Motorcyclists have long championed riding as their main road to stress relief and positive mental health. Today, the results of a neurobiological study conducted by a team of three researchers from UCLA’s Semel Institute for Neuroscience and Human Behavior yielded pioneering scientific evidence revealing the potential mental and physical benefits of riding. 28% reduced stress, 27% more adrenaline, higher alertness, greater sensory focus and 11% increased heartrates… Funded by Harley-Davidson, the study found that motorcycling increased metrics of focus and attention, and decreased relative levels of cortisol, a hormonal marker of stress.
RIDING A MOTORCYCLE IMPROVED METRICS OF FOCUS AND DECREASED STRESS BIOMARKERS, ACCORDING TO A NEW NEUROBIOLOGICAL STUDY
Motorcycling Decreased Stress-Measures, Similar to Light Exercise
MILWAUKEE (January 17, 2019) – Researchers recorded participants’ brain activity and hormone levels before, during, and after motorcycling, driving a car, and resting. While riding a motorcycle, participants experienced increased sensory focus and resilience to distraction. Riding also produced an increase in adrenaline levels and heart rate, as well as a decrease in cortisol metrics – results often associated with light exercise and stress-reduction.
“Stress levels, especially among young adults, continue to rise, and people are exploring pathways to better their mental and physical health. Until recently, the technology to rigorously measure the impact of activities like motorcycling on the brain didn’t exist,” said Dr. Don Vaughn, the neuroscientist who led the research team. “The brain is an amazingly complex organ and it’s fascinating to rigorously investigate the physical and mental effects riders report.”
- Riding a motorcycle decreased hormonal biomarkers of stress by 28%
- On average, riding a motorcycle for 20 minutes increased participants’ heart rates by 11 percent and adrenaline levels by 27 percent—similar to light exercise
- Sensory focus was enhanced while riding a motorcycle versus driving a car, an effect also observed in experienced meditators vs non-meditators
- Changes in study participants’ brain activity while riding suggested an increase in alertness similar to drinking a cup of coffee
“While scientists have long-studied the relationship of brain and hormone responses to attention and stress, doing so in real-life conditions such as these is rare,” explained UCLA Professor and senior team member, Dr. Mark Cohen. “No lab experiment can duplicate the feelings that a motorcyclist would have on the open road.”
“The differences in participants’ neurological and physiological responses between riding and other measured activities were quite pronounced,” continued Dr. Vaughn. “This could be significant for mitigating everyday stresses.”
The research team monitored participants’ electrical brain activity and heart rate, as well as levels of adrenaline, noradrenaline, and cortisol. To be presented later this year, the Harley-Davidson funded study, entitled “The mental and physical effects of riding a motorcycle” measured the biological and physiological responses of more than 50 experienced motorcyclists, using mobile EEG technology.
“We’re leveraging the latest technologies as we shift our focus from exclusively motorcycles to growing ridership, so it only made sense to tap technology to explore the impact of riding itself,” said Heather Malenshek, Harley-Davidson’s Senior Vice President of Marketing & Brand. “The research findings Dr. Vaughn and his team identified helps explain what our riders have felt for the past 116 years – there’s a vitality and heightened sensory experience that comes from the freedom of riding a motorcycle. We hope their findings inspire the next generation of riders to experience these benefits along with us.”
The mental and physical effects of riding a motorcycle Catalyst Agency LLC
The brain’s response to external stimuli is known to vary with internal state. While
low-level processing of auditory stimuli has been shown to vary in low-intensity
laboratory conditions, it is not clear how sensory modulation varies during the
physical and attention-demanding task of riding a motorcycle. We here
investigated how riding a motorcycle affects “pre-attentional” sensory processing
using an auditory oddball task and the electroencephalography signals it evokes:
the N1 and mismatch negativity. We additionally investigated how riding a
motorcycle modulates the concentrations of hormones, including cortisol,
DHEA-S, testosterone, and epinephrine. We report that the N1 amplitude—the
brain’s response to auditory tones—was significantly reduced during motorcycle
riding relative to baseline recordings, and that the mismatch negativity (the
change in N1 in response to unexpected tones) was greater during riding. We
additionally report that riding significantly increased both the level of
catecholamine and the ratio of DHEA-S to cortisol. Together, these results
suggest that riding increases attention by strengthening focus and heightening
the brain’s passive monitoring of changes in the sensory environment while
reducing the immediate stress response.
For years, anecdotal reports have suggested that riding a motorcycle may have beneficial
effects on riders’ brains and physiological states. However, the nature of these effects has not
been characterized. Here, in an experimental setting, we tested several possibilities. First, we
predicted that if riding enhances focus, then riders should be less sensitive to distractions while
riding than in a non-riding condition. Second, if riding heightens sensory processing, then riders
should be more sensitive to changes in their sensory environment while riding.
To address these hypotheses, we used electroencephalography (EEG) to measure brain
responses to auditory tones presented passively during riding. Because auditory brain
responses are well characterized (Risto Näätänen, Kujala, & Winkler, 2011; R. Näätänen,
Paavilainen, Rinne, & Alho, 2007) , auditory tones are a reliable tool for passively probing
sensory and attention processing during riding. For instance, approximately 100 ms after a tone,
EEG shows a distinct response (called N1) associated with activity of the brain’s auditory cortex
in processing that tone. This response can be enhanced in two ways. First, the response is
stronger if the listener attends closely to the auditory tones, and is weakener if the tones are
ignored. If the act of riding a motorcycle effectively focuses the rider’s attention on the visual
modality at the expense of the auditory modality, then we would expect a weaker response
while riding (Alain & Arnott, 2000; Sussman, 2007) . Second, the response is enhanced if the
brain detects a sudden change in the auditory environment (e.g., a sudden noise from a glass
shattering in an already-noisy cafeteria) (Garrido, Kilner, Stephan, & Friston, 2009; R. Näätänen
et al., 2007) . This orienting response can be thought of as a heightened sensory state.
Combining these two modes of attenuation, we would expect motorcycle riders, relative to car
drivers, to present a weakened overall N1 to auditory tones because riders must pay more
attention to visual inputs than auditory ones; at the same time, regardless of attention, we would
expect riders to display a stronger change in N1 upon the introduction of auditory irregularities,
consistent with a heightened state of auditory processing. Finally, we tested whether these brain
changes are associated with an increase in physiological arousal and decrease in the stress
response, as reported anecdotally by riders.
Methods & Materials
77 participants (23 female, age 42 ∓ 14 years) were recruited from Southern California using an
IRB-approved recruitment flyer. Participants were required to answer screening questions.
Inclusion criteria were as follows: between 18 and 70 years old, neurologically healthy, not
taking antipsychotics, and comfortable riding a motorcycle (on a 5-point scale, we accepted
participants who rated their comfort as a 3 or greater). We conducted no validation of
participants’ self reports. Following exclusion of data due to noise, artifacts, and acquisition
errors, the final sample comprised 42 participants.
The protocol was approved by IntegReview Independent Review Board Services (
https://integreview.com/ ), and all participants provided written informed consent.
Stimuli and Experimental Design
Participants performed the oddball task during both riding and non-riding conditions. The
oddball task is a simple paradigm in which auditory tones are presented to both ears at regular
intervals (on average, 1.5 seconds apart). The task is passive, meaning that participants do not
respond to each stimulus. The tones occur at both high and low frequencies, and critically, the
ratio of the frequencies is uneven (70% vs. 30%). This design defines the less-frequent tones as
the “oddball” stimuli, nested within “standard” stimuli. This design is validated to elicit an N1
responses to all stimuli, corresponding to the auditory brain response, as well as a mismatch
negativity (MMN) response to the oddball tones. The MMN is a difference wave; it reflects a
change in the N1 during the oddball stimuli relative to the standard stimuli. This design thus
tests the auditory brain response to the auditory tones, and pre-attentive sensory processing to
the oddball stimuli. The oddball task was presented during riding and non-riding conditions.
After 6 minutes of the oddball task, participants had 1 minute without tones.
All participants rode their own motorcycle and drove a provided car (Lexus NX200) on a set
route under normal driving conditions. The order of these two blocks was pseudo-randomized to
eliminate temporal effects. Before and after the driving and riding blocks, participants sat in a
chair, immobile, and provided saliva samples. Participants provided a urine sample after all
three immobile conditions ( Figure 1 ). This experimental design allowed our analysis to draw
inferences about the effects of riding/driving during the activity itself as well as afterward, all
while controlling for baseline levels of activity. By design, there were no intersubject variables.
The experiment was conducted at two separate locations: Angeles Crest Highway outside Los
Angeles, and Mesa Grande at Lake Henshaw. Both routes took approximately 22 minutes,
round trip, to complete.
Figure 1: Experimental Design . Block durations reflect planned time, not actual time.
Brain activity and heart rate data were recorded using a 64-channel EEG cap and 2-channel
active electrocardiogram (EKG) from the eego sports package by ANT Neuro
( https://www.ant-neuro.com/ ).
Hormone concentration was determined by liquid chromatography with tandem mass
spectrometry (LC-MS/MS) from saliva and dried urine samples. Hormone concentrations from
urine samples were normalized for kidney output by corresponding creatinine (Cr) levels.
EEG Preprocessing & Analysis
All processing of the EEG data was performed in Matlab (v.R2018b, Mathworks Inc.) using
EEGLAB software (v.14.1.2) (Delorme & Makeig, 2004) . Power was computed using EEGLAB
spectopo. All data was processed according to the following five steps.
(1) EEG data was down-sampled to 250 Hz and trimmed to remove any pre- or post-recording
signals, dominated by task-unrelated noise.
(2) A high-pass filter (.75 Hz) was applied to remove artifacts due to slow drifts.
(3) The Artifact Subspace Reconstruction (ASR) algorithm (Chang, Hsu, Pion-Tonachini, &
Jung, 2018) was applied to first identify and then remove bad channels, as well as to remove
extreme artifacts in the data. Our parameters dictated that channels should be removed if they
(i) contained more than 15 minutes of flat line data or (ii) failed to meet a correlational threshold
(i.e., a correlation > 0.7 with other channels for a majority of the data). The ASR algorithm is a
PCA-like algorithm that constructs a subspace representation of artifact-free data to create a
reference. The algorithm uses this reference to identify windows along the time series that
depart from this subspace statistically, indicating the presence of artifact; these windows are
then reconstructed based on the clean data. The key parameter in this approach is the definition
of artifact, which we based on the number of standard deviations by which a window deviated
from the reference data. Based on a formal assessment (Chang et al., 2018) , we adopted a
threshold of 100 standard deviations, which provides a very conservative approach that
identifies only the most extreme artifacts. Critically, because this algorithm operates within a
moving window (1–2 seconds in width) along the time series, it is capable of identifying
non-stationary, extreme artifacts that are not easily removed by any other traditional approach.
Thus, we used the ASR algorithm to eliminate large transient artifacts (like motion, which we
expect from children) while preserving the data. The technique was developed for real-time,
brain-computer interface applications, making it well-suited to our experiment. The cleaned data
were re-referenced to the average reference.
(4) Following the removal of gross, transient artifacts, we used independent component analysis
(Extended Infomax Algorithm) (Lee, Girolami, & Sejnowski, 1999; Makeig, Jung, Bell,
Ghahremani, & Sejnowski, 1996) to identify remaining artifacts that were more stationary in
nature: (i) eye blinks and lateral movements, (ii) pulsation, and (iii) any remaining channel
(5) Finally, from the cleaned data, we extracted features of interest. The N1 was calculated by
extracting 1-second epochs from -100 ms to 900 ms following each auditory stimulus. These
epochs were averaged for oddball auditory tones, and separately for standard auditory tones.
Mean baseline (-100–0 ms) voltage was subtracted from each average event-related potential
(ERP), and the peak amplitude of the N1 was extracted to index the auditory response of each
individual for each type of auditory tone (oddball/standard). The mismatch negativity (MMN) was
calculated as the peak in the difference of these waveforms.
(6) We analyzed N1 responses with a general linear model that included two factors—state
(riding or driving) and stimulus (oddball or standard)—to test for main effects of riding on
auditory responses. Additional paired sample t-tests were used to evaluate the effect of riding
on the MMN response.
All tests of statistical significance employed paired, parametric t-tests or F-test, unless otherwise
noted. Missing samples were excluded rather than imputed, and thus, degrees of freedom may
vary slightly between tests even within the same modality. Effect size and significance were
computed by mean.
After data exclusion due to electrode detachment, excessive noise, and acquisition errors, we
retained useable EKG data from 38 participants. Heart rate in the riding condition was
significantly different from rest (𝞵 = 8.7 BPM, t(37) = 4.7, p < 10 -4) and driving (𝞵 = 5.0 BPM,
t(37) = 3.2, p < 0.01 ), corresponding to increases of 11% and 7%, respectively ( Figure 2 ).
Figure 2: Heart Rate. All data are paired. Each red line indicates the median, blue marks the
interquartile range, and black denotes the most extreme non-outlier. Heart rate, shown here in
beats per minute (BPM), was significantly different during riding than both resting and driving.
After data exclusion due to sample dilution and acquisition errors, we analyzed the urine data
from 43 participants and salivary data from 46 participants.
Riding significantly increased levels of the catecholamine, epinephrine, by 27% relative to
baseline (𝞵 = 15.0 μg/g Cr vs. 𝞵 = 11.6 μg/g Cr, t(40) = 5.6, p < 0.05, Figure 3A ). Driving did not
increase epinephrine relative to baseline (𝞵 = 12.4, t(42) =1.2, p = 0.23).
Riding significantly decreased levels of cortisol by 28% relative to sulfated
dehydroepiandrosterone (DHEA-S) (𝞵 = 0.177 log, t(42) = 4.5, p < 10 -4 , Figure 3B) . This ratio is
an indicator of stress (Buford & Willoughby, 2008; Morgan et al., 2004; Ritsner et al., 2004;
Warnock et al., 2010) , and thus its decrease can be interpreted as a reduction in stress. We did
not collect longitudinal data to confirm or refute the transient or long-lasting nature of this
We observed no significant change in salivary testosterone levels.
Figure 3: Hormone Testing. All data are paired. Each red line indicates the median, blue
marks the interquartile range, and black denotes the most extreme non-outlier. A. Changes in
epinephrine urine concentration as a function of activity condition. Riding is significantly different
than rest. B. Changes in the DHEA-S to cortisol concentration ratio as a function of activity
condition. By mean, riding is significantly different than rest.
The N1 (i.e., the brain’s response to auditory tones) was significantly reduced during riding
relative to baseline recordings (𝞵 = -1.32 uV vs. -.62 uV, F(1,40) = 18.5, p < .0001, Figure 4A ).
The whole-brain response 100 ms after the onset of the tone in each of the three main
conditions is visualized in Figure 4B . This response was also significantly weaker during riding
than during driving (𝞵 = -1.32 uV vs. -.61 uV, F(1,40) = 22.48, p < .001). Consistent with these
effects, the N1 during driving did not differ from baseline (F(1,39) = 1.39, p = .25). In addition,
we found that the MMN (i.e., the change in N1 in response to unexpected tones) was greater
during riding (𝞵 = -.50 uV vs. -.09 uV, t(40) = 2.3, p = .027).
Figure 4: Electroencephalogram ERP Results. A. Event-related potentials in response to
standard (dashed) and oddball (solid) tones in driving, riding, and immobile (nostate) conditions.
B. The whole-brain response 100 ms after the onset of the tone in each of the three main
conditions, showing that the brain is significantly less responsive to tones while riding.
A spectral analysis of frequency content during periods of silence revealed changes by
condition. Specifically, the spectral power percentage in the alpha frequency band (8 – 12Hz)
during riding was significantly less than either baseline (𝞵 = 0.32%, t(39)=4.0, p<10 -3 , Figure 5 )
or driving (𝞵 = 0.51%, t(39)=5.7, p<10 -5 )
Figure 5: Electroencephalogram Spectral Results. All data are paired. Each red line
indicates the median, blue marks the interquartile range, and black denotes the most extreme
non-outlier. Alpha band power, as a percentage of total spectral power, is greater in riding than
either rest or driving.
In the sensory processing of auditory stimuli, the N1 has well-characterized involvement and
known modulability with varying degrees of attention. In our experiment, N1 amplitude in
response to both standard and oddball tones was lower in the riding condition than driving and
resting conditions, suggesting that riding reduced susceptibility to distraction. Relatedly, we
found that the MMN was also greater during riding than baseline, consistent with the prediction
that riding heightens sensory processing. This interpretation of sensory-focus enhancement
aligns with research showing an increased MMN response in meditators (Biedermann et al.,
2016; Luo, Wei, & Weekes, 1999; Singh & Telles, 2015; Srinivasan & Baijal, 2007) , as well as
deviant MMN amplitude and/or latency in cases of cognitive dysfunction (Ford & Mathalon,
2012; Huttunen-Scott, Kaartinen, Tolvanen, & Lyytinen, 2008; Risto Näätänen, Sussman,
Salisbury, & Shafer, 2014; Shelley et al., 1991; Umbricht & Krljes, 2005) . Together, these
results suggest that riding increases attention via two mechanisms: strengthening focus and
heightening the brain’s passive monitoring of changes in the sensory environment.
Furthermore, the decrease in whole-brain alpha band power during periods of silence, while
riding relative to baseline and driving, is similar to the effects of caffeine. The magnitude and
widespread nature of this change in brain activity while riding suggests an increase in alertness
similar to a cup of coffee (Angelakis, Lubar, Stathopoulou, & Kounios, 2004; Barry et al., 2005;
Dimpfel, Schober, & Spüler, 1993; Reeves, Struve, Patrick, & Bullen, 1995) .
The hormonal data support a comparison of motorcycling with light exercise. Specifically, the
observed increase in catecholamines and heart rate suggest that riding, but not driving,
increases arousal of the sympathetic nervous system. The magnitude of the increase in these
quantities, relative to rest and despite reduced catecholamine production while sitting
(Christensen & Brandsborg, 1973; Von Euler & Hellner, 1952) , is commensurate with light
exercise (Hill et al., 2008; Zouhal, Jacob, Delamarche, & Gratas-Delamarche, 2008) .
Additionally, while we have no findings to report about long-term changes in hormonal
biomarkers of stress as a function of riding a motorcycle, future longitudinal studies may choose
to investigate this potential prophylactic link, as elevated glucocorticoid levels have been shown
to contribute to neuronal death (Dinkel, MacPherson, & Sapolsky, 2003; Kerr, Campbell,
Applegate, Brodish, & Landfield, 1991; Krystal, 1993; Sorrells, Munhoz, Manley, Yen, &
Sapolsky, 2014; Uno, Tarara, Else, Suleman, & Sapolsky, 1989) .
The Ultimate New Year’s Resolution: Learning to Ride
For those who wish to experience the heightened sensory experience of riding first-hand, H-D Riding Academy will introduce you to motorcycle riding and build your skills in just a few days, regardless of experience level. Offered at select Harley-Davidson dealers, H-D Riding Academy provides expert guidance from Harley-Davidson certified coaches. In the classroom, you get to know basic motorcycle functions and learn the basics of rider safety skills. On the practice range, you build skills and confidence, learning everything from braking, turning and skilled maneuvers. Best of all, you will be connected to a growing community of new riders. To find available courses near you, contact your local dealer or search for classes at www.h-d.com.
Rider Inspiration: Research Study Finds Riding a Motorcycle Promotes Brain Health
About Harley-Davidson Motor Company
Harley-Davidson, Inc. is the parent company of Harley-Davidson Motor Company and Harley-Davidson Financial Services. Since 1903, Harley-Davidson Motor Company has fulfilled dreams of personal freedom with custom, cruiser and touring motorcycles, riding experiences and events and a complete line of Harley-Davidson motorcycle parts, accessories, general merchandise, riding gear and apparel. Harley-Davidson Financial Services provides wholesale and retail financing, insurance, extended service and other protection plans and credit card programs to Harley-Davidson dealers and riders in the U.S., Canada and other select international markets. For more information, visit Harley-Davidson’s Web site at www.harley-davidson.com.
Study of healthy, experienced adults, riding their own motorcycles on a designated 22-minute route, under normal conditions. Provided for informational purposes only. Sponsor makes no guarantee that you will experience similar results; actual effects will vary based on equipment, driving conditions and age/health/experience of rider. Views expressed and conclusions reached are solely those of the author, Dr. Don Vaughn, in his personal capacity, and do not necessarily represent the views of UCLA. Sponsor: Harley-Davidson Motor Company. Copyright 2019, all rights reserved.
1 Radosevich, P. M. et al. Effects of low- and high-intensity exercise on plasma and cerebrospinal fluid levels of ir-beta-endorphin, ACTH, cortisol, norepinephrine and glucose in the conscious dog. Brain Res. 498, 89–98 (1989).
2 Hill, E. E. et al. Exercise and circulating cortisol levels: the intensity threshold effect. J. Endocrinol. Invest. 31, 587–591 (2008).
3 As measured by the concentration ratio of DHEA-S to cortisol
4 Hill, E. E. et al. Exercise and circulating cortisol levels: the intensity threshold effect. J. Endocrinol. Invest. 31, 587–591 (2008).
5 Zouhal, H., Jacob, C., Delamarche, P. & Gratas-Delamarche, A. Catecholamines and the effects of exercise, training and gender. Sports Med. 38, 401–423 (2008).
6 Boutcher, S. H. & Landers, D. M. The effects of vigorous exercise on anxiety, heart rate, and alpha activity of runners and nonrunners. Psychophysiology 25, 696–702 (1988).
7 As measured by the mismatch negativity (MMN) – the change in the amplitude of evoked auditory responses, to standard versus deviant tones
8 Biedermann, B. et al. Meditation and auditory attention: An ERP study of meditators and non-meditators. Int. J. Psychophysiol. 109, 63–70 (2016).
9 Srinivasan, N. & Baijal, S. Concentrative meditation enhances preattentive processing: a mismatch negativity study. Neuroreport 18, 1709–1712 (2007).
10 Luo, Y., Wei, J. & Weekes, B. Effects of musical meditation training on auditory mismatch negativity and P300 in normal children. Chin. Med. Sci. J. 14, 75–79 (1999).
11 As measured by the commensurate reduction in alpha frequency band power between baseline and riding to caffeine vs placebo
12 Barry, R. J. et al. Caffeine effects on resting-state arousal. Clin. Neurophysiol. 116, 2693–2700 (2005).
13 Dimpfel, W., Schober, F. & Spüler, M. The influence of caffeine on human EEG under resting condition and during mental loads. Clin. Investig. 71, 197–207 (1993).
14 Angelakis, E., Lubar, J. F., Stathopoulou, S. & Kounios, J. Peak alpha frequency: an electroencephalographic measure of cognitive preparedness. Clin. Neurophysiol. 115, 887–897 (2004).
15 Reeves, R. R., Struve, F. A., Patrick, G. & Bullen, J. A. Topographic quantitative EEG measures of alpha and theta power changes during caffeine withdrawal: preliminary findings from normal subjects. Clin. Electroencephalogr. 26, 154–162 (1995).
16 Kaplan, G. B. et al. Dose-dependent pharmacokinetics and psychomotor effects of caffeine in humans. J. Clin. Pharmacol. 37, 693–703 (1997).
Special thank you to Harley-Davidson, Dr. Don Vaughn and UCLA’s Semel Institute for Neuroscience and Human Behavior for this weeks Total Motorcycle Rider Inspiration Story!