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620784 research-article2016 Research Report Psychological Science 2016, Vol. –294 Wearing a Bicycle Helmet Can Increase © The Author(s) 2016 Reprints and permissions: Risk Taking and Sensation Seeking sagepub.com/journalsPermissions.nav DOI: 10.1177/ pss.sagepub.com in Adults Tim Gamble and Ian Walker Department of Psychology, University of Bath Abstract Humans adapt their risk-taking behavior on the basis of perceptions of safety; this risk-compensation phenomenon is typified by people taking increased risks when using protective equipment. Existing studies have looked at people who know they are using safety equipment and have specifically focused on changes in behaviors for which that equipment might reduce risk.

Here, we demonstrated that risk taking increases in people who are not explicitly aware they are wearing protective equipment; furthermore, this happens for behaviors that could not be made safer by that equipment. In a controlled study in which a helmet, compared with a baseball cap, was used as the head mount for an eye tracker, participants scored significantly higher on laboratory measures of both risk taking and sensation seeking. This happened despite there being no risk for the helmet to ameliorate and despite it being introduced purely as an eye tracker. The results suggest that unconscious activation of safety-related concepts primes globally increased risk propensity. Keywords risk taking, sensation seeking, social priming, bicycling, protective equipment, behavior change, open data Received 9/8/15; Revision accepted 11/12/15 People’s perceptions of safety influence their risk taking. risk-taking behavior has been in the same domain as the This phenomenon, studied under such rubrics as risk safety measure (e.g., studies of seat-belt use in driving compensation (Adams & Hillman, 2001), risk homeosta- speed; Janssen, 1994). sis (Wilde, 1998), and risk allostasis (Lewis-Evans & Here, we changed both these approaches.

First, we Rothengatter, 2009), is typified by people taking increased induced people to wear a helmet without their necessar- risks when using protective equipment (Adams, 1982) or ily being aware they were wearing safety equipment: at least reducing their risk taking when protective equip- Participants were (falsely) told they were taking part in ment is absent (Fyhri & Phillips, 2013; Phillips, Fyhri, & an eye-tracking study so we could exploit the fact that Sagberg, 2011). Behavioral adaptation in response to the head-mounted eye-tracking device we employed safety equipment has been reported in studies examining comes with both a bicycle helmet and a baseball cap as drivers operating a vehicle with and without built-in its standard mounting solutions.

At random, participants safety devices (Sagberg, Fosser, & Sà¦termo, 1997), chil- were assigned to wear one mount or the other and were dren running obstacle courses with and without safety simply told it was the anchor for the eye tracker. Second, gear (Morrongiello, Walpole, & Lassenby, 2007), and we divorced risk-taking behavior from the safety device bicyclists descending a steep hill with and without hel- by using a computerized laboratory measure called the mets (Phillips et al., 2011). Work to date has been based Balloon Analogue Risk Task (BART; Lejuez et al., 2002), on the assumption that people respond only to safety measures of which they are aware—an idea encapsulated Corresponding Author: in Hedlund’s first rule of risk compensation: “If I don’t Tim Gamble, Department of Psychology, University of Bath, Bath know it’s there, I won’t compensate for a safety measure†BA2 7AY, United Kingdom (Hedlund, 2000, p.

87). Moreover, in research to date, the E-mail: [email protected] 290 Gamble, Walker in which the helmet could do nothing to change risk. We earnings for that trial were lost. At any point, participants also measured sensation seeking and anxiety as possible could choose to stop pumping and “bank†their accrued explanatory variables for any effect. money. After the balloon burst, or after a decision to bank, the next trial began.

Each participant completed 30 trials, and his or her risk-taking score was the mean num- Method ber of pumps made on trials on which the balloon did not burst. This score would be higher when participants Participants risked losses by trying to maximize their score and lower Eighty participants (15 male and 24 female in the helmet when participants avoided risk and played more condition, 19 male and 22 female in the cap condition) conservatively. between the ages of 17 and 56 years (M = 25.26, SD = Sensation seeking was measured using the Sensation- 6.59) took part in the study; no monetary reward was Seeking Scale Form V (Zuckerman, Eysenck, & Eysenck, offered for participation. An a priori power analysis 1978).

This scale measures four dimensions (10 self- showed that 40 participants per condition should have report items each) of sensation-seeking behavior: thrill 80% power to detect an effect size (Cohen’s d) of 0.63. and adventure seeking, disinhibition, experience seek- This was deemed sufficient, as we hoped to see relatively ing, and boredom susceptibility. Bicycling frequency was substantial effects of the helmet manipulation. measured using a Likert scale ranging from 1 (never) to 6 (five times a week or more). If a person selected anything other than “never†on this instrument, helmet-wearing Materials frequency was measured using a Likert scale ranging State anxiety was measured using the State-Trait Anxiety from 1 (never) to 6 (always).

Inventory (STAI) Form Y-1 (Spielberger, 1983). This form Either an Abus (Phoenix, AZ) HS-10 S-Force peak contains 40 questions, 20 that measure a person’s feelings bicycle helmet or a Beechfield (Bury, United Kingdom) of anxiety right at the moment of response and 20 that B15 five-panel baseball cap was used to support the measure his or her chronic levels of anxiety. Participants SensoMotoric (Teltow, Germany) head-mounted iView X here answered the former set. In the BART (Lejuez et al., HED-4.5 eye-tracking device (with its delicate 45° mirror 2002), which we programmed in Real Studio (Xojo, 2011), removed; see Fig. 1).

Participants responded to the scales participants pressed a button to inflate an animated bal- using the Bristol Online Surveys Web site. All measures loon on a computer screen. Each button press inflated and the BART were completed on a 19-in. 4:3 LCD moni- the balloon more and increased the amount of fictional tor. The experimenter “operated†an Applied Science currency earned.

If the balloon burst (which it would at Laboratories (Bedford, MA) Eye-Trac 6 desk-mounted a random point between 1 and 128 inflations), all optics system with Eye-Trac PC. A fake nine-point Fig. 1. Photos showing how the eye tracker was mounted in each of the two conditions: to a baseball cap (left) and a bicycle helmet (right). Helmets, Risk Taking, and Sensation Seeking 291 eye-tracking calibration program was written in Real modeling, interactions of any of these variables (e.g., the Studio to increase the verisimilitude of the eye-tracking Condition à— Bicycling Experience interaction was not sig- procedure. nificant; t = 0.39, p = .70).

Prior research has shown that helmets do not affect cognitive performance in demand- ing laboratory tasks (Bogerd, Walker, Brà¼hwiler, & Rossi, Procedure 2014), which means the results cannot be attributed to This study was conducted in the University of Bath this factor either. Department of Psychology’s eye-tracking laboratory. Participants were brought into the laboratory and told Discussion that they would complete a number of computer-based risk-taking measures while their point of gaze was mea- Laboratory measures showed greater risk taking and sen- sured using a head-mounted eye tracker. After reading sation seeking when participants wore a helmet, rather information about the study on the computer screen and than a baseball cap, during testing.

These effects arose agreeing to participate, they entered their age and gender even though the helmet was introduced as a mount for and completed the STAI Y-1. A screen then appeared say- an eye-tracking apparatus and not as safety equipment, ing that the eye tracker would now be set up; the experi- and even though it could do nothing to alter participants’ menter placed the cap- or helmet-mounted eye tracker level of risk on the experimental task. Notably, the effect on the participant’s head, making a show of carefully was an immediate shift in both risk taking and sensation aligning everything as in a real eye-tracking procedure. seeking. This finding contrasts with those of previous The experimenter then moved to the eye-tracking com- work on unconscious influence, such as experiments on puter, where he or she ran the fake calibration software the persuasive effects of head movements (Wells & Petty, and conspicuously adjusted the eye-tracking controls to 1980) and environmental cues on consumer behavior make it appear to participants that their eye movements (Berger & Fitzsimons, 2008), which looked instead at were really being tracked.

Participants then completed longer-term attitudinal changes from more overt signals. the Sensation-Seeking Scale, the BART, and the STAI Y-1 Our findings are plausibly related to social priming, again. Afterward, a screen appeared saying that the eye wherein social behaviors are cued by exposure to stereo- tracker was to be turned off, and the experimenter types or concepts (Bargh, 2006). However, whereas social removed the apparatus from participants’ heads. priming is generally understood in terms of behavior Participants then completed the final STAI Y-1 before directed toward another person, the effects in this study being debriefed, at which point they were informed of were individual, focused on the risk-taking propensity of the deception and asked not to share details of the exper- a person acting alone during exposure to a safety-related iment with anyone else.

They then reported their bicy- prime. Schrà¶der and Thagard (2013) produced computa- cling frequency and, if they did cycle, their helmet-wearing tional models of social priming in which primes activate frequency. shared cultural concepts in people’s minds, which in turn are associated with actions; through these links, the actions become available to the behavioral selection pro- Results cess. Speculatively, if what we saw in this study were to Wearing a helmet was associated with higher risk-taking be understood through such mechanisms, with the hel- scores (M = 40.40, SD = 18.18) than wearing a cap (M = met invoking concepts of protection from risk and thereby 31.06, SD = 13.29), t(78) = 2.63, p = .01, d = 0.59 (Fig.

2a). subconsciously shaping behaviors, our findings might Similarly, participants who wore a helmet reported higher suggest that Schrà¶der and Thagard’s social-priming frame- sensation-seeking scores (M = 23.23, SD = 7.00) than par- work operates even when its interaction target compo- ticipants who wore a cap (M = 18.78, SD = 5.09), Welch’s nent (another person with whom to interact) is absent. t(69.19) = 3.24, p = .002, d = 0.73 (Fig. 2b). These effects Our findings initially appear different from those of cannot be explained by the helmet affecting anxiety, as some other studies. Fyhri and Phillips (2013; Phillips anxiety did not change significantly as a function of con- et al., 2011) found that risk taking in downhill bicy- dition, F(1, 78) = 0.19, p = .66, time of measurement, F(2, cling, measured through riding speed, did not simply 156) = 2.37, p = .10, or an interaction between the two, increase when a helmet was worn; rather, the people F(2, 156) = 1.18, p = .31 (Fig.

2c). Note that we used the who normally cycled with a helmet took fewer risks square roots of the anxiety scores for analyses because of when riding without one. Why did the participants in the skew seen in Figure 2c. There was no relationship Fyhri and Phillips’s study who were not habitual helmet between risk taking and gender, t(78) = 0.45, p = .66, bicy- users not react to wearing a helmet with increased risk cling experience (Ï = .12, p = .27), and extent of helmet taking, as our experiment might suggest they would? use when bicycling (Ï = .06, p = .60), nor, in regression Clearly more work is needed to definitively pin down 292 Gamble, Walker a b Cap Cap − Helmet Helmet − Score Score c Cap (Time Cap (Time Cap (Time Helmet (Time Helmet (Time Helmet (Time Score Fig.

2. Distribution of scores for the helmet and cap conditions on (a) the Balloon Analogue Risk Task (BART), (b) the Sensation-Seeking Scale, and (c) state anxiety, measured using the State-Trait Anxiety Inventory (STAI). For anxiety, scores are shown separately for time points before donning the eye tracker (Time 1), while wearing the eye tracker (Time 2), and after removing the eye tracker (Time 3). For each measure, the mean score across conditions is indicated by a vertical dotted line, and the mean score for each condition separately is indicated by a thick vertical line. Individual participants’ scores are shown as thin vertical lines (rug points; stacked when more than 1 participant obtained the same score).

Overlaid on the rug-point plots are kernel-density curves (with arbitrary scaling) that illustrate the overall distribution of scores within each condition. Helmets, Risk Taking, and Sensation Seeking 293 all the mechanisms here, but for now, we speculate that complete Open Practices Disclosure for this article can be found the difference might be related to considerable varia- at This article has received a badge for Open Data. More information tions between the two studies’ procedures. Fyhri and about the Open Practices badges can be found at Phillips greatly emphasized the physicality of their task .io/tvyxz/wiki/1.%20View%20the%20Badges/ and (“to increase the difference in measures between the .sagepub.com/content/25/1/3.full . helmet-on and -off conditions, all participants were instructed to cycle using one-hand in both conditionsâ€; References p.

60), which provides a direct link between the action Adams, J. (1982). The efficacy of seat belt legislation. In (bicycling) and the condition (helmet wearing) that was Transactions of the Society of Automotive Engineers (Vol. absent in our study. Moreover, that study used a 91, pp. ). Warrendale, PA: Society of Automotive repeated measures design, in which participants were Engineers. doi:10.4271/820819 aware they were riding a bicycle both with and without Adams, J. (1995).

Risk. London, England: UCL Press. a helmet. This could have meant that behavior changed Adams, J., & Hillman, M. (2001). The risk compensation the- through mechanisms different from those seen here, ory and bicycle helmets. Injury Prevention, 7, 89-91. where participants took part only in one condition and doi:10.1136/ip.7.2.89 were not aware of any manipulation, nor even that they Bargh, J.

A. (2006). What have we been priming all these years? were specifically wearing a safety device. On the development, mechanisms, and ecology of non- The practical implication of our findings, in which risk conscious social behavior. Journal of Social Psychology, 36, taking changed in a global way when the helmet was worn, . doi:10.1002/ejsp.336 Berger, J., & Fitzsimons, G. (2008). Dogs on the street, pumas might be to suggest more extreme unintended conse- on your feet: How cues in the environment influence prod- quences of safety equipment in hazardous situations than uct evaluation and choice.

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B., Ramsey, S. E., Stuart, G. L., . . . Brown, R. A. (2002).

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What role should the concept of risk play in theories of accident involvement? Ergonomics, 31, 469- Declaration of Conflicting Interests 484. doi:10.1080/ The authors declared that they had no conflicts of interest with Morrongiello, B. A., Walpole, B., & Lassenby, J. (2007). Under- respect to their authorship or the publication of this article. standing children’s injury-risk behavior: Wearing safety gear can lead to increased risk taking. Accident Analysis & Open Practices Prevention, 39, . doi:10.1016/j.aap.2006.10.006 Phillips, R.

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Paper for above instructions

Assignment Solution: The Impact of Wearing Bicycle Helmets on Risk-Taking and Sensation Seeking

Introduction

The relationship between the use of safety equipment and behavioral changes, particularly in relation to risk-taking, is a well-explored theme in psychology. The risk compensation hypothesis posits that individuals may engage in riskier behaviors when they perceive themselves as being safer due to protective gear (Adams & Hillman, 2001). This paper discusses the findings of Gamble and Walker (2016), who explore the implications of helmet use for risk-taking behaviors and sensation seeking. Their study emphasized unconscious priming influenced by safety-related concepts, demonstrating how individuals might increase their propensity for risk even without awareness of their protective equipment.

Study Overview

Gamble and Walker (2016) conducted an experiment involving eighty participants who were either fitted with a bicycle helmet or a baseball cap as mounts for an eye-tracking device. Participants were falsely informed that the study aimed to assess their eye movements while engaged in risk-related activities. The primary goal of this research was to analyze whether the mere act of wearing a helmet—traditionally associated with safety—could prime participants to take more risks, even in scenarios where the helmet did not afford any potential for safety.
The researchers utilized the Balloon Analogue Risk Task (BART) to measure risk-taking behavior. In this task, participants could inflate a virtual balloon to accumulate points, with the risk that the balloon would burst, resulting in a loss of accrued points. Additionally, the sensation-seeking scale was employed to assess the participants' natural predisposition towards sensation seeking.

Key Findings

1. Increased Risk Taking: Participants wearing helmets reported significantly higher risk-taking scores compared to those wearing baseball caps (Gamble & Walker, 2016). Specifically, the mean number of pumps made on trials where the balloon did not burst was higher in the helmet-wearing condition, indicating heightened risk-taking behavior. These results suggested a potential mechanism of social priming, where the mere presence of a helmet unconsciously activated notions of protection and safety.
2. Heightened Sensation Seeking: In addition to increased risk-taking, participants in the helmet condition exhibited higher sensation-seeking scores. The Sensation-Seeking Scale revealed that the group wearing helmets recorded greater means than their counterparts, reinforcing the idea that safety-related cues can modulate not just risk behavior but also broader personality traits related to seeking new and thrilling experiences (Gamble & Walker, 2016).
3. Minimal Anxiety Influence: Interestingly, the study found no substantial changes in anxiety levels based on the condition of helmet or cap. This suggests that the observed increase in risk behavior and sensation seeking was not attributable to changes in anxiety, thereby strengthening the argument that physical cues (such as wearing a helmet) can influence behavior independently of emotional state.

Theoretical Implications

The findings from Gamble and Walker (2016) add complexity to existing models of risk compensation. By demonstrating that participants increased their risk-taking behaviors even when they were unaware they were using safety equipment, the study pushes the boundaries of the risk compensation theory. Prior studies often insinuated that awareness of safety measures is crucial for compensatory behaviors; however, this research shows that the unconscious activation of safety concepts can directly influence risk propensity.
Innovatively, the study aligns its findings with social priming theories, suggesting that exposure to certain stimuli (like the helmet) can evoke cognitive frameworks that influence behavior (Bargh, 2006). This perspective opens avenues for further research on how various forms of social and environmental cues can subconsciously shape individual choices, especially in contexts involving safety and risk.

Practical Implications

The practical implications of these findings are profound, especially in public health and safety domains. The idea that users might engage in riskier behaviors when using protective gear—be it helmets for cyclists or seatbelts in cars—raises concerns about how safety equipment is perceived and utilized in everyday life. While protective gear is essential for preventing accidents, understanding the psychological effects associated with their use will be crucial for designing better safety campaigns and interventions.
For instance, awareness campaigns could emphasize that protective gear should not encourage reckless behavior. Alternatively, strategies to cultivate a mindset that promotes risk-averse behavior while wearing safety equipment could be beneficial.

Conclusion

The research by Gamble and Walker (2016) underscores the need for a nuanced understanding of how subconscious perceptions of safety can alter risk-taking behaviors. The observed effects of helmet-wearing suggest that safety equipment may unintentionally lead individuals to embrace risks, which complicates traditional safety narratives. Continued exploration into the intersections of psychology, safety equipment use, and risk behavior is needed to develop practical interventions that promote safer behaviors without compromising the efficacy of protective gear.

References

1. Adams, J. (1982). The efficacy of seat belt legislation. In Transactions of the Society of Automotive Engineers (Vol. 91, pp. 1-7).
2. Adams, J., & Hillman, M. (2001). The risk compensation theory and bicycle helmets. Injury Prevention, 7, 89-91.
3. Bargh, J. A. (2006). What have we been priming all these years? Journal of Social Psychology, 36, 4-30.
4. Berger, J., & Fitzsimons, G. (2008). Environmental cues and consumer behavior. Journal of Marketing Research, 45, 1-14.
5. Gamble, T., & Walker, I. (2016). Wearing a bicycle helmet can increase risk taking and sensation seeking in adults. Psychological Science, 27(3), 490-495.
6. Fyhri, A., & Phillips, R. O. (2013). Emotional reactions to cycle helmet use. Accident Analysis & Prevention, 50, 59-63.
7. Janssen, W. (1994). Seat-belt wearing and driving behavior: An instrumented-vehicle study. Accident Analysis & Prevention, 26, 1-7.
8. Lejuez, C. W., et al. (2002). Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 8, 75-84.
9. Phillips, R. O., Fyhri, A., & Sagberg, F. (2011). Risk compensation and bicycle helmets. Risk Analysis, 31, 715-724.
10. Wells, G. L., & Petty, R. E. (1980). The effects of overt head movements on persuasion. Basic and Applied Social Psychology, 1, 219-234.