Acute exercise effects on inhibitory control and the pupillary response in young adults

Abstract

Previous research has established an impact of acute exercise on cognitive performance, which has inspired investigations into neurobiological mechanisms that may underlie the observed benefits. Pupillary responses have been posited to reflect activation of such underlying neurobiological mechanisms. The current study recruited healthy young adults to investigate the effects of a single bout of moderate-to-vigorous intensity aerobic exercise on subsequent performance and pupillary responses during an inhibitory control task. Results showed that an acute bout of exercise was related to shorter reaction times and increased tonic pupil dilation during an inhibitory control task. Although pupillary responses did not mediate the acute exercise effect on inhibitory control, higher cardiorespiratory fitness was associated with greater phasic pupil dilation following exercise relative to seated rest. The current study supported the plausibility of the pupillary response as a marker of LC-NE system activation that is sensitive to acute exercise. Whether pupillary responses could account for transient benefits of acute exercise on brain and cognition remains unclear.

The benefits of physical activity to cognitive and brain health have received considerable attention in recent years (Colcombe and Kramer, 2003; Hillman et al., 2008; Voss et al., 2011b). Research over the past two decades has framed a strong case that single bouts of physical activity acutely improve cognitive and brain function across the life span (Erickson et al., 2019). The recent 2018 Physical Activity Guidelines for Americans concluded that there is strong evidence that acute bouts of moderate- to vigorous-intensity exercise provide transient benefits for cognition during the post-recovery period following the cessation of the exercise (Erickson et al., 2019; Powell et al., 2019). More specifically, acute exercise interventions have exhibited positive effects on aspects of cognition in children and adolescents (Ludyga et al., 2016a; Verburgh et al., 2014), as well as younger and older adults (Hyodo et al., 2012; Kamijo et al., 2009; Lambourne and Tomporowski, 2010; Ludyga et al., 2016a). A substantial body of the literature has shown that a single bout or ‘dose’ (i.e., intensity, duration, and type) of exercise can induce transient benefits on subsequent goal-directed behaviors, such as performance of executive function dependent tasks (Chang et al., 2012). In young adults, a majority of acute exercise studies have investigated how a bout of 16–35 min of moderate- to vigorous-intensity aerobic exercise affects inhibitory control (Pontifex et al., 2019) – an aspect of goal-directed behavior denoting the ability to suppress internal and external distraction to successfully attend to a task at hand (Miyake et al., 2000). Further, aerobic exercise at such an intensity yields task performance benefits that can persist for at least an hour following exercise cessation (Joyce et al., 2009; Ludyga et al., 2019). While research continues to validate the dose-response effects of acute aerobic exercise on behavior and cognition, questions remain regarding the physiological factors that could potentially mediate this relationship.

A principal interest in understanding the relationship between exercise and cognitive function is investigating the neurobiological mechanisms that may underlie the observed benefits. As such, the locus-coeruleus norepinephrine (LC-NE) system is a candidate mechanism thought to be involved in regulating fluctuations in attention and arousal (Aston-Jones and Cohen, 2005). Direct physiological recordings of the LC-NE system in nonhumans have identified two types of activation. Specifically, tonic activity is characterized by spontaneous, basal functioning, which is theorized to vary with one’s arousal state and behavior along an inverted U-shaped curve (Nieuwenhuis et al., 2005). That is, low levels of tonic activation relate to lower levels of attentional vigilance, whereas greater levels of tonic activity may promote distractibility or suboptimal performance. Alternatively, phasic activity is characterized by a task- or stimulus-evoked response, which is theorized to vary from moment-to-moment with one’s attentional control, reflecting bursts of NE release in response to the properties of the eliciting stimulus (i.e., salience). Successful uptake of NE, and resultant increases in attentional control, is thought to facilitate a focused state by effectively filtering irrelevant stimuli, and is closely associated with more optimal task performance (Nieuwenhuis et al., 2005).

In the context of a behavioral task, cognitively demanding stimuli (i.e., stressors) can facilitate an upshift in phasic activation (Aston-Jones and Cohen, 2005; Sara, 2009). Exercise is one such acute stressor that drives arousal (McMorris, 2016). However, little research has focused on whether a ‘dose’ (i.e., intensity, mode, and duration) of exercise may prompt arousal such that it engenders an upregulation of the LC-NE activation response underlying improvements in cognitive performance (McGowan et al., 2019). To do so would require a method of measuring LC-NE activity simultaneously with goal-directed cognitive function. Accordingly, pupil size fluctuations have been shown to closely track tonic and phasic activity within the LC-NE system in animal (Joshi et al., 2016; Liu et al., 2017) and human (Murphy et al., 2014; Reimer et al., 2016) models, as well as associated functional brain networks linked to salience detection and arousal in rodents (Zerbi et al., 2019) and humans (Kucyi and Parvizi, 2020; Schneider et al., 2016). Thus, pupillometry may provide an easily accessible, noninvasive method to indirectly measure changes in activity of the LC-NE system and associated brain networks during performance of behavioral tasks that tap aspects of cognition (Joshi and Gold, 2020).

Pupil dilation is posited to reflect increases in mental effort, which is supported by findings of increased pupil dilation for conditions requiring greater amounts of inhibitory (i.e., attentional) control (van der Wel and van Steenbergen, 2018). For instance, van Steenbergen and Band (2013) observed increased pupil dilation during trials requiring greater amounts of inhibition, indicating that inhibitory control modulates phasic activation of pupil dilation in young adults. They further found that trials requiring greater amounts of inhibition produced longer RTs and a greater number of errors than trials with lesser inhibitory demands, indicating that phasic pupil dilation coincided with changes in behavior across trial types. Thus, phasic pupil size was associated with the difficulty and level of effort required to successfully complete the inhibitory control task. Their findings corroborated other research showing modulation of phasic pupil diameter in response to differential inhibitory control demands. Specifically, in young adults, inhibitory control tasks have been shown to elicit increased phasic pupil diameter as well as longer RTs during trials that require inhibiting perceptual interference caused by irrelevant or distracting stimuli (Laeng et al., 2011; van Steenbergen et al., 2015). Therefore, the pupillary response may indirectly reflect mechanisms underlying modulation of behavior during tasks that modulate inhibitory control demands.

Only one study that we are aware of has investigated the pupillary response as a potential index of the effect of a single bout of aerobic exercise on LC-NE activity (McGowan et al., 2019). McGowan et al. (2019) administered an exercise intervention (treadmill exercise at varying grades) for 20 min at a moderate-to-vigorous intensity (70% of age-predicted maximum heart rate) in a sample of young adults. Their comparison condition was an active control intervention of equal length where participants walked at the lowest possible speed and grade on the treadmill (0.5 mph, 0% grade). Inhibitory control was investigated via a modified flanker task, and their results indicated shorter RT for the moderate-to-vigorous intensity condition compared to the active control, corroborating previous findings of acute aerobic activity interventions at a similar intensity in young adults (Kamijo et al., 2007). However, despite improvements in task performance, they failed to find any modulation of the tonic or phasic pupillary responses across the exercise and control conditions. These findings suggest an insensitivity of the pupillary response to the cognitive effects of the acute exercise intervention. However, there may be other factors to consider in testing whether the LC-NE system is an underlying mechanism for transient improvements in inhibitory control following acute bouts of exercise.

Cardiorespiratory fitness may be a moderating factor when considering the acute effects of aerobic exercise on inhibitory control (Chu et al., 2015; Hogan et al., 2015). Cardiorespiratory fitness is associated with greater cognitive functioning across the lifespan (Hillman et al., 2008). Previous research in adolescents and older adults has found that higher-fit individuals exhibit greater inhibitory control following an acute bout of aerobic exercise (Chu et al., 2015; Hogan et al., 2015). Contrary to these findings, a recent meta-analysis suggested that cardiorespiratory fitness may not influence the beneficial effects of acute exercise on cognition, and the observed benefits may also be specific to preadolescent children and older adults (Ludyga et al., 2016b). Thus, findings regarding the proposed role of cardiorespiratory fitness on the transient benefits of acute exercise on cognition remain inconsistent. Therefore, other mechanisms linked to cardiorespiratory fitness could influence whether it modulates acute aerobic exercise effects on cognition.

The LC-NE system is a posited therapeutic target for cardiovascular disease (Wood et al., 2017), whereas greater cardiorespiratory fitness is associated with optimal cardiovascular health (Lee et al., 2010). Cardiovascular health prompted by greater cardiorespiratory fitness may modulate optimal functioning of the LC-NE system. Interestingly, a recent cross-sectional study by Chandler et al. (2021) found that higher cardiorespiratory fitness was associated with better performance on an inhibitory control task, but this effect was not mediated by the phasic pupillary response. Moreover, Chandler et al. did not find any association between pupillary responses and cardiorespiratory fitness. They concluded that the absence of a relationship between cardiorespiratory fitness and phasic pupil dilation indicated a lack of LC-NE system activation. However, an open question remains as to whether cardiorespiratory fitness status may modify the acute exercise effects on phasic activation. For instance, higher-fit individuals may have an upregulation in phasic activity and NE, or better regulate tonic NE activity, which could lead to brain structural changes that facilitate cognitive performance. Thus, if the LC-NE system underlies the beneficial effects of acute aerobic exercise on inhibitory control, levels of cardiorespiratory fitness may influence how the transient activation of LC-NE system manifests in physiological biomarkers (i.e., phasic pupil dilation). Higher-fit individuals could experience an upregulation in phasic activity (i.e., NE release and uptake) following a single bout of aerobic exercise, which would be accompanied by an increased task-evoked phasic pupil dilation response.

The current study investigated whether acute exercise was related to activation of the LC-NE system would benefit inhibitory control in young adults. Tonic and phasic pupil diameter were measured during an inhibitory control task as a potential biomarker of LC-NE activity. That is, this study aimed to elucidate potential neural mechanisms underlying the transient effects of acute exercise on inhibitory control by illustrating modulations in the pupillary response following a bout of exercise compared to a non-physically active control condition (i.e., seated rest). Note, unlike McGowan et al. (2019), the current study did not include a baseline assessment, and only measured cognitive performance following the intervention. We hypothesized that the current findings would replicate previous reports indicating that acute moderate-to-vigorous intensity aerobic exercise is positively related to performance on task conditions that required greater amounts of inhibitory control. Specifically, we expected that acute exercise would have a greater effect on incongruent compared to congruent trials given the larger inhibitory control demands. We further hypothesized that the acute bout of aerobic activity would increase tonic and phasic pupil dilation compared to the passive control condition. Lastly, we conducted exploratory analyses into the moderating effects of cardiorespiratory fitness on the acute exercise and behavioral and pupillary relationship. If improved behavioral performance is observed as a function of acute exercise, any effects on tonic and/or phasic pupillary responses would provide support for using pupillometry as a biomarker of LC-NE activity.

1. Method

1.1. Participants

The present sample included undergraduate students (n = 43; age = 19.7 ± 1.6) recruited during summer (n = 17), and fall (n = 26) semesters from the Northeastern University Department of Psychology undergraduate subject pool. Participants reported that they were free of neurological disorders, cardiovascular disease, and any medications that influence central nervous system function. They were fluent English speakers and had normal or corrected-to-normal vision. They also had not performed the cognitive task utilized in the current protocol prior to the study. Participants provided informed consent for procedures approved by the Northeastern University Institutional Review Board for human subject safety.

1.2. Cardiorespiratory fitness measurement

Participants completed a maximal graded exercise (i.e., VO_2max) test on a motorized treadmill (Trackmaster TMX428 with a COSMED Quark CPET OMNIA metabolic cart, Concord, CA, USA). The test utilized a modified Balke protocol and followed the guidelines of the American College of Sports Medicine (ACSM, 2017). Participants began with a walking warm up for one and a half minutes, then ran at a constant speed with incremental grade inclines of 2.5–3.5% every 2 min until volitional fatigue. Participants wore a heart rate (HR) monitor during the test to determine maximal heart rate. Ratings of perceived exertion (RPE) were collected via the Borg scale (Borg, 1998) and a feeling scale (±5-point scale) was assessed every 2 min. Relative peak oxygen consumption (i.e., VO_2max) was expressed in ml/kg/min and based upon maximal effort as evidenced by achieving at least two of the following criteria: 1) a plateau in oxygen uptake corresponding to an increase of less than two ml/kg/min despite an increase in exercise workload; (2) a peak HR ≥ age-predicted HR_max (220 - age); (3) a respiratory exchange rate ≥ 1.1; and/or (4) RPE ≥ 17. Participants’ relative VO_2max metrics were subsequently converted into percentiles informed by normative fitness classifications by age and sex (Shvartz and Reibold, 1990).

1.3. Pupillometry

The pupillary response was recorded using an ETL-200 Eye Tracker (ISCAN, Inc., Woburn, MA, USA). The eye imaging unit was placed on a tabletop in front of the stimulus monitor and facing the participant. The unit was placed below screen-level such that it did not occlude the monitor. The eye imaging unit was comprised of a high-sensitivity eye imaging camera, manually focused optics, and an infrared eye illuminator, all mounted on a moveable two-axis pan/tilt base. The eye imaging camera sampled at a rate of 500 Hz. Monocular (right) eye movements were monitored by the imaging unit by reflecting infrared light off of the lens and cornea of the eye. The experimenter remotely controlled the pan/tilt base from a control room (immediately adjacent to the testing chamber) to maintain the participant’s eye features within the camera’s view. The experimenter also manually adjusted the optics coupled to the eye camera in order to zoom and focus the eye image and control the amount of light admitted to the eye camera. The zoom was completed once at the beginning and then kept at the same setting throughout the task.

The pupil diameter data were output into arbitrary units, which was later converted into millimeters (mm). This conversion used a self-constructed false pupil, which consisted of a mechanically punched hole of exactly 7 mm in diameter on a manila folder. This false pupil was then placed in the exact position where the camera had fixated the participants’ eye. Once in focus, the diameter of the image of the false pupil was manually measured in mm using a tape measure. This measurement of the false pupil was then divided by the known false pupil diameter (7 mm) to create a mm conversion factor that was unique for each participants’ pupil diameter output.

Prior to calibrating the eye tracker, participants were seated in a chair with a stabilized table placed in front of them to avoid any subsequent movement. A foam-padded chinrest was used by participants, which placed in a marked position on the table in front of the participant. The height of the chinrest was adjusted to accommodate participants’ comfort while also facilitating an upright posture. Once settled, participants’ eyes were 74.7 cm from the stimulus monitor and 50.8 cm from the eye imaging unit. Calibration of the eye tracker followed the manufacturer’s recommendations, which consisted of a five-point calibration with each calibration point level to the participants’ line of site. Each edge calibration point was placed such that it subtended ±8 to ±10 degrees of the participants’ field of view. The distance (d) between edge points was determined by the following formula: d = (74.7 ÷ 2.5) × tan (9). Thus, edge points were placed at equidistant diagonal edges, 11.7 cm from the center point. Participants remained seated in a stable position throughout the task to ensure consistent tracking; thus, no further recalibration occurred throughout the task.

1.4. Inhibitory control task

Inhibitory control was measured using a modified flanker task. The task was presented using E-Prime 3.0 software (Psychology Software Tools, Inc. Pittsburgh, PA) on a 24-in. ASUS VG248QE monitor. Stimuli were black symbols presented on a gray background, which were designed to minimize reflexive pupillary contractions due to stimulus-background combinations of a higher contrast (Binda et al., 2013). In support of this consideration, McGowan et al. observed a negative dip at stimulus onset in the task-evoked, phasic pupillary response waveform (see Fig. 4 in McGowan et al., 2019) that is indicative of a pupillary light response (Mathôt, 2018), which may have obscured the ability to observe intervention-based differences in the pupillary response. That is, the degree of contrast between the eliciting stimulus and background may have interfered with the pupil’s capacity to dilate in response to the manipulations of the inhibitory control demands of the task or reflect differences as a function of the exercise intervention. Such a pupillary light response could be avoided by, instead, using black stimuli on a gray background to reduce the visual contrast, which has been found efficacious in reducing the pupillary light response in previous pupillometric studies with inhibitory control tasks (van Steenbergen and Band, 2013; van Steenbergen et al., 2015). Interestingly, other research has shown that a gray background (lower contrast/luminance) modulates tonic pupil dilation under higher cognitive load compared to black stimuli presented on a white background (higher contrast/luminance), while phasic dilation remained unaffected by higher cognitive load conditions (Peysakhovich et al., 2016). As such, the question remains whether utilizing a different stimulus environment (i.e., gray and black color contrasts) may afford adequate pupil modulation following exercise intervention. Accordingly, we aimed to test the acute exercise-pupillary response with this methodology to gain a clearer understanding of this relationship.

Responses were made using a 4-button inline fiber optic respond pad (Current Designs, Philadelphia, PA, USA) with left and right thumbs each placed on the outermost buttons on opposite sides of the keypad. Participants were given instruction prior to beginning the task and completed 36 practice trials before completing the main task. The instructions included an emphasis on speed such that participants were asked to respond to each trial “as quickly as possible, while still being as accurate as possible.”

The main task consisted of four blocks of 100 trials. The trial order was pre-randomized separately for each trial block, and the block order was randomized such that participants performed a different combination of blocks following each experimental condition on separate days.

The modified flanker task involved five arrows, three centimeters tall, presented focally on the monitor. Participants were instructed to respond according to the directionality of the centrally-presented target arrow, which was presented amid either congruent (pointing in the same direction) or incongruent (pointing in the opposite direction) flanking arrows. Congruency and directionality of the arrows were equiprobable. After instruction, participants completed 36 practice trials with a sequence consisting of a 400 ms fixation cross followed by a 100 ms stimulus, followed by a blank screen for an inter-stimulus interval of 2400 ms during which participants could make a response. The experimental sequence was the same as for the practice trials with the exception of a variable inter-stimulus interval (2000, 2200, and 2400 ms).

1.5. Procedure

1.5.1. Session 1

On the first laboratory visit, participants completed an informed consent, a health history and demographics questionnaire, the Physical Activity Readiness Questionnaire (PAR-Q; Thomas et al., 1992), and the Edinburgh Handedness Inventory (Oldfield, 1971). Participants were then fit with a HR monitor and had their height and weight measured. Subsequently, participants were calibrated on the eye tracker to ensure that their eyes could be reliably tracked, and then completed a practice round of the inhibitory control task for familiarization. Lastly, participants completed a maximal graded exercise test to assess their cardiorespiratory fitness. Maximal heart rate determined from the graded exercise test was used to set the prescribed intensity of the exercise intervention. Testing lasted approximately 1 h.

1.5.2. Session 2 and 3

The protocol employed a within-subject design with participants pre-randomized into two different experimental intervention orders for sessions 2 and 3 (Rest-Aerobic Exercise, Aerobic Exercise-Rest) to account for effects caused by the order of exposure. During each session, participants were fit with a HR monitor, given instructions for the experimental intervention, and oriented to the equipment. Participants then underwent the intervention. For the exercise intervention, the protocol consisted of 20-min of walking/jogging on a treadmill at a moderate-to-vigorous intensity (60–70% of their maximum heart rate achieved during the graded exercise test), and 1-min of cooldown. In the rest condition, participants sat in a comfortable chair placed in the same room and position as the motor-driven treadmill for 21-min. RPE and feeling scale were measured every 2 min during each intervention.

Upon completion of the intervention, participants were escorted to the testing chamber located in an adjacent room. There was a 15.0 ± 0.3 min interval from cessation of intervention to the initiation of the first trial of the inhibitory control task. The lighting in the room during this interval was static and dim, which was kept constant across participants as a means to control luminance. During this interval, participants were calibrated to the eye-tracker. Participants were placed in the chin rest to keep their head stable while the table-top camera tracked their eye movements. Participants’ HR at the start of the task was <15% of their resting HR. The average duration of cognitive testing was 22.4 ± 1.6 min for the seated rest condition and 22.6 ± 2.2 min for the exercise condition, which was not significantly different, t = 0.43, p = 0.67. Sessions 2 and 3 lasted ~90 min, and participants were briefed on the purpose of the experiment at the conclusion of their third session.

1.6. Data reduction and statistical analyses

Data were processed using MATLAB 2017a (The MathWorks Inc., 2017). Behavioral outcomes of mean response accuracy and reaction time (RT) were derived for each trial type (i.e., congruent/incongruent trials) of the inhibitory control task. Perceptual interference (i.e., flanker effect) was also calculated for each outcome. Response accuracy interference was calculated as congruent – incongruent accuracy, and RT interference was calculated as incongruent – congruent RT. Superior performance, across both intervention and congruency conditions, was indicated by higher response accuracy, shorter RT, as well as less accuracy and RT interference.

Continuous pupillary diameter output for all trials was corrected for missing data; trials were rejected if they were missing more than 60% of the data points due to artifact (i.e., blinks) (van Steenbergen and Band, 2013; van Steenbergen et al., 2015). The mean number of trials included were 398 ± 4.7 trials for exercise condition and 399 ± 4.4 trials for the rest condition. Data from the remaining included trials were then preprocessed. Of the initial 43 participants, five participants were removed because of technical issues or loss of data due to prolonged eye closure. The preprocessing protocol included identifying and removing segments of the signal containing blinks, which were linearly interpolated, then smoothed using 22-point unweighted average filter – a technique adapted from prior pupillometry research (Ruchkin and Glaser, 1978; Siegle et al., 2008; Siegle et al., 2003) and scaled-up from a 60 Hz eye-tracking design to fit the current eye-tracker’s sampling rate of 500 Hz. Tonic and phasic outcomes were calculated from the preprocessed pupillometric data. Processing of the tonic data consisted of selecting the 200 ms preceding stimulus onset, which was averaged across trials. Processing of the phasic measurement consisted of subtracting each trial’s pre-trial baseline 200 ms from the selected range of data points (i.e., 200 to 2000 ms post-stimulus), and determining the peak baseline-corrected amplitude (in mm) between 600 and 1400 ms of the signal given the timeframe of a typical pupil orienting response (Mathôt, 2018). The selected peak was then averaged across trials.

Initial inspection of the data revealed that response accuracy measures across interventions were non-normally distributed with a negative skew (<–1.9). Thus, a base 10 log transformation was conducted on the response accuracy measures to correct for skewness. The remaining critical outcomes (i.e., RT, phasic/tonic pupillary response) were distributed normally.

All statistical analyses were performed in R 4.0.3 (R Core Team, 2020). Behavioral and pupillary response outcomes were separately analyzed using 2 (intervention: aerobic exercise/rest) × 2 (inhibitory control: congruent/incongruent) linear mixed effect models that included the random intercept at the participant level. Participant-level intraclass correlation coefficients (ICCs) were assessed to determine whether inclusion of participants at a higher level was warranted. ICCs were considerably high, ρs > 0.26, thus participant IDs were entered into all models as random intercept effects. Secondary analyses used the same model structure without the examined response accuracy and RT interference scores using paired sample t-tests of intervention. All linear mixed effect modeling was conducted using R packages lme4 (Bates et al., 2015), lmerTest (Kuznetsova et al., 2017), and emmeans (Lenth, 2020). For significant intervention effects, we performed post hoc power analyses (Helios and Rosario, 2020) to demonstrate power achieved for the effect size (i.e., Cohen’s f² or d) based on model degrees of freedom and a conservative assumed Type I error rate of 0.001. Exploratory analyses were conducted to assess the moderating influence of cardiorespiratory fitness on the effect of the intervention on behavioral and pupillary response outcomes. In these follow-up analyses, VO_2max percentile was added as a covariate, as well as an interaction with the intervention term, to the initial linear mixed effect model to test its association to the outcome. The original model (full model specifications as detailed previously) and the same model with VO_2max percentile included were compared to assess whether the addition of VO_2max percentile improved model fit (i.e., significantly changed R²). If model fitness improved, and VO_2max percentile significantly interacted with the intervention term, then VO2max percentile moderated the effect of the intervention on the outcome.

The R package “mediation” (Tingley et al., 2014) was used for mediation and sensitivity analyses (Imai et al., 2010) to determine whether the pupillary response mediated effects of exercise on behavioral task performance. Total, direct, and indirect (or “mediation”) effects were calculated, yielding nonparametric confidence intervals of the mediation effect through 1000 bootstrap resamples of the indirect effect. Sensitivity analysis was performed to assess the robustness of the mediation result to potential violations of sequential ignorability; that is, whether or not an unmeasured confounder effected the result (Albert and Wang, 2015).

2. Results

Demographic and fitness data are presented in Table 1.1.

Table 1.1.

Demographic information.

Mean (±SD)	Female (n = 21)	Male (n = 17)	Total (n = 38)
Age	19.8 ± 1.2	19.6 ± 1.7	19.7 ± 1.5
BMI (kg/m2)	23.4 ± 5.0	24.4 ± 4.2	23.8 ± 4.6
Cardiorespiratory fitness percentile	53.2 ± 32.6	51.4 ± 34.4	52.4 ± 32.9
Maximum heart rate	192.1 ± 6.8	189.4 ± 8.1	190.9 ± 7.4

Note: BMI = body mass index; Cardiorespiratory Fitness (VO_2max) Percentile is based on normative fitness classifications (Shvartz and Reibold, 1990).

Descriptive statistics are presented in Table 1.2.

Table 1.2.

Descriptive statistics for behavioral and physiological statistics.

Mean (±SD)	Rest		Exercise
Intervention heart rate (bpm)	78.65 ± 11.8		127.86 ± 8.2
Percent of heart rate max	41.2 ± 0.1		66.99 ± 0.0
	Congruent	Incongruent	Congruent	Incongruent
Response accuracy (%)	99.0 ± 1.6	94.5 ± 6.5	98.6 ± 2.5	93. 9 ± 7.5
Reaction time (ms)	432. 7 ± 47.1	495.8 ± 55.7	418.8 ± 40.3	480.8 ± 48.5
Tonic dilation (mm)	5.23 ± 1.0	5.22 ± 1.0	5.59 ± 1.0	5.57 ± 1.0
Phasic dilation (mm)	0.16 ± 0.1	0.18 ± 0.1	0.15 ± 0.1	0.17 ± 0.1

2.1. Behavioral performance

For response accuracy, no significant main effect of the intervention was observed, F(1, 111) = 0.59, p = 0.44, f² = 0.005 [95% CI: 0.00, 0.07]. However, as expected, the main effect of congruency was significant, F(1, 111) = 139.87, p < 0.001, f² = 1.26 [95% CI: 0.78, 1.85], indicating that response accuracy for congruent trials was significantly greater than incongruent trials (see Fig. 1.1). No intervention × congruency interaction was observed, F(1, 111) = 0.03, p = 0.85, f² < 0.001 [95% CI: 0.00, 0.03]. Follow-up analyses showed no influence of VO_2max percentile on the model (see Table 1.3) or meaningful change model fit, x² = 0.002, p = 0.99.

Fig. 1.1.

Flanker task response accuracy outcomes.

Note. Log₁₀ transformed mean flanker task response accuracy outcomes for exercise and rest interventions across inhibitory control conditions. Lower numbers indicate better performance. ***p < 0.001.

Table 1.3.

Linear mixed effects model of intervention and congruency effects on behavioral and pupillary response outcomes - testing moderating effects of cardiorespiratory fitness.

	Response accuracy	Accuracy interference	RT	RT interference	Tonic pupil dilation	Phasic pupil dilation
Intervention	F(1, 110) = 0.16 p = 0.69 f2 = 0.001 [0.00, 0.05]	F(1, 112) = 0.61 p = 0.44 f2 = 0.02 [0.00, 0.21]	F(1, 110) = 3.50 p = 0.06 f2 = 0.03 [0.00, 0.14]	F(1, 112) = 0.01 p = 0.91 f2 < 0.001 [0.00, 0.07]	F(1, 110) = 9.26 p = 0.002 f2 = 0.08 [0.01, 0.23]	F(1, 110) = 10.33 p = 0.002 f2 = 0.09 [0.01, 0.25]
Congruency Condition	F(1, 110) = 138.61 p < 0.001 f2 = 1.26 [0.78, 1.85]	–	F(1, 110) = 348.71 p < 0.001 f2 = 3.17 [2.19, 4.32]	–	F(1, 110) = 0.02 p = 0.89 f2 < 0.001 [0.01, 0.03]	F(1, 110) = 13.28 p < 0.001 f2 = 0.12 [0.02, 0.29]
VO2max Percentile	F(1, 36) = 0.002 p = 0.97 f2 < 0.001 [0.00, 0.01]	F(1, 36) = 0.27 p = 0.61 f2 = 0.007 [0.00, 0.17]	F(1, 36) = 8.98 p = 0.005 f2 = 0.25 [0.00, 0.01]	F(1, 36) = 0.25 p = 0.62 f2 = 0.007 [0.00, 0.20]	F(1, 36) = 1.58 p = 0.22 f2 = 0.04 [0.00, 0.29]	F(1, 36) = 3.35 p = 0.08 f2 = 0.09 [0.00, 0.41]
Intervention * Condition	F(1, 110) = 0.04 p = 0.85 f2 = 0.001 [0.00, 0.03]	–	F(1, 110) = 0.03 p = 0.87 f2 < 0.001 [0.00, 0.03]	–	F(1, 110) = 0.007 p = 0.93 f2 < 0.001 [0.01, 0.03]	F(1, 110) = 0.13 p = 0.72 f2 = 0.001 [0.00, 0.05]
Intervention * VO2max Percentile	F(1, 110) = 0.04 p = 0.85 f2 = 0.001 [0.00, 0.03]	F(1, 112) = 1.45 p = 0.24 f2 = 0.04 [0.00, 0.28]	F(1, 110) = 0.19 p = 0.66 f2 = 0.002 [0.00, 0.05]	F(1, 112) = 0.01 p = 0.91 f2 < 0.001 [0.00, 0.07]	F(1, 110) = 0.34 p = 0.56 f2 = 0.003 [0.00, 0.06]	F(1, 110) = 10.15 p = 0.002 f2 = 0.09 [0.01, 0.24]

Note: Cohen’s f and d presented with [95% CI]. Significant effects are bolded.

For accuracy interference there was no significant effect of the intervention t(37) = 0.46, p = 0.65, d = 0.11 [95% CI: −0.36, 0.57]. Follow-up analyses showed no influence of VO_2max percentile on the model (see Table 1.3) or meaningful change model fit, x²(2) = 1.78, p = 0.41.

For RT, there was a significant main effect of intervention, F(1, 111) = 18.63, p < 0.001, f² = 0.17 [95% CI: 0.05, 0.36], power = 84%, indicating the expected pattern of shorter RTs following the aerobic exercise intervention compared to rest (see Fig. 1.2). Further, the expected main effect of congruency was significant, F(1, 111) = 351.28, p < 0.001, f² = 3.16 [95% CI: 2.19, 4.31], indicating longer RT for incongruent trials than congruent trials. However, there was no intervention × congruency interaction, F(1, 111) = 0.16, p = 0.69, f² = 0.004 [95% CI: 0.00, 0.03]. Follow-up analyses showed the model fitness improved when VO_2max was included, x²(2) = 8.66, p = 0.01. The model with VO_2max terms included (see Table 1.3) showed a reduction in the intervention effect on RT to trend level, and no interaction was found between the intervention and VO_2max percentile. There was a significant main effect of VO_2max percentile. These results suggested an association between RT and VO_2max percentile, but VO_2max percentile did not moderate the intervention effect on RT.

Fig. 1.2.

Flanker task reaction time outcomes.

Note. Mean flanker task reaction time outcomes for exercise and rest interventions across inhibitory control conditions. **p < 0.01, ***p < 0.001.

There was also a non-significant effect of intervention for RT interference, t(37) = 0.40, p = 0.69, d = 0.09 [95% CI: −0.37, 0.55].

2.2. Pupillary response

For the tonic response collected during the pre-trial period, there was a significant main effect of intervention, F(1, 111) = 23.52, p < 0.001, f² = 0.21 [95% CI: 0.07, 0.43], power = 93%, indicating that pupil diameter was significantly larger for the aerobic exercise intervention compared to rest (see Fig. 1.3). There was no significant effect of congruency, F(1, 111) = 0.02, p = 0.89, f² < 0.001 [95% CI: 0.00, 0.27], or intervention × congruency interaction, F(1, 111) = 0.007, p = 0.93, f² < 0.001 [95% CI: 0.00, 0.04]. Follow-up analyses showed no influence of VO_2max percentile on the model (see Table 1.3) or meaningful change model fit, x²(2) = 1.98, p = 0.37.

Fig. 1.3.

Flanker task tonic pupillometric outcomes.

Note. Mean flanker task tonic pupillometric outcomes for exercise and rest interventions across inhibitory control conditions. **p < 0.01.

The task-evoked, phasic response time course showed dilation in response to stimuli and no signs of a pupillary light response (see Fig. 1.4). There was no significant main effect of intervention, F(1, 111) = 0.85, p = 0.36, f² = 0.008 [0.00, 0.07]. However, the main effect of congruency was significant, F(1, 111) = 12.27, p < 0.001, f² = 0.11 [0.02,0.27], indicating that pupil diameter for incongruent trials were significantly greater than congruent trials (see Fig. 1.5). No intervention × congruency interaction was observed, F(1, 111) = 0.12, p = 0.73, f² = 0.001 [0.00, 0.04]. Further, VO_2max percentile was significantly associated with phasic pupil dilation across congruency conditions following the aerobic exercise intervention, rs ≥ 0.34, ps ≤ 0.04. No such associations were observed following rest, rs ≤ 0.19, p ≥ 0.20. This finding suggested that higher VO_2max percentile was specifically associated with greater phasic dilation following aerobic exercise. Follow-up analyses showed the model fitness improved when VO_2max was included, x²(2) = 13.44, p = 0.001. Contrary to the previous model, the model with VO_2max percentile terms (see Table 1.3) showed a significant effect of intervention on phasic pupil dilation. There was also a significant interaction between the intervention and VO_2max percentile, which indicated that VO_2max percentile moderated the effect of the intervention on phasic pupil dilation. Together, these findings suggest that individuals with higher VO_2max percentile exhibit greater pupil dilation following exercise compared to rest.

Fig. 1.4.

Flanker task phasic pupillometric time course.

Note. Mean flanker task pupil diameter change time course for exercise and rest interventions across inhibitory control conditions.

Fig. 1.5.

Flanker task phasic pupillometric outcomes.

Note. Mean flanker task phasic pupillometric outcomes for exercise and rest interventions across inhibitory control conditions. ***p < 0.001.

2.3. Mediation and sensitivity analysis

Pupillary response outcomes were assessed as an underlying mechanism for intervention effects on behavioral outcomes. Specifically, mediation analyses were conducted to examine whether pupillary responses (i.e., tonic and phasic pupil dilation) mediated associations between the acute exercise intervention and the main behavioral outcomes (i.e., response accuracy and RT). The effect of the intervention (X) on the behavioral outcomes (Y) constituted the total effect; the effect of the intervention on the behavioral outcome with pupillary response (M) taken into account constituted the direct (average causal direct) effect; and the indirect (average causal mediation) effect represented the total effect minus the direct effect.

2.3.1. Tonic pupil dilation

For response accuracy, tonic pupil dilation did not elicit an average causal mediation effect: 0.02, 95%CI [−0.01, 0.05], p = 0.18, or an average direct effect: −0.04, 95%CI [−0.17, 0.08], p = 0.18. This finding suggests that tonic pupil dilation did not mediate the relationship between acute exercise intervention and flanker response accuracy. The sensitivity analysis showed a minimal amount of correlation between the error terms (0.01) and a minimal amount of variance (1%) required by an unobserved confounder to see a null result.

For RT, tonic pupil dilation did not elicit an average causal mediation effect: 1.64, 95%CI [−1.51, 7.30], p = 0.40, or an average direct effect: 12.77, 95%CI [−5.12, 30.80], p = 0.17. This finding suggests that tonic pupil dilation did not mediate the relationship between acute exercise intervention and flanker RT. The sensitivity analysis showed a minimal amount of correlation between the error terms (−0.1) and a minimal amount of variance (1%) required by an unobserved confounder to see a null result.

2.3.2. Phasic pupil dilation

For response accuracy, phasic pupil dilation did not elicit an average causal mediation effect: 0.003, 95%CI [−0.02, 0.03], p = 0.68, or an average direct effect: −0.03, 95%CI [−0.16, 0.09], p = 0.57. This finding suggests that phasic pupil dilation did not mediate the relationship between acute exercise intervention and flanker response accuracy. The sensitivity analysis showed a minimal amount of correlation between the error terms (0.02) and a minimal amount of variance (2%) required by an unobserved confounder to see a null result.

For RT, phasic pupil dilation did not elicit an average causal mediation effect: 0.29, 95%CI [−1.86, 3.05], p = 0.73, or an average direct effect: 14.11, 95%CI [−4.82, 32.79], p = 0.14. This finding suggests that phasic pupil dilation did not mediate the relationship between acute exercise intervention and flanker RT. The sensitivity analysis showed a minimal amount of correlation between the error terms (0.01) and a minimal amount of variance (1%) required by an unobserved confounder to see a null result.

3. Discussion

The current study investigated the differential relation of a single bout of moderate-to-vigorous exercise compared to a resting control condition on pupil diameter and inhibitory control. The main aim of this study was to assess tonic and phasic pupillary responses to inhibitory control task performance as potential objective biomarkers of acute exercise on modulation of the LC-NE system and associated brain networks. We hypothesized that tonic and phasic pupillary responses would be sensitive to exercise-induced manipulations of inhibitory control. Our results indicated that tonic pupil diameter was larger during the flanker task following the exercise intervention compared to the resting control. Task-evoked, phasic pupil diameter was modulated by inhibitory control demands with significantly larger pupil diameter for the incongruent condition of the flanker task, which required greater amounts of inhibitory control. This pattern of results indicated that the flanker task was efficacious in manipulating phasic pupillary responses based on the inhibitory control demands of the task. Initial examination showed no group-level differences in phasic pupillary response as a function of the intervention. However, follow-up analyses showed that individual differences in VO_2max percentile moderated an effect of the intervention on the phasic pupil dilation. This finding suggested that individuals with higher fitness levels exhibited greater task-evoked phasic pupil dilation following aerobic exercise compared to rest. As such, our results suggest that the tonic and phasic pupillary responses may be sensitive to acute bouts of exercise, whereas the phasic pupillary response may also be sensitive to the manipulation of inhibitory control demands. These findings show the capability of the current study protocol in invoking distinct effects on tonic and phasic pupillary responses.

Behavioral findings generally supported the pupillary data. The results showed response accuracy and RT were sensitive to the manipulation of inhibitory control demands in the flanker task, such that participants exhibited the expected decrease in response accuracy and longer RT for trials requiring greater amounts of inhibitory control. The phasic pupillary responses supported this pattern of results such that greater inhibitory control demands induced larger phasic pupil dilation. Moreover, participants exhibited shorter RT following the exercise intervention compared to the control condition, which replicated previous findings of transient improvements in RT during inhibitory control tasks following a single bout of exercise (Drollette et al., 2014; Hillman et al., 2009; Kamijo et al., 2007; Pontifex et al., 2013). We also examined whether pupillary response outcomes mediated acute exercise intervention effects on the flanker behavioral outcomes, but our mediation analysis yielded null results. These findings suggest that the effects of a single bout of exercise on behavioral and pupillary responses were independent.

The findings of the current study complement and extend the earlier investigation by McGowan et al. (2019). The current study replicated McGowan et al.’s results of exercise-induced enhancements in RT during an inhibitory control task. Moreover, greater phasic pupil dilation was observed for incongruent compared to congruent trials, which replicates the robust literature on the pupillary response to modulation of inhibitory control demands (Laeng et al., 2011; Scharinger et al., 2015; van Steenbergen and Band, 2013; van Steenbergen et al., 2015). There was also replication of McGowan et al.’s finding indicating no differences in phasic dilation across intervention groups. One explanation for the absence of phasic dilation effects could be attenuation as a function of residual effects of larger tonic pupil dilation. That is, larger tonic pupil dilation has been associated with smaller phasic responses (Gilzenrat et al., 2010; Knapen et al., 2016). Interestingly, unlike McGowan et al., the current study did not observe a pupillary light response in the phasic pupil response, yet consistent with McGowan et al. findings, there was no differential effect of acute exercise on the phasic pupillary response. There is a possibility that the inhibitory control tasks (i.e., modified flanker tasks) used in both studies may not be optimally sensitive to modulations of acute exercise on the phasic pupillary response.

The main extension of the current study was the novel finding of larger tonic pupil dilation following a bout of aerobic exercise compared to rest. Acute exercise indeed modulated the pupillary response, but only in a more global manner (i.e., tonic activation) rather than via moment-to-moment fluctuations (i.e., phasic activation). The tonic pupillary response corresponds to LC tonic activity indicative of task engagement (Aston-Jones and Cohen, 2005; Nieuwenhuis et al., 2005). The current study’s findings suggest that acute moderate-to-vigorous aerobic exercise may elicit tonic activity levels that coincide benefits for cognitive function. However, it should be noted that McGowen et al. did not observe exercise-induced differences in tonic pupil diameter in their study. This lack of consensus across studies may be due to methodological differences. Specifically, the current study opted for a static, restful reading control condition compared to the active control employed by McGowan et al. Thus, differences in arousal, reflected in tonic activation, may have been obscured in McGowan et al. if light intensity exercise also modulates tonic pupil diameter in a manner similar to moderate intensity exercise. This remains an open question. Future studies will need to modulate the exercise ‘dose’ to determine how different intensities affect the tonic pupillary response. Given that the magnitude of pupil dilation has been shown to increase along with exercise (Hayashi et al., 2010), pupillary response could also be measured during exercise to assess dose-response-effects of exercise intensity on post-exercise tonic pupillary response. Further, differences in the methodology used to assess tonic pupil dilation may be found across studies. The current study measured the average pupil diameter of the 200 ms preceding stimulus onset, a method of measuring tonic pupil diameter that has been used in similar paradigms assessing pupillometry and cognitive performance (Chiew and Braver, 2013, 2014; Murphy et al., 2011). By contrast, McGowan and colleagues averaged pupil diameter during a non-task related fixation period (prior to the start of each block of trials) to assess tonic pupil dilation. They suggested that this method would alleviate any confound of tonic pupil size by phasic changes in the pupillary responses associated with performing the inhibitory control task. That is, larger phasic dilation on a preceding trial may influence tonic dilation on a current trial. However, the former, compared to the latter, type of tonic pupil measurement is not necessarily confounded by carryover effects from the preceding trial; rather, phasic and tonic dilation show opposite trends as function of task engagement (Gilzenrat et al., 2010). Future work should incorporate both types of tonic measurement to assess whether there are meaningful differences across methods. Lastly, the current study assessed pupillometry and cognitive performance only following the intervention, whereas McGowan et al. tested at pre- and post-test. While the clear difference in tonic pupil dilation between acute exercise and control conditions shown in the current study differ from the results of McGowan et al., their null findings were based on a differential pre-post study design. Thus, the current findings should be interpreted with caution until future work clarifies the relationship of acute exercise on pupillary outcomes.

Critically, the current study found that pupillary response outcomes did not mediate acute exercise intervention effects on the flanker behavioral outcomes. While single bouts of exercise increased transient tonic activity and beneficial cognitive function in parallel, they were not supported by a causal relationship. Imaging research has shown that moderate tonic activity coincides optimal levels of cognitive function (Murphy et al., 2011), implicating the LC-NE system as an underlying mechanism. Theoretically, the beneficial effects of acute exercise on cognitive function may also result from regulating tonic LC-NE activity (Pontifex et al., 2019). Although the current study does not directly support such a claim, there plausibly could be a mutually influential physiological mechanism that links the process involving acute exercise, LC-NE activity, and cognitive function. In attempting to bridge this gap, it is possible that our chosen behavioral task may not have been an ideal outcome for the model assessing the mediating influence of LC-NE activity (indexed by pupillary response) on the beneficial effects of exercise on inhibitory control. Future research should consider a multifaceted approach, using more than one measure of inhibitory control and/or tasks that tap different domains of cognitive control (i.e., working memory and mental flexibility).

Consistent with previous research in young adults, Chandler et al. (2021) found that the positive association between higher cardiorespiratory fitness and shorter RT on an inhibitory control task was not mediated by phasic pupillary responses. In fact, they did not observe any associations between pupillary responses and cardiorespiratory fitness. They concluded that the lack of an association between cardiorespiratory fitness and phasic pupil dilation indicated the absence of underlying LC-NE system activation. The current study also found that increased cardiorespiratory fitness was associated with shorter RT on an inhibitory control task and not directly associated with phasic pupil dilation. An interesting novel finding of the current study was that the emergence of cardiorespiratory fitness as a moderator of intervention effects on phasic pupil dilation. Such a finding suggests that individuals with greater cardiorespiratory fitness may have a physiological upregulation in phasic pupil dilation following an acute bout of aerobic exercise. However, VO_2max percentile did not moderate the effects of the intervention on RT. These findings suggest that the degree to which pupil dilation can index the transient benefits of a single bout of moderate intensity aerobic exercise on inhibitory control performance may depend on an individual’s level of cardiorespiratory fitness. Thus, phasic pupil dilation could plausibly index LC-NE system and associated brain networks’ activity underlying the transient benefits of acute aerobic exercise on inhibitory control, which may be selectively greater for individuals with greater cardiorespiratory fitness. Cardiorespiratory fitness has been linked to brain activity in regions underlying inhibitory control, such as the default mode network (Voss et al., 2010, 2011a, 2011b, 2016). The LC-NE system is important for attentional control and the regulation of the default mode network, which facilitate mind-wandering if not properly suppressed (Unsworth and Robison, 2017). Future research should examine whether cardiorespiratory fitness levels have a causal significance for whether phasic pupil dilation can index inhibitory control and underlying activity in the LC-NE system as well as associated brain networks. Another interesting approach would be to identify whether there are unique structural changes or distinct physiological responses (i.e., increased NE) in individuals with higher cardiorespiratory fitness that underlie their capacity for phasic upregulation during task engagement, as indexed by phasic pupil dilation.

A limitation of the current study was the absence of concurrent neuroimaging during inhibitory control task performance. The LC-NE system has been shown to robustly modulate the P3 ERP component (Nieuwenhuis et al., 2005), which is known to be a neuroelectric biomarker of flexible modulation of attentional control resources (Polich, 2007). Inhibitory control task performance following single bouts of exercise have been shown to influence the magnitude of the P3 response (Hillman et al., 2012; Pontifex et al., 2018). The P3 response has also been associated with the pupillary response (Murphy et al., 2011; Nieuwenhuis et al., 2005). In McGowan et al.’s (2019) study, no changes were observed for pupillary responses despite finding behavioral and ERP-related changes in cognition. As such, incorporating these measures into our study would have strengthened our ability to understand the role of the a single dose of exercise in modulating the LC-NE system and determining its role as a neurobiological mechanism that contributes to changes in cognitive function. Credence should also be given towards exploring whether neuromodulatory circuits other than the LC-NE system are implicated in acute exercise effects on pupillary responses. That is, multiple areas of the brain may underlie pupillary responses during a task, which complicates localization of a particular pupillary and behavioral responses to a singular neuronal circuity (Larsen and Waters, 2018).

A second limitation of the current study was that luminance was not objectively measured in the testing chamber where the pupillary response was recorded. Luminance was kept constant across participants with a static setting of dim lighting in the room. However, we did not objectively measure room illumination or the task monitor luminance, thus we cannot discount that luminance factors influenced pupillary responses. Though the current study lacked fine-grained control of luminance, this limitation was addressed by in the study design by keeping luminance consistent across participants as well as making sure doors to the testing chamber were closed during the completion of the task. Further, the contrast in color of the task stimuli were designed specifically to avoid attenuation of the pupillary response due to luminance changes as a function of stimuli onsets.

A final limitation of the current study was in the design and sample. The current study used a crossover design wherein task performance and pupillary responses were only measured post-intervention, informed by previous literature demonstrating beneficial effects of acute exercise on inhibitory control (Hillman et al., 2003; Kamijo et al., 2007; O’Leary et al., 2011). We recognize that the limitation of this design is the absence of a pre-intervention baseline. Hypothetically, any number of pre-study factors could have influenced performance (e.g., diet, daily stressors, sleep, etc.), although participants did come in at the same time of day for both sessions, and they were asked to refrain from caffeine intake as well as exercise prior to the study. Moreover, differences in intervention outcomes could be due to either increased performance due to exercise or decreased performance due to the seated rest control. The seated control is also debatable as an appropriate control condition (Pontifex et al., 2019). For instance, a seated control may have attenuated sympathetic nervous system activity to a greater extent than an active control condition. Aside from the inability to control for baseline performance, our observed intervention effects on tonic pupil dilation and RT should be interpreted with caution given the narrow age range of our participants, relatively small sample size. These findings warrant further replication and may not be generalizable to other periods of the lifespan. Nevertheless, we observed sufficient power (>84%) to detect intervention effects.

The current study builds upon previous research to provide a platform for future investigations of acute exercise effects on the LC-NE system and associated brain networks as measured by the pupillary response. Future research should be aware of the sensitivity of the pupillary response to stimulus contrast when assessing the effect of exercise on inhibitory control outcomes. Moreover, future studies should incorporate concurrent imaging measurements as well as different types of cognitive control tasks to provide multifaceted assessments of the pupillary response as a biomarker of the LC-NE system. Another interesting possibility would be to assess how different intensities or types of exercise influence the pupillary response. Murphy et al. (2011) showed an inverted-U relationship between tonic pupil dilation and the P3 response, which corresponds to levels of arousal as proposed by the adaptive gain theory of LC-NE function (Aston-Jones and Cohen, 2005). From a theoretical perspective, under assumptions of the adaptive gain theory, higher intensity exercise would cause higher arousal, and consequently, larger pupil dilation, smaller P3, and poorer behavioral performance. However, recent evidence has shown that single bouts of high-intensity interval training can benefit neurocognitive function in a manner similar to moderate intensity aerobic exercise interventions (Kao et al., 2017). More work is needed to understand whether transient dose-response effects of exercise may modulate activation in the LC-NE and associated brain networks, and whether the effects extend to global improvements in cognition or rather only to specific domains such as inhibitory control.

In conclusion, the current study investigated the effects of a single bout of moderate-to-vigorous intensity aerobic exercise on subsequent performance and pupillary responses during an inhibitory control task. Results showed an improvement in task performance as well as increased tonic pupil dilation following a bout of exercise compared to restful reading. However, tonic pupil dilation did not mediate the association between the acute exercise intervention and task performance. Such a pattern of results suggests that single bouts of moderate-to-vigorous aerobic exercise engenders increased transient tonic activity and beneficial cognitive function in parallel, but a causal relationship was not supported. Further, cardiorespiratory fitness moderated the effect of the intervention on phasic pupil dilation, indicating larger phasic pupil dilation following aerobic exercise for individuals with greater cardiorespiratory fitness. The current study extends previous research by providing evidence supporting the plausibility of the pupillary response as a marker of LC-NE system activation that is sensitive to acute exercise. Whether pupillary responses can account for the observed transient benefit of acute exercise on brain and cognition remains unclear.