Using Virtual Reality to Systematically Examine Impacts of Noise and Visual Clutter on Message Elaboration and Cognitive Capacity

Abstract

This study used virtual reality to examine how environmental attributes interact with health communication to influence psychiatric help-seeking behavior, using the example of a subway station. We used a 2 × 2 factorial design crossing two noise conditions (high noise [75 dB] or low noise [30 dB]) and two visual clutter conditions (low clutter [a tidy trash can and orderly construction materials] or high clutter [scattered trash and construction materials]). We found that participants in the high (vs. low) visual clutter condition reported lower cognitive capacity levels, and there was a significant correlation between cognitive capacity and message elaboration. However, we found no effects of noise conditions. Serving as a proof-of-concept study to investigate the contexts in which environmental stressors may influence information processing, this study contributes to the field of health communication environmental design research.

Clinical Trial Registration:

https://osf.io/rsa48

Introduction

Health communication often occurs in public spaces, but little is known about how environmental features of these spaces (e.g., noise, visual clutter) interact with messages.^1–7 Considering the features of the environment in which health information will be encountered is important for understanding information processing.^5,7 We report a proof-of-concept study using a virtual reality (VR) environment to systematically study the naturalistic combination of noise and visual clutter in a VR subway station.⁵ This project illustrates the potential value of immersive VR for examining the environments in which health information may be encountered.^5,6

Research significance

Environmental factors may significantly affect how people process health information, which may be posted in noisy and disorderly public places.^5–7 Noise can increase state anxiety,^7,9–11 but whether increased state anxiety due to noise can impact message elaboration of health information is unclear.^5–7 Similarly, visual clutter can lower persistence on challenging tasks,³ (e.g., performance on the Stroop task) but whether exposure to visual clutter can reduce cognitive performance, thus reducing message elaboration, is unknown.^5,6

The Elaboration Likelihood Model theory¹² predicts how message elaboration (the effort required to process and evaluate information)¹³ is driven by motivation and capacity to influence attitude change. While increased motivation may make up for capacity deficits during high state anxiety conditions,⁸ reduced capacity¹⁴ can lead to poorer performance on tasks requiring cognitive resources, such as message elaboration.^5,14,15 Variables (e.g., noise) that reduce capacity available for processing a persuasive message can thus reduce message elaboration.^5,7,15

The impact of noise on state anxiety and message elaboration

In experimental settings, state anxiety increases as noise increases.^5,9–11 High state anxiety decreases information processing ability, reducing message elaboration.^5,14 Thus, we propose:

H1: There will be an impact on noise on state anxiety. On average, participants in the high noise condition will report a higher state anxiety that those in the low noise condition.

H2: There will be a statistically significant correlation between self-reported state anxiety and self-reported message elaboration.

The influence of visual clutter on cognitive capacity, self-regulation, and message elaboration

Visual clutter, the perception of disorder due to features such as space, color, and cues such as litter,^1,3,5,6 is negatively associated with quality of life.¹⁶ Disorderly environments promote disorderly and/or impulsive behaviors.^3,17 For example, greater levels of perceived powerlessness were reported by those who think of their neighborhoods as visually disordered.^5,18 Perceived neighborhood disorder mediated the relationship between poverty and depression,^5,19 and exposure to disorderly environments reduced self-regulation.³ Notably, perceived powerlessness and self-regulatory failure are also associated with health risk perception.⁶

Difficulty in processing health messages would exhaust cognitive resources required for self-regulation.^20–22 Effortful regulation of negative thoughts and feelings may reduce one's cognitive capacity to exert self-regulated behavior.²⁰

Ego depletion theory states that self-regulation relies on a finite pool of mental resources.²³ Using regulatory resources in one task (e.g., regulating negative thoughts when processing visual clutter) reduces the amount of available resources to perform an immediate subsequent task (e.g., health risk perception), even if the tasks are mostly unrelated.²⁴ Thus, cognitive capacity tends to covary with message elaboration.¹² We predict that there will be a correlation between cognitive and message elaboration, with lower cognitive leading to fewer topic-related thoughts when seeing information on psychiatric help seeking.^5,14

H3: There will be an impact of visual clutter on cognitive capacity. On average, participants in a high visual clutter condition will demonstrate a lower level of cognitive capacity than those in the low visual clutter condition [number of errors on Stroop Test (3A) and near misses (3B)].

H4: Cognitive capacity will be correlated with self-regulation.

Exploratory Research Question 1: Will cognitive capacity correlate with self-reported message elaboration?

Noise and visual clutter context: Subway station sound effect and litter and construction materials

As people often see health information in subway stations (Fig. 1), we selected a subway platform as our “environment of interest.”^5,6 We used psychiatric help-seeking messages, which encourage viewers to seek help for conditions such as depression, as our topic.^5–7 We displayed seven posters in the virtual subway boarding area (Fig. 2)^5,6 showing messages adapted from Leite.²⁵

FIG. 1.

Psychiatric help-seeking messages on the subway platform.

FIG. 2.

Subway station.

Materials and Methods

We used a 2 × 2 factorial design (two noise conditions × two visual clutter conditions).⁵ Participants experienced either high (75 dB) or low (30 dB) noise in the form of ambient subway sound effects.^5,6 Participants in the low clutter condition saw trash placed in a trash can and construction materials placed in an orderly manner.^5,6 In the high clutter condition, trash was scattered around the receptacle and construction materials were disordered.^5,6 We conducted the IRB-approved study in Spring 2019: preregistered here: https://osf.io/rsa48.⁵

Participants

We recruited 124 first-year undergraduates (∼30 per condition), giving us the power to detect f = 0.06.⁵ Table 1 presents the demographic distributions. All participants provided informed consent and received a $10 gift card or two course credits.⁵

Table 1.

Summary of Demographics

Gender
Female	67.7%
Male	31.5%
Prefer not to say	0.8%
Ethnic backgrounds
White or Caucasian	30.6%
Black or African American	4.8%
Hispanic or Latino	3.2%
Multiple races/ethnicities	11.3%
Other	0.8%
Prefer not to say	3.3%

Experimental environment

Participants wore an Oculus Rift head-mounted display and headphones without accompanying hand controllers.^5,6 They were not embodied in an avatar.^5,6 A virtual subway station, purchased from the Unity Asset Store, was modified as described above (Figs. 2 –5).^5,6

FIG. 3.

Trash can.

FIG. 4.

Trash.

FIG. 5.

Construction materials.

Procedure

Each session took ∼30 minutes.⁵ After consenting, participants were told to “wait for the Q train” as they stood in the simulated station.⁵ No special measure directed their attention to the posters.⁵ After 8 minutes, participants completed a Stroop Test and their response accuracy was noted.^5,22 They then completed a survey.⁵

Measures

Table 2 presents the dependent variables and manipulation checks.

Table 2.

List of Dependent Variables, Manipulation Checks, and Psychiatric Help-Seeking Messages

Measure	Source	Examples of items	Scale	Interpretation	Cronbach's alpha
Dependent variable
State anxiety (20 self-reported items)	State-Trait Anxiety Inventory.³⁰	I am tense. I am worried. I feel calm.	1: Almost Never to 4: Always	Higher scores indicate greater anxiety	0.924
Message elaboration (12 self-reported items)	Message elaboration scale.¹⁰	While reading the PHS message, I was: Attempting to analyze the issues in the message. Not very attentive to the ideas. Deep in thought about the message.	1: Strongly Disagree to 7: Strongly Agree	Higher scores indicate greater message elaboration	0.738
Self-regulation in the context of psychiatric help-seeking Cause: To assess one's beliefs regarding the causes of the experience described in the poster (16 self-reported items)	Self-regulation measure.²⁵	Please continue to imagine that you are having the experience described in the posters. For each item below, please circle the number that indicates how likely you think the circumstance could have caused the experience. Being overworked. Lack of sleep. Personality.	1: Very Unlikely to 7: Very Likely	Higher scores indicate higher self-regulation.	0.877
Self-regulation in the context of psychiatric help seeking Consequence: To assess one's beliefs regarding the consequences of the experience described in the poster (12 self-reported items)	Self-regulation measure.²⁵	Please continue to imagine that you are having the experience described in the posters. Using the following scale, please circle the number that indicates how likely you think each item would be a consequence of the experience. Have difficulties performing day to day tasks. Have difficulties with my school assignments. Have difficulty interacting with others.	1: Very Unlikely to 7: Very Likely	Higher scores indicate higher self-regulation.	0.891
Self-regulation in the context of psychiatric help seeking Timeline: To assess one's beliefs regarding the duration of the experience described in the poster (1 self-reported item).	Self-regulation measure.²⁵	Please continue to imagine that you are having the experience described in the posters. How long do you think that experience would last? Please check the box that best reflects your answer.	Less than 1 hour 2–3 days About 1 week 2–3 weeks 2–3 months 6 months 1 year or longer	Higher scores indicate higher self-regulation.	0.880
Self-regulation in the context of psychiatric help seeking Control: To assess one's beliefs regarding the controllability of depressive symptoms (3 self-reported items)	Self-regulation measure.²⁵	Please continue to imagine that you are having the experience described in the posters. Using the following scale, please rate the extent to which you agree with each statement. That experience would be controllable. Nothing I do would affect that experience. There is a lot I could do to control that experience.	1: Strongly Disagree to 7: Strongly Agree	Higher scores indicate higher self-regulation.
Response accuracy (No. of errors)	Stroop test.²⁹	A participant answered “red” when “blue” was the correct response.		Higher score, lower cognitive capacity.	N/A
Response accuracy (near misses)	Stroop test.²⁹	A participant answered “red… oh, no blue.”		Higher score, lower cognitive capacity.	N/A
Manipulation check
Noise (2 self-reported items)		The subway station was quiet.The subway station was noisy.	1: Strongly Disagree to 5: Strongly Agree	Higher scores indicate higher perceived level of noise.	1.00
Visual clutter (2 self-reported items)		The subway station was well organized. The subway station was messy.	1: Strongly Disagree to 5: Strongly Agree	Higher scores indicate higher perceived level of visual clutter.	1.00
Environmental familiarity (1 self-reported item)		The subway environment I saw in the virtual environment was familiar to me.	1: Strongly Disagree to 5: Strongly Agree	Higher scores indicate higher perceived level of environmental familiarity.	N/A
Psychiatric help-seeking messages²⁴
Have you1. been less interested in doing things that you used to enjoy, and don't get as much pleasure out of life? Seek help,2. found it hard to concentrate and have struggled to make decisions? Seek help,3. sometimes think that you are a failure and that everyone else is better than you? Seek help,4. felt tired, and have had trouble sleeping? Seek help,5. been feeling sad and sometimes cry? Seek help,6. had less energy than usual, and sometimes find it difficult to get out of bed? Seek help, and7. not felt like talking, and haven't gone out with your friends and family as often as you used to? Seek help.

Results

Alpha was set at 0.05.⁵ Outliers were assessed by inspecting a boxplot, normality was assessed using Shapiro–Wilk's normality test for each cell of the design, and homogeneity of variance was assessed by Levene's test.⁵ Table 3 presents the number of participants per condition, statistical test used, and results. Participants in the high (vs. low) visual clutter condition reported lower cognitive capacity level.⁵ The correlation between cognitive capacity and message elaboration was statistically significant.⁵ However, we found no effects of noise conditions.⁵

Table 3.

Summary of Hypotheses, Participants Per Conditions, Statistical Tests Used, and Results

Hypothesis	Participants per condition	Test used	Results	Hypothesis supported?
Manipulation check
Participants will report a difference in perceived noise level across conditions	High noise and high visual clutter (n = 31)High noise and low visual clutter (n = 31)Low noise and high visual clutter (n = 30)Low noise and low visual clutter (n = 32).	Independent samples t-test	Successful manipulations for N (M_high N = 4.50, SD_high N = 0.81; M_low N = 1.26, SD_low N = 0.59; p < 0.001, d = 4.57)Successful manipulations for VC (M_high VC = 4.03, SD_high VC = 0.84; M_low VC = 2.14, SD_low VC = 0.98; p < 0.001, d = 2.06).	Yes
H1: Influence of noise on state anxiety
Noise impacts state anxiety. On average, participants in a high (vs. low) noise condition will report a higher level of state anxiety.	High noise—75 dB (n = 62)Low noise—30 dB (n = 62)	Independent samples t-test	No difference in state anxiety levels between participants in the high (M = 1.94, SD = 0.58) and low (M = 1.87, SD = 0.49) noise conditions, t(124) = 0.744, p = 0.458.	No
H2: Correlation between state anxiety and message elaboration
Self-reported state anxiety will be statistically significantly correlated with self-reported message elaboration.		Pearson's r correlation test	State anxiety was not correlated statistically significantly with message elaboration (r = 0.106, p = 0.242).	No
RQ3A: Influence of visual clutter on cognitive capacity (number of errors on Stroop Test)
Visual clutter impacts cognitive capacity. On average, participants in a high (vs. low) visual clutter condition will demonstrate a lower level of cognitive.	High visual clutter–presence of trash (n = 61)Low visual clutter–absence of trash (n = 63)	Welch's t-test:Due to the assumption of homogeneity of variances being violated, as assessed by Levene's test for equality of variances (p < 0.001)	Cognitive capacity levels as measured by lower error scores were higher for participants in the low visual clutter condition (M = 0.21, SD = 0.74) than high visual clutter condition (M = 0.93, SD = 1.45), a statistically significant difference, M = 0.73, 95% CI [0.32–1.14], t(88.981) = 3.505, p = 0.001, d = 0.63.	Yes
RQ3B: The influence of visual clutter on cognitive capacity (near misses)
Visual clutter impacts cognitive capacity. On average, participants in a high (vs. low) visual clutter condition will demonstrate a lower level of cognitive.	High visual clutter–presence of trash (n = 61)Low visual clutter–absence of trash (n = 63)	Independent-samples t-testNote: There were two outliers in the data, as assessed by inspection of a boxplot. However, we did not perform any transformation (i.e., outliers were not removed) given that “near misses” scores were normally distributed.	Cognitive capacity levels were higher for participants in the low visual clutter condition (M = 0.46, SD = 0.714) than the high visual clutter condition (M = 0.90, SD = 1.136), a statistically significant difference, M = 0.44, 95% CI [0.11–0.78], t(122) = 2.599, p = 0.011, d = 0.46.	Yes
H4: Correlation between cognitive capacity and self-regulation
Cognitive capacity will be correlated with self-regulation.		Pearson's r correlation test	Cognitive capacity was not correlated statistically significantly with self-regulation (r = −0.021, p = 0.531).	No
Exploratory Research Question 1: Correlation between cognitive capacity and message elaboration
Cognitive capacity will be correlated with self-reported message elaboration.		Pearson's r correlation test	Cognitive capacity was correlated statistically significantly with self-reported message elaboration (r = −0.226, p = 0.012).	Yes

Discussion

This study examined the impact of noise on state anxiety and message elaboration, the impact of visual clutter on cognitive capacity and self-regulation, and whether cognitive capacity and message elaboration were correlated.⁵ We found that noise did not impact state anxiety, and state anxiety and message elaboration were not correlated.⁵ Participants in the high (vs. low) visual clutter condition reported lower cognitive capacity level.⁵ Cognitive capacity and self-regulation were not correlated, but cognitive capacity and message elaboration were correlated.⁵

The impact of noise on state anxiety and message elaboration

Participants were exposed to noise for only 8 minutes.⁵ Past research has not previously studied the impact of noise on state anxiety over such periods of time, implying that the role of the noise might be different for other durations.^5,9–11 In addition, since subway noise is relatively common, participants may be “accustomed to” the noise.⁵ However, this explanation is less likely as the same statistical analysis was conducted with potential covariates (i.e., environmental familiarity, subway station usage frequency) and still found no significant results.⁵

We found no significant correlation between state anxiety and message elaboration.⁵ As participants were asked to look toward the train track, they may have been “primed” to look toward the location of the psychiatric help-seeking posters, leading to the nonsignificant differences across conditions.⁵

The impact of visual clutter on cognitive capacity

The results show a significant main effect of visual clutter on cognitive capacity (performance on the Stroop test) between the high and low visual clutter conditions, supporting H3a and H3b.⁵ Fewer accurate responses point to a lower cognitive capacity level, indicating that resource-demanding activities like vigilance, monitoring, and distraction are required when people experience and cope with threats (e.g., seeing visual clutter).^5,27 The number of “near misses”—an exploratory proxy measure of cognitive capacity—was significantly lower in the high visual clutter condition, highlighting the potential for this measure to be used in future research.⁵

The correlation between cognitive capacity and self-regulation

Findings did not show that cognitive capacity and self-regulation were correlated.⁵ To our best knowledge, past research has not examined the impact of visual clutter on general measures of self-regulation or specifically on self-regulation in the context of psychiatric help seeking.⁵ Although Ref.²⁷ used the measure of self-regulation (psychiatric help seeking) to investigate cognitive representations of depression in the context of mental health literacy, the internal consistency coefficients and test–retest reliability coefficients of this measure have not been validated.⁵ Overall, the construct and content validity of these two measures require further validation.⁵ In addition, this finding should be considered in light of recent critiques of ego-depletion theory, citing conceptual issues that hinder the derivation and testing of specific falsifiable predictions.^5,30

The correlation between cognitive capacity and message elaboration

Findings show that cognitive capacity and message elaboration were significantly correlated, supporting the exploratory analysis.⁵ Ongoing elaboration of distraction, such as visual clutter, may hinder participants' information processing.⁵ Thus, visual clutter lowers the level of cognitive capacity available to process a message, hence, impacting a participant's level of message elaboration.^5,15

Theoretical implications

Factors influencing health behaviors are complex.^5,7,26 Models and theories (e.g., The Elaboration Likelihood Model) can help us better understand how the world operates and how improvements can be made, particularly if multidimensional factors that impact people's lives can be included.²⁸ However, theoretical advancements tend to be rooted within specific disciplines lowering the likelihood of developing behavioral interventions applicable to the broader population.^5,29,30 Disciplines like health communication (informed by social psychology) and design lack systematic investigation into environmental attributes that may influence information processing, particularly in lived environments.^5,7 VR can address this need.^5,6 In our study, the use of VR allowed us to systematically investigate the effects of noise and visual clutter in a tightly controlled setting, leading to the novel finding that participants experiencing high visual clutter indicated a lower cognitive capacity level.⁵

Practical implications

Our research findings point to a larger set of issues to which health communicators, designers, and policymakers should attend—physical environments shape how we process messages.^5,7 Past research (e.g., Refs.^9–11) found that environmental stressors can influence one's psychological state and information processing—although in a highly simulated environment. This proof-of-concept study indicates the need for more of a robust understanding of which environmental features critically influence information processing in lived environments.⁹ Systematic isolation of environmental-design attributes can help establish more evidence-based guidelines for the strategic communication of health information in lived environments.⁵

In the context of psychiatric help seeking, awareness of the appropriate environments for sharing information will help in locating such messages where they will be most effective.^5,7 Rather than placing messages in chaotic environments associated with crowds such as subways, less chaotic spaces might be more useful (e.g., park benches or libraries).⁵ In an epoch where mental health problems appear to be increasing, good communication of health treatment options is critical.^5,7

Because visual clutter influences cognitive capacity and downstream psychiatric help-seeking measures, communication practitioners should think about the impact of visual clutter in physical and digital environments where health information is located.⁵ Using VR as a research tool to examine the influence of common environmental stressors on psychosocial factors, researchers can virtually conceptualize, create, and examine the effects of environments on health communication.⁵ By enhancing our knowledge of the design and use of VR, VR research may improve our understanding of (a) how health communication in various environments can be impacted by the intersection between communication and environmental-design attributes and (b) environmental factors that influence behavior and how to alter environmental- and/or policy-based interventions.^5,8

Conclusions

This study examined how communication and environmental attributes can interact to influence psychiatric help seeking through communication in public spaces, using the example of a subway station.^5,6,8 Specifically, we examined the respective influences of noise and visual clutter on viewer elaboration and cognitive capacity.^5,6

Limitations and future research

Participants indicated state anxiety after exposure to noise for eight minutes, but the exposure duration may be a threat to construct validity.⁵ Future research may include noise of other durations or ranges.⁵

Participants mostly indicated self-reported measures (e.g., state anxiety) after being exposed to the virtual environment.⁵ Research in the future should explore approaches to measure physiological, behavioral, and attitudinal responses while participants are still in the virtual environment.⁵

Footnotes

Acknowledgments

The authors thank Mr. Frank Rodriguez for helping to design the virtual environment and Ms. Francoise Vermeylen and Ms. Erika Mudrak of the Cornell Statistical Consulting Unit for their help with the analysis.

Authors' Contributions

J.N., M.S., and A.W. conceived and planned the experiment. J.N. carried out the experiments. J.N., M.S., and A.W. contributed to the interpretation of the results and writing of the article. All authors provided critical feedback and helped shape the research, analysis, and article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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