Habitual exercise affects inhibitory processing in young and middle age men and women

International Journal of Psychophysiology 146 (2019) 73–84

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Habitual exercise affects inhibitory processing in young and middle age men and women

T

Kate Lennoxa, Rosemaree Kathleen Millerb,∗, Frances Heritage Martinb a b

College of Medicine and Health, The University of Tasmania, Churchill Ave, Hobart, TAS, 7005, Australia School of Psychology, The University of Newcastle, 10 Chittaway Road, Ourimbah, NSW, 2258, Australia

ARTICLE INFO

ABSTRACT

Keywords: Habitual exercise Inhibition Middle age P3b N2

Inhibitory processing is an aspect of cognitive control susceptible to cognitive decline due to aging. Engaging in habitual exercise could attenuate these declines in middle age. In the present study, the event-related potential (ERP) activity of 40 middle age adults (21 females) and 42 young adults (24 females) was recorded with electroencephalography (EEG) as participants completed two cognitive tasks that elicit inhibitory processing, one indexing interference control (i.e., the Flanker Task), and the other response inhibition (i.e., the Stop-Signal task). Congruent arrays elicited significantly earlier peaks in P3b activity compared to incongruent arrays in the Flanker task for non-exercisers and young habitual exercisers. For middle age habitual exercisers, this difference was of much smaller magnitude, and non-significant. This finding suggests that the timing of interference control, as indexed by P3b latency, was similar in the congruent and congruent conditions for middle age adults who engaged in regular exercise. On the Stop-Signal task, the P3b activity of habitual exercisers was larger and peaked earlier than that of non-exercisers, indicating that ERP activity signalling response inhibition was enhanced in young and middle age adult regular exercisers. Sex differences were also observed in peak P3b activity on the Flanker task, results which suggest the relationship between regular exercise and interference control differs between men and women. The findings of this study suggest that it is important to consider individual differences, for example sex, when examining the effectiveness of exercise interventions targeting cognitive decline.

1. Introduction The investigation of exercise interventions to address cognitive decline as a person grows older has garnered widespread research interest. In comparison to other cognitive processes, cognitive decline appears to have a disproportionately larger influence on the executive functions of the brain (Glisky, 2007; Reuter-Lorenz and Park, 2014; West, 1996). Inhibition is often viewed as an integral component of cognitive control (Aron, 2007), as inhibitory processing is arguably the neural basis of one's capacity to regulate their interactions with the external world (e.g., behaviour, motivation). Exercise may be an effective intervention to address age-related deficits in inhibition, as this form of physical activity appears to negate a range of factors that contribute to cognitive decline as an individual grows older. Event-related potential (ERP) activity, derived from electroencephalography (EEG), is often employed to delineate the ways in which exercise may influence cognitive processing (Hillman et al., 2012). However, much of the relevant EEG research has focused on comparisons between

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young and older (i.e., over 60 years old) adults. Cognitive decline due to aging is estimated to begin from middle age onward (Karlamangla et al., 2014; Salthouse, 2009; Singh-Manoux et al., 2012), highlighting a need to examine the differences in ERP modulation indexing inhibitory processing during middle age between people who exercise and those who do not. 1.1. Why exercise? Exercise is the purposeful execution of physical activity in order to promote or maintain one's physical fitness (Caspersen et al., 1985). Engaging in exercise bestows several neurobiological benefits that may translate into improved cognitive function in humans, and these effects could offset cognitive decline as a person grows older. Animal research indicates that exercise enhances levels of several neurotransmitters vital for optimal cognition, including brain-derived neurotrophin factor and insulin-like growth factor (Basso and Suzuki, 2017; Cotman et al., 2007; Hamilton and Rhodes, 2015). These types of neurotransmitters

Corresponding author. School of Psychology, The University of Newcastle, Ourimbah, NSW, 2258, Australia. E-mail address: [email protected] (R.K. Miller).

https://doi.org/10.1016/j.ijpsycho.2019.08.014 Received 18 December 2018; Received in revised form 19 August 2019; Accepted 20 August 2019 Available online 23 October 2019 0167-8760/ © 2019 Elsevier B.V. All rights reserved.

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contribute to neurogenesis and neuroprotection, or more specifically, the stimulation of new neuron growth and the ongoing survival of these cells (Marques-Aleixo et al., 2012; van Praag, 2008). In the human brain, exercise is also thought to expedite this form of neuroplasticity (Cotman et al., 2007; Voss et al., 2013), which in turn facilitates general cognitive function and brain health. The potential influence of exercise on higher cognitive tasks is also supported by the findings of several EEG studies (for a review see Hillman et al., 2012). In particular, both acute and chronic forms of exercise are shown to moderate inhibitory processing, as indexed by ERP measures (Guiney and Machado, 2013; Hillman et al., 2012). Exercise may be an effective intervention for age-related cognitive decline. Many types of physical exercise involve the brain-body connection, and several aspects of cognitive control are important to this relationship. Cognitive control can be defined as the regulatory influence of the executive functions on the integration of incoming information, perception and response execution (also see Gratton et al., 2018). Executive functions are higher-order cognitive tasks which do not become automated over time, such as inhibition, working memory, coordination and planning (Colcombe and Kramer, 2003; ReuterLorenz and Lustig, 2016). Inhibition reflects two related, but distinct, types of processing: interference control and response inhibition (a.k.a. inhibitory control). Interference control denotes the capacity to focus on a primary response, task or stimulus in the presence of competing contextual information, while response inhibition refers to the ability to suppress reflexive responses towards an eliciting stimulus (Nigg, 2000). Inhibitory processing is also one aspect of cognitive control that is susceptible to cognitive decline as an individual grows older (Braver and Barch, 2002; Lustig et al., 2007; Weeks and Hasher, 2016). A dilemma affecting the evidence for exercise as an intervention for cognitive decline is that exercise is often only defined as physical activity that increases aerobic fitness, or an individual's capacity for the reuptake of oxygen (Boutcher, 2001). In reality, though, physical activity can be classed as “… any bodily movement produced by skeletal muscles that results in energy expenditure” (Caspersen et al., 1985, p. 126). Recently Bowman (2018) has gone a step further and proposed that physical activity, and by extension exercise, are subcategories of bodily movement. The implication is that it is engagement in regular bodily movement, rather than the exercise itself, which drives effects often attributed to chronic exercise. In terms of aging, this distinction is important, as regular engagement in specific types of exercise may be linked to observed changes in various aspects of cognitive control. An alternative hypothesis is that it is the habitual engagement in exercise throughout one's lifetime that best promotes improvements in aspects of cognitive control, such as inhibition. Habitual exercise1 is best defined as patterns of behaviour that promote regular exercise-related physical activity (Aarts et al., 1997).

referred to as P3, P300) has been shown to index the influence of chronic exercise on the brain activity of young and older adults (Alderman et al., 2016). P3b activity peaks approximately 300–800 ms after stimulus onset and reflects the integration of several cognitive processes, including attention and stimulus expectancy, evaluation, and classification (Picton, 1992; Polich, 2007, 2012). During inhibitory processing, P3b modulation occurs when participants are required to attend to and discriminate between stimuli (Polich, 2007). Age-related changes in P3b activity also reflect the degradation of functional cortical connections underlying various cognitive functions as a person ages, resulting in signs of cognitive decline such as reduced attentional control and processing speed (Pontifex et al., 2009). Findings from two EEG studies suggest that patterns of habitual exercise matter for inhibitory processing in older adults (Chuang et al., 2015; Tsai et al., 2015). Tsai et al. (2015) compared the behavioural performance and P3b activity of older adult men on a three-stimulus oddball task, a more challenging version of a standard oddball paradigm, before and after engaging in a 12-month resistance training program. After the training program was completed, the exercise group responded with faster and more accurate responses to oddball targets than the control group. Moreover, after the exercise intervention, the P3b amplitude of the control group was reduced compared to their baseline data, a decrease that did not occur for the exercise group. In another study, Chuang et al. (2015) examined the behavioural performance and P3 activity of older women on a modified Flanker task after they had engaged in a regular dancing or walking intervention over three months. Participants from each exercise group responded to the Flanker task faster after three months relative to baseline, a difference not found for the control group. Moreover, post-intervention, the latency of parietal P3 activity in the two exercise groups was reduced compared to the control group (Chuang et al., 2015). A limitation of the Tsai et al. (2015) and Chuang et al. (2015) studies is that each sample consisted of only male or female participants. Prior research indicates that the association of exercise with cognitive function differs between men and women from older populations (Barha and Liu-Ambrose, 2018). For instance, Colcombe and Kramer (2003) suggest that, based on the results of their meta-analysis, older women experience larger improvements in cognitive function compared to older men. Engaging in aerobic exercise has also been associated with improvements in executive control function in women, but not men (Baker et al., 2010). Findings from more recent studies further support the importance of including sex differences in the study design when investigating the relationship between cognitive function and exercise. Fagot et al. (2019) found that performance on an inhibitory processing task was affected by engagement in habitual exercise for older women, but not older men, while Dimech et al. (2019) showed that relationships between cardio-respiratory fitness and brain function were more strongly associated in older men than older women.

1.2. Exercise, inhibition, aging and ERPs

1.3. The present study

ERP activity derived from EEG is an ideal method for assessing the relationship between habitual exercise and inhibitory processing, and other aspects of cognitive control, due to the capacity of this method to time-lock related processing to a specific stimulus with millisecond accuracy. Moreover, ERP measures index the interaction between the evaluation of a stimulus and the subsequent response to the same stimulus, both of which may be facilitated by engagement in habitual patterns of exercise. Amplitude modulation for the P3b (also variously

The findings of Tsai et al. (2015) and Chuang et al. (2015) support the notion that there is a relationship between habitual exercise and inhibitory processing in older adults. However, there is a scarcity of EEG research regarding the effect of chronic exercise on middle age adults (approximately 40–60 years). Reduced neurotransmitter synthesis and a general decline in central nervous system function are associated with increasing age throughout the lifespan (Begega et al., 2016), and these effects, in turn, may contribute to the deterioration of several cognitive processes from the age of 30 (Salthouse, 2009). Moreover, Cox et al. (2016) found evidence of a small but positive association between habitual physical activity and levels of cognitive function for young and middle age adults participating in studies that employed exercise interventions of at least 12 months duration. To date though, it is unclear whether this relationship is reflected in ERP indices of inhibitory processing. The aim of the present study was to test

1 Chronic and habitual exercise can be differentiated based on whether adherence to physical activity is voluntary or not. For research-based exercise interventions, chronic and habitual forms of exercise can be considered interchangeable as participation in chronic exercise requires the individual to provide informed consent to comply with ethical research standards in several countries.

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whether voluntary habitual exercise, outside of intervention, differentially indexes interference control and response inhibition in young and middle age adults. Habit formation is an important component of regular engagement in exercise (Phillips and Gardner, 2016). Moreover, assessing the relationship between habitual exercise and inhibitory processing in middle age will also provide evidence as to whether implementing chronic exercise interventions before individuals reach old age might be useful. Participants completed two cognitive tasks as EEG and behavioural performance were recorded. The Flanker task (Eriksen and Eriksen, 1974) indexed the interference control aspects of inhibition, while the Stop-Signal task was employed to examine response inhibition (Verbruggen and Logan, 2008). Young and middle age adult men and women were recruited to allow for the possibility that habitual exercise, or lack thereof, may be differentially associated with inhibitory processing in men and women. Behavioural and ERP measures were expected to reflect enhanced interference control and response inhibition for young adults compared with middle age adults, via faster reaction times, more positive P3b amplitudes and earlier-occurring P3b latencies. It was also predicted that similar results would be found for comparisons between habitual exercisers and non-exercisers (Hillman et al., 2004; Hillman et al., 2006b; Hillman et al., 2006a). If habitual exercise is associated with increased allocation of attentional resources and faster stimulus evaluation (Hatta et al., 2005) for middle age adults, then it was expected that the P3b activity of middle age exercisers would be more positive and earlier occurring than that of young exercisers. This result would be matched by no difference between the amplitude and latency of P3b activity for young and middle age nonexercisers. Finally, prior research indicates that there are sex differences in the relationship between cognitive function and exercise engagement in older populations (Barha and Liu-Ambrose, 2018; Dimech et al., 2019; Fagot et al., 2019). If this association extends to interference control and response inhibition in middle age, then it was predicted that participant sex would moderate the relationship between habitual exercise and inhibitory processing, as indexed by P3b modulation, for middle age adults but not young adults.

2.2. Materials and apparatus Exercise measures. Responses to the Physical Exercise Questionnaire (PAQ) and the Baecke Habitual Physical Activity Questionnaire - Sports subscale (B-SS; Baecke et al., 1982) were employed to categorise participants into exercise and non-exercise groupings. Groupings were further validated with the participant's resting heart rate and the calculation of their body mass index (BMI). The PAQ is a modified version of the Ainsworth et al. (1993) compendium of physical activities and was used to assess the average amount of minutes participants engaged in physical exercise per week over the previous two years. Moderate and vigorous exercise were defined as physical activities with metabolic equivalent values (METs) of 3–6 METs or more than 6 METs, respectively (Ainsworth et al., 1993). These amounts of physical exercise are consistent with the most recent guidelines provided by the World Health Organization (2010) that adults should engage in a minimum of 150 minutes of moderate intensity aerobic exercise per week. The full Baecke physical activity questionnaire evaluates the types of physical activity respondents engage in during their sport (i.e., the B-SS), work and leisure time. Raw answers for each question are converted to produce a score between 1 and 5 for each subscale. For the B-SS, a score of 1 indicates no sportsrelated physical activity, while a score of 5 denotes very high levels of sports-related physical activity. B-SS scores are positively and significantly correlated with physical activity measured by pedometers and accelerometers (Baecke et al., 1982). Cognitive tasks. The two cognitive tasks were run using Neuroscan Stim2 software. All stimuli presented during the two tasks were shown centrally in white on a black screen. Trials for both tasks began with a white fixation cross that appeared for 300 ms, followed by an interstimulus interval (ISI) with a blank screen for 100 ms (Fig. 1). For both tasks, the inter-trial interval was 1000 ms. The Flanker task. In the Flanker task, participants respond to a central target stimulus while ignoring irrelevant distractors (e.g., ≪ > ≪; Guiney and Machado, 2013). Incongruent arrays need more extensive interference control to suppress the conflicting information of the Flankers compared to the target stimulus (Hillman et al., 2009). A version of the Eriksen Flanker paradigm with no neutral condition was employed in the present study (Eriksen and Eriksen, 1974). Participants completed three blocks of 100 trials each and were instructed to respond as quickly and accurately as possible to each trial. Flanker task, arrays consisted of five congruent (≫≫ >) or incongruent (≪ > ≪) arrowheads that were presented in a random and equiprobable order for 500 ms (Fig. 1a). The arrowheads were 1 cm in height, and each Flanker array was 5 cm in length. Participants responded to each trial by indicating the direction of the central target arrow. The Stop-Signal task. The Stop-Signal task requires participants to perform a two-choice reaction time task to a target ‘go’ stimulus that is sometimes followed by a stop stimulus that signals the need to inhibit the pre-potent response (e.g., de Jong et al., 1990). In the Stop-Signal task, participants were presented with a two-choice reaction time paradigm consisting of ‘Go’ and ‘Stop’ trials that were grouped into six blocks of 80 trials each. Stop trials were presented on 20% of the StopSignal task trials. Thus, 96 of 480 trials contained stop signal stimuli. For this task, target stimuli were two alphabetic letters, A and B, which were presented centrally on a computer screen (Fig. 1b). Letters were 4 cm in height, and each letter was shown on 50% of Go or Stop trials in random order. During Go trials, the target letter was presented for 1000 ms after the ISI, and participants responded to the target as quickly as possible without delay. For Stop signal trials, the target letter was shown for 200 ms, after which a red cross was superimposed on the stimulus for 800 ms to signal participants to inhibit their response to the target letter. Participants responded to the type of target letter shown.

2. Method 2.1. Participants Forty-two young adults (21 females, age range = 18–28 years) and 40 middle age adults (24 females, age range = 45–55 years) were recruited by researchers from the Cognitive Neuroscience Laboratory at the Psychology Research Centre, University of Tasmania, Australia. Advertising for the study specified that the researchers were seeking participants who did or did not exercise regularly. Before EEG testing participants completed the Edinburgh Handedness Inventory (Oldfield, 1971) and a Medical History Questionnaire. All participants had normal or corrected to normal vision, and most were right-handed (nine lefthanded). The National Adult Reading Test (NART; Nelson, 1982) and the participant's total years of formal education were used to compare general intelligence between exercisers and non-exercisers (see Supplementary Materials). Standard guidelines for EEG exclusion criteria were followed during recruitment, including past or recent experience of epilepsy, severe head trauma, psychiatric illness or a neurological disorder (Keil et al., 2014; Picton et al., 2000). Participants were also excluded based on recent or chronic use of illicit drugs, alcohol, cigarettes or prescriptive medication (apart from oral contraceptive or blood pressure medication). All participants provided informed written consent for the use of their behavioural and EEG data for research purposes. Ethics approval for the study was granted by the Human Research Ethics Committee (Tasmania) Network (H0011735).

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each task, and participants were provided brief rest periods between blocks. Task order was counterbalanced equally between participants. Presentation of congruent, incongruent, Go and Stop trials was randomised within and between blocks for the respective task. Responses to the two tasks were collected using a customised response pad. For the Flanker task, the participant pressed a button with their left hand if the arrow pointed left, and another button with their right hand if the arrow pointed to the right. During the Stop-Signal task, participants responded to Go trials with their left or right index finger using two different buttons. 2.5. Design and data analysis A 2[Age: young, middle age] x 2[Exercise: exerciser, non-exerciser] x 2[Participant sex: female, male] design was followed for data analysis. The Participant sex factor was included in the study design due to evidence that an individual's sex may influence the effectiveness of exercise interventions on cognition in older adults (Barha et al., 2017; Barha and Liu-Ambrose, 2018). Behavioural and EEG data were analysed using Statistica 13. The level of significance was set at p < .05. Significant interactions were investigated with Tukey's HSD or independent samples t-tests as appropriate. Effect size was indexed by partial eta squared (ηp2) and Hedges' g (g) values. Separate 2[Age: young, middle age] x 2[Exercise: exerciser, non-exerciser] x 2[Participant sex: female, male] ANOVAs were employed to analyse resting heartrate, BMI, PAQ scores and B-SS scores. To ease interpretation of the study findings, only inferential statistics for the highest-order significant main effects or interactions involving the Exercise or Age factors are reported. Any significant interactions involving the Age or Exercise factors being moderated by the Participant sex factor are also described. ERP data. Correct responses for each participant were epoched and averaged, and those with more than 15 trials per condition were accepted for statistical analyses. Average waveforms were calculated for the congruent and incongruent conditions in the Flanker task and for the Go and Stop trials in the Stop Signal task. These were scored for peak amplitude and latency of ERP activity indexing the P3b in both cognitive tasks. An (Electrode: Fz, FCz, Cz, CPz, Pz) within-subjects factor was added to the analysis of P3b data for each task. The neural activity recorded with EEG varies as a function of electrode location, due to the ERP waveforms derived from EEG data being two-dimensional representations of three-dimensional amplitude fluctuations. The inclusion of peak detection for the P3b across the five midline sites was based on inspection of the grand waveforms and in accordance with past literature regarding the location of P3b activity (Hillman et al., 2004; Lardon and Polich, 1996; Polich and Lardon, 1997). Activity for a N2 component was also observed in the ERP waveforms generated for the Stop-Signal task, and peak amplitudes and latencies for the N2 were derived for analysis. The frontal-central N2 refers to middle-latency (approx. 150–350 ms) negative activity modulated by cognitive tasks that involve response inhibition (Folstein and Van Petten, 2008). Prior research also suggests that N2 activity differentiates response inhibition and interference control during inhibitory processing in middle age and in older adults, at least in comparison to young adult participants (Hsieh et al., 2012; Wild-Wall et al., 2008). The Flanker task. A 2(Congruency: congruent, incongruent) within-subjects factor was added to analyses for the Flanker task to incorporate the two congruency conditions into the study design. Reaction times, accuracy and P3b activity were computed from correct responses to the Flanker task. Reaction times and accuracy from the Flanker task were examined using separate four-way mixed-design ANOVAs, which included the 2(Congruency: congruent, incongruent) within-subjects factor. Due to technical difficulties with EEG recording five participants were excluded from the analysis of P3b activity for the Flanker task, leaving the P3b data of 38 young adults (18 female) and 39 middle age adults (23 females) available for analysis. In the Flanker

Fig. 1. Procedures for trials on the Flanker task (Panel A) and the Stop-Signal task (Panel B). On the Flanker task congruent arrays consisted of left- or rightfacing arrows, while incongruent arrays featured target and flanker arrows facing opposite directions. For the Stop-Signal task the alphabetic letters A and B were presented on Go and Stop trials with equal probability.

2.3. EEG recording and processing EEG activity was recorded from 32 sites positioned according to the International 10–20 system of electrode placement (Jasper, 1958; Tavakoli and Campbell, 2015). EEG data were collected with impedances less than 10 kΩ using a 32-channel AEGIS Array electrode cap featuring sintered Ag/AgCl electrodes, SynAmps2 amplifiers and NeuroScan SCAN 4.4 software (Compumedics Neuroscan, 2007). EEG activity was sampled continuously at 1000 Hz and amplified at 200 Hz. During EEG testing, all electrodes were referenced to linked mastoids, and horizontal and vertical electro-oculargraphic activity was recorded via electrodes placed on the outer canthi of both eyes and below and above the left eye, respectively. Before processing, reaction time and accuracy data were merged with EEG data for each participant. Ocular artefact rejection was then conducted, followed by the application of a low pass filter set at 30 Hz. Continuous data were then processed offline for 1000 ms epochs with a 100 ms pre-stimulus baseline. Artefact rejection was conducted with high and low voltage cut-offs set at 100 μV and −100μV, followed by baseline correction. 2.4. Procedure All participants attended a single session that lasted two hours. Once relevant paperwork for ethics, medical history, handedness, education and exercise amount were complete, measures for resting heart rate and BMIs were collected. Participants were then prepared for EEG testing and seated in a well-lit, sound-proof room. The two cognitive tasks were presented on an 18 cm computer screen positioned at a viewing distance of 100 cm. Instructions for each task were provided by the supervising researcher. Practice trials were included at the beginning of 76

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task, the P3b was defined as the most positive peak amplitude occurring 250–500 ms post-stimulus. The Stop-Signal task. Reaction times for correct responses to Go trials and the percentage of successful inhibitions towards Stop signals were calculated. Two participants were excluded from the analysis of N2 and P3b activity for the Stop-Signal task, leaving the P3b and the N2 data of 41 young adults (20 females) and 39 middle age adults (24 females) for analysis. During Stop trials, the close temporal proximity of the Go stimulus (i.e., A or B in the present study) and the Stop signal (i.e., the red cross) may have resulted in residual activity being present in the ERP waveform for Stop trials (Kok et al., 2004). To eliminate this overlap, difference waveforms for the N2 and the P3b were calculated by subtracting ERP activity for Go trials from that observed for Stop trials. As in the Flanker task, P3b activity in the difference waveform was identified as the most positive peak amplitude 250–500 ms following stimulus onset. N2 was defined as the most negative-going amplitude occurring 150–250 ms post-stimulus in the difference waveform. Analyses of N2 peak amplitude and latency were confined to the frontal midline site (Fz), consistent with the literature regarding the topography of the response inhibition N2 (Folstein and Van Petten, 2008; Larson et al., 2014).

exercisers engaged in more than 150 minutes of moderate to vigorous physical exercise per average week, while non-exercisers reported no regular moderate or vigorous exercise, as indexed by the PAQ (Table 1). Significant main effects for Exercise and Age were qualified by a significant two-way interaction between these two factors, F (1,74) = 10.31, MSE = 265722, p < .001, ηp2 = 0.12. The PAQ scores of non-exercisers in both age groups was zero. Therefore, their data were not analysed further. An independent samples t-test indicated that the PAQ scores of younger exercisers were significantly higher than those of middle age exercisers, t(40) = 3.32, p = .002, g = 1.03, 95% CI = [84.03, 345.47]. Exercisers reported B-SS scores of more than 3, while young and middle age non-exercisers scored a B-SS of less than 3, F(1,73) = 140.95, MSE = 141, p < .001, ηp2 = 0.66 (Table 1). The BSS scores of male participants (M = 3.07, 95% CI [3.42, 2.71]) were also significantly higher than those of female participants (M = 2.69, 95% CI [2.99, 2.38]), F(1,74) = 6.63, MSE = 3, p = .01, ηp2 = 0.08. 3.2. The Flanker task Reaction times. Congruent Flanker arrays elicited faster (M = 430.09, 95% CI [446.58, 413.59]) responses than incongruent Flanker arrays (M = 555.25, 95% CI [582.10, 528.39]; also see Table 2). This pattern of results was confirmed by the main effect of Congruency reaching significance for reaction times (F(1,74) = 238.32, MSE = 608052, p < .001, ηp2 = 0.76). The responses of young adults were also significantly faster than those of middle age adults to the Flanker task, F(1,74) = 28.48, MSE = 359682, p < .001, ηp2 = 0.28 (Table 2).This main effect was qualified by a significant three-way interaction between Age, Exercise and Participant sex in reaction times, F (1,74) = 4.64, MSE = 58648, p = .03, ηp2 = 0.06 (Fig. 2). The magnitude of the difference between reaction times for young and middle age adults was much larger for female non-exercisers (Tukey's HSD, p < .001) compared to male non-exercisers (p = .66), female exercisers (p = .84) and male exercisers (p = .21). Accuracy. Responses to congruent Flanker arrays (M = 90.22, 95% CI [93.02, 87.41]) were more accurate than responses towards incongruent Flanker arrays (M = 80.57, 95% CI [85.00, 76.14]; also see Table 2). Similar to reaction times, this pattern of results was confirmed by the main effect of Congruency reaching significance for accuracy (F (1,74) = 27.45, MSE = 3478, p < .001, ηp2 = 0.27). A significant main effect of Participant sex was also modified by a significant threeway interaction between this factor, Age and Exercise, F(1,74) = 6.06, MSE = 2330, p = .02, ηp2 = 0.08 (Fig. 2). Levels of accuracy were similar between males and females for young exercisers and middle age non-exercisers (Tukey's HSD, p = 1). However, females responded with lower levels of accuracy than males in the middle age exerciser (p = .03) and young non-exerciser groupings (p = .51). P3b activity. For middle age adults maximal P3b amplitude was

3. Results 3.1. Participant characteristics The average age of young exercisers and non-exercisers was similar, as was the average age of middle age exercisers and non-exercisers (Table 1). Levels of resting heart-rate were also comparable between exercisers and non-exercisers. The BMI scores of exercisers were significantly lower than that of non-exercisers (F(1,74) = 14.04, MSE = 305, p < .001, ηp2 = 0.16), and young adults had significantly lower BMI scores than middle age adults (F(1,74) = 6.86, MSE = 149, p = .01, ηp2 = 0.08. The two significant main effects were qualified by a significant three-way interaction between Age, Exercise and Participant sex, F(1,74) = 5.09, MSE = 111, p = .03, ηp2 = 0.06 (Table 1). The BMI scores of exercisers who were young males (M = 22.52, SD = 2.76), middle age males (M = 24.99, SD = 2.87), or middle age females (M = 24.03, SD = 3.36) were lower than those of non-exercisers who were young males (M = 27.94, SD = 7.42), middle age males (M = 27.66, SD = 3.68), or middle age females (M = 31.13, SD = 4.81). This difference only reached significance for middle age females (Tukey's HSD, p = .009) and not for young males (p = .15) or middle age males (p = .94). BMI scores were similar between female young exercisers (M = 23.00, SD = 3.48) and female young non-exercisers (M = 23.43, SD = 6.55; p = 1). Exercise measures. There was a strong, positive and significant relationship between scores from the PAQ and the B-SS (Pearson's correlation coefficient, r = .73, p < .001). Young and middle age

Table 1 Unadjusted mean values (standard deviation) for measures of participant characteristics (age, resting heart-rate, BMI, PAQ scores, B-SS scores), categorised by age and exercise grouping. Mean BMI scores (standard deviation) for male and female participants in the young exerciser (11 female), young non-exerciser (10 female), middle age exerciser (12 female) and middle age non-exerciser (12 female) are also shown. Young adults

Age (years) Heart rate (beats/minute) PAQ score B-SS score BMI

Middle age adults

Exercisers (n = 22)

Non-Exercisers (n = 20)

Exercisers (n = 20)

Non-Exercisers (n = 20) <

20.59 (2.38) 63.91 (9.99) 475.00 (264.45) 3.75 (0.67) 22.76 (3.07)

20.75 (2.71) 64.65 (10.75) 0.00 (0) 2.04 (0.66) 25.69 (7.20)

50.75 (2.73) 64.35 (7.94) 251.25 (134.58) 3.55 (0.55) 24.42 (3.13)

49.90 (2.85) 64.65 (10.75) 0.00 (0) 2.00 (0.72) 29.74 (4.63)

P-value (E vs. NE)

NS NS < .001 < .001 < .001

Note 1. n = number per grouping; PAQ score = physical activity questionnaire score; B-SS= Baecke sports score; BMI = body mass index; E = Exerciser, NE = NonExerciser, NS = p > .05. PAQ scores indicate the average number of minutes engaged in vigorous to moderate physical activity per week. B-SS scores range from 1 to 5, with a score of above 3 indicating that the person performs above average levels of sports-related physical activity on a regular basis. 77

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Table 2 Unadjusted mean reaction times and accuracy [shown with 95% confidence intervals] for behavioural performance on the Flanker task and the Stop-Signal task, categorised by age and exercise grouping. Young adults

The Flanker task Congruent RT (ms) Incongruent RT (ms) Congruent Acc (%) Incongruent Acc (%) The Stop-Signal task Go trial RT (ms) Successful inhibitions (%)

Middle age adults

P-value (Y vs. M)

Exercisers (n = 22)

Non-Exercisers (n = 20)

Exercisers (n = 20)

Non-Exercisers (n = 20)

397.17 [419.68, 374.65] 523.44 [552.96, 493.93] 91.38 [95.78, 86.98] 82.17 [87.42, 76.91]

376.28 [403.34, 349.22] 481.98 [518.18, 445.78] 86.74 [94.65, 78.82] 77.26 [87.82, 66.69]

463.35 [492.25, 434.45] 589.47 [641.06, 537.88] 89.34 [96.12, 82.56] 76.14 [89.69, 62.59]

486.85 [520.48, 453.23] 629.27 [705.93, 552.61] 93.30 [97.07, 89.53] 86.56 [92.39, 80.73]

p < .001 p < .001 NS NS

401.20 [424.23, 378.18] 64.66 [74.21, 55.10]

393.17 [421.89, 364.45] 68.71 [77.75, 59.67]

463.68 [496.47, 430.89] 74.19 [84.55, 63.83]

472.77 [507.60, 437.94] 75.06 [83.45, 66.66]

p < .001 NS

Note. n = number per grouping; RT = reaction time; Acc = accuracy, Y = young adults, M = Middle age adults, NS = p > .05.

incongruent arrays P3b amplitude was significantly reduced for middle age adults (M = 9.59, 95% CI [10.14, 9.05]) compared with young adults (M = 12.41, 95% CI [13.30, 11.52]; Tukey's HSD, p = .02). The main effect of Participant sex also reached significance in P3b amplitudes, and was modified by a trend for the two-way interaction between this factor and Exercise, F(1,69) = 3.96, MSE = 612, p = .05, ηp2 = 0.07. Levels of P3b activity were similar for non-exercisers who were male (M = 11.31, 95% CI [12.09, 10.53]) or female (M = 11.34, 95% CI [12.01, 10.67], Tukey's HSD, p = 1). However, the P3b amplitude of female exercisers (M = 13.65, 95% CI [14.34, 12.95]) was significantly more positive than that of male exercisers (M = 9.92, 95% CI [10.61, 9.24]; Tukey's HSD, p = .02). 3.3. The Stop-Signal task Behavioural data. The responses of young adults (M = 397.34, 95% CI [414.85, 379.91]) to Go trials were faster than those of middle age adults (M = 468.22, 95% CI [491.09, 445.22], see Table 2). This finding was confirmed by a significant main effect of Age in reaction times for Go trials, F(1,74) = 23.28, MSE = 100972, p < .001, ηp2 = 0.24. Middle age adults (M = 74.62, 95% CI [80.99, 68.26]) tended to execute higher amounts of successful inhibitions to Stop trials compared with young adults (M = 66.59, 95% CI [72.94, 60.24]), although the latter effect did not reach statistical significance, F (1,74) = 2.53, MSE = 1056, p = .12, ηp2 = 0.03. ERP data. Activity for the N2 was maximal between 180 and 200 ms at the midline sites FCz and Cz. Amplitudes for the P3b were most positive at the same two electrodes and peaked between 300 and 350 ms (Fig. 5). At electrode Fz the main effect of Age was significant for N2 amplitude (F(1,72) = 41.66, MSE = 1299, p < .001, ηp2 = 0.37) and latency (F(1,72) = 8.29, MSE = 12216, p = .004, ηp2 = 0.11). These results indicated that the peak N2 activity of young adults was significantly more negative (M = −8.17, 95% CI [-6.45, −9.90]) and earlier-occurring (M = 176.15, 95% CI [186.16, 166.13]) than that of middle age adults (Amplitude: M = −0.19, 95% CI [1.61,-1.99]; Latency: M = 201.67, 95% CI [215.38, 187.95]). The main effect of Age was modified by a trend for the three-way interaction between this factor, Exercise and Participant sex for latency, F(1,72) = 3.27, MSE = 4471, p = .07, ηp2 = 0.04). (Fig. 6). N2 latency occurred slightly earlier for young adults relative to middle age adults for male exercisers (Tukey's HSD, p = .86), female exercisers (p = 1) and male non-exercisers (p = .99). This same difference also occurred for female nonexercisers but was of much greater magnitude and statistically significant (p = .02). In P3b activity the main effect of Exercise reached significance for P3b amplitude (F(1,72) = 4.61, MSE = 1720, p = .04, ηp2 = 0.06) and latency (F(1,72) = 8.04, MSE = 150168, p = .006, ηp2 = 0.10). Peak P3b activity for exercisers was significantly earlier (M = 319.37, 95% CI [327.04, 311.70]) and more positive (M = 14.83, 95% CI [15.89, 13.76]) than that of non-exercisers (Amplitude: M = 10.85, 95% CI [12.30,9.39]; Latency: 362.96, 95% CI [373.01, 352.91]).

Fig. 2. Unadjusted mean reaction time and accuracy in the Flanker task for female and male participants, categorised by age and exercise grouping. Vertical bars denote 95% confidence intervals.

observed at the central midline site (Cz) and peaked between 350 and 450 ms for congruent arrays, and between 450 and 550 ms for incongruent arrays (Fig. 3). Maximal activity for the P3b was slightly more posterior for young adults at the central parietal midline site (CPz). For young adults, P3b amplitude peaked between 300 and 400 ms for congruent arrays, and between 400 and 500 ms for incongruent arrays. Significant main effects of Congruency and Age in P3b amplitude and latency were both modified by higher-order interactions. For P3b latency, the Congruency and Age main effects were qualified by a significant three-way interaction between these two factors and Exercise, F (1,69) = 4.63, MSE = 23826, p = .04, ηp2 = 0.06, ε = 1 (Fig. 4). The peak latency of P3b activity was significantly earlier for congruent compared to incongruent arrays for non-exercisers and young exercisers, but not for middle age exercisers. In P3b amplitudes, the two-way interaction between Age and Congruency reached significance, F(1,69) = 7.35, MSE = 184, p = .009, ηp2 = 0.10, ε = 1. The magnitude of P3b amplitude was similar for young (M = 12.65, 95% CI [13.49, 11.80]) and middle age (M = 11.79, 95% CI [12.31,11.28]) adults for congruent arrays, but for 78

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Fig. 3. Average waveforms for young and middle age adults in response to the Flanker task, categorised by participant sex, exercise groupings and congruency of the Flanker array. Female participants are shown to the left, and male participants to the right.

4. Discussion

perform young non-exercisers. Although not the main focus of the present study, participant sex emerged as a potential moderator of inhibitory responses in relation to habitual exercise and comparisons between young and middle age adults. The observed sex differences will be discussed in relation to the study hypotheses as it is important to acknowledge the possibility that the relationship between habitual exercise and aging in inhibitory processing differs between men and women. Further replication will be required to validate these results due to small numbers of male and female participants in the exercise and age groupings. Exercise interventions may be more effective for targeting cognitive function in older women than older men (Barha

The relationship between habitual exercise and inhibitory processing in middle age and young adults, as indexed by ERP activity, was examined in the present study. The timing of peak P3b activity in the Flanker task indicated that habitual exercise may attenuate interference control in middle age adults. Amplitude modulation for the N2 was also observed, and this ERP activity was moderated by the age, rather than the exercise status, of participants. The speed of responses to the two cognitive tasks was consistent with age-related differences reported in prior research; however, young exercisers did not consistently out79

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Fig. 4. Unadjusted means of peak P3b latency on the Flanker task, categorised by Flanker congruency and groupings for age and exercise. Vertical bars denote 95% confidence intervals.

Fig. 5. Average difference waveforms following successful inhibition on Stop trials of the Stop-Signal task, categorised by participant sex, exercise groupings and age groupings. Female participants are shown to the left, and male participants to the right.

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4.2. Habitual exercise and sex differences in inhibition The pattern of P3b latency to the Flanker task could indicate that habitual exercise facilitates more cautionary stimulus processing during interference control in middle age adults. In contrast, habitual exercisers were faster at evaluating stop trials in the Stop-Signal task, indicating that this group allocated more attentional resources to these stimuli during response inhibition, as indexed by P3b amplitude and latency. Interestingly though, age was not the only individual difference that modulated P3b activity during interference control. The P3b amplitude of female exercisers was more positive than that of male exercisers, a sex difference not observed in the non-exerciser groups. Habitual exercise was associated with greater attentional allocation during interference control for women, but not men. The observed pattern of P3b activity may also reflect a tendency by women to be less physically active, and consequently more sedentary, than men in everyday life. Men are on average more likely to report higher levels of physical activity than women (Bauman et al., 2012; Hakola et al., 2015; Trost et al., 2002), a finding supported by the higher B-SS scores of men compared to women in the present study. Sex differences were also observed in N2 modulation on the StopSignal task. N2 latency is thought to index the timing of processes engaged during decision making, with faster latency associated with accelerated stimulus recognition and categorisation (Hillyard and Kutas, 1983). Age-related decline in N2 activity may also index attenuation of response inhibition as an individual ages (e.g., Lucci et al., 2013; Willemssen et al., 2011). In the present study, the N2 latency of middle age women was significantly delayed compared to young women in the non-exerciser grouping, an age-related difference that did not occur for female exercisers or men. This finding corresponds to the findings of Chuang et al. (2015), who found that a three-month exercise intervention led to earlier-occurring N2 latency for older women compared with controls, albeit on a Flanker task. Habitual exercise is thus associated with improved timing of decision-making processes indexed by N2 latency in women, but not men. Unlike the present study, Taddei et al. (2012) reported that N2 latency was earlier for young and middle age fencers compared to their nonathletic equivalents during a GoNoGo task. More than 60% of the sample from Taddei et al. (2012) consisted of males, however, suggesting these latter findings may be specific to men rather than women. The behavioural performance observed in the present study also suggests sex differences in inhibitory processing translates to physical responses toward the Flanker task. Female middle age exercisers responded faster than female non-exercisers, but accuracy was higher for female non-exercisers compared to female exercisers. Young female non-exercisers were also among the fastest responders to the Flanker task, but like the middle age female exercisers, these women achieved relatively low levels of accuracy compared to other participants. Previously, participant sex has been identified as a potential confounding variable in the speed and accuracy of responses to the Flanker task. After controlling for IQ and participant sex, Hillman et al. (2006a) reported that, in a large sample of individuals aged from 15 to 71 years, slower reaction times were associated with increasing age, but higher levels of weekly physical activity were related to faster responses. Moreover, a positive relationship was found between physical activity levels and accuracy on the Flanker task, but only for older adults (Hillman et al., 2006a).

Fig. 6. Unadjusted means of peak N2 latency on the Stop-Signal task for male and female participants, categorised by age and exercise grouping. Vertical bars denote standard errors.

et al., 2017), however, to date this sex difference has only recently been linked to inhibitory processing in aging individuals (Fagot et al., 2019). 4.1. Habitual exercise, age, inhibition and the P3b Age-related changes in interference control may be offset by engagement in regular exercise. The timing of P3b activity on the Flanker task indicated that responses to arrays which required less interference control were not faster for middle age exercisers, suggesting the processing of congruent arrays was slowed for these participants compared to other non-exercisers and young exercisers. In contrast, age-related changes in response inhibition were not linked to habitual patterns of exercise. There remains a need to differentiate between the overall influence of habitual exercise and engagement in specific types of exercise to address potential age-related declines in inhibitory processing. For instance, there is some EEG evidence that certain forms of exercise, such as fencing, may moderate inhibition during middle age (Taddei et al., 2012). In the present study, the age-related discrepancy between the two subtypes of inhibitory processing could be related to the malleability of each to the neurobiological benefits of regular exercise practice. As an example, in the revised scaffolding theory of aging and cognition (STAC-r; Reuter-Lorenz and Park, 2014), increasing levels of cognitive decline are accompanied by positive changes in neural structure and function, termed “compensatory scaffolding”. Lifestyle factors, including engagement in exercise, are thought to moderate the interaction between cognitive decline and compensatory scaffolding during the aging process. ERP measures may index the development of compensatory scaffolding during middle age. In relation to the P3b, the latency of this ERP component reflects the time taken to assess and categorise stimuli, with earlier-occurring latency associated with the faster evaluation of a specific stimulus (Kutas et al., 1977). P3b amplitude is reported to index the allocation of attentional resources, with higher amounts of attention associated with more positive amplitude (Polich, 2007). For the Stop-Signal task, differences between habitual exercisers and nonexercisers in P3b modulation were not moderated by participant age, a finding consistent with prior research (Hillman et al., 2006b; Pontifex et al., 2009). In the Flanker task, however, the delay in P3b latency observed for middle age exercisers relative to young exercisers for congruent arrays contrasts to previous evidence (Chuang et al., 2015; Hillman et al., 2004). Chuang et al. (2015) and Hillman et al. (2004) both found that P3b latency was earlier occurring for older adults who participated in a three-month exercise intervention or engaged in high levels of regular exercise compared with controls or less active older adults. These results were consistent across congruent and incongruent conditions in the relative Flanker tasks.

4.3. Age-related changes in inhibition Consistent with prior research, in the present study, age-related changes in behavioural performance and ERP activity were found. Young adults responded to each cognitive task faster than middle age adults, results that correspond with previous studies employing middle age (Taddei et al., 2012) or older adult participants (Hillman et al., 2004; Hillman et al., 2006b; Hsieh et al., 2012; Pontifex et al., 2009; 81

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Wild-Wall et al., 2008). The amplitude and latency of peak N2 activity was also reduced and delayed for middle age adults compared to young adults on the Stop-Signal task (Lucci et al., 2013; Willemssen et al., 2011). However, middle age adults were better able to successfully inhibit responses to Stop trials compared with young adults on this same cognitive task. This pattern of results is consistent with the STAC-r model, as possible signs of cognitive decline (i.e., slowed reaction time, reduced and delayed N2 activity) were accompanied by higher levels of response accuracy for the middle age adults. In the present findings, however, habitual exercise was not associated with age-related changes in the timing and resources allocated to decision-making, as indexed by the N2 during response inhibition. On the Flanker task, the P3b amplitude of middle age adults was reduced relative to that of young adults for incongruent, but not congruent, arrays. This result is consistent with the study predictions, as an age-related decrease in P3b amplitude occurred for the Flanker condition that required the highest levels of inference control. However, this pattern of P3b modulation may also reflect more efficient interference control in middle age compared with young adulthood. Hsieh et al. (2012) reported reduced P3 amplitude for incongruent compared with congruent arrays for older adults aged more than 60 years, P3 modulation that was not found for young adults. However, the researchers also measured activity for the lateralised readiness potential, an ERP component that indexes pre-conscious neural activation associated with motor responses. Patterns of activity for the lateralised readiness potential were similar for older and young adults in response to the modified Flanker task, indicating that the pattern of activation elicited by interference control did not differ significantly between these two age groupings (Hsieh et al., 2012).

pattern of P3 modulation in terms of the compensation-related utilisation of neural circuits hypothesis (Reuter-Lorenz and Cappell, 2008), in which higher levels of brain activation denote neural compensation in older adults relative to younger individuals. The findings of Huang et al. (2014) indicate that engaging in more challenging forms of exercises, such as open-skill activities, may, in turn, negate neural compensation during inhibitory processing in older adults. A possible limitation of the present study is the use of ANOVA to examine the potential relationship between habitual exercise and inhibitory processing. ANOVA is a statistical technique often employed when investigating psychological constructs experimentally. However, ANOVA and related approaches may not adequately model individual variation between participants during analysis. For instance, Boisgontier and Cheval (2016) caution against over-reliance on ANOVA involving repeated measures in neuroscience research for this same reason. The typical application of ANOVA relies on subjecting a dataset to a hypothetical full model of the analysis design, meaning effects or interactions that do not contribute meaningfully to variation within the dataset are still incorporated into the analyses. Alternative statistical approaches, such as Bayesian methods (e.g., Dienes, 2016) or linear mixed-effects modelling (e.g., Boisgontier and Cheval, 2016), may better suit the characterisation of the associations between many of the variables investigated in the present study, as well as in similar research. Another important issue to consider in the transition from ANOVA to other analysis methods in psychological research is that statistics are not a panacea, but rather a tool with which to understand the data we observe. The concept of habitual exercise may also be too broad a term to study empirically. In the present study, self-report measures were corroborated by participant BMIs. However, including measures that assess cardiorespiratory fitness could better substantiate participant's reports of their exercise engagement (e.g., Pontifex et al., 2009). Including additional measures of habit formation in relation to exercise and physical activity may also help to validate the habitual exercise concept (Gardner et al., 2016; Phillips and Gardner, 2016). Bowman (2018) also suggests that the habitat of an individual influences the geometry of one's movement throughout their lifetime. A person who undertakes a wide variety of movements on an ongoing basis may experience the same proposed benefits on cognitive function as a habitual exerciser, in addition to improvements to their physical fitness. To delineate these relationships in terms of participant sex and age, future research may utilise regression techniques to more accurately predict the interaction between exercise, fitness, age, sex, and inhibitory processing. An alternative approach for repeated measures designs is linear mixed-effects analysis (Boisgontier and Cheval, 2016), a technique in which normal differences between individuals can be modelled with random variance.

4.4. Implications and limitations Habitual exercise may be associated with age-related changes in inhibitory processing as early as middle age. The current findings also support the idea that the effectiveness of exercise interventions differ between men and women as they age (Barha et al., 2017; Colcombe and Kramer, 2003). More importantly, though, the observed pattern of results highlights two potential barriers to the implementation of habitual exercise as a method for improving cognitive function. Firstly, age-related changes in ERP activity may index processing associated with compensatory scaffolding that tempers cognitive decline. In some cases, delays in peak ERP latency could represent a more cautionary or strategic approach in inhibitory processing as individuals age, a dynamic that may also apply to the other executive functions involved in cognitive control. Secondly, the type of physical activity engaged in by habitual exercisers could differentially modulate cognitive function as an individual grows older. In the present study, individuals participating in moderate to vigorous levels of physical exercise per week were classed as habitual or regular exercisers. However, forms of exercise that require high levels of behavioural regulation may further enhance the benefits of habitual exercise (Huang et al., 2014; Taddei et al., 2012). Habitual patterns of exercise could be most beneficial for those cognitive functions that require consistent physical-based practice (Levin et al., 2017). The inhibitory aspects of cognitive control fulfil this criterion, as this type of processing requires the ongoing adaption of outward behaviour in response to incoming perceptual information. Some forms of exercise are inherently more challenging in this regard. For instance, Huang et al. (2014) compared P3 activity during a modified Flanker task between older adults who regularly engaged in closed-skill (e.g., jogging, swimming) or open-skill activities. In this context, open-skill activities refer to those where there is a high demand to regulate behavioural responses based on environmental cues (e.g., tennis, basketball). P3 activity for open- and closed-skill participants was more positive at central than frontal sites, a difference that reached significance for open-skill participants. The authors interpreted this

5. Conclusion Age-related changes in cognitive function are often characterised in terms of decline as an individual grows older. Exercise interventions represent an exciting opportunity to assist in counteracting cognitive decline in the human brain. Middle age can be viewed as the pinnacle of one's development, as during this life period many individuals retain the physical and psychological capacities of young adulthood while taking on greater levels of financial, care-giving and mentoring responsibilities (Allemand, 2001). Habitual engagement in exercise during middle age and earlier life periods may predispose an individual towards more robust compensatory scaffolding, due to repeated exposure to the neurobiological benefits of exercise on brain health (Cotman et al., 2007; Voss et al., 2013). The potential of exercise as a remedy for age-related changes in inhibitory processing needs to be characterised across the lifespan, not only for young and older adults. The type of exercise practised by an aging individual may also affect the association between habitual exercise and cognitive function. The 82

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individual differences, such as participant sex, which potentially moderate how engagement in a regular exercise practice is related to cognitive function, must also be considered.

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