69 Effects of Natural and Urban Imagery on the Error-Related Negativity

Graduate Student Mentor:  Amy McDonnell (Psychology, University of Utah)

Much research has gone into studying the benefits of exposure to natural environments, especially benefits related to attention, stress, and mood. However, there is an open question related to whether an individual must be exposed to real nature to receive these benefits, or if viewing images of nature has the same power. The present study uses electroencephalography (EEG) to explore the effect of viewing nature imagery on brain activity related to attention. Specifically, it measures the amplitude of the error-related negativity (ERN), a brainwave that indexes attentional control, after viewing images of nature. Building upon prior literature that suggests an improvement in attention (as indexed by an increase in amplitude of the ERN) during immersion in real nature, we predicted a positive relationship between viewing nature imagery and ERN amplitude, such that ERN amplitude would increase as a result of viewing nature images compared to urban images or a no-image control. However, data from 56 participants revealed that there was no influence of viewing nature imagery on the amplitude of the ERN, suggesting that there are characteristics of immersion in real nature that influence brainwaves related to attention that aren’t present in just pictures of nature.

Introduction

The theory that attention is a limited resource is widely accepted. For example, when we are forced to exert effortful attention for long periods of time, our attentional resources can become depleted, leading to decreased attentional performance (Baumeister, Bratslavksy, Muraven, & Tice, 1998). This limitation of attention affects things such as what we can give our attention to, how well we can perceive and process information, and how efficient we are when performing various tasks (Oberauer, 2019). Knowing this, studying how to restore our easily depleted attentional resources is important. Attention Restoration Theory (ART; Kaplan, 1995) proposes one way to restore attention—spending time in nature. ART argues that natural environments contain less attentional demands, allowing us space to recuperate our attentional resources, leading to increased attentional performance. Since many people are spending less and less time in nature and more time in urban environments, understanding the significance of nature’s restorative effects could change how we prioritize spending time in nature.

Many behavioral studies have explored the ideas set forth by Attention Restoration Theory (ART), which proposes that exposure to nature can “restore” various aspects of attention that are depleted by our everyday, urban environments (Kaplan, 1995). These studies have shown improved performance on cognitive behavioral tasks requiring creativity (Atchley, Strayer, & Atchley, 2012), sustained attention (Berman, Jonides, & Kaplan, 2008), and working memory (Bratman et al., 2015) after nature exposure. In general, the findings of these studies support ART’s proposition that exposure to nature positively affects our attentional performance.

However, showing nature’s restorative effects using simple behavioral metrics is one way to study ART. Using neurophysiological metrics adds information about the actual changes that are happening in the brain when people interact with nature and how it later influences their behavior. Thus, using neurophysiology allows for a deeper understanding of nature’s influence on the individual from multiple levels of analysis. Less research has investigated the neurophysiological mechanisms underlying the behavioral benefits of time in nature. One study run by Bratman et al. (2015) employed fMRI before and after a 50-minute walk and found reduced activity in the subgenual prefrontal cortex, an area in the brain associated with depression, in participants who completed a nature walk as opposed to an urban walk. Other preliminary research has found evidence of attention-related electrophysiological brain responses (using EEG) to nature (Aspinall et al., 2015; LoTemplio et al., 2020; McDonnell et al., under review; Scott et al., in prep). These findings are important in helping us better understand nature’s restorative properties on a neural level as opposed to just a behavioral level.

The present study builds off a prior study conducted at the University of Utah. In this study, LoTemplio and colleagues (2020) took participants on a 4-day camping trip and used EEG to measure brain activity related to attention before, during, and after the exposure to nature. In particular, they measured changes in the error-related negativity (ERN), a brain component related to attentional control. The ERN is a negative-deflection in frontally- and centrally distributed brainwaves (electrodes Fz and Cz) that appears within 100ms after an individual makes an error on a task. The ERN is not present when an individual makes a correct response, so it is thought to index our ability to recognize an error and allocate our attention accordingly to correct subsequent behavior (Gehring et al., 1993). The ERN is most often generated by the Flanker Task (Eriksen & Eriksen, 1974), so participants in this study completed a Flanker Task before, during, and after the camping trip in order to elicit the ERN. LoTemplio and colleagues (2020) found that ERN amplitude increased during the nature exposure, thought to represent an increase in attentional control abilities in nature and thus potentially indicative of attention restoration (LoTemplio et al., 2020). The present study serves as the control study to LoTemplio et al. (2020) and seeks to answer whether or not someone needs to be fully immersed in nature to receive attentional benefits, or if viewing nature images is enough to improve attentional control. In this study, we measured the same ERN brainwave elicited by the Flanker Task to see if it acted similarly to what was found in the nature immersion study.

As mentioned, our study utilizes electroencephalography (EEG) to measure changes in the amplitude of the error-related negativity (ERN). The ERN peaks within 100 milliseconds after an individual makes an error on a task. The ability to recognize the error is related to the attention allocated to the task. Studies have shown that when someone is more motivated to avoid errors on a task, the ERN increases (Gehring et al., 1993; Hajcak & Foti, 2008). A larger ERN is also associated with better self-regulation (Legault & Inzlicht, 2013; Potts et al., 2006) and increased working memory capacity (Coleman et al., 2018). The use of EEG allows us to explore whether the ERN is influenced by exposure to nature imagery. This may provide further evidence of Attention Restoration Theory beyond just behavioral metrics, as well as answer the question of whether simply viewing images of nature is enough to see the restorative changes proposed by ART (as opposed to being immersed in real nature).

We hypothesized that if nature images are enough exposure to nature to elicit attentional changes in the brain, there will be an increase in the amplitude of the ERN after viewing 10 minutes of nature imagery, which would conceptually replicate the results of LoTemplio et al. (2020). On the other hand, if images of nature are not enough exposure to restore attention there will be no change in the amplitude of the ERN across the three testing sessions. This would suggest that there is something unique about immersion in nature as opposed to just viewing images of nature when it comes to restoring attentional resources.

Method

Participants

Participants (N=56) were recruited from the greater Salt Lake area and paid $70 compensation at the end of the study. The sample was compromised of 41 females (73%) and 15 males (27%). The participants ages ranged from 18-50 (M = 25.13, SD = 5.46). The majority of the sample identified as White/Caucasian at 77% with 17% identifying as Asian, 4% identifying as Hispanic/Latino, and 2% identifying as Black/African American.

Materials

EEG data were recorded using the BIOPAC EEG system (BIOPAC Systems, Goleta, CA, USA) and reusable electrodes (NATUS Neurology Grass gold-surface electrodes, model F- E5GH-48) and observed through the AcqKnowledge (Version 5.0) software while participants were testing.

To elicit the ERN (our dependent variable), participants completed 800 trials of the Flanker Task (Eriksen & Eriksen, 1974) while the EEG was recorded, consistent with LoTemplio et al. (2020). The Flanker Task asked participants to respond to the central letter in a five-letter sequence. There were either congruent stimuli consisting of all identical letters (e.g., SSSSS or HHHHH), or incongruent stimuli (e.g., SSHSS or HHSHH). Participants were instructed to respond quickly and accurately to the central letter using keys on the keyboard. Importantly, the ERN is generated on each trial that the participant makes an error on this task (e.g., when they respond that the middle letter is an H when it is actually an S).

The nature and urban images that were utilized in this study were the same images used by Berman, Jonides, and Kaplan (2008) in a study that found attentional benefits associated with viewing nature compared to urban imagery. The nature and urban images were presented to the participant via a 10-minute PowerPoint slideshow before they completed the Flanker Task.

EEG Data Processing

EEG data was processed in MatLab using the EEGLab and ERPLab toolboxes (Lopez- Calderon & Luck, 2014). The data was first down sampled to 250 Hz and filtered from 0.1-30 Hz in order to only capture brain activity and not surrounding electrical activity from the environment. We then identified all the blinks and eye-movements in the data and corrected
them using eye-movement correction procedure (Gratton, Coles, & Donchin, 1983). Then, once the data was clean of eye-movements, we identified all the timepoints in which a participant made a correct response and all the timepoints in which they made an incorrect response and then averaged them together separately to be left with a single waveform of correct responses and a single waveform of incorrect responses. We then subtracted the correct brain activity from the incorrect brain activity to be left with the ERN (which is technically a difference wave of incorrect minus correct).

Design

Consistent with LoTemplio et al. (2020), participants completed three testing sessions, spanning three weeks, as the within-subjects component. However, in the present study at Session 2, half the participants were randomized to view nature images and half viewed urban images, making Image Type a between-subjects component. This resulted in a mixed design with both a within- and between-subjects component. At Sessions 1 and 3, participants looked at a concrete wall for 10 minutes before completing the Flanker Task. At Session 2, they viewed environmental imagery (nature or urban) for 10 minutes before completing the Flanker Task. Thus, like LoTemplio et al. (2020), the environmental manipulation occurred at Session 2.

Procedures

At each session participants read through and signed a consent document and had questions answered by the researcher. Researchers then explained the study set up and what the participant could expect at each session. Participants were then set up with the EEG system in the lab while filling out some self-report questionnaires for a different study. Scalp electrodes were placed at Fz, Cz, and Pz with a reference electrode on the right mastoid bone, a ground electrode on the middle of the forehead, and eye electrodes above and below the right eye for recording blink activity.

During the first session all participants spent the first 10 min staring at a concrete wall outside of the BEHS building on the University of Utah campus. They did this in a weatherproof enclosure sitting in a chair to protect the EEG equipment from the elements. After those 10 min they completed the Flanker Task to elicit the ERN. At the second session they completed the same procedure, but instead of staring at the wall for 10 min they either viewed urban images for 10 min or images of nature for 10 min (Berman et al., 2008) before completing the Flanker Task. Participants were randomly assigned to view either urban (N=28) or nature images (N=28) making the study both between subjects (urban vs. nature) and within subjects (no imagery vs. imagery). The third testing session looked identical to the first, in which the 10 mins were spent staring at the concrete wall. Thus, the second testing session contained the nature manipulation (similar to LoTemplio et al., 2020). At the end of each session, they filled out a post-task mindfulness survey for a different study while the EEG electrodes were removed. At the end of the third session participants were compensated and debriefed.

Results

This research was aimed at examining the relationship between ERN amplitude and viewing nature imagery. We started by analyzing the data to get descriptive information including participant’s age, gender, and ethnicity (reported above). We then processed the ERN data for each participant in MatLab using the procedures outlined in the Methods section. We conducted two separate analyses to compare ERN amplitude between nature imagery and no- imagery (by viewing the results of the nature imagery participants at sessions 1, 2, and 3) and to compare ERN amplitude between nature imagery and urban imagery only at Session 2.

To analyze the ERN data for the nature imagery viewing participants from Session 1 to 2 to 3, we used a linear mixed effects model using the lme4 package (version 1.1-17) in the R

To compare ERN amplitude at Session 2 between the nature viewing participants and the urban viewing participants, we ran an independent samples t-test. We found that there was no

significant difference in ERN amplitude between the two groups (t(50.85)= 1.55, p=.128).

Discussion

In this study we looked at the ideas set forth by Attention Restoration Theory (ART) which proposes that time in natural settings restores our attentional resources (Kaplan, 1995). A previous study by LoTemplio and colleagues (2020) tested ART by taking participants out into nature on a 4-day camping trip and measuring their ERN using EEG equipment while performing a standardized Flanker Task (Eriksen & Eriksen, 1974). They found a significant increase in ERN amplitude when participants were on the camping trip compared to before and after, suggesting better attentional control during immersion in nature. We ran the present study to compare the findings of LoTemplio et al. (2020) to the ERN amplitude of participants who viewed images of nature as opposed to spending time in a natural environment. Our hypothesis was that if nature images are enough exposure to nature to elicit attentional changes in the brain, there would be an increase in the amplitude of the ERN after viewing 10 minutes of nature imagery. On the other hand, if images of nature are not enough exposure to restore attention, there would be no change in the amplitude of the ERN across the three testing sessions, suggesting that there is something unique about immersion in nature as opposed to just viewing images of nature when it comes to restoring attentional resources.

The results of the study found that there was no statistically significant difference in amplitude of the ERN after viewing nature imagery compared to no imagery, nor after viewing nature imagery compared to urban imagery. These findings are informative in that they show the importance of immersing oneself in nature as opposed to just viewing pictures of nature when it comes to restoring attentional resources. Since previous studies have shown the positive effect of immersion in nature on attention restoration (Aspinall et al., 2015; Bratman et al., 2015; LoTemplio et al., 2020; McDonnell et al., under review; Scott et al., in prep), it’s worth thinking about why viewing images of nature didn’t produce the same results.

ART focuses on four components that lead to attention restoration: soft fascination, being away, extent, and compatibility (Kaplan, 1995). Soft fascination causes a decreased need for directed attention and opportunities for reflection which helps restore depleted attentional resources. Being away refers to when someone is free of the mental effort required for directed attention. Extent means the environment must be engaging enough for the mind, allowing for significant depth of processing. Compatibility indicates that the natural environment someone is in must be compatible with them. For example, someone who has a strong aversion to the cold would probably not feel restored in a snowy, mountainous environment. With these four components in mind, we can start to see why we may have gotten the results that we did. Viewing pictures of nature does not produce soft fascination, a sense of being away, or extent nearly as much as spending time immersed in the natural settings would. This could be why immersion in nature is so essential to producing the desired restorative results.

With that in mind it’s also important to consider the limitations and possible future directions of our study. One limitation to our study that could be considered is the fact that we had participants view nature imagery for only 10 minutes, which is not comparable to the four days that participants spent out in nature during the LoTemplio et al. (2020) study. Further research could explore if spending a longer time viewing nature imagery would illicit different results. It is also possible that viewing a video of nature or that a more immersive virtual reality nature experience may influence amplitude of the ERN more so than static images do. These slightly more dynamic and immersive visual experiences may increase the amount of soft fascination and/or extent of the experience, thus potentially making them more restorative. On this note, it would be valuable to better understand the differences in nature immersion vs. viewing nature imagery in the components of soft fascination, being away, and extent. Research could be done to measure these three components in those two conditions to see if there is in fact a significant difference in the components when you are spending time in nature.

Attention is becoming an increasingly important resource in our daily lives and so, finding ways to restore it effectively is essential. The more we understand how things such as nature affect us the better able we are to make choices that benefit us. Understanding that there is a difference between spending time in nature and viewing nature imagery changes the conversation around how important it is to protect natural spaces and how much we should invest in making them accessible to all people.

Bibliography

Aspinall, P., Mavros, P., Coyne, R., & Roe, J. (2013). The urban brain: analysing outdoor physical activity with mobile EEG. British Journal of Sports Medicine, 49(4), 272–276. https://doi.org/10.1136/bjsports-2012-091877

Atchley, R. A., Strayer, D. L., & Atchley, P. (2012). Creativity in the Wild: Improving Creative Reasoning through Immersion in Natural Settings. PLoS ONE, 7(12), e51474. https://doi.org/10.1371/journal.pone.0051474

Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of personality and social psychology, 74(5), 1252.

Berman, M. G., Jonides, J., & Kaplan, S. (2008). The Cognitive Benefits of Interacting With Nature. Psychological Science, 19(12), 1207–1212. https://doi.org/10.1111/j.1467- 9280.2008.02225.x

Bratman, G. N., Hamilton, J. P., Hahn, K. S., Daily, G. C., & Gross, J. J. (2015). Nature experience reduces rumination and subgenual prefrontal cortex activation. Proceedings of the National Academy of Sciences, 112(28), 8567–8572. https://doi.org/10.1073/pnas.1510459112

Cohen, S., Kamarck, T., Mermelstein, R., (1994). Perceived stress scale. In: Measuring Stress: A Guide for Health and Social Scientists, pp.235-283.

Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & psychophysics, 16(1), 143-149.

Gehring, W. J., Goss, B., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1993). A Neural System for Error Detection and Compensation. Psychological Science, 4(6), 385–390. https://doi.org/10.1111/j.1467-9280.1993.tb00586.x

Gratton, G., Coles, M. G., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4), 468-484.

Hajcak, G., & Foti, D. (2008). Errors Are Aversive. Psychological Science, 19(2), 103–108. https://doi.org/10.1111/j.1467-9280.2008.02053.x

Kaplan, S. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15(3), 169-182.

Legault, L., & Inzlicht, M. (2013). Self-determination, self-regulation, and the brain: Autonomy improves performance by enhancing neuroaffective responsiveness to self-regulation failure. Journal of Personality and Social Psychology, 105(1), 123–138. https://doi.org/10.1037/a0030426.

Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in human neuroscience, 8, 213.

LoTemplio, S. B., Scott, E. E., McDonnell, A. S., Hopman, R. J., Castro, S. C., McNay, G. D., … & Strayer, D. L. (2020). Nature as a potential modulator of the error-related negativity: a registered report. International Journal of Psychophysiology, 156, 49-59.

Marshall, A.L., Smith, B.J., Bauman, A.E., Kauer, S., 2005. Reliability and validity of a brief physical activity assessment for use by family doctors. British Journal of Sports
Medicine 2005; 39:294-297.

McDonnell, A.S., LoTemplio, S.B., Scott, E.E., Greenberg, K., McNay, G.D., Castro, S.C., & Strayer, D.L. (In prep). Nature modulates neurophysiological correlates of reward.

Potts, G. F., George, M. R. M., Martin, L. E., & Barratt, E. S. (2006). Reduced punishment sensitivity in neural systems of behavior monitoring in impulsive individuals. Neuroscience Letters, 397(1-2), 130–134. https://doi.org/10.1016/j.neulet.2005.12.003

Scott, E.E., McDonnell, A.S., LoTemplio, S.B., McNay, G.D., Greenberg, K., Castro, S.C., & Strayer, D.L. (In prep). Prolonged immersion in nature modulates neural correlates of working memory.

Watson, D., Clark, L.A., 1999. The PANAS-X: Manual for the Positive and Negative Affect Schedule-expanded Form.

Oberauer K. (2019). Working memory and attention—A conceptual analysis and review. Journal of cognition, 2(1), 36. https://doi.org/10.5334/joc.58