Exercise-Induced Physiological Arousal Biases Attention Toward Threatening Scene Details

Abstract

The present experiment examined whether physiological arousal induced by acute bouts of aerobic exercise would influence attention and memory for scenes depicting or not depicting weapons. In a repeated-measures design, participants exercised at either low or high exertion levels. During exercise, they were presented with images, some of which depicted weapons; immediately following exercise, they completed a recognition test for portions of central and peripheral scene regions. Two primary results emerged. First, in the low exertion condition, we replicated extant research showing inferior peripheral scene memory when images contained, versus did not contain, weapons. Second, the high exertion condition increased central scene memory relative to low exertion, and this effect was specific to images containing weapons. Thus, we provide evidence for accentuated weapon focus effects during states of exercise-induced physiological arousal. These results contribute new applied and theoretical understandings regarding the interactions between physiological state, breadth of attention, and memory.

Keywords

Exercise physical exertion attention and vigilance memory

Introduction

The recent bombing tragedy at the 2013 Boston Marathon highlights the importance of understanding of how the physiological arousal accompanying aerobic exercise influences memory for scenes. Although emotional arousal has been repeatedly implicated in altering the breadth of attentional focus and memory (Hoscheidt, Dongaonkar, Payne, & Nadel, 2013), surprisingly few studies have related physiological arousal alone to applied memory phenomena. The existing studies in this area show equivocal results using relatively mild physiological arousal inductions and inconsistent methodologies (e.g., Cavenett & Nixon, 2006; Libkuman, Nichols-Whitehead, Griffith, & Thomas, 1999). The present study extends earlier research by examining the influence of low- versus high-intensity exercise on memory for central and peripheral scene details.

Physiological arousal and cognition

Affective states can be characterized by two orthogonal components: arousal and valence (Lang, Bradley, & Cuthbert, 1997). Arousal describes the intensity (low vs. high) of affective experience, whereas valence refers to the direction (positive vs. negative) of the experience. Early accounts of arousal influences on cognitive performance suggested that physiological arousal decreases the number of cues that can be monitored, restricting processing to salient cues in the central focus of attention (Easterbrook, 1959). Continuing research has supported and extended this hypothesis to several domains, including working memory (Mather et al., 2006), narrative comprehension (Cahill & McGaugh, 1995), spatial cognition (Gardony, Brunyé, Mahoney, & Taylor, 2011), trait anxiety (Derryberry & Reed, 1998), and autobiographical memory (Talarico, Berntsen, & Rubin, 2009). The relationships between arousal and memory are debated, however, and a range of alternative and more specific explanations have been offered. These include consideration of specific affective states (e.g., fear, anxiety, sadness, and happiness), motivational states (e.g., approach, and avoidance), stimulus types, task goals, and stimulus relevance (for a review, see Levine & Edelstein, 2009).

Further complicating the relationship between arousal and cognition is that most research has relied on affective state induction to induce both emotional and physiological arousal (Cahill & McGaugh, 1995; Christianson, Loftus, Hoffman, & Loftus, 1991; Corson & Verrier, 2007; Gardony et al., 2011; Mather et al., 2006); for instance, exposing participants to emotional images, videos, music, or words. Inducing affective states, however, is not entirely reliable and is also subject to large individual differences in interpretation, physiological response, and the experience and expression of emotion (Lang, Greenwald, Bradley, & Hamm, 1993). These complexities have motivated research using more direct inductions of physiological arousal in order to operationally separate arousal into physiological versus psychological components (Lacey, 1967; Libkuman et al., 1999).

In an early examination of the physiological component, Salmela and Ndoye (1986) examined choice spatial reaction time task performance when participants rested or exercised at low or moderate exertion rates, with results suggesting that physiological arousal directly induced via exercise causes a narrowing of attention to central stimuli. Similar results have been found with orienteers experiencing physical exertion and fatigue (Hancock & McNaughton, 1986) and skydivers preparing for a jump (Cavenett & Nixon, 2006). Finally, some recent evidence suggests that moderate-intensity exercise increases processing of centrally presented, visually salient stimuli (Hütterman & Memmert, 2012). Overall, research supports a relationship between acute exercise and narrowed attentional focus.

Several mechanisms might account for this pattern. Acute exercise may influence attentional control processes in working memory (Bacon, 1974). The relationship between acute exercise and attentional control is complex and modulated by a number of factors including exercise intensity, task difficulty, and physical fitness (Chang, 2009; Chang, Labban, Gapin, & Etnier, 2012; Davranche & McMorris, 2009; Hutchinson & Tenenbaum, 2007; Kamijo, Nishihira, Higashiura, & Kuroiwa, 2007; Salmela & Ndoye, 1986; Tomporowski, 2003). For instance, one study demonstrated positive linear effects of exercise intensity on lower level sensory information processing, but quadratic effects of exercise intensity on attentional control processes (Chang, 2009). For the latter pattern, low- to moderate-intensity exercise increased attentional control, whereas moderate- to high-intensity exercise caused performance decrements.

Acute exercise may also increase internal attention focus at the expense of attention resources available for external focus (i.e., association vs. dissociation; Tenenbaum, 2007). For instance, one study used a think-aloud protocol during variable intensity exercise and found increased occurrence of thoughts related to internal attention at the expense of external attention (Hutchinson & Tenenbaum, 2007). This result aligns with the parallel processing model, which suggests that high-intensity exercise will cause internal sensory and physiological cues to occupy attention, possibly at the expense of external task performance (Rejeski, 1985).

Finally, neurocognitive theories suggest that transient reductions in metabolic resources available to the prefrontal cortex during exercise could alter the ability to effortfully control attention (Dietrich, 2006; Hütterman & Memmert, 2012). Specifically, the reticular-activating hypofrontality model posits dynamic compensatory shifts of brain metabolic resources toward sensory and motor processes, and away from higher order functions of the prefrontal cortex (Dietrich & Audiffren, 2011). This compensatory shift tends to occur primarily during moderate- to high-intensity and extended bouts of physical exertion, and may partially explain why salient environmental features (such as a weapon) may spontaneously attract attention, and refocusing attention away from the feature may prove difficult during moderate- to high-intensity exercise (Ekkekakis, 2009; McMorris, 2016).

Weapon focus effects

To provide further examination of physiological arousal influences on memory for central versus peripheral scene details, we chose to leverage the weapon focus effect. The weapon focus effect refers to memory advantages for a weapon appearing in a scene, with concomitant memory disadvantages for extra-weapon scene information (Harada, Hakoda, Kuroki, & Mitsudo, 2015; Loftus, 1979; Loftus, Loftus, & Messo, 1987; Maass & Kohnken, 1989). Two prominent hypotheses have been proposed to explain the weapon focus effect, one emphasizing the perceived threat and resulting emotional arousal experienced when viewing a weapon (the arousal/threat hypothesis) and the other emphasizing the unusualness of depicted objects influencing attention and memory (the unusual item hypothesis) (Fawcett, Russell, Peace, & Christie, 2013).

The arousal/threat hypothesis proposes that an arousing object in a scene, such as a gun, knife, or syringe induces a state of physiological arousal. The immediate behavioral response is an attention fixation on the source of arousal (e.g., the gun), considering a narrowing of attention toward the salient object (Fawcett et al., 2013). This focus on a salient object is associated with a neglect of attention toward peripheral scene features, resulting in heightened memory for the salient object and diminished memory for peripheral features (Brown, 2003; Peters, 1988). This pattern is in accordance with Easterbrook's cue-utilization hypothesis (Easterbrook, 1959) and supported by some meta-analyses of studies examining arousal influences on weapon focus effects (Steblay, 1992).

The unusual item hypothesis challenges the arousal/threat hypothesis, emphasizing attention toward unusual (low predictability) scene features. Evidence for this hypothesis is threefold. First, some research demonstrates that even in low arousal scenarios, the presence of a weapon can influence memory for details (Kramer, Buckhout, & Eugenio, 1990). Weapon focus effects are also more likely to emerge when the weapon violates an expectation (e.g., a priest vs. police officer wielding a gun) (Pickel, 1999). Furthermore, objects that violate contextual expectations tend to attract attention, even when that object is not particularly threatening (Antes, 1974). Thus, the unusual item hypothesis suggests that attention toward an object may be driven by a violation of context-driven expectations, in addition to any influence of arousal or perceived threat.

Unfortunately, previous studies have used highly varied methods for operationalizing, eliciting, and measuring arousal-based impacts on weapon focus effects, making it difficult to understand the nature and extent of any arousal- versus unusual item-based influences (Deffenbacher, Bornstein, Penrod, & McGorty, 2004; Fawcett et al., 2013).

The present study

The present study extended previous research in several ways. First, rather than examining a very brief bout of low- to moderate-intensity exercise (e.g., running in place for 1 minute; Libkuman et al., 1999), we examined physiological load imposed by prolonged, high-intensity physical exercise. Second, rather than examining memory encoding preceding an arousing event (e.g., prior to a skydive; Cavenett & Nixon, 2006), we examined memory encoding during periods of physiological arousal, measuring subsequent retrieval at rest. Third, to control for individual differences in response to arousal inductions, we catered the intensity of our physiological arousal induction to each participant's aerobic capacity. We objectively measured the effectiveness of the induction by monitoring heart rate (HR) and also collected subjective ratings of perceived exertion (RPE).

Two primary hypotheses were made. First, we expect to replicate a basic weapon focus effect in conditions of low arousal: reduced memory for peripheral details and increased memory for central details, when a weapon is present (Fawcett et al., 2013; Loftus et al., 1987). Second, we expect that high physical exertion will accentuate the weapon focus effect relative to low exertion. Such a result would support earlier research examining how exercise narrows attentional focus with spatial choice reaction time tasks (Salmela & Ndoye, 1986), extending it to the weapon focus effect under conditions of intense physiological load.

Methods

Participants

Twelve physically fit (Shvartz & Reibold, 1990) adult males (Age M = 19.8; body mass index M = 27.1; two-mile run times M = 13.9 minutes) volunteered to participate. The sample size (n = 12) was determined via sample size estimates using extant data (McMorris & Graydon, 1997) demonstrating significant influences of exercise on response times (power = .93) and accuracy (power = .67) during a cognitive task with the same sample size. Informed consent was collected in accordance with approvals issued by the U.S. Army; recruitment was restricted to individuals without history of lower back, hip, knee, or ankle problems, including current orthopedic injuries.

Design

We manipulated one independent variable related to exercise, Exercise Intensity, across two levels (low and high), in a within-participant design. For the memory task, we manipulated two independent variables in a within-participant design: first, Weapon Depiction, which manipulated whether a weapon was present or absent in an image (present and absent). Second, we manipulated whether the Details tested were extracted from central or peripheral scene regions (central and peripheral). Thus, our overall design was a 2(Exercise Intensity: low, high) × 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) within-participant factorial design.

Apparatus and materials

Participants exercised on a lifecycle integrity fully recumbent bicycle ergometer, with a Polar RS800CX wireless telemetry strap to monitor, communicate (to the ergometer), and record HR. RPE (Borg, 1982) were collected.

We gathered (using Google™ Image Search) and normed a set of 400 images. Of these, 100 images depicted a hand-held weapon (i.e., knife and gun) and 300 images did not. Care was taken to match the 100 images containing a weapon with images not containing a weapon; for instance, a similar background scene and object(s), but with a person holding a tool or other non-threatening object (see Figure 1). Each image was standardized to 1024 × 768 pixels while maintaining aspect ratio. Image arousal ratings were collected using the Amazon Mechanical Turk crowdsourcing website. Participants rated each image on a scale ranging from 1 (Very Calm) to 9 (Very Excited). They were told that a 1 corresponded to the picture making them feel very calm, serene, or unaroused and a 9 corresponded to the picture making them feel very excited, stimulated, jittery, awake, or aroused (Warriner, Kuperman, & Brysbaert, 2013). As would be expected, ratings from 45 participants demonstrated higher arousal ratings for images containing a weapon (M = 5.4, SD = 1.4) versus not containing a weapon (M = 3.7, SD = 1.2), F(1, 44) = 160.5, p < .001, η² = .78. The set of 400 images was divided equally into two sets of 200 images (50 with weapon and 150 without weapon), for use in the low and high exertion sessions (we call these image sets A and B). Within each image set, half of the images (100) were used for learning and half (100) were used as lures during test.

Figure 1.

Two sample paired images with weapon absent (left) and present (right) versions. Red boxes depict central (weapon or non-weapon object) and peripheral image portions used in the recognition test.

To develop a recognition test, for each image, we selected two small portions (200 × 200 pixels) of the larger image, one representing a central (weapon) or peripheral (non-weapon) region. For images not containing a weapon, central regions corresponded to a non-threatening object, as exemplified in Figure 1.

Procedure

To customize our exercise intensity manipulation to individual physical fitness levels, at least two days prior to the experiment participants performed a VO₂ peak test involving pedaling on a cycle ergometer until volitional exhaustion. During this test, participants maintained a constant pedaling cadence of 60 ± 5 rpm, while the ergometer automatically increased resistance starting at 40 W and increasing by 20 W every minute. Volitional exhaustion occurred when participants either failed to maintain the cadence range or ceased pedaling. A metabolic cart measured HR, oxygen uptake, carbon dioxide output, and minute ventilation. The peak HR achieved at the point of volitional exhaustion was used to define each participant's maximum HR. This number was used to calculate participant-specific target HR ranges to be used during exercise.

Each participant visited the laboratory for two experimental sessions in a temperature-controlled room. The first session involved measuring body weight, height, and handedness (to determine which hand to place the response pad under). During a session, participants donned the HR monitor and pedaled on the bicycle for three successive 30-minute bouts of exercise at either the low (up to 25–35% of peak HR) or high (75–85% of peak HR) intensities (counterbalanced by session across participants). Each bout of exercise began with a 5-minute warm-up, during which the ergometer slowly increased resistance until target HR range was reached; participants then continued to pedal for an additional 25 minutes, followed by a 5-minute cooldown, and a 5-minute rest. Participants therefore exercised at the target intensity for a total of 75 minutes during each session. The RPE was administered immediately prior to, and during the final minute of, each exercise bout.

During exercise, participants were presented with 100 images (from set A or B) on a 22′′ monitor positioned at eye level at approximately 60-cm distance. Image presentation began when there were 6 minutes remaining the exercise bout; whether they viewed the 100 images during the first, second, or third bout of exercise was counterbalanced across participants; note that in follow-up analyses, the between-participant factor for order did not interact with any of the other factors. The 100 images were presented in random order for 3 seconds each, separated by a 500 ms fixation cross. Note that eye tracking data were also collected during the encoding phase (using an SMI RED500 remote eye tracking device), but excessive exercise-related head movement rendered the data uninterpretable.

During the 10-minute cooldown and rest, participants completed an old/new recognition test. The test contained 200 items: central (50) or peripheral (50) portions of each of the 100 studied images, plus central (50) or peripheral (50) portions of each of the 100 lures. Each image was displayed in the center of the screen for up to 3 seconds or until the participant responded Yes or No using a modified number keypad. Whether participants received image set A or B during the high or low exertion session was counterbalanced across participants.

Data processing and analysis

To assess memory performance, we calculated sensitivity (d-prime), which subtracts z-scored false alarm rates from z-scored hit rates and response times to hits. This was done for the two image types (with or without weapons) and the two test types (central and peripheral details). Statistical analyses were conducted to test the two primary hypotheses. First, to test for replication of a basic weapon focus effect in the low arousal condition, we conducted a 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) repeated-measures analysis of variance (ANOVA) within the low-intensity exercise condition. Second, to test whether conditions of high-intensity physical exertion would accentuate the weapon focus effect, we conducted an omnibus 2(Exercise Intensity: low, high) × 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) repeated-measures ANOVA. All analyses were done separately for sensitivity and response times to hits. Effect sizes are provided for all analyses, using η² for ANOVAs, and Cohen's d for t-tests. Table 1 details hit rates, false alarm rates, and response times to hits for all conditions.

Table 1.

Mean (standard deviation) hit rates, false alarm rates, sensitivity (d-prime), and response times to hits, for all conditions.

	Weapon present		Weapon absent
	Central detail	Peripheral detail	Central detail	Peripheral detail
Hit rate (SD)
Low-intensity exercise	.69 (.12)	.65 (.13)	.70 (.09)	.77 (.09)
High-intensity exercise	.84 (.05)	.53 (.15)	.71 (.09)	.72 (.10)
False alarm rate (SD)
Low-intensity exercise	.03 (.04)	.14 (.15)	.05 (.04)	.04 (.03)
High-intensity exercise	.01 (.02)	.06 (.08)	.03 (.03)	.04 (.03)
Sensitivity (SD)
Low-intensity exercise	2.55 (.55)	1.77 (1.0)	2.32 (.53)	2.56 (.16)
High-intensity exercise	3.25 (.38)	1.84 (.85)	2.47 (.43)	2.44 (.41)
Response Time in ms (SD)
Low-intensity exercise	1146 (154)	1115 (134)	1096 (109)	1113 (90.6)
High-intensity exercise	1049 (120)	1120 (114)	1117 (79.6)	1082 (90.5)

Results

Manipulation checks

To ensure that our exercise manipulation had its intended effects on HR, we examined mean HR in a 2(Exercise Intensity: low, high) × 3(Phase: exercise, cooldown, rest) repeated-measures ANOVA. There were main effects of Exercise Intensity, F(1, 11) = 154.38, p < .001, η² = .52, and Phase, F(2, 22) = 200.23, p < .001, η² = .27, and an interaction, F(2, 22) = 196.49, p < .001, η² = .15. As detailed in Table 2, the high-intensity condition significantly increased HR relative to low-intensity exercise. Follow-up paired t-tests demonstrated that this effect was present during exercise, t(11) = 17.49, p < .001, d = 5.0, cooldown, t(11) = 9.72, p < .001, d = 2.8, and rest, t(11) = 17.49, p < .001, d = 1.6, phases.

Table 2.

Mean (standard deviation) heart rates during exercise, cooldown, and rest phases.

	During exercise	During cooldown	During rest
Low-intensity exercise	92.2 (3.8)	91.4 (5.0)	84.9 (8.1)
High-intensity exercise	143.6 (9.7)	118.1 (9.2)	97.7 (5.6)

To ensure that our exercise manipulation had its intended effects on perceived exertion, we examined mean RPE in a 2(Exercise Intensity) × 2(Phase: pre-exercise, during exercise) repeated-measures ANOVA. There were main effects of Exercise Intensity, F(1, 11) = 213.36, p < .001, η² = .34, and Phase, F(1, 11) =214.39, p < .001, η² = .35, and an interaction, F(2, 22) = 238.67, p < .001, η² = .26. As detailed in Table 3, the high-intensity condition significantly increased RPE relative to low-intensity exercise, but only during, t(11) = 17.5, p < .001, d = 5.1, and not before, t(11) = 1.95, p = .08, d = .57, exercise.

Table 3.

Mean (standard deviation) ratings of perceived exertion for immediately before and during exercise.

	Before exercise	During exercise
Low-intensity exercise	6.64 (.76)	7.22 (1.1)
High-intensity exercise	7.19 (1.3)	15.4 (1.8)

Replicating weapon focus effects

To assess whether weapon focus effects emerged in our data, we tested for changes in central and peripheral detail memory as a function of weapon presence. To do so, we tested for weapon focus effects in the baseline low-intensity exercise condition only. We entered mean sensitivity data into a 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) repeated-measures ANOVA. The ANOVA revealed an interaction between Weapon Depiction and Detail, F(1, 11) = 8.64, p < .05, η² = .16. All other effects were non-significant ( ps > .15). To aid interpretability, Figure 2 bars are letter coded; we reference these letter codes parenthetically for planned comparisons. Planned comparisons demonstrated a significant reduction in peripheral detail sensitivity in the presence of a weapon (B vs. D), t(11) = 2.51, p < .05, d = .73, but no change in central detail sensitivity (A vs. C), t(11) = 1.44, p = .18, d = .42. Thus, sensitivity data generally replicate the weapon focus effect, with poorer peripheral detail memory when a weapon is present versus absent. There was no evidence for enhanced detail memory in the presence of a weapon.

Figure 2.

Mean sensitivity (d-prime, and standard error bars) for the two exercise conditions, two weapon conditions, and two detail conditions.

A second ANOVA with the same design examined response times to correct judgments and revealed no significant main or interactive effects of Weapon Depiction or Detail ( p_min = .45).

Exercise influences on weapon focus effects

To test whether conditions of high-intensity exercise influence the weapon focus effect, we entered sensitivity data into a 2(Exercise Intensity: low, high) × 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) repeated-measures ANOVA. The ANOVA revealed a main effect of Detail, F(1, 11) = 5.88, p < .01, η² = .13, an interaction between Weapon Depiction and Detail, F(1, 11) = 31.96, p < .001, η² = .19, and a marginal interaction between Exercise Intensity and Detail, F(1, 11) = 3.73, p = .08, η² = .03. All other effects were non-significant ( ps > .10).

Although our specific interaction of interest was marginal (Exercise Intensity × Detail), we conducted follow-up analyses to specifically test our hypotheses; to reduce the likelihood of a Type I error, we used a Bonferroni correction term (α = .025). Specifically, we wanted to test whether the weapon focus effect was pronounced in the high- versus low-intensity exercise conditions. To do so, we conducted two planned comparisons. First, we tested whether central detail memory on weapon present trials was increased in the high- versus low-intensity exercise conditions (A vs. E); this difference was significant, t(11) = 3.89, p < .01, d = 1.1. Second, we tested whether peripheral detail memory on weapon present trials was decreased in the high- versus low-intensity exercise conditions (B vs. F); this difference was non-significant, t(11) = .17, p = .87, d = .05. Thus, high-intensity exercise increases memory for central details on weapon-present trials, but does not decrease memory for peripheral details.

To test whether conditions of high-intensity exercise influence the weapon focus effect, we also entered response time data into a 2(Exercise Intensity: low, high) × 2(Weapon Depiction: present, absent) × 2(Details: central, peripheral) repeated-measures ANOVA. The ANOVA revealed a three-way interaction between Exercise Intensity, Weapon Depiction, and Detail, F(1, 11) = 7.13, p < .05, η² = .04 (see Figure 3). To examine this interaction, we conducted two separate simple effects ANOVAs with a 2(Weapon Depiction: present, absent) × 2(Detail: central, peripheral) design, one within each Exercise Intensity condition. In the low-intensity condition, the Weapon Depiction ×Detail interaction was non-significant, F(1, 11) = .62, p = .45, η² = .01. In the high-intensity condition, the interaction was marginally significant, F(1, 11) = 4.19, p = .07, η² = .10. The difference between central and peripheral response times in the high-intensity, weapon-present condition was non-significant ( p = .15).

Figure 3.

Mean response times (and standard error bars) for the two exercise conditions, two weapon conditions, and two detail conditions.

Discussion

Results demonstrated evidence for a weapon focus effect in the low-intensity exercise condition. Participants showed lower sensitivity for peripheral details when a weapon was present versus absent, replicating this common applied memory phenomenon (Fawcett et al., 2013; Loftus et al., 1987; Steblay, 1992). We did not, however, find evidence for heightened central detail memory when weapons were present. This latter pattern supports research demonstrating that the weapon focus effect is not particularly stable with regard to central versus peripheral detail memory (Small, Kenny, & Bryant, 2011; Wessel, van der Kooy, & Merckelbach, 2000).

Critically, high-intensity exercise induced arousal states that accentuated central detail memory in the presence of a weapon. This was a categorically large (Cohen, 1988) effect, suggesting a dramatic narrowing of attention during intense exercise. Overall, high-intensity exercise increased sensitivity for scene details in the presence of a weapon by approximately 27%. We propose that this result provides some support for the arousal/threat hypothesis (Easterbrook, 1959; Fawcett et al., 2013; Peters, 1988). Specifically, a state of high physiological arousal induced by intense exercise appears to exaggerate the attentional narrowing that already occurs in response to viewing emotional stimuli. This contrasts with a recent meta-analytic review suggesting relatively limited support for the arousal/threat hypothesis (Fawcett et al., 2013). Perhaps previous research examining physiological arousal did not induce sufficient levels of arousal to generate reliable differences in central versus peripheral detail memory (Libkuman et al., 1999). While we did not find that exercise also decreases peripheral detail memory in the presence of a weapon, we note the data were trending in that direction.

Theoretical implications

The present results fit within a larger theoretical framework proposing the critical role of physiological arousal in mediating the influence of emotion on memory (Hamann, 2001). For instance, theories of arousal-biased competition suggest that arousal during encoding (whether due to induced arousal or presented stimuli) selectively influences memory for high priority, salient information (Mather & Sutherland, 2011). Physiological arousal, along with its reliable release of stress hormones (such as cortisol), can accentuate the effects of emotional arousal on memory. This pattern is seen in some prior research with acute stress inductions (Buchanan, Tranel, & Adolphs, 2006; Kuhlmann, Piel, & Wolf, 2005). The present results may also be accounted for by neurocognitive theories linking physical exercise, brain metabolic processes, and cognition. For instance, some theories and meta-analyses posit a transient loss of executive control functions during conditions of moderate- to high-intensity exercise, with a prioritization of relatively low-level sensorimotor functions (Chang et al., 2012; Dietrich, 2006; McMorris, 2016). This pattern may be driven by a dynamic reallocation of brain metabolic resources from a frontal-parietal control network toward lower level salience networks (Elton & Gao, 2014). In the present research, a failure of top-down guidance to disengage attention away from salient image features may have served to increase memory for the most salient, central, scene details (Borji & Itti, 2013; Brunyé et al., 2014; Phelps, 2013). Salient features of a scene may be deemed of particular value for rapid processing to facilitate any necessary action in response to a threat (Davis & Whalen, 2001); perhaps because of the physiological strain and reduced affordances during intense exercise, processing is prioritized toward information more likely to be important and consequential to safety (Phelps, 2013).

Applied implications

In military and other applied contexts, intense periods of physiological arousal are both frequent and necessary for sustained occupational performance (Foran, 2014). The impact of physiological arousal, load carriage, and fatigue on perception and cognition are only beginning to be understood and predictively modeled. Research examining the impact of sustained physical exertion on the deployment of attention and memory encoding and retrieval contributes to applied research in several ways. For instance, better predictive models linking physiological states to cognitive performance outcomes will afford: (1) supervisors making more informed decisions regarding work rates, work-rest cycles, and task allocation, (2) equipment designers working to reduce physiological burden imposed by heavy gear (e.g., oxygen tanks, backpacks, body armor), and (3) trainers working to increase individuals' aerobic capacity (Eddy et al., 2015; Hursh et al., 2004; Lieberman et al., 2006).

Conclusion

The present study found evidence for increased central detail memory in the presence of a weapon during intense bouts of physical exercise. This pattern carries implications for arousal/threat hypotheses and theories disentangling the role of physiological arousal on perception and cognition. It also carries possible impacts in multiple applied realms, including decision-making and equipment design in occupational contexts.

Footnotes

Acknowledgments

The authors thank Miss Jessica Howe for assistance with data collection.

Article Notes

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