decrease in RT executive control index from pre-walk to post-walk (β = − 8.07, SE = 3.28, df = 89.88, t = − 2.46, p < 0.05, 95% CI [− 14.53 − 1.59], Cohen’s d = − 0.19).

ere was no signi cant main e ect of Condition (χ2( 1 ) = 0.17, p = 0.684), nor a signi cant Time by Condition interaction (χ2( 1 ) = 0.358, p = 0.549) on the RT executive control index. Neurophysiologically, there was no signi cant main e ect of Time (χ2( 1 ) = 0.28, p = 0.600) or Condition (χ2( 1 ) = 1.32, p = 0.251) on the ERN executive control index. ere was, however, a signi cant interaction between Time and Condition (χ2( 1 ) = 4.47, p = 0.034) such that nature walkers showed a more enhanced ERN from pre- to post-walk (indicating an improvement in executive control) than urban walkers did (β = 2.39, SE = 1.13, df = 79.20, t = 2.12, p = 0.0368, 95% CI [0.18 4.59],

Cohen’s d = 0.41). ere was a signi cant positive correlation between the RT executive control index and the ERN executive control index (Pearson’s r = 0.30, p = 0.0083), such that an increase in the RT executive control index was associated with an increase in mean amplitude of the ERN.

Relationship to perceived restorativeness

Participants’ average score on the PRS-11 did not correlate with changes in either alerting index, either orienting index, or the RT executive control index. Interestingly though, average score on the PRS-11 did signi cantly correlate with change in ERN amplitude from pre- to post-walk (Pearson’s r = − 0.26, p = 0.0247) such that the more restorative a participant rated their walk to be, the larger their ERN. A series of regression models were run to better understand the relationship between self-reported perceived restorativeness, walk condition, and change (post-walk minus pre-walk) in ERN amplitude. ere was a main e ect of perceived restorativeness on change in ERN amplitude (β = − 0.61, SE = 0.27, t = − 2.29, p = 0.0247, 95% CI [− 1.15 − 0.080], Cohen’s d = − 0.12) and a main e ect of Condition on change in ERN amplitude (β = 2.54, SE = 1.14, t = 2.23, p = 0.0289, 95% CI [0.27 4.81], Cohen’s d = 0.49). However, the interaction between perceived restorativeness and Condition was not statistically signi cant (β = 0.58, p = 0.507, 95% CI [− 1.15 2.32]), indicating that the relationship between perceived restorativeness and change in ERN amplitude was consistent across both groups.

Discussion

is study took a rigorous experimental approach to assessing the in uence of immersion in nature on attentionprocesses in the brain. We assessed three facets of attention (alerting, orienting, and executive control), from multiple levels of analysis (behavioral and neural), with a sample size that exceeds prior experimental studies in the Attention Restoration eory literature. We set out to answer which aspects of attention, if any, are in uenced by a short-term (40-min) immersion in nature.

To experimentally recreate Kaplan’s idea of the depleted cognitive state from which individuals may seek out nature, participants underwent a depletion protocol to ensure they were ‘primed’ for maximal restoration potential. Participants were randomized to either a 40-min walk in nature or a 40-min walk in a control, urban environment of comparable distance. e two walking routes utilized in this study were designed in accordance with the four quali cations of a restorative environment outlined in Attention Restoration eory15. It was theorized that a 40-min immersion would be long enough to meet the quali cation of being away, but also short enough to still be compatible with most people. e natural environment used in the experiment had substantial extent and depth of processing, as participants walked along a trickling creek, through an oak tree tunnel, and around a pond with ducks and a small waterfall. It is assumed that these features engaged the senses in the ‘so ly fascinating’ way proposed by Attention Restoration eory. As expected, participants reported that the natural environment was, indeed, signi cantly more restorative than the urban environment, as measured by the Perceived Restorativeness Scale (PRS-11).

To assess each aspect of attention within a single cognitive task, participants completed the Attention Network Task (ANT) before and a er their walk. e ANT can elucidate whether the attentional bene ts of exposure to nature are speci c to a particular aspect of attention or if they encompass all three dimensions in a domaingeneral manner. Notably, this study is the rst in the Attention Restoration eory literature to synchronize ERPs with the ANT, allowing for the dual examination of task performance from a behavioral and neurophysiological standpoint. is simultaneous co-registration of behavioral and neural data not only facilitates the assessment of task performance but also yields insights into the underlying neural mechanisms engaged during the task. Consequently, this approach yields a more comprehensive understanding of potential cognitive changes in response to immersion in nature, shedding light on the intricate interplay between nature exposure and cognitive functioning. We hypothesized that nature exposure would improve the executive control indices of the ANT, as executive control is thought to align closest with the construct of directed attention described in Attention Restoration eory18. We predicted that there would be no changes in alerting or orienting.

Contrary to our hypothesis, results suggest that both groups showed enhanced alerting a er the walk, denoted behaviorally by an increase in the RT alerting index and neurophysiologically as an increase in the P300 alerting index from pre- to post-walk. It can be concluded that 40-min (~ 2-miles) of low-intensity walking may improve alerting on both a behavioral level and a neural level regardless of the type of environment one walked in. is is supported in the exercise and cognition literature, which shows that low- to moderate-intensity exercise enhances the alerting index on the ANT54. e low-intensity walk utilized in this study likely enhanced arousal and freed up available attentional resources, re ected by an increase in RT and P300 alerting indices for both groups.

In terms of orienting attention, our hypothesis was supported in that neither group showed changes in either the behavioral or neural index of orienting. is suggests that neither immersion in nature nor even exercise in uence orienting e ciency. is is likely because orienting is not a particularly e ortful facet of attention and is thus less sensitive to being depleted in the rst place.

We are particularly interested in where environment-speci c e ects diverge from general exercise e ects— namely, where there is a distinction between the nature and urban group. We see this di erentiation in the executive control results. In line with our hypothesis that there would be an improvement in executive control associated with immersion in nature, nature walkers showed enhanced (more negative) ERN amplitude a er their walk while urban walkers showed no signi cant changes in ERN amplitude. is replicates prior ndings of enhanced ERN amplitude during a 4-day immersion in nature compared to immersion in a control, urban environment51. e ERN occurs within the rst 100 ms following the commission of an error on the ANT, indexing error detection and performance monitoring in the brain74. e larger the ERN response, the more likely an individual is actively monitoring their performance and adjusting their behavior to reduce future errors.

is is a key aspect of executive control. rough the framework of Attention Restoration eory, this suggests that nature likely allowed the neural mechanisms related to executive control to rest and recuperate, leading to increased executive control capacity (illustrated by an increased ERN) when tasked with completing the ANT a er the walk. is conclusion is further supported by the observed signi cant relationship between score on the PRS-11 and change in ERN amplitude, suggesting that the changes in ERN amplitude are, at least to some degree, associated with restoration. However, this relationship should be interpreted with caution given the e ect size is small (Cohen’s d = − 0.12) and there is no signi cant interaction between perceived restorativeness and condition in predicting change in ERN amplitude. Future work should further disentangle the relationship between the ERN, self-reported perceived restoration, and environment type.

e ERN is generated in the anterior cingulate cortex, a subcortical structure the brain that plays a crucial role in integrating cognitive, emotional, and motivational information from the environment to guide subsequent behavior75,76. Importantly, the anterior cingulate cortex is functionally connected to the prefrontal cortex during tasks that engage aspects of executive control such as error detection and response inhibition—processes that are assessed in this study by the executive control portion of the ANT. Perhaps the decrease in attentional demands present in natural environments allows for anterior cingulate-prefrontal cortex functional network to rest and recover, allowing for enhanced functionality when required to come online during ANT completion a er the walk. is suggests a potential underlying neural mechanism for executive control restoration in nature that should be expanded upon in future work.

It is interesting to note that the RT executive control index and the ERN executive control index show different patterns such that both groups improved on a behavioral level but only the nature group improved on a neural level. e lack of a behavioral e ect is consistent with some prior work that fails to nd di erences in the RT executive control index on the ANT between a nature walk and an urban walk38–40. e dissociation between behavioral and neural results presented in this study illustrates the strength of utilizing multiple dependent measures when assessing cognition, suggesting that di erent dependent measures have di erent sensitivity to experimental manipulations. It is not uncommon to see this play out in the cognitive neuroscience literature at large. For one, neurophysiological metrics may detect subtler, possibly pre-conscious processes, while behavioral metrics capture more integrated and conscious responses45. Additionally, neurophysiological measures may detect changes that are not large enough to a ect behavior, which o en requires a change to pass a certain threshold before it is re ected in task performance. Nevertheless, such patterns indicate a complex and nuanced relationship between brain activity and behavior.

In this study, we can only speculate as to why we see this dissociation between the behavioral and neural results. For one, it is possible that because the RT executive control index is calculated from just the correct trials and the ERN is calculated based on error trials, they are capturing di erent aspects of executive control, with some aspects of executive control being more strongly in uenced by immersion in nature than others. e RT executive control index taps into inhibitory control processes (ignoring distracting anker arrows to respond quickly and accurately to the central target), whereas the ERN indexes error processing and performance monitoring (recognizing and responding to an error). While our results show a signi cant correlation between the RT executive control index and the ERN executive control index (Pearson’s r = 0.30), this correlation is small, accounting for only 9% of the variance. erefore, while both processes re ect aspects of executive control, they are dissociable constructs that serve di erent roles in how an individual attends to their environment. Interestingly, there is prior literature that similarly fails to nd nature-related improvements in inhibitory control processes such as on the Stroop Task77 and literature that does nd improvements in performance monitoring51,78.

is suggests that perhaps not all forms of executive control are comparably in uenced by exposure to nature. Again, these divergent results accentuate the strength of co-registering behavioral with neurophysiological metrics and utilizing multiple dependent measures in research, as it allows for deeper insight into cognition than any single measure can provide on its own. Nevertheless, future work should collect behavioral and neural measures simultaneously to see if the pattern holds.

is study was designed speci cally to test which aspects of attention, if any, are restored following a 40-min nature compared to urban walk. As hypothesized, we found that the nature walk, but not the urban walk, enhanced amplitude of the ERN—a neural index of the executive control aspect of attention. Our research question and hypotheses were based on extant theory and prior literature that has validated the propositions set forth by Attention Restoration eory. Based on this robust body of prior work, as well as participant reports of greater perceived restoration a er the nature walk compared to the urban walk, we believe we are, at least in part, observing attention restoration e ects of immersion in nature. As is unavoidable when exploring the impact of immersion in real-world environments on cognition, there are inevitably several uncontrollable variables that may in uence results. To control for some of these, we randomly assigned participants to conditions and carefully designed our two walking routes to be comparable in distance, elevation change, amount of time outside, and pace (see Table 2). Additionally, our two groups were well-matched on exercise metrics such as average heart rate and calories burned. Furthermore, in a series of exploratory analyses presented in Supplemental Materials , we statistically ruled out other potential variables that may in uence our ERN results, such as age, temperature, and time of day. However, it is still possible that there are other, unaccounted for variables at play that may be in uencing the observed enhanced ERN. For instance, it is possible that there are group di erences in stress. Roger Ulrich’s Stress Recovery eory complements Attention Restoration eory in that it posits that natural environments are restorative because they aid in recovery from stress79. While this study did not directly measure or manipulate stress, it is likely that there is an element of stress recovery co-occurring alongside attention restoration, an idea that has been proposed in a recent uni ed framework of restoration in nature80. is study was rigorous in controlling for many variables; however, the results should be interpreted with caution, given there may still be unidenti ed factors in uencing ERN amplitude beyond attention restoration.

What constitutes “natural” and “urban” environments exists on a spectrum from untouched wilderness to concrete jungle. us, the ideal walking environments would perhaps be a nature walk in a remote wilderness area and an urban walk in a highly-congested downtown neighborhood. Given the logistical constraints of the current study, we were limited in the type of environments to choose from. is constraint le us with a protected natural environment with some “built” characteristics such as paved walkways and bridges. is arboretum was adjacent to an urban environment that had some trees lining the sidewalk and a distant view of mountains. While these environments are not at the polar ends of the spectrum described above, they do accurately represent environments that are realistically accessible to most individuals on a day-to-day basis, increasing the ecological validity of this work.

Additionally, given certain outcome measures improved in both walking groups (namely, the two alerting metrics and the behavioral executive control metric), the data suggest that exercise, in and of itself, improves certain aspects of attention regardless of the environment. e methodological decision to have participants walk in these environments rather than just sit in them may be perceived as a limitation of this work. We made this decision because we felt it was important for participants to engage and interact with their environments in a way that would not have been possible just sitting. is was in line with Attention Restoration eory’s criterion for an environment to have extent to be restorative. is decision was also based on the substantial body of prior studies that require participants to walk in nature rather than sit in nature and still nd di erential e ects between natural and urban environments35,38,44. Lastly, because individuals o en exercise when they are in natural settings (e.g., a walk in a park, trail running, mountain biking, skiing), we felt it would be important for the ecological validity of the work to incorporate this to see if nature di erentially in uences attention above and beyond the e ects of just exercise. However, future work could replicate this protocol but have participants sit in each environment for 40-min rather than walk in them or employ a direct manipulation of exercise in nature.

is study demonstrates an initial exploration into understanding the underlying neural mechanisms of nature’s attentional bene ts. Future work should seek to answer what the optimal dosage of nature is to realize these bene ts (e.g., minutes, hours, or days), how di erent types of nature may di erentially impact cognition (e.g., ocean, forest, mountains, desert), and how long these e ects last. In the present study, participants completed their second ANT within 20 min of their walk. It is possible that the observed enhancements in executive control may be a eeting bene t that quickly dissipates as one re-enters their typical urban environment. Furthermore, an interesting and important step in the eld will be to delineate the restorativeness of nature from the (2024) 14:1845 | https://doi.org/10.1038/s41598-024-52205-1 12 restorativeness of being away from technology. Participants in this study were instructed to leave their cellphones in the laboratory so that it would not interfere with the restorative potential of their walk. erefore, it is possible that some of the bene ts we see in nature may be driven by separation from technology. Anecdotal evidence would suggest that being immersed in nature without cellphone service or the ability to refresh an email inbox is more restorative than being outside and still connected to work and social media. However, this needs to be directly tested. Jiang et al. (2019)14 experimentally manipulated technology use during a 15-min immersion in nature and found that participants who were in nature on a laptop did not receive the same cognitive bene ts as those without technology14. Future work can replicate this type of design using neurophysiological measures to see if engaging with technology disrupts the neural changes we have identi ed.

While beyond the scope of the present study, this eld of environmental neuroscience holds implications for environmental policy, clinical psychology, and urban design. Compared to studies based exclusively on selfreport and behavioral measures, studies that utilize cognitive neuroscience methods may be more successful in in uencing government action and public concern for the health of the environment. As Roger Ulrich—a seminal gure in environmental psychology—suggests, research employing neurophysiological measures carries more weight than behavioral or subjective measures in political decision-making and will therefore be more e ective in the implementation of environmentalist policy42. Moreover, studies suggest that nature-based interventions may be e ective in improving health outcomes across a variety of conditions, such as attention-related disorders or depression81. Lastly, understanding the relationship between physical environments and the individual can also contribute to urban design geared toward optimizing human functioning. Perhaps the location and design aesthetics of buildings such as workplaces, hospitals, and prisons will prioritize exposure to nature. Work schedules that allow for more time outside and less time in front of a computer screen could optimize cognitive performance and productivity of employees.

In sum, this well-powered, tightly-controlled study sought to improve our understanding of the e ects of a nature walk on the brain and behavior. It employed sophisticated electrophysiological techniques to o er intriguing results that a walk in nature enhances certain executive control processes in the brain above and beyond the bene ts associated with exercise. is study demonstrates the power of utilizing EEG to uncover the bene ts of nature on human cognition and provides a framework for future work to explore which types of nature enhance executive control and how much time in nature is needed to do so.

Data availability

De-identi ed data as well as MATLAB and R code for data processing and analysis are available on OSF at https:// osf.io/px4ev/?view_only=ec6ec86bd497482795d821386543d802.

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Acknowledgements

e authors thank Red Butte Garden in Salt Lake City, UT for their generosity and hospitality in providing laboratory space for this study. ey also thank Dr. Cory Inman, Dr. Jeanine Stefanucci, Dr. Paula Williams, and Dr. Ruthann Atchley for their insight into the design of this study and their feedback on a previous version of this article.

Author contributions

A.S.M. conceptualized and designed the project, developed the methodology, programmed the experiment, collected, and analyzed the data. She also wrote the original dra of the manuscript. D.L.S. supervised and provided resources for the project. He also aided in conceptualization, so ware programming, and revising the manuscript.

Competing interests

e authors declare no competing interests.

Additional information

Supplementary Information 10.1038/s41598-024-52205-1.

e online version contains supplementary material available at https://doi.org/ Correspondence and requests for materials should be addressed to A.S.M.

Reprints and permissions information is available at www.nature.com/reprints.

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1. United Nations , Department of Economic and Social A airs, Population Division ( 2019 ). World urbanization prospects: e 2018 revision (Report No . ST/ESA/SER.A/420). https://population.un.org/wup/publications/Files/WUP2018-Report.pdf. 2. Lambert , K. G. , Nelson , R. J. , Jovanovic , T. & Cerdá , M. Brains in the city: Neurobiological e ects of urbanization . Neurosci. Biobehav. Rev . 58 , 107 - 122 . https://doi.org/10.1016/j.neubiorev. 2015 . 04 .007 ( 2015 ). 3. Peen , J. , Schoevers , R. A. , Beekman , A. T. & Dekker , J. e current status of urban-rural di erences in psychiatric disorders . Acta Psychiatr. Scand . 121 ( 2 ), 84 - 93 . https://doi.org/10.1111/j.1600- 0447 . 2009 . 01438 . x ( 2010 ). 4. Kuo , F. E. & Sullivan , W. C. Aggression and violence in the inner city: E ects of environment via mental fatigue . Environ. Behav . 33 ( 4 ), 543 - 571 . https://doi.org/10.1177/00139160121973124 ( 2001 ). 5. Krabbendam , L. & Van Os , J. Schizophrenia and urbanicity: A major environmental in uence-conditional on genetic risk . Schizophr. Bull . 31 ( 4 ), 795 - 799 . https://doi.org/10.1093/schbul/sbi060 ( 2005 ). 6. Stevens , R. G. , Brainard , G. C. , Blask , D. E. , Lockley , S. W. & Motta , M. E. Adverse health e ects of nighttime lighting: Comments on American Medical Association policy statement . Am. J. Prev. Med . 45 ( 3 ), 343 - 346 . https://doi.org/10.1016/j.amepre. 2013 . 04 . 011 ( 2013 ). 7. McKenzie , K. , Murray , A. & Booth , T. Do urban environments increase the risk of anxiety, depression and psychosis? An epidemiological study . J. A ect. Disord . 150 ( 3 ), 1019 - 1024 . https://doi.org/10.1016/j.jad. 2013 . 05 .032 ( 2013 ). 8. Breslau , N. , Wilcox , H. C. , Storr , C. L. , Lucia , V. C. & Anthony , J. C. Trauma exposure and posttraumatic stress disorder: A study of youths in urban America . J. Urban Health 81 ( 4 ), 530 - 544 . https://doi.org/10.1093/jurban/jth138 ( 2004 ). 9. World Health Organization ( 2015 ). Public health implications of excessive use of the Internet, computers, smartphones and similar electronic device: Meeting report . Report of the meeting conducted at Main Meeting Hall, Foundation for Promotion of Cancer Research, National Cancer Research Centre , Tokyo, Japan, 27 - 29 August 2014 . https://apps.who.int/iris/handle/10665/184264 10. Van der Aa , N., Overbeek , G. , Engels , R. C. Scholte , M. E. ,, Meerkerk G.-J., & Van den Eijnden R. J. Daily and compulsive Internet use and well-being in adolescence: A diathesis-stress model based on big ve personality traits . J. Youth Adolesc . 38 , 765 - 776 . https://doi.org/10.1007/s10964-008-9298- 3 ( 2009 ). 11. Cheever , N. A. , Rosen , L. D. , Carrier , L. M. & Chavez , A. Out of sight is not out of mind: e impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users . Comput. Hum. Behav . 37 , 290 - 297 . https://doi.org/10.1016/j. chb. 2014 . 05 .002 ( 2014 ). 12. Sparrow , B. , Liu , J. & Wegner , D. M. Google e ects on memory: Cognitive consequences of having information at our ngertips . Science 333 ( 6043 ), 776 - 778 . https://doi.org/10.1126/science.1207745 ( 2011 ). 13. Ai , M. How computer and Internet use in uences mental health: A ve-wave latent growth model . Asian J. Commun . 23 , 175 - 190 . https://doi.org/10.1080/01292986. 2012 . 725179 ( 2012 ). 14. Jiang , B. , Schmillen , R. & Sullivan , W. C. How to waste a break: Using portable electronic devices substantially counteracts attention enhancement e ects of green spaces . Environ. Behav . 51 ( 9-10 ), 1133 - 1160 . https://doi.org/10.1177/0013916518788603 ( 2019 ). 15. Kaplan , S. e restorative bene ts of nature: Toward an integrative framework . J. Environ. Psychol . 15 ( 3 ), 169 - 182 . https://doi.org/ 10.1016/ 0272 - 4944 ( 95 ) 90001 - 2 ( 1995 ). 16. Kahneman , D. Attention and E ort (Prentice-Hall , 1973 ). 17. Baumeister , R. F. , Bratslavsky , E. , Muraven , M. & Tice , D. M. Ego depletion: Is the active self a limited resource? . J. Personal. Soc. Psychol . 74 ( 5 ), 1252 - 1265 . https://doi.org/10.1037/ 0022 - 3514 . 74 .5. 1252 ( 1998 ). 62. Ladouceur , C. D. , Dahl , R. E. & Carter , C. S. Development of action monitoring through adolescence into adulthood: ERP and source localization . Dev. Sci . 10 ( 6 ), 874 - 891 . https://doi.org/10.1111/j.1467- 7687 . 2007 . 00639 . x ( 2007 ). 63. Van Veen , V. & Carter , C. S. e timing of action-monitoring processes in the anterior cingulate cortex . J. Cogni. Neurosci . 14 ( 4 ), 593 - 602 . https://doi.org/10.1162/08989290260045837 ( 2002 ). 64. Carter , C. S. , Botvinick , M. M. & Cohen , J. D. e contribution of the anterior cingulate cortex to executive processes in cognition . Rev. Neurosci . 10 ( 1 ), 49 - 58 . https://doi.org/10.1515/REVNEURO. 1999 . 10 .1. 49 ( 1999 ). 65. Jasper , H. H. e ten-twenty electrode system of the International Federation . Electroencephalogr. Clin. Neurophysiol . 10 , 370 - 375 ( 1958 ). 66. Delorme , A. & Makeig , S. EEGLAB : An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis . J. Neurosci. Methods 134 ( 1 ), 9 - 21 . https://doi.org/10.1016/j.jneumeth. 2003 . 10 .009 ( 2004 ). 67. Lopez-Calderon , J. & Luck , S. J. ERPLAB : An open-source toolbox for the analysis of event-related potentials . Front. Human Neurosci. 8 , 1 - 14 . https://doi.org/10.3389/fnhum. 2014 . 00213 ( 2014 ). 68. Gratton , G. , Coles , M. G. & Donchin , E. A new method for o -line removal of ocular artifact . Electroencephalogr. Clin. Neurophysiol . 55 ( 4 ), 468 - 484 . https://doi.org/10.1016/ 0013 - 4694 ( 83 ) 90135 - 9 ( 1983 ). 69. Luck , S. J. & Gaspelin , N. How to get statistically signi cant e ects in any ERP experiment (and why you shouldn't) . Psychophysiology 54 ( 1 ), 146 - 157 . https://doi.org/10.1111/psyp.12639 ( 2017 ). 70. Meyer, A., Riesel , A. & Proud t, G. H. Reliability of the ERN across multiple tasks as a function of increasing errors . Psychophysiology 50 ( 12 ), 1220 - 1225 . https://doi.org/10.1111/psyp.12132 ( 2013 ). 71. R Core Team ( 2020 ). R: A language and environment for statistical computing. R Foundation for statistical computing . https:// www.R-project. org/. 72. Bates , D. , Maechler , M. , & Bolker , B. lme4: Linear mixed-e ects models using S4 classes . R package version 0 . 999999 - 0 . https:// cran.r-project.org/web/packages/lme4/index.html. ( 2012 ). 73. Witt , J. K. Graph construction: An empirical investigation on setting the range of the y-axis. Meta-psychology . https://par.nsf.gov/ servlets/purl/10107678 ( 2019 ). 74. Miltner , W. H. et al. Implementation of error-processing in the human anterior cingulate cortex: A source analysis of the magnetic equivalent of the error-related negativity . Biol. Psychol . 64 ( 1-2 ), 157 - 166 . https://doi.org/10.1016/S0301- 0511 ( 03 ) 00107 - 8 ( 2003 ). 75. Bush , G. , Luu , P. & Posner , M. I. Cognitive and emotional in uences in anterior cingulate cortex . Trends Cogn. Sci. 4 ( 6 ), 215 - 222 . https://doi.org/10.1016/S1364- 6613 ( 00 ) 01483 - 2 ( 2000 ). 76. Shackman , A. J. et al. e integration of negative a ect, pain, and cognitive control in the cingulate cortex . Nat. Rev. Neurosci . 12 ( 3 ), 154 - 167 . https://doi.org/10.1038/nrn2994 ( 2011 ). 77. Augustinova , M. et al. Does exposure to pictures of nature boost attentional control in the Stroop task? . J. Environ. Psychol . 84 , 1 - 6 . https://doi.org/10.1016/j.jenvp. 2022 . 101901 ( 2022 ). 78. McDonnell , A. S. , LoTemplio, S. B. , Scott , E. E. , McNay , G. D. , Greenberg , K. , & Strayer , D. L . (under review). e in uence of natural environments on neurophysiological correlates of reward processing . 79. Ulrich , R. S. et al. Stress recovery during exposure to natural and urban environments . J. Environ. Psychol . 11 ( 3 ), 201 - 230 ( 1991 ). 80. Scott , E. E. , McDonnell , A. S. , LoTemplio, S. B., Uchino , B. N. & Strayer , D. L. Toward a uni ed model of stress recovery and cognitive restoration in nature . Parks Stewardship Forum 37 ( 1 ), 46 - 60 . https://doi.org/10.5070/P537151710 ( 2021 ). 81. LoTemplio , S. et al. Healthy by nature: Policy practices aimed at maximizing the human behavioral health bene ts of nature contact . Policy Insights Behav. Brain Sci . 10 ( 2 ), 247 - 255 . https://doi.org/10.1177/23727322231197578 ( 2023 ).