Further tests were undertaken to ascertain that the observed differences in time spent were also present for trials of similar difficulty (i.e., trials with words that had the same total number of letters). When trials with words of 9 and 10 letters are considered, the Yuen's *t* test for trimmed means (bootstrapping set at 2,000, and trim level for the mean as 0.20) showed that there was a significant group difference, *M*diff = 1871.91 (171.56, 3572.26), *Y*t = 2.39, *p* = 0.031. The effect size of 0.70 was observed. For trials with words of 10 and 11 letters, the result again revealed a significant difference between groups, *M*diff = 2378.98 (414.04, 4343.91), *Y*t = 2.53, *p* = 0.017. The effect size was found to be 0.74. Lastly, for trials with words of 11 and 12 letters, the result is similar in that a significant difference between groups was observed, *M*diff = 2693.13 (712.90, 4673.36), *Y*t = 2.76, *p* = 0.010. The effect size value was large at 0.80. In all cases, the experimental group spent lesser time than the control group as depicted in **Figure 3**.

Lastly, as the analyses above were based on response timings of all trials, regardless of whether they were correctly or incorrectly performed, we repeated these analyses based on response timings of trials that were correctly performed to remove the possible noise in the data due to incorrect trials. Similar findings were observed. This additional observation suggests that the experimental effects are present even when we removed trials that were performed incorrectly due to the participants giving up or responding carelessly on certain trials. The effects of incorrect trials were negligible.

#### DISCUSSION

As there is a dearth of empirical research on the non-striving aspect of mindfulness, we tested the effects of a presumably futile strange loop task that has the potential of being developed as a mindfulness task, on subsequent non-striving behavior and performance. Although there was no significant group difference observed in terms of performance (based on total number of correct trials achieved), the overall results (close to significant *p* value and medium effect size) suggest that the control group showed tendencies of better performance than the experimental group. It is likely that those in the control group applied more effort and adopted the counting strategy to achieve the accuracy while the experimental group tended not to. Findings from the test of the main hypothesis suggest that participants who underwent the Infinite Water Scooping Task took significantly shorter time to perform the task compared to the control group. The effect was observed when all trials were considered, and when trials of similar difficulty level were considered. Collectively, our hypothesis that performing the futile strange loop task would prime subsequent non-striving behavior is supported.

The current outcome of eliciting non-striving behavior is noteworthy as non-striving is an important aspect of mindfulness practice that is rarely examined in research. Here, we observed that 10 min of repetitively scooping and pouring water over a string tied across a container, peppered only with a brief initial reminder to focus on the process of the actions (without mention of it being a mindfulness practice), led to non-striving consequently when compared to the control task. One speculation could be that the implicit realization of task's futility or paradoxity arising from repetitive performance of the strange loop task (Hofstadter, 2007) is having an effect on one's tolerance of ambiguity. Having to repeatedly scoop and pour water for 10 min without perceiving any sign of advancement may have gradually activated a sense of acceptance toward the ambiguities of the task. The inner dialogue could be something like: "this task is paradoxical in nature and does not make sense, but I continue with it nevertheless." A sense of acceptance may have been resulted through repeated actions required of the task, akin to acceptance of unfoldment of in- and out-breaths in mindful breathing practices. Following the completion of this task, this tolerance for ambiguity or paradox, in turn, may have lowered one's efforts to judge critically (or count carefully) during the subsequent task. Previously, Carson and Langer (2006) noted that actively thinking about paradoxes could increase one's ability to tolerate ambiguity, which they viewed as a hallmark of mindfulness. In a way, performing the Infinite Water Scooping Task for a few minutes possibly succeeded in exposing the participants to a sense of paradox, akin to the sense of perceiving paradoxes when contemplating Zen koans (Christopher, 2003; Maex, 2011), which has been linked to psychological awakening.

In the present context, by psychological awakening, we mean leading participants out of their fixed ways of thinking or habits of striving, at least temporarily. Since the control group performed an objective-oriented task (i.e., clearly perceiving changes in water levels as a result of their action, and being told that the task is a means of developing wrist control and strength), performing the task could have heightened the sense of purpose-driven thinking and certainty that is implicitly habitual in most people (Covington, 2000; Monteiro et al., 2018; Ronkainen et al., 2018). Upon completing the control task that is perceptually more normal and non-ambiguous, the tendency to count the letters purposefully to achieve better accuracy in the subsequent word length comparison task may have been more readily evoked, as shown by the longer trial durations. Based on the shorter trial duration observed, it appears that those in the experimental group tended not to count before making a response. If they were striving to achieve task accuracy, they would have spent more time in the task by counting the letters carefully. As earlier alluded to, shorter time spent can be viewed as a sign of their willingness to tolerate ambiguity and errors in their task performance. They are striving less toward achieving accuracy as per required by the task. In a way, those in the experimental group may be less judgmental about themselves in terms of whether they performed the task accurately, while those in the control group seem more objective-oriented about their task performance. Taken together, the Infinite Water Scooping Task seems to be instrumental in leading participants out of their habits of striving, at least temporarily, when compared to the control task.

There are some practical implications arising from the current findings worthy of mention. First, given the current results, the Infinite Water Scooping Task could potentially be used to introduce the notion of non-striving within mindfulness practice in various contexts, such as when teaching mindfulness in clinical interventions (Roemer and Orsillo, 2003), in education (McKeering and Hwang, 2019), and in sport as part of mental skills training (Ortega and Wang, 2018). For instance, the Infinite Water Scooping Task can be used as an introductory task to teach what is meant by notions of repetition, focus on process, and purposelessness within mindfulness practice, relative to a task that is done with a purpose. Beginning mindfulness practitioners could especially benefit by experiencing the nuts and bolts of mindfulness practice through the physical nature of the task (Kee, in press), alleviating some of the difficulty raised by Solhaug et al. (2016). Second, the possibility of eliciting one's tolerance for ambiguity and non-striving through the Infinite Water Scooping Task may be useful when it comes to helping one prepare for performance or creative situations when a creative, relaxed, and open mind-set is needed. For example, in sports, some athletes described their best performance during a flow experience (a peak psychological state in which one is fully immersed in an activity experience) as one of intense focus without making effort in keeping focused (Jackson and Roberts, 1992). The relaxed and non-striving experience accompanied by the Infinite Water Scooping Task or other similar tasks may help athletes navigate through the nuances accompanying such peak psychological states when they use it as part of their mental skills repertoires. For example, an archer can use this task to get him/herself mentally prepared to focus on the process and not the outcome for his/her shoot. Likewise, this task could add to the list of other known strategies for enhancing creativity such as one described and tested by Chirico et al. (2018), in that this task may help one to suspend judgment momentarily to allow creativity to flow. Lastly, beyond these two specific implications, there is potential for the mindful repetition of the Infinite Water Scooping task to be used as a mindfulness practice task for those who find difficulty in performing seated meditation, given that it is an overt task.

Although the findings and implications seem promising, there are some limitations that must be noted. First, as no manipulation check was conducted, we cannot be certain that the aforementioned non-striving or paradoxical effects were indeed evoked. One main reason for not conducting a manipulation check immediately after participants completed the brief intervention task is the concern that such check (e.g., using questions) may result in unexpected priming of psychological effects that affect the performance of secondary task. While we maintain that the paradoxity explanation is plausible for the experimental task given that the design principle of the Infinite Water Scooping Task took reference from concepts of strange loop (Hofstadter, 2007), we acknowledge that a better approach would be to conduct a manipulation check after the secondary task. Secondly, although we argued that the control task is aligned with participants' *modus operandi* of being goal driven and performing the control task could be treated as typical experiences, the control condition can potentially prime goal-focused orientation, and thus the possibility of participant striving as a result cannot be ruled out. It would be worth considering including a neutral control task that does not prime striving and non-striving in future studies. Thirdly, although the main difference between the experimental and control condition is essentially pouring across a string within the same container compared to pouring across containers, the brief instruction of focusing on the process was given to the experimental group but not to the control group. This raises the question whether the observed effects came from the instruction to focus on the process. Although the likelihood is slim as the instruction was only given at the start in a brief fashion, future studies could consider keeping the instructions to focus on the process consistent for both groups to rule out this possibility. Fourthly, there could be an alternative interpretation in that those who underwent the Infinite Water Scooping Task actually strove harder than the control group, since they could have completed the word length comparison task faster due to them putting in more efforts to respond sooner without compromising on overall performance. It is a limitation in that there was no measure of perceived efforts. Nevertheless, since there was no explicit instruction to complete the task fast, we expected participants to direct their effort toward responding correctly as required by the task. The observed speed-accuracy trade-off supports this view. We maintain that the control group indeed made more efforts to count carefully compared to the experimental group, judging by the seemingly better task performance in the control group. In the future, potential mediators such as a perceived effort, drowsiness, sleepiness, and apathy can also be examined.

In summarizing the lessons learned in terms of designing and conducting this research on non-striving, we conclude that non-striving *per se* is very difficult to operationalize, which perhaps explains the lack of such works in the literature. The difficulty roots from the fact that, by definition, non-striving is the antithesis of striving. To understand non-striving without making reference to striving is not possible in some sense. Experimentally, it is difficult to create a neutral control task that is completely devoid of any form of striving to compare non-striving with. Any form of active control task would necessarily involve some level of striving, simply because there would be something to be done. That inevitably primes striving. On the other hand, adopting a passive task of sitting still as a control task is also not suitable as a control condition, because the non-striving condition such as the Infinite Water Scooping task would then be construed as requiring more striving than the control task. To this end, we propose a possible approach to make further inroads into understanding non-striving by focusing on the nature of the secondary task rather than on the manipulations. That is, by examining whether non-striving can manifest even in a seemingly effortless task. Putting it in the context of the present experimental setup, we can get closer to know if we effected non-striving if performance on word length comparison task involving very easy tasks (say, obviously long versus short words) is different for those undergoing control and experimental treatments. The assumption is that easy tasks take very minimal striving to perform. If the Infinite Water Scooping task resulted in even lesser striving for easy tasks, that could be a clearer indication of non-striving. Future studies could explore this approach to advance the operationalization of non-striving.

As mindfulness research advances over the years, many constructs related to mindfulness were examined to better understand mindfulness (Kee et al., 2019). Since works on non-striving have been especially scarce, the current study presents an initial foray into the empirical examination of non-striving in the context of mindfulness practice. The main takeaway message is that the Infinite Water Scooping Task seems instrumental in eliciting non-striving (relative to the control task), which may be useful for teaching mindfulness practitioners about the notion of non-striving. Clearly, more works in examining the nature and practical value of non-striving is warranted. Beyond investigating non-striving in the context of mindfulness, non-striving as a research topic is also relevant for line of research questioning the values of objectives and goals setting, such as works championed by Ordóñez et al. (2009) and Swann et al. (in press). There is a possibility that the line of research questioning the values of goals and non-striving through mindfulness may converge, hopefully illuminating further lessons beyond the duality

#### REFERENCES

of striving and non-striving. The conversations arising from the testing of Infinite Water Scooping Task may also contribute to discussions on altered sense of consciousness-related issues such as *wu-wei*, flow, mystical experiences, and awakening, since there had been discussion of "letting go," non-striving, and such states in neuroscience literature (Austin, 2006, p. 275).

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

#### ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Nanyang Technological University – Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

#### AUTHOR CONTRIBUTIONS

YK, KA, and RF conceptualized and designed the study. KA and RF collected the data. YK and KA analyzed the data. YK, KA, and CL prepared the manuscript. All authors approved the final version of the manuscript for submission.

#### FUNDING

This research was funded by the Nanyang Technological University's NIE AcRF Grant (RI 9/17 KYH).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02314/ full#supplementary-material

Hofstadter, D. R. (2007). *I am a strange loop*. New York, NY: Basic Books.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Kee, Aye, Ferozd and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Neural Processes of Proactive and Reactive Controls Modulated by Motor-Skill Experiences

Qiuhua Yu1,2, Bolton K. H. Chau2 , Bess Y. H. Lam2 , Alex W. K. Wong3,4, Jiaxin Peng2,5 and Chetwyn C. H. Chan2,6 \*

1 Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 2 Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China, 3 Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States, 4 Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States, 5 Department of Education, Shaoguan University, Shaoguan, China, 6 University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Hong Kong, China

This study investigated the experience of open and closed motor skills on modulating proactive and reactive control processes in task switching. Fifty-four participants who were open-skilled (n = 18) or closed-skilled athletes (n = 18) or non-athletic adults (n = 18) completed a cued task-switching paradigm task. This task tapped into proactive or reactive controls of executive functions under different validity conditions. Electroencephalograms of the participants were captured during the task. In the 100% validity condition, the open-skilled participants showed significantly lower switch cost of response time than the closed-skilled and control participants. Results showed that the open-skilled participants had less positive-going parietal cue-locked P3 in the switch than repeat trials. Participants in the control group showed more positive-going cuelocked P3 in the switch than repeat trials, whereas the closed-skilled participants had no significant differences between the two types of trials. In the 50% validity condition, the open- and closed-skilled participants had less switch cost of response time than the control participants. Participants in the open- and closed-skilled groups showed less positive-going parietal stimulus-locked P3 in the switch than repeat trials, which was not the case for those in the control group. Our findings confirm the dissociation between proactive and reactive controls in relation to their modulations by the different motor-skill experiences. Both proactive and reactive controls of executive functions could be strengthened by exposing individuals to anticipatory or non-anticipatory enriched environments, suggesting proactive and reactive controls involved in motor-skill development seem to be transferable to domain-general executive functions.

Keywords: proactive control, reactive control, task switching, ERP, motor skills

# INTRODUCTION

A high level of motor skills has been associated with improved executive functions. Sports are physical activities that require high levels of motor skills. Individuals who engaged in fencing (Chan et al., 2011), baseball (Kida et al., 2005), and soccer were reported to have better executive functions than the controls (Verburgh et al., 2014). Researchers have suggested that the enhancement in

#### Edited by:

Stephen Fairclough, Liverpool John Moores University, United Kingdom

#### Reviewed by:

Chun-Hao Wang, National Cheng Kung University, Taiwan Valentina Bianco, Santa Lucia Foundation (IRCCS), Italy

> \*Correspondence: Chetwyn C. H. Chan<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Motor Neuroscience, a section of the journal Frontiers in Human Neuroscience

Received: 05 September 2019 Accepted: 29 October 2019 Published: 14 November 2019

#### Citation:

Yu Q, Chau BKH, Lam BYH, Wong AWK, Peng J and Chan CCH (2019) Neural Processes of Proactive and Reactive Controls Modulated by Motor-Skill Experiences. Front. Hum. Neurosci. 13:404. doi: 10.3389/fnhum.2019.00404

executive functions could be related to the neural plasticity brought about by the long-term aerobic fitness and cognitive trainings involved in these sports (Voss et al., 2010; Chan et al., 2011). Common features of the physical activities described above are playing facing opponents and in changing external environments, which demand open motor skills. Open motor skills involve generation of physical responses to dynamically and externally paced environment (Wang et al., 2013a; Yu et al., 2017). Contrary to open motor skills is closed motor skills, which requires participants to generate responses that are relatively consistent, stationary, and self-paced (Wang et al., 2013a; Yu et al., 2017). The typical physical activities involving closed motor skills are swimming, and track and field. In view of the differences between open and closed motor skills, it is intuitive that participants of "closed" physical activities would have gained lower level of executive functions than those of "open" physical activities. For instance, open-skilled participants were revealed to have higher levels of inhibitory control than the closedskilled counterparts (Yamashiro et al., 2015). Nevertheless, in an earlier behavioral study, we reported that, by employing a dual cognitive control model for executive functions (Chevalier et al., 2015; Tarantino et al., 2016), both the open- and closedskilled participants showed significantly higher levels of reactive control of executive functions than the controls (Yu et al., 2017). The differences between the two experimental groups are in the proactive versus reactive control. In this paper, we aimed to employ electroencephalogram to understand the neural mechanisms underlying how different types of motor skills would associate with proactive or reactive controls among participants engaging in physical activities.

Proactive control is an early selection process, which can optimally bias attention, perception, and action systems in a goal-driven manner (Braver, 2012). Information selected early in the process is to be deployed before the response-demanding event occurs. In contrast, reactive control is to resolve the interference imperatively after the response-demanding event appears (Braver, 2012). Physical activities dominated by open motor skills involve predictions of outcomes upon the actions of opponents and teammates for producing rapid and accurate responses (Jin et al., 2011; Abreu et al., 2012; Yu et al., 2016). These cognitive processes are comparable to those described in proactive control. In contrast, the physical activities dominated by closed motor skills less involve the prediction of actions of others before giving the responses. In addition, open motor skills require the participants to rapidly inhibit inappropriate actions, and switch from an intended movement to an appropriate one in an unpredictable environment (Taddei et al., 2012; Yu et al., 2017). The imperative inhibition and switching process are comparable to those described in reactive control. Tsai and Wang (2015) explored the effects of open- (e.g., badminton and table tennis) or closed-skilled (e.g., jogging and swimming) training in the reactive control of task switching for elderly subjects. Their results showed that open-skilled group had larger P3 amplitude in switch condition than closed-skilled and control groups, but had comparable P3 amplitude in repeat condition with the other two groups. All the studies revealed employed a unidimensional perspective of executive control. The results are that open-skilled groups had higher level of executive functions in reactive control associated with enhanced P3 than closed-skilled groups. No study has been conducted to explore and explain the possible gains in different time processes of executive functions differentially in the open- and closed-skilled groups. The dual cognitive control model of executive function offers a theoretical basis for addressing the potential differentiation in the cognitive gains due to the engagement in these physical activities.

Neural processes related to proactive and reactive controls can be examined by using a cued task-switching paradigm with electroencephalogram (EEG). The cued task-switching paradigm involves participants predicting a switch of task rule based on the information embedded in the cue and subsequently giving a response according to the new rule. The task cue can be fully predictive (100% validity) or fully non-predictive (50% validity), which makes possible elicitation of the eventrelated potentials (ERPs) for reflecting the proactive or reactive control processes, respectively. The common ERPs associated with proactive control in the task-switching paradigm reported are the parietally distributed P3 elicited by the cue (called cuelocked P3) (Gajewski and Falkenstein, 2011; Tarantino et al., 2016) and the frontocentrally distributed contingent negative variant (CNV) (Tarantino et al., 2016). In contrast, the frontally distributed N2 (Hsieh and Wu, 2011) and parietally distributed P3 (called stimulus-locked P3) (Scisco et al., 2008; Tarantino et al., 2016) elicited by the stimulus were reported to associate with the reactive control in task switching. The cue-locked P3 can be identified within the 300–600 ms time-window after the appearance of a predictive cue (West et al., 2012; Tarantino et al., 2016). This component was suggested to reflect task reconfiguration – anticipatory updating of task goals and/or action rules in working memory (Nicholson et al., 2006; Gajewski and Falkenstein, 2011). The CNV is a slow wave elicited prior to the onset of the target stimulus (Funderud et al., 2013). It reflects anticipatory attention and motor preparation for the upcoming target stimulus (Funderud et al., 2013; Grane et al., 2016). Thus, the amplitude of CNV could be modulated by the responserelated parameters embedded in the task cue (Scheibe et al., 2009; Linssen et al., 2013), e.g., the cue validity. Previous studies also showed that Bereitschaftspotential (BP), comparable to CNV, was more negative-going in athletes than non-athletes (Bianco et al., 2017a,b). This finding suggested that the athletes would have better motor preparation than the non-athletes. However, Wang et al. (2013b) did not reveal significant differences in motor preparation between the open- and closed-skilled players.

The N2 component has been employed as a marker reflecting reactive control – suppression of conflict responses (Rushworth et al., 2002; Hsieh and Wu, 2011). More negative-going frontal N2 was shown to associate with the switching to a new response set. The stimulus-locked P3 can be identified after the appearance of a response-demanding stimulus (Swainson et al., 2006; Tarantino et al., 2016). In the predictive cue condition, the stimulus-locked P3 accounts for stimulus-response set implementation (Jamadar et al., 2010; Gajewski and Falkenstein, 2011; Tarantino et al., 2016) or task-specific evaluation of a target stimulus (Swainson et al., 2006). The amplitude difference of stimulus-locked P3 between switch and repeat trials was negatively related to the switch cost

of the response time when the cue was predictive (Li et al., 2012), suggesting that stimulus-locked P3 in reactive control was related to the performance in task switching. In the non-predictive cue condition, stimulus-locked P3 is associated with updating of the task goal or task rules (Hillman et al., 2006; Scisco et al., 2008). It was more positive-going in the switch than repeat trials, because more attentional resources required for subsequent memory updating in reactive control (Hillman et al., 2006; Scisco et al., 2008). However, Kamijo and Takeda (2010) reported intensive experience of physical training, regardless of type of sport, showed less positive stimulus-locked P3 in the switch than repeat trials in an alternating-runs switching paradigm. The reason was likely that these studies showed differences in the task difficulties, which P3 component was sensitive to Kamijo and Takeda (2010).

In this study, a cued task-switching task was employed for eliciting the proactive and reactive control processes modulated by the participants with experience of open- or closed-skilled physical activities. As the participants had received intensive training of two types of motor skills, we hypothesized that in the trials with predictive cues (100% validity), the openskilled participants would show fewer positive differences in the between-trial (switch verse repeat) cue-locked P3 compared with the closed-skilled participants due to employing more anticipation in the open-skilled training. It was also hypothesized that in the trials with non-predictive cues (50% validity), the open-skilled participants would show fewer positive differences in the between-trial stimulus-locked P3 than the closedskilled and control ones due to the imperative switch in the unpredictable environment. More negative-going CNV in the open- and closed-skilled than controls participants were anticipated due to better preparation in the former two groups. Comparable N2 amplitudes were anticipated in the open- and closed-skilled participants. The performances in the neural processes of the 75% validity condition would be between those of the 100 and 50% validity conditions.

# MATERIALS AND METHODS

#### Participants

Fifty-four university students were recruited via convenience sampling. Among them, 18 (8 females and 10 males) were members of the university badminton team (open-skilled group), 18 (7 females and 11 males) were members of the university track and field team (closed-skilled group), and 18 (9 females and 9 males) declared they had not engaged in any professional or amateur sport (control group). The selection of badminton and track and field athletes as the open- and closed-skilled participants was made reference to those recruited in Wang and Tu (2017) and Wang et al. (2017). The results obtained would have more meaningful comparisons with those reported by Wang and Tu (2017) and Wang et al. (2017). Participants in each group had matched age and education levels (**Table 1**). The participants of the open- and closed-skilled groups had five or more years for professional motor skill practices. Each athlete had won prizes in open competitions and had no regular training in other sports. The levels of skill competences (in terms of winning international/local sport awards) were comparable between two groups (n = 2/16 for open-skilled versus n = 3/15 for closed-skilled). All of the participants were right-handed with normal or corrected-to-normal visual acuity, and had no history of neurological or cardiovascular disorders. Participants were not on regular medication. The participant's cardiorespiratory fitness was assessed by the Queen's College step test, which had been introduced in Yu et al.'s (2017). Ethical approval of this study was granted by the Departmental Research Committee of The Hong Kong Polytechnic University. Written informed consent was obtained from the participants before commencing the experiment for data collection.

# Experimental Task

This study used a cued task-switching paradigm to manipulate the proactive and reactive controls. Details of the task design were described in Yu et al. (2017). The time course of one typical trial is summarized in **Figure 1**. A trial began with presentation of a task cue (4 cm × 4 cm) at the center of the screen for 1500 ms. Then the task cue was replaced by a target stimulus (4 cm × 4 cm). Upon presentation of the target stimulus, the participant was asked to give a two-key sequential response correctly as soon as possible within 3000 ms. The next trial began once the response from the participant was registered. Next, a blank interval of 1000 ms appeared before the onset of the next trial.

In this paradigm, proactive and reactive controls were manipulated by means of cue validities. Three cue validities were used: 100, 75, and 50%. A 100% valid cue appeared as a solid square (or diamond ), a 75% valid cue appeared as a hollow square (or diamond ), and a 50% valid cue appeared (as a solid star ). The participants were asked to prepare the response selection rules based on the cue (except for a 50% valid cue). A 100% valid cue meant the rule sets conveyed in the cue would be the same as those that appeared in the target stimulus. In this case, the participant could prepare to repeat the same task rule as the previous trial or switch to a new task rule based on the rule set conveyed in the cue. Thus, a 100% valid cue would elicit more proactive but fewer reactive control processes. A 50% valid cue meant that no information on the task sets in the subsequent response was provided. In such an ambivalent situation, the participant could not give a specific preparation for the response. The participant could repeat the same task rule or switch to a new task rule based on the target stimulus. It was expected that a 50% valid cue would elicit reactive rather than proactive control processes. A 75% valid cue was expected to elicit mental processes that were a combination of those in the 50 and 100% validity conditions. A digit (1 or 2) that appeared inside the shape (a square or diamond) formed the target stimulus (**Figure 1**). Two sets of response selection rules were used. Each rule involved two sets of two-key sequential responses delivered by the target stimulus with the same shape (square or diamond). For instance, one response selection rule was that a square with digit "1" was for the participant to press the "z" and then "n" keys on the keyboard, and a square with digit "2" was "x" and then "m"; the other response selection rule was that a diamond with digit "1" was "x" and then "n", and a diamond with digit "2" was "z" and then "m." The mappings

#### TABLE 1 | Demographic characteristics of the open-skilled, closed-skilled and control participants.

∗Denotes the regular motor skill practices under the coach guidance.

between cue stimulus (square or diamond) and two response selection rules were counterbalanced across participants. Only the 75% validity condition had congruent and incongruent trials. Congruent trials featured the same hollow square or diamond shapes appearing in the cue and target. Incongruent trials were when the shapes displayed in the cue and target differed. Only valid congruent trials in the 75% validity condition were included in the data analyses. Each trial was defined as "repeat" or "switch" depending on the response selection rule. A repeat trial was that the response selection rule was the same as that in the previous trial; whereas that of a switch trial was different from the previous trial.

The ratio of the three types of cue validities was 1:1:1. There was same ratio of repeat to switch trials in each block. Trials were organized in counterbalanced orders and grouped into nine blocks, and each block had 140 trials. It took around 9 min to complete one block followed by a 4-to-5 min break. NeuroScan Stim2 software (NeuroScan, Inc., Sterling, VA, United States) was used for the fabrication of the trials. The time for completing the experimental task was approximately 1.5 h.

#### Data Collection Procedures Preparation

Each participant was asked to complete the demographic information sheet, which included years for professional motor skill practices, hours for professional motor skill practices per week, sports categories, and other expertise outside of sports (**Table 1**).  A 15 inch computer monitor for showing trials was placed on the table at a distance of 65–75 cm. Each participant was required to first complete 100 practice trials. Standardized instructions and feedback were given to the participant throughout the training block. This was followed by a test block in which the participant completed 50 task trials and reached 90% accuracy before entering into the experiment. If the participant achieved an accuracy rate of less than 90%, he or she repeated the training block. The participant was also reminded to minimize eye blinks and to keep his or her eyes at the center of the monitor throughout the task.

#### Acquisition of ERP Data

fnhum-13-00404 November 12, 2019 Time: 17:4 # 5

Participants' EEG signals were captured by a 64-channel Quik-cap equipped with 90 mm Ag/AgCl sintered electrodes, SynAmps2 Digital DC EEG amplifier, and Curry 7 software (NeuroScan, Inc., Sterling, VA, United States). Vertical and horizontal electrooculograms (EOGs) were captured with two pairs of electrodes placed on the supra- and infra-orbital areas of the left eye and the left and right orbital rims of both eyes, respectively. A ground electrode was positioned on the forehead in front of the Cz electrode. All the channels were referenced to the electrodes on the left and right mastoids. The EEG and EOG signals were sampled at a rate of 1000 Hz/channel. All EEG/EOG electrode impedances were set to below 5 k. EEG signals were recorded from the beginning of each block of experimental tasks. The timing of all stimuli was recorded by Curry 7 software.

Offline signal preprocessing also employed Curry 7 software. EEG signals were digitally filtered with a band pass from 0.01 to 30 Hz. The covariance analysis algorithm was used when eye movement was detected. Then the EEG signals were segmented into the cue- and stimulus-locked epochs. Cuelocked epochs were defined as −200 ms before the cue to 1,500 ms after the cue, and stimulus-locked epochs were defined as −200 ms before the target to 1,000 ms after the target. Baseline corrections were referenced to the pre-stimulus interval. Epochs with an amplitude exceeding ± 80 µv and trials with incorrect responses were excluded from the subsequent averaging procedure. The cue- and stimulus-locked waveforms of each electrode were averaged separately for three cue validities (100, 75 versus 50%) and two task conditions (repeat versus switch). The number of epochs extracted for data analysis for each cue validity in each of the repeat or switch trials was around 140 for each group.

#### Data Analysis

As the behavioral data of this study shared the same data set of a previous study conducted by the same research team, the detailed methods of analyzing the behavioral results of the participants can be found in Yu et al. (2017) and will not be repeated here. Analyses of the ERP data included the cue-locked P3, CNV, N2 and the stimulus-locked P3 elicited when participants engaged in the behavioral task. Independent component analysis (ICA) was conducted to confirm the timewindows set for extracting signals related to the cue-locked P3 (350–550 ms post-cue), CNV (1200–1500 ms post-cue), N2 (200–300 ms post-target), and stimulus-locked P3 (300–600 ms post-target). A short time-window was set for the cue-locked P3 for lowering the possible interferences to the CNV, as ICA results showed a slight overlap in the time-windows of these two components. In the analysis, the electrodes at the midline sites (Fz, Cz, and Pz) were included making reference to the results of previous studies that switch effects were maximal at the midline electrode sites (Gajewski and Falkenstein, 2011; Hsieh and Wu, 2011; Li et al., 2012). A four-way repeated measures ANCOVA for validity (100, 75 versus 50%) × trial (repeat versus switch) × site (Fz, Cz versus Pz) × group (open-skilled, closed-skilled versus control) was conducted to test the mean amplitudes of the cue-locked P3, CNV, N2, and stimulus-locked P3. The years of participants' professional motor skill practices was the only covariate entered because of the significant between-group differences in this variable (**Table 1**). Another reason was that the years of professional motor skill practices rather than the MBI and VO2max was revealed to significantly predict the participants' performances on the behavioral tasks in Yu et al. (2017). Post hoc pairwise comparisons with the Bonferroni adjustment were applied when significant main or interaction effects were observed. This study only included the amplitudes rather than latencies in the ERP data analyses because previous study reported no significant differences between groups and between trials on the P3 latency (Scisco et al., 2008). As the switch cost of response time was related to amplitudes of cue- or stimulus-locked P3 components (Jamadar et al., 2010; Li et al., 2012), the present study examined the relations among cue- and stimulus-locked P3 and behavioral performance (i.e., switch cost of response time) for different motor skills by using a hierarchical, stepwise regression analysis for each of the validity conditions. For each regression equation, two regressors were the mean amplitude differences between switch and repeat trials of cue- (cP3S-cP3R) and stimulus-locked P3 (sP3S-sP3R); two other regressors were the identities of the open- and closed-skilled groups (with the control group as the reference). The two-way interaction terms for the neural processes and group identities were cP3S-cP3R × openskilled; cP3S-cP3R × closed-skilled; sP3S-sP3R × open-skilled; and sP3S-sP3R × closed-skilled. The variance inflation factor (VIF) ≥ 10 and Pearson's correlation ≥ 0.85 were considered as indicators of strong multicollinearity between any of the two independent variables in a hierarchical regression model. The results showed no variable displayed strong multicollinearity, suggesting that none of the variables were related to each other. All analyses were performed with IBM SPSS statistics version 20.0 (IBM, Chicago, IL, United States).

#### RESULTS

The main behavioral variable was the switch cost of response time, which was defined as the difference in the reaction times between the switch and repeat trials. Two-way repeated measure ANOVA of validity (100, 75 versus 50%) × group (open-skilled, closed-skilled versus control) testing the effects on the switch cost of response times indicated that the validity × group effect on the switch cost of response times was marginally significant (p = 0.053). Participants in the open-skilled group showed significantly fewer switch cost values than the closedskilled (p = 0.023) and control (p < 0.001) groups in the 100% validity condition (**Figure 2**). Participants in both the open- (p < 0.001) and closed-skilled (p = 0.033) groups showed

significantly fewer switch cost values than the control group in the 50% validity condition (**Figure 2**). No significant differences in switch costs were revealed between the open- and closed-skilled groups (p = 0.473) (**Figure 2**). Their details can be found in Yu et al. (2017).

#### Cue-Locked P3 (350–550 ms)

**Figure 3** presents topographic maps (3A) and waveforms (3B) of the cue-locked P3 for the open-skilled, closed-skilled, and control groups. The covariate of years of professional motor skill practices [F(1,50) = 0.54, p = 0.466, η 2 p = 0.011] was not significant. The validity × trial × site × group effect [F(6.264,156.602) = 2.66, p = 0.016, η 2 p = 0.096] was found significant. The site main effect was also significant [F(1.488,74.389) = 9.61, p = 0.001, η 2 p = 0.161]. However, the validity [F(2,100) = 0.13, p = 0.880, η 2 p = 0.003], trial [F(1,50) = 0.002, p = 0.968, η 2 p < 0.001], and group effects [F(2,50) = 0.42, p = 0.657, η 2 p = 0.017] on the amplitudes of cue-locked P3 were not significant.

Post hoc analyses on trial × site × group effect were conducted separately at each level of validity. The trial × site × group effect was only significant in the 100% validity condition [F(3.351,85.442) = 5.46, p = 0.001, η 2 p = 0.177] but not in the 75% [F(2.752,70.179) = 1.11, p = 0.348, η 2 p = 0.042] and 50% validity conditions [F(3.249,82.856) = 0.13, p = 0.952, η 2 p = 0.005]. Hence, the trial × group effect for the 100% validity condition was further examined at each of the electrode sites. The trial × group effect was significant at Fz [F(2,51) = 6.82, p = 0.002, η 2 p = 0.211] and Pz [F(2,51) = 8.03, p = 0.001, η 2 p = 0.239] but not at Cz [F(2,51) = 1.32, p = 0.28, η 2 p = 0.049]. At Fz, the open-skilled group showed marginally less positive-going cue-locked P3 in the switch than repeat trials (p = 0.056) in the 100% validity condition, whereas the closed-skilled group showed an opposite trend whereby the cue-locked P3 was significantly more positivegoing in the switch than repeat trials (p = 0.003). The control group did not show significant between-trial-type differences in the amplitudes of cue-locked P3 at Fz (p = 0.822) (**Figure 3C**). At Pz, the open-skilled group showed a less positive-going cuelocked P3 in the switch than repeat trials (p = 0.011). The closed-skilled group, however, did not show significant betweentrial-type differences in the amplitudes of cue-locked P3 at Pz (p = 0.523). The control group showed significantly more positive-going cue-locked P3 at Pz in the switch than repeat trials (p = 0.004).

#### CNV (1200–1500 ms)

In CNV, the covariate of years of professional motor skill practices was found significant [F(1,50) = 4.21, p = 0.045,

η 2 p = 0.078]. The validity × trial × site × group effect [F(6.118,152.946) = 0.55, p = 0.772, η 2 p = 0.022] on the amplitudes of CNV was not significant (**Figure 4**). The validity effect [F(2,100) = 7.20, p = 0.001, η 2 p = 0.126] was significant. However, trial [F(1,50) = 0.06, p = 0.811, η 2 p = 0.001], site [F(2,100) = 1.54, p = 0.221, η 2 p = 0.030], and group effects [F(2,50) = 1.06, p = 0.354, η 2 p = 0.041] were not significant. Post hoc analysis showed that CNV in the 100% validity condition was more negativegoing than those in 75% (p < 0.001) and 50% (p < 0.001) validity conditions.

#### N2 (200–300 ms)

fnhum-13-00404 November 12, 2019 Time: 17:4 # 8

The covariate of years of professional motor skill practices was found not statistically significant [F(1,50) = 0.29, p = 0.596, η 2 p = 0.006]. The validity [F(1.656,82.825) = 1.85, p = 0.170, η 2 p = 0.036], trial [F(1,50) = 1.63, p = 0.208, η 2 p = 0.031], group [F(2,50) = 0.42, p = 0.658, η 2 p = 0.017], and the validity × trial × site × group effects [F(5.866,146.655) = 1.23, p = 0.295, η 2 p = 0.047] on the amplitudes of N2 were also not significant. However, the site [F(1.275,63.730) = 5.86, p = 0.012, η 2 p = 0.105], and validity × trial × site × covariate (years of professional motor skill practices) effect were significant [F(2.933,146.655) = 2.80, p = 0.043, η 2 p = 0.053]. Post hoc analysis showed that N2 in 100% validity conditions was more negativegoing in the switch than repeat trials at Fz (p = 0.034), Cz (p < 0.01), and Pz (p < 0.01), but this effect was not found in 75 and 50% validity conditions. In both 75 and 50% validity conditions, N2 was more negative-going at Fz (ps < 0.05) than Cz and Pz. **Figure 5A** presents the topographic maps of the N2 component.

#### Stimulus-Locked P3 (300–600 ms)

The topographic maps (5A) and waveforms (5B) of the stimuluslocked P3 for the open-skilled, closed-skilled, and control groups are presented in **Figure 5**. The covariate of years of professional motor skill practices was significant [F(1,50) = 4.46, p = 0.040, η 2 p = 0.082]. The validity × trial × site × group effect [F(5.671,141.771) = 2.25, p = 0.045, η 2 p = 0.082] on the amplitudes of stimulus-locked P3 was found significant. The site [F(1.474,73.719) = 4.14, p = 0.031, η 2 p = 0.076] and group effects [F(2,50) = 5.27, p = 0.008, η 2 p = 0.174] were significant. The trial effect was marginally significant [F(1,50) = 3.93, p = 0.053, η 2 p = 0.073]. However, the validity [F(2,100) = 0.33, p = 0.719, η 2 p = 0.007] effect was not significant.

By adjusting years of professional motor skill practices, post hoc analysis showed significant trial × site × group effect only in the 50% validity condition [F(3.267,81.670) = 3.48, p = 0.017, η 2 p = 0.122] (**Figure 5C**), but not in the 100% [F(2.824,70.610) = 1.81, p = 0.322, η 2 p = 0.045] and 75% validity conditions [F(2.897,72.437) = 1.37, p = 0.260, η 2 p = 0.052]. Interestingly, the results were different from those found in cuelocked P3, in which the same three-way interaction effect was significant in the 100% validity condition. The trial × group effect in the 50% validity condition was further tested separately for Fz, Cz, and Pz. The trial × group effect was found significant at the Cz and Pz [F(2,51) = 3.52, p = 0.037, η 2 p = 0.121; F(2,51) = 4.14, p = 0.021, η 2 p = 0.140, respectively], but not at the Fz [F(2,51) = 2.02, p = 0.143, η 2 p = 0.073]. At Pz, both the open-skilled (p = 0.008) and closed-skilled (p = 0.002) groups showed significantly less positive-going stimulus-locked P3 in the switch than repeat trials (**Figure 5C**). The control group, however, showed no significant between-trial-type difference in the stimulus-locked P3 amplitudes at Pz (p = 0.630). At Cz, the open-skilled (p < 0.001) and closed-skilled (p = 0.038) groups had significantly less positive-going stimulus-locked P3 in the switch than repeat trials; whereas the control group did not show any significant between-trial-type differences in the amplitudes of stimulus-locked P3 (p = 0.838).

#### Hierarchical Stepwise Regression

In the 100% validity condition, the regression model was significant, R 2 = 0.226, F(4,49) = 3.579, p = 0.012, with the only significant regressor in the model being the open-skilled group as a group identity (β = −0.573, p = 0.001) (**Table 2**). Other regressors (e.g., cP3S-cP3R and sP3S-sP3R, closed-skilled group) were not significant (| β| < 0.285, ps > 0.057). The changes in the variance explained by the open-skilled group regressor were also significant [1R 2 = 0.182, F(4,45) = 3.455, p = 0.015]. The effect of cP3S-cP3R × open-skilled was significant (β = 0.475, p = 0.007), whereas cP3S-cP3R (β = −0.431, p = 0.072) and cP3S-cP3R × closed-skilled (β = 0.247, p = 0.205) did not show significant impacts (**Figure 6A**). These results suggested that the cP3S-cP3R, which was associated with proactive control for task switching, showed significant correlation with the switch cost of response times among the open-skilled participants but not among the closed-skilled and control participants.

In the 50% validity condition, the regression model was significant, R 2 = 0.211, F(4,49) = 2.782, p = 0.037, for both the open- and closed-skilled groups as group identities were identified as significant regressors (β = −0.474, p = 0.003; β = −0.397, p = 0.012, respectively) (**Figure 6B**). The other two regressors, cP3S-cP3R and sP3S-sP3R, were not significant (β = −0.008, p = 0.949; β = −0.062, p = 0.643, respectively). The changes in the variance are explained by the significance of the open- and closed-skilled groups' regressors [1R 2 = 0.157, F(4,45) = 3.057, p = 0.043]. The effects ofsP3S-sP3R (β = −0.550, p = 0.020), open-skilled (β = −0.363, p = 0.031), closed-skilled (β = −0.347, p = 0.023), sP3S-sP3R × open-skilled (β = 0.464, p = 0.022), andsP3S-sP3R × closed-skilled (β = 0.519, p = 0.012) were the significant predictors, suggesting that the correlations between sP3S-sP3R and the switch cost of response time in the open- and closed-skilled groups were significantly different from those of the control group. A follow-up analysis suggested that such correlation was significantly negative in the control participants (r = −0.534, p = 0.022), but not significant in the open-skilled (r = 0.262, p = 0.294) and closed-skilled (r = 0.274, p = 0.270) participants (**Figure 6B**).

In the 75% validity condition, the regression model was also significant, R 2 = 0.255, F(4,49) = 4.203, p = 0.005, with cP3S-cP3R (β = 0.320, p = 0.023) and sP3S-sP3R (β = −0.378, p = 0.004)

being significant regressors in the model. No other significant regressors were found [R 2 = 0.277, F(8,45) = 2.151, p = 0.050] (**Figure 6C**). Both cP3s-cP3r (r = 0.317, p = 0.020) and sSP3ssP3r (r = −0.333, p = 0.014) showed significant correlations with the participants' switch cost of response times, regardless of the subgroups.

# DISCUSSION

The current study investigated how motor skill experiences modulated proactive and reactive controls of executive function in healthy adults. New findings are that the open-skilled participants, when compared with the other two groups, showed significantly less positive-going parietal cue-locked P3 in switch than repeat trials, which coupled with better performances on task-switching in the predictive condition. These suggest that proactive control was unique to the open-skilled participants. It appears that they might have been able to deploy fewer attentional resources in proactively updating the new action rule than the closed-skilled participants. These findings corroborate with the results of the regression analysis, which show that better proactive control for task switching was associated with the between-trial difference in the cue-locked P3 amplitudes for the open- but not closed-skilled participants. On the contrary, in the non-predictive condition both the open- and closed-skilled participants showed significantly less positivegoing parietal stimulus-locked P3 in the switch than repeat trials, which could not be found in the control participants. These results indicate that, prior experiences in motor skill training, regardless of the types of motor skills developed, would result in fewer deployments of attentional resources for reactively updating the new action rule under non-anticipatory circumstances. Our findings further confirm the dissociation of proactive and reactive controls in relation to their modulations by different motor-skill experiences. In particular, both proactive and reactive controls of executive functions could be enhanced by intensively exposing individuals to anticipatory and nonanticipatory enriched environments.

The results of the 75% validity condition will not be discussed because no significant findings were revealed in the comparisons of the ERP data.

# Proactive Control and Open-Skill Experience

The experimental task used in the present study required the participants to switch between two sets of action rules. Our ERP results show less positive cue-locked P3 in switch than repeat trials observed among the open-skilled participants, which was not the case in the closed-skilled and control groups. These findings indicate that open-skilled participants deployed fewer attentional resources when proactively updating the new action rule than their closed-skilled counterparts. In addition, behavioral results on the same groups of participants reported by in Yu et al. (2017) showed that open-skilled participants exhibited smaller switch cost of response times in the predictive condition (100% validity) than closed-skilled participants. The ERP findings of the cue-locked P3 and the published behavioral

TABLE 2 | Results of hierarchical stepwise regression of amplitudes of the cue- and stimulus-locked P3 for predicting the switch cost of response times in the 100, 50, and 75% cue validity conditions.

∗ p < 0.050; ∗∗p < 0.010; RT denotes response time; cP3S-cP3R denotes difference of mean amplitudes between switch and repeat trial-types of cue-locked P3; sP3S-sP3R denotes difference of mean amplitudes between switch and repeat trial-types of stimulus-locked P3; 100% denotes 100% valid cue; 75% denotes 75% valid cue; 50% denotes 50% valid cue.

data suggest higher efficiency in updating the new action rule in proactive control than the closed-skilled participants. Our results are consistent with those reported in previous studies, which found participants engaged in open-skilled physical activities have higher efficiency in the updating process related to motor preparation. Open-skilled participants were proposed to have better motor-reprograming processes in terms of smaller timing errors than closed-skilled participants (Nakamoto and Mori, 2012). Jin et al. (2011) also found that professional badminton players had more accurate judgments of the placement of badminton strokes and larger P3a amplitude in the proactive anticipation than non-professional players, suggesting good anticipation ability. Mcrobert et al. (2011) also reported the openskilled participants had the higher online updating ability, with which they were more adaptive to dynamic environments in the anticipation. Bertollo et al. (2016) further explained that the higher efficiency in switching between the automatic and controlled processes among sport experts than amateurs is a reason for the former to proficiently adapt to the changing environment in open sports. When compared with amateur athletes, the high efficiency found among the open-skilled athletes was revealed to result in the employment of less cognitive resources in controlled processes (Babiloni et al., 2010).

In the present study, the open-skilled participants are badminton athletes whose experience is in updating opponent's changed kinematics information and overcoming interferences from previous deceptive movement patterns (Müller and Abernethy, 2012). They succeeded in winning the game mostly depending on how well they update environmental changes and anticipate their opponent's actions (Bianco et al., 2017b). With such a background, the open-skilled participants tend to deploy fewer attentional resources when updating the new action rules in proactive control for the switch trials, which was not the case in the closed-skilled and control participants. The significant positive correlations between the amplitudes of cue-locked P3 and the switch cost of response time in our results were revealed only in the participants of the open-skilled group, which further substantiate the uniqueness of proactive control to the openskilled participants.

No differences were revealed in the between-trial and between-group comparisons for the CNV. The non-significant between-group difference, supported by those reported in other

study (Bianco et al., 2017b), suggests that the level of the top–down attentional control preparing for task switching was comparable across the three groups of participants. No difference between switch and repeat trials indicates that motor preparation levels were not affected by switch and repeat conditions, which was consistent with the findings in

Gajewski and Falkenstein (2015). These results further support the notion that the CNV appears to be not an important neural marker related to the executive functions in the proactive control process. The years of professional motor skill practices is a significant covariate for CNV component, which suggests that participants with professional motor skill practices, regardless of any type of motor skills, had more negative-going CNV than those without professional motor skill practices. These findings were in line with those reported by Bianco et al. (2017a,b). The tonic activity in motor preparation was related to speed control in pre-supplementary motor area (Bianco et al., 2017a). In the present study, the speed requirement in both open (badminton) and closed (most were runners) skills was high, which may contribute to no group differentiation in CNV amplitude.

# Reactive Control and Motor Skill Experiences

The open- and closed-skilled participants showed less positivegoing parietal stimulus-locked P3 in the switch than repeat trials in the non-predictive condition. Less positivity in the switch than repeat trials could not be found in the control group, who reported having no habit of practicing any types of physical activities. The results indicate that fewer attentional resources would have been deployed by the participants in updating the action rule in reactive control for switching in both open- and closed-skilled groups than in the control group. No significant differences were shown between these two motor-skilled groups, however. The comparable performance in stimulus-locked P3 for the open- and closed-skilled groups was not in line with our hypothesis, but was partly consistent with the findings in Wang et al.'s (2017). The findings of Wang and colleagues study revealed that open- and closed-skilled participants had comparable frontal N2 component and theta power for reactive control in a flanker task. The open-skilled participants, however, showed greater theta phase coherence (0–500 ms, 4 Hz; 300–400 ms, 5 Hz) for incongruent trials compared to congruent trials, but this effect could not be found in the closed-skilled group. These findings suggested that the dissociation between open- and closed-skilled participants appears to be the stability level of neural process rather than the level of allocated cognitive resources. Compared with those engaged in closed motor skills, the open motor skills required the participants to give a response within a limited time (Jin et al., 2011). Thus, the superior performance in reactive control of open-skilled participants could be showed in the paradigm with short interval between the response and the next target stimulus, like 500 ms in Tsai and Wang's (2015), but not long interval between the response and the next target stimulus, like 2500 ms in the present study.

The behavioral results reported in Yu et al. (2017) indicated that open- and closed-skilled participants had significantly smaller switch cost of response times than controls. The ERP findings reported in this study reveal that both open-and closedskilled groups had higher efficiency in updating the new action rule in reactive control, reflected by less positive-going stimuluslocked P3. These findings were supported by those reported in the previous studies (Kamijo and Takeda, 2010; Zhang et al., 2015). Kamijo and Takeda (2010) reported that the participants with regular physical training showed less switch cost and less positive stimulus-locked P3 in the switch than repeat trials than the sedentary controls, suggesting better reactive control in task switching. One plausible reason to account for the enhanced reactive control among the participants could have been the inevitable gains in cardiorespiratory fitness due to the intensive training received by both open- and close-skilled groups (Scisco et al., 2008; Tsai and Wang, 2015). Zhang et al. (2015) indicated that experienced fencers deployed less cognitive effort in the reactive inhibition process (as reflected from the less positivegoing P3 in Nogo condition) than their novice counterparts, which is consistent with the results revealed in this study. Nevertheless, Yamashiro et al. (2015) revealed more positive somatosensory Nogo-P3 in the open-skilled participants for reactive control in the Go/Nogo task than the closed-skilled participants. The inconsistent findings reported in Yamashiro et al. (2015) study were likely due to a lack of controlling the years of professional motor skill practices among the participants, which could confound the results in the reactive control condition. Another reason may be that the superior performance in baseball group resulting from the baseball specific training, which could not be generalized to the closed-skilled participants.

Significant correlations were revealed between the amplitudes of stimulus-locked P3 and the switch cost of response time only in the control group. The results are unexpected, as significant correlations were anticipated among the open- and closed-skilled participants. A plausible explanation for the non-significant findings could be due to the heterogeneity of the strategies employed by the open- or closed-skilled participants. To further test this proposition, a median-split method (Tamura et al., 2010) was applied to subdivide the open- and closed-skilled groups into higher and lower ability subgroups based on the participants' performances on between-trial difference in the stimulus-locked P3 amplitudes (sP3S-sP3R). Significant correlations were found in the higher but not the lower open-skilled ability subgroups; whilst significant correlations but opposite in direction were found in both the higher and lower closed-skilled ability subgroups. The small sample size for each of the subgroups (n = 9) only allowed us to suspect within-group heterogeneity as a possibly confounding factor to the non-significant relationships between the ERP and behavioral results. Future studies should explore possible deployment of different strategies by the participants in the same open- or closed-skilled group and the differences in the neural processes associated with proactive or reactive controls.

Our negative-going frontal N2 findings in the switch than repeat trials (in the 100 validity) at the Fz, Cz, and Pz electrodes are consistent with those reported by Hsieh and Wu (2011). The results suggested the possible involvement of response-set switching and suppression of conflict response processes unique to the behavioral task used in this study (Nakamoto and Mori, 2008; Wang et al., 2017), as no significant group difference was revealed in the N2 among all the participant groups.

#### Limitations

First, the proactive and reactive control processes were prescribed by the switching task employed in this study. The results may

not be directly generalized to other executive functions, such as inhibition or self-regulation. Second, the sample sizes of the open- and closed-skilled participants of this study were relatively small, which could have lowered the power of the analyses. Readers should be cautious when interpreting the results. Third, it is unclear whether badminton and track and field can best represent open- and closed-skilled physical activities, respectively. Any generalization of the findings should be restricted to participants of the same type of physical activities and level of competence. Future studies may consider recruiting participants of other types of physical activities and levels of competence. Fourth, the participants in the control group were those who had not been engaged in professional or amateur sports. The levels of physical activity engaged by these participants were not controlled. The existing differences between the open/closed-skilled and control groups could have been confounded by the differences in other parameters such as the levels of physical fitness rather than the types of motor skills, in case that the majority of the participants in the control group had been leading a sedentary lifestyle. Future study should recruit individuals who have comparable levels of physical fitness and/or physical training, but not at the professional level, as the control group. Fifth, the proposition of potential heterogeneity in strategies taken by the open- and closed-skilled groups was based on small sample sizes and without triangulation. Future studies should employ a more stringent research design and a larger sample size to address this issue.

#### CONCLUSION

This study explored how experiences in open and closed motor skills modulate individuals' proactive and reactive control processes. Compared with closed-skilled experiences, intensive open-skilled experiences were related to better proactive control for task switching characterized by lower switch cost and significant difference between switch and repeat trials of cuelocked P3 amplitudes. The enhanced proactive control is likely the result of high demand of anticipating environmental changes in open-skilled physical activities. Intensive open- and closedskilled experiences were related to better reactive control for task switching than the experiences of control participants, which

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was most likely resulted from higher cardiorespiratory fitness. Proactive and reactive controls as part of the process of motorskill development seem to be transferable to domain-general executive functions.

# DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

# ETHICS STATEMENT

The studies involving human participants were reviewed and approved by The Research Grant Committee of the Hong Kong Polytechnic University. The patients/participants provided their written informed consent to participate in this study.

### AUTHOR CONTRIBUTIONS

AW and BL contributed to the data interpretation and article writing. BC contributed to the conceptualization, data interpretation, and article writing. CC contributed to the conceptualization, study design, data interpretation, and article writing. JP contributed to the data collection, data analysis, and data interpretation. QY contributed to conceptualization, study design, data collection, data analysis, data interpretation, and article writing.

### FUNDING

This work was supported by the General Research Fund of the Research Grant Council of Hong Kong (grant number 151044).

### ACKNOWLEDGMENTS

The authors thank the University Research Facility in Behavioral and Systems Neuroscience for its support in providing the equipment and research personnel.

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Yu, Chau, Lam, Wong, Peng and Chan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fnhum-13-00404 November 12, 2019 Time: 17:4 # 15

# The Effect of Meditation on Comprehension of Statements About One-Self and Others: A Pilot ERP and Behavioral Study

Alexander Savostyanov 1,2,3\*, Sergey Tamozhnikov 2 , Andrey Bocharov 2,3 , Alexander Saprygin2 , Yuriy Matushkin1 , Sergey Lashin1,3 , Galina Kolpakova3 , Klimenty Sudobin3 and Gennady Knyazev 2

1Laboratory of Psychological Genetics, Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences (SB RAS), Novosibirsk, Russia, 2Laboratory Differential Psychophysiology, State-Research Institute of Physiology and Basic Medicine, Novosibirsk, Russia, 3Laboratory of Biological Markers of Human Social Behavior, Humanitarian Institution at the Novosibirsk State University, Novosibirsk, Russia

#### Edited by:

Stephen Fairclough, Liverpool John Moores University, United Kingdom

#### Reviewed by:

Bahar Güntekin, Istanbul Medipol University, Turkey Christopher George Burns, University of Warwick, United Kingdom

#### \*Correspondence:

Alexander Savostyanov<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognitive Neuroscience, a section of the journal Frontiers in Human Neuroscience

Received: 04 July 2019 Accepted: 28 November 2019 Published: 09 January 2020

#### Citation:

Savostyanov A, Tamozhnikov S, Bocharov A, Saprygin A, Matushkin Y, Lashin S, Kolpakova G, Sudobin K and Knyazev G (2020) The Effect of Meditation on Comprehension of Statements About One-Self and Others: A Pilot ERP and Behavioral Study. Front. Hum. Neurosci. 13:437. doi: 10.3389/fnhum.2019.00437 The main goal of this study was to examine the effect of long-term meditation practice on behavioral indicators and ERP peak characteristics during an error-recognition task, where participants were presented with emotionally negative (evoking anxiety or aggression) written sentences describing self-related or non-self-related emotional state and personality traits. In total, 200 sentences written in Russian with varying emotional coloring were presented during the task, with half of the sentences containing a grammatical error that the participants were asked to identify. The EEG was recorded in age-matched control individuals (n = 17) and two groups of Samatha meditators with relatively short- (3–5 years' experience, n = 18) and long-term (10–30 years' experience, n = 18) practice experience. Task performance time (TPT) and accuracy of error detection (AED) were chosen as behavioral values. Amplitude, time latency and cortical distribution of P300 and P600 peaks of ERP were used as a value of speech-related brain activity. All statistical effects of meditation were estimated, controlling for age and sex. No behavioral differences between two groups of meditators were found. General TPT was shorter for both groups of meditators compared to the control group. Non-meditators reacted significantly slower to sentences about aggression than to sentences about anxiety or non-emotional sentences, whereas no significance was found between meditator groups. Non-meditators had better AED for the sentences about one-self than for the sentences about other people, whereas the meditators did not show any significant difference. The amplitude of P300 peak in frontal and left temporal scalp regions was higher for long-term meditators in comparison with both intermediate and control groups. The latency of P300 and P600 in left frontal and temporal regions positively correlated with TPT, whereas the amplitude of P300 in these regions had a negative correlation with TPT. We demonstrate that long-term meditation practice increases the ability of an individual to process negative emotional stimuli. The differences in behavioral reactions after onset of negative information that was self-related and non-self-related, which is typical for non-meditators, disappeared due to the influence of meditation. ERP results could be interpreted as a value of increase in voluntary control over emotional state during meditational practice.

Keywords: Samatha meditation, assessment of self and other, recognition of written speech, EEG and ERP, task performance time and accuracy of error detection

# INTRODUCTION

Assessment of self and others (non-self) is one of the most important elements of regulation of social behavior. One of these methods, attribution theory, was suggested by social psychologist Heider (1958) to evaluate how people perceive their own behavior, as well as the behaviors of others. According to this theory, an act of attribution is a prediction of ongoing behaviors, such as emotions and motives for self or any other person. The main question of research in this area of self-feeling (our ''I'') is whether it has a special status in human consciousness or if it is functionally equivalent to semantic processing of any other classes of stimuli. Is our consciousness capable of paying more attention to information in which we are somehow involved? Symons and Johnson (1997) showed that information related to self in any way is remembered better than impersonal. This phenomenon became known as ''self-reference effect''. Neuroscientific studies on self-concept have mainly relied on experimental paradigms in which participants evaluated sentences that described some traits about the self. These studies revealed consistent activation in a medial prefrontal cortex (mPFC) during self-evaluation (see meta-analyses: Denny et al., 2012; Murray et al., 2012). In the study of Fossati et al. (2003), influence of emotional valence of stimuli was studied in addition to localization of the information processing relating to self. Activation in the right dorsomedial prefrontal cortex was found for self-referencing stimuli, which was independent from the valence of the words. Differences in processing of positive and negative adjectives were noticed in this study in areas outside of the medial prefrontal cortex. Other studies (Moran et al., 2006; Qin and Northoff, 2011) showed that besides mPFC, such areas as the cingulate cortex, precuneus, and temporalparietal cortex are also involved in the processing of information about self.

One of the experimental approaches to studying evaluation of self and non-self is a comparison of groups of people that vary significantly in behavioral values related to social perception. Such groups can include people of different age (Pfeifer et al., 2009), gender (Cross and Madson, 1997), or ethno-cultural traits (Markus and Kitayama, 1991; Cross et al., 2011) that affect selfevaluation. In addition, healthy subjects can be compared to psychiatric patients with impaired self-perception processes (Sass and Parnas, 2003; Nelson et al., 2012; Mishara et al., 2014). In this study, we aimed to examine people practicing Buddhist Samatha meditation through neuroscientific approaches.

Meditation is a religious practice of great interest to neuroscience, as it is a model that allows the studying of changes occurring in regards to brain function, which occur due to voluntary and conscious efforts of the subject. Meditation's effects on attention have been previously studied using attention network task (ANT) and other experimental paradigms (Chiesa et al., 2011). Some studies report improvement in conflict monitoring (Wenk-Sormaz, 2005; Chan and Woollacott, 2007; Slagter et al., 2007; Tang et al., 2007; Moore and Malinowski, 2009). For example, it is shown that several days (20 min each day) of integrative mental-body training led to improved conflict monitoring (Tang et al., 2007). Additionally, longitudinal studies of 3-month long awareness meditation have shown decrease in attention blinking due to exercise (Slagter et al., 2007; van Leeuwen et al., 2009). In cross-section studies, experienced meditators demonstrated the best results in monitoring conflicts (van den Hurk et al., 2010).

According to the report of Aftanas and Golosheykin (2005), Sahaja Yoga meditators manifested smaller emotional arousal while watching aversive video clips, which was reflected in changes of spectral power in their EEG records. In addition, meditators manifested larger power values in theta-1 (4–6 Hz), theta-2 (6–8 Hz) and alpha-1 (8–10 Hz) frequency bands compared to control subjects in eye-closed conditions without external stimulation. These results were interpreted as proof of meditators having better capabilities to moderate intensity of emotional arousal.

Changes in amplitude of N1, P2, and P3 peaks in meditators compared to non-meditators were revealed in many ERP studies of different kinds of meditation (Cahn and Polich, 2009; Atchley et al., 2016; Biedermann et al., 2016). P300 amplitude increased in meditators immediately after meditation practice compared to the pre-meditation condition (Telles et al., 2019). The study of Vipassana meditation practice has found that the dynamics of P300 in conjunction with the theta and alpha bands spectral power indices reflected differences between people with different prolongation of meditation experience (Kakumanu et al., 2019). The amplitude of frontal P300 reflects a degree of concentration of voluntary attention. Therefore, most researchers interpret the influence of meditation on the amplitude of P300 as an index of the increase in the ability of meditators to voluntary concentrate attention on themselves and/or external events.

Several functional and structural MRI studies of awareness meditation have focused on neuroplasticity in areas responsible for attention control. Awareness practice effects on attention are mostly related to the anterior cingulate (van Veen and Carter, 2002; Cahn and Polich, 2006; Hölzel et al., 2007; Tang et al., 2010, 2012a,b, 2013; MacCoon et al., 2014; Tang and Posner, 2014). The anterior cingulate is responsible for executive attention and control through detecting conflicts caused by incompatible information processing flows (van Veen and Carter, 2002; Posner et al., 2007; Tang and Tang, 2014). The anterior cingulate cortex and the frontal cortex form a part of the network, which, due to effective distant connections with other areas of the brain, facilitates cognitive processing (Sridharan et al., 2008; Tang et al., 2012b). Compared to the control group, experienced practitioners have shown an increase in activation of areas of the anterior cingulate during meditation (Hölzel et al., 2007) or with a conscious expectation of pain stimulus (Gard et al., 2012). In a controlled randomized longitudinal study, high activation of the ventral and/or rostral anterior cingulate cortex have also been identified during the rest period following 5 days of integrative mental-body training (MacCoon et al., 2014). Activation of the anterior cingulate cortex may increase during the initial stages of the practice and decrease with prolonged training (Brefczynski-Lewis et al., 2007).

Other brain areas related to attention, in which functional changes due to awareness meditation were witnessed, include the dorsolateral prefrontal cortex (dlPFC), where an increased response during executive processing was observed (Allen et al., 2012), and the posterior parietal cortex (PPC), which had shown higher activation after the MBSR course in subjects with social anxiety (Goldin and Gross, 2010).

Thus, the areas of the brain where activity changes under the influence of meditation and the areas involved in the self-assessment and evaluation of other people overlap to a significant degree. According to the self-report of participants, Samatha is a religious practice, with the end goal being the ''dissolution'' of Myself in Universe. Self-awareness is considered by adherents of such meditation an illusion that must be discarded. We have proposed that the long practice of Samatha can change the processes of evaluation of one-self and/or of other people, which can be witnessed using brain electrical activity analysis. We chose event-related potentials (ERP), occurring during the recognition of written sentences, which include emotionally negative descriptions of one-self's conditions or non-self traits and characteristics, as the method of our study. According to our hypothesis, in such a paradigm non-meditators should show differences in responses to messages about self and non-self, whereas the consequence of meditation should be the disappearance of such differences.

### MATERIALS AND METHODS

#### Participants

The study was conducted at the Baikal Meditational Center1 . The experimental sample included 53 healthy right-handed participants from 25 to 66 years (32 men; mean age = 41.0, SD = 8.3). The participants were divided into three groups: (1) non-meditators-17 persons (10 men, mean age = 40.5, SD = 8.5) who refused to take part in a meditational practice; (2) intermediate group–18 persons (11 men, mean age = 40.3, SD = 8.0) with 3–5 years of experience of meditation; and (3) long-term meditators–18 persons (11 men, mean age = 42.7, SD = 9.3) who had long-term (more than 10 years) experience of a meditation practice. All participants from the long-term group have the status of great masters of meditation, recognized in the Buddhists community. After completion of our study, they took part in ''a retreat,'' i.e., semi-annual continuous meditation. The groups of participants were balanced in age and sex. All participants had no history of neurological, psychiatric, or major somatic disorders. According to the selfreport, they denied use of narcotic drugs or other psychoactive substances. All participants were native Russian-speakers or natural bilinguals (Russian and one of the Siberian languages) and had normal or corrected-to-normal visual acuity. All participants (including non-meditators) are related to the lamaistic Buddhism community of Russia.

After the experiment all participants filled a Russian version of Goldberg's IPIP Big-Five Factor Markers (validated by Knyazev et al., 2010) for estimation of their psychological traits. In addition, a set of psychological questionnaires was used for estimation of participants' differences in emotional intelligence scores, trait anxiety level, vulnerability to depression and anxiety disorder, etc.

All subject protection guidelines and regulations were followed in accordance with the Declaration of Helsinki. The study goals were explained to all participants and they signed the informed consent. The study and the consent form were approved by the Institute of Physiology and Basic Medicine ethics committee and by the spiritual leader of the lamaistic Buddhism community of Russia.

# Experimental Procedure

Two-hundred sentences in Russian language were selected for the experiment (see **Table 1**). Half of the sentences (100) contained a grammatical error. In preliminary testing on another group, the grammatically wrong sentences were selected so that they were easily recognized (more than 80% accuracy) by all participants. The text stimuli were presented in white and black (Arial, 36 pt) via a 24.4 cm × 18.3 cm monitor located 60 cm away in front of a participant. A warning signal (cross) appeared at the center of the screen for 0.5 s before the task onset. The participants were instructed to answer whether a presented sample contains an error by pressing one of two buttons with their dominant hand. Participants had three practice trials before task execution. Time for decision making was not limited for participants, but they were instructed to perform the task as quickly as possible and with maximal accuracy.

The experiment also contained a hidden condition: the sentences were grouped by emotional coloring, of which participants were not informed. There were five groups of sentences: (1) sentences describing aggression of a participant; (2) sentences describing aggression of other people; (3) sentences describing anxiety of a participant; (4) sentences describing anxiety of other people; and (5) neutral sentences about inanimate objects. The sentences from different categories were presented in random order. The sentences about anxiety were selected from the Russian version of Spielberger's State-Trait Anxiety Inventory (Spielberger et al., 1970; translated to Russian and validated by Hanin, 1976). The sentences about aggression were taken from the Buss-Perry aggression questionnaire (Buss and Perry, 1992; translated to Russian and validated by Yenikolopov and Tsibulsky, 2007). All sentences

1http://www.geshe.ru/

about one-self were taken from validated Russian versions of psychological tests. Translations of these tests into Russian were performed by professional translators. All questionnaires were repeatedly validated in different samples in Russia and in other countries where Russian is widely spoken. The sentences about non-self anxiety or aggression were created by the replacement of a pronoun ''I'' or ''Me'' on accidentally chosen pronouns ''He,'' ''She,'' ''They,'' ''Him,'' ''Her'' or ''Them.'' The sentences about objects, anxiety, and aggression were balanced in number of words and grammatical structure. The sentences about self and non-self differed only by pronouns and connected verbs. The order of sentences was randomized across participants.

#### EEG Recording

EEGs were recorded using 130 channels (128 EEG, VEOG, ECG) via Ag/AgCl electrodes. The EEG electrodes were placed on 128 head sites according to the extended International 5–10% system and referred to Cz with ground at FzA. The Quik-Cap128 NSL was used for electrode fixation. The electrode resistance was maintained below 5 k. The signals were amplified using NVX 136 amplifier (MCS, Russia), with 0.1–100 Hz analog bandpass and continuously digitized at 1,000 Hz.

#### Behavioral Data Processing

The task performance time (TPT, in milliseconds) and accuracy of error detection (AED, a percent of correctly recognized error in wrong sentences and a percent of sentences without error correctly marked as ''right'') were used as the two behavioral values.

Initially, these values were used for repeated measures ANOVA with the Greenhouse-Geisser correction to test the main effects of such factors as ''group'' (non-meditators vs. meditators), ''correctness'' (right or wrong sentences), ''sentence category'' (five levels for different categories of sentences), ''me\_other'' (sentences about participant or about other people, results for neutral sentences were excluded from such analysis), ''aggression\_anxiety'' (sentences about aggression vs. about anxiety, results for neutral sentences were excluded from such analysis), age, sex, and interactions between these factors. However, any significant differences in the behavioral values between the participants from intermediate and long-term experience groups were not revealed. Both groups of meditators showed same differences from the non-meditators. For this reason, behavioral values were compared between the united group of meditators and the control group of non-meditators. Besides, no inter-group differences in TPT and AED were found for neutral sentences. Therefore, behavioral values for neutral sentences were excluded from statistical analysis. Finally, for statistical comparison of TPT and AED values, the repeated measures ANOVA with the Greenhouse-Geisser correction with factors ''group'' (non-meditators vs. meditators of both groups), ''correctness,'' ''me\_other,'' ''aggression\_anxiety,'' age, sex, and interactions between these factors was used. All statistical effects of meditation were estimated controlling for age and sex.

# EEG Pre-processing and ERP Analysis

Recordings were processed in the EEGLAB toolbox (Delorme and Makeig, 2004). The trials containing artifacts were rejected from analysis. For each subject, 180–195 trials with sentence onset were used. The time intervals from 1.5 s before to +3.0 s after the fixation cross onset were analyzed. Time intervals from −1.5 to −0.75 s before fixation cross onset were used for baseline-correction.

EEGs were preliminary band-pass filtered in 1–40 Hz using elliptic filters. Following the suggestion by Delorme and Makeig (2004), re-reference (averaged reference) and baseline adjustment procedures were performed during data pre-processing. Independent component analysis (ICA) was used for correction of eye-movement and eye-blinking artifacts. First, the component's weights were computed individually for each record. Then, components corresponding to eye's artifacts were disclosed by visual inspection of component sets together with VEOG and ECG records. Artifactual components were removed in the pre-processing of EEGs.

To assess changes in signal amplitude, associated sentence onset, event-related potentials (ERPs) were calculated in the ERPLAB toolbox2 . After removing artifacts, we computed ERP-values using the Erplab toolbox separately for every EEG-channel, subject and experimental condition, and cutoff filter at 12 Hz was applied to them. After that, maximal peak amplitudes, mean peak amplitude, and peak latencies in 150–500 ms (that corresponded to location of P300 peak during the visual peak analysis) and 600–1,000 ms (i.e., peak P600) time ranges were averaged across nine scalp regions of interest (ROI: left frontal, medial frontal, right frontal, left temporal, central, right temporal, and left, medial and right parietal-occipital scalp regions) for each subject. These values were used for repeated measures ANOVA with the Greenhouse-Geisser correction to test the main effects of such factors as ''region of interest,'' ''group,'' ''correctness,''

2https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995046/

''sentence category,'' age, sex, and interactions between these factors.

### RESULTS

## Behavioral Results

No statistically significant inter-group differences in Big-Five factor markers were found in all scales of Goldberg's inventory. The main effects of factors ''age'' and ''sex'' and their interactions were statistically insignificant both for TPT (for age p = 0.95, for sex p = 0.51) and AED (for age p = 0.33, for sex p = 0.12).

As already mentioned, significant differences in the behavioral values between the participants from intermediate and long-term experience groups were not revealed. The main effect of ''correctness'' factor controlling for age and sex was highly significant as for TPT, F(1,51) = 23.78; p < 0.0001, as for AED, F(1,51) = 15.65; p < 0.0001. The correct sentences were recognized slower (correct sentences: mean TPT 4.0 ± 0.2 s; wrong sentences: 3.4 ± 0.2 s), but with better accuracy (correct sentences: mean accuracy 96.5 ± 1.3%; wrong sentences: 90.6 ± 0.9%), than the sentences with error. However, the interactions between factors ''correctness,'' ''group,'' ''age,'' and ''sex'' were insignificant for both values (p > 0.5).

For TPT, the main effect of the ''group'' factor was statistically significant, F(1,51) = 2.95; p = 0.052. Meditators solved the task faster (mean time 3.3 ± 0.2 s) than non-meditators (4.0 ± 0.2 s). In addition, for value of TPT, the main effect of the ''me/other'' factor was highly significant, F(1,51) = 22.69; p < 0.0001. Sentences about one-self were recognized faster (mean time 3.5 ± 0.2 s), than about others (3.9 ± 0.2 s). Interaction between factors ''Me/Other'' and ''group'' for value of reaction time was statistically insignificant (p > 0.3). Additionally, a highly significant main effect of the ''Anxiety/Aggression'' factor, F(1,51) = 51.24; p < 0.0001, and an interaction between ''Anxiety/Aggression'' and ''group'' factors, F(1,51) = 5.86; p = 0.019 were revealed for TPT. For all groups of participants, recognition time for sentences about anxiety (mean 3.4 ± 0.2 s) was lower than for sentences about aggression (4.0 ± 0.2 s). However, meditators had smaller differences in recognition time for sentences about anxiety and aggression (anxiety: 3.4 ± 0.2 s; aggression: 3.6 ± 0.2 s) than non-meditators (3.7 ± 0.3 s 4.4 ± 0.3 s, respectively; **Figure 1**).

The main effect of the ''group'' factor for AED value was not significant (p > 0.4). For AED, a statistically significant main effect of the ''me/other'' factor, F(1,51) = 7.46; p = 0.009, and an interaction between ''Me/Other'' and ''group'' factors, F(1,51) = 9.11; p = 0.004 were revealed. Averages between all group AED values were higher for sentences about one-self (94.3 ± 1.0%), than about others (92.8 ± 0.8%). However, these differences manifested only in the non-meditation group (oneself: 94.5 ± 1.7%; other: 91.3 ± 1.3%), while meditators had no such differences (one-self: 94.1 ± 1.1%; others: 94.3 ± 0.8%; **Figure 2**). The main effect of the ''Anxiety/Aggression'' factor (p > 0.9), and the interaction between ''Anxiety/Aggression'' and ''group'' (p > 0.8) were not significant. Interactions between ''Me/Other,'' ''Anxiety/Aggression,'' ''age,'' and ''sex'' were also insignificant.

FIGURE 1 | Differences in time of task performance for the sentences about anxiety (light gray) and aggression (darkly gray) for both groups of meditators (left side) and non-meditators (right side).

#### ERP Results

Time-amplitude plots of ERP in different cortical areas among different groups of participants are presented in **Figure 3**; cortical topography is presented in **Figure 4**. P300 and P600 peaks can be identified by visual inspection of these plots. P300 peak is clearly noticeable in the left temporal and all frontal cortical regions with amplitude maximum about 300 ms after sentence onset. Negative peak is detected in occipital-parietal cortical regions with amplitude maximum about 350 ms after sentences onset. The P300 has the highest amplitude in the group of long-term meditators in comparison with other groups. P600 peak is noticeable only in left temporal regions, i.e., in Broca's and Wernicke's areas. Amplitude of P600 among different groups is maximal in short-term meditators, but this difference was not significant. The topographic distribution of P300 and P600 peaks over cortical regions was not principally different among groups of participants. Though, general time-amplitude parameters and

sentences about one-self (light gray) and others (darkly gray) for both groups of meditators (left side) and non-meditators (right side).

cortical distribution of P300 and P600 peaks were in consistence with standard patterns for tasks on language recognition.

In all subject groups, amplitudes of P300 P600 peaks were significantly higher for sentences about aggression than for sentences about anxiety and inanimate objects. Amplitudes of these peaks were higher for incorrect sentences, however, no significant interactions between ''group'' and ''category'' or ''correctness'' were revealed. Also, there were no differences between ERP values for sentences about self and non-self. That's why we averaged ERP values for all sentence categories.

For the majority of measured ERP values (i.e., time latency of P300 and P600, averaged and maximal amplitude of P600, and averaged amplitude of P300) any inter-group differences were not revealed. Significant inter-group differences were revealed for the maximal amplitude of the P300 peak. The main effect of age was significant, F(1,46) = 6.16; p = 0.017 while the main effect of sex was insignificant (p > 0.7).  The main effect of the ''group'' factor for P300 values was insignificant (p > 0.2). However, the interaction of effects of factors ''Region'' and ''Group'' compared in three groups of participants was statistically significant, F(16,368) = 1.83; p = 0.026. Using one-way ANOVA, we have compared all pair combinations of three groups (i.e., control vs. intermediate; control vs. long-term meditators; intermediate vs. long-term). The comparison was performed separately for each cortical region, controlling for age and sex. One-way ANOVA revealed significant differences between the long-term meditators and other participants in left frontal, medial frontal, right frontal, and left temporal cortical regions. In all of these regions, long-term meditators showed higher amplitude of peak P300 in comparison with non-meditators and people with short-term experience of meditation (see **Table 2**). These effects were significant both for comparison between control participants and long-term meditators, and between short-term and long-term meditators. The significant differences between a control group and short-term meditators were revealed in right frontal and right temporal cortical regions. The amplitude of P300 in these regions was higher in control participants than in short-term meditators.

#### Correlations Between Behavioral and ERP Values

Two-tailed Pearson's correlation coefficients were calculated between the values of TPT and AED, averaged across all

Significantly different values are marked by a bold font.

categories of sentences, and values of maximal amplitude and time latency of P300 and P600 peaks of ERP separately in all cortical ROI. Maximal amplitude of P300 had significantly negative correlation with the TPT in left frontal (r = −0.34; p = 0.014) and left temporal (r = −0.31; p = 0.028) cortical regions. The latency of P300 significantly positively correlated with TPT values in left frontal (r = 0.34; p = 0.014) and medial frontal (r = 0.31; p = 0.029) cortical regions. The latency of P600 had a positive highly significant correlation with TPT in the left frontal (r = 0.53; p < 0.0001) and left temporal (r = 0.56; p < 0.0001) regions, and negative correlation with accuracy of error recognition in the left temporal area (r = −0.30; p = 0.035). Separated statistical analysis did not reveal any differences in correlations between behavioral and ERP values among different groups of participants and different categories of sentences.

#### Results Summary

Short-term and long-term experience groups of meditators were not statistically different in their behavioral values, however, they showed significant differences to the control group. Non-meditators showed decrease in TPT for sentences about aggression when compared to sentences about anxiety, while both meditators groups had these differences decreased due to faster recognition of sentences containing aggression. Additionally, non-meditators performed better in sentences about self than about non-self, while meditators had no such differences due to better performance in sentences about non-self. In ERP values long-term meditators showed higher amplitudes of P300 peak in frontal and left temporal cortical regions compared to other groups, while differences between non-meditators and short-term meditators were found in the right frontal and temporal regions. For all groups and all sentences, categories peak latencies of both P300 and P600 correlated positively with TPT. P300 peak amplitude in left frontal and temporal regions correlated negatively with TPT.

# DISCUSSION

The aim of our study was to investigate how meditation changes perception of information connected with negative assessment of one-self or other people. The task reflecting implicit perception of emotional coloring of written speech was used as an experimental method. In our experiment, the voluntary attention of a participant was concentrated on detection of a grammatical error in the presented sentence, with the tasks being simple for the participants (mean AED was more than 90% for all participants). However, we compared speeds and accuracy of the answers for different categories of sentences which differed in their emotional coloring (neutral, anxious and aggressive), and whether the sentence was in relation to themselves or to other people. In this study, we have analyzed differences in behavior that participants did not control voluntarily and did not concentrate their attention on. It is possible to assume that the recorded differences in these measurements between meditators and non-meditators reflect some long-term changes in their behavior, which can be shown in other test tasks and ordinary life.

We have found two significant effects of meditation on the behavioral values and one effect of meditation on the ERP values. The behavioral differences were revealed in both groups of meditators (i.e., both for people with relatively shorter and longer experience of such practice), whereas the ERP differences distinguished the group with long-term experience both from non-meditators and meditators with shorter experience. A slight difference in the P300 amplitude in the right frontal and right temporal areas was also found between the control participants and meditators with shorter experience.

The significant differences in time and accuracy in performing the set tasks for anxiety and aggression colored sentences, as well as for sentences about self and non-self were observed in non-meditators compared to meditators (the values for neutral sentences did not significantly differ from values for sentences about self-related anxiety). Non-meditators have recognized the sentences about anxiety quicker (difference approximately in 0.7 s) than sentences about aggression. The quality of recognition of sentences about anxiety and aggression in non-meditators did not differ. Also, non-meditators recognized the sentences about themselves quicker (difference in 0.4 s) and with better accuracy (difference about 3%), than the sentences about others. It is essential that the sentences about themselves and about others differed only with a pronoun and connected verb and contained identical grammatical errors. The order of presentation of these sentences have been randomized between participants. Thus, the differences in behavioral values for these sentences cannot be connected neither with their grammatical properties, nor with a presentation order, and reflect personal features of perception of negative information about self and others.

It is also important to note that all statistically significant effects of meditation were calculated controlling for participants' age and sex. Thus, behavioral and ERP differences between groups cannot be explained as a random result of influence of age and sex differences between groups of participants. We revealed a general correlation between TPT and AED, which also correlated with P300 amplitudes for all groups, as noted in the results on correlations between different indicators. However, long-experienced meditators showed a general decrease in TPT as compared to other groups, but the main effect of the ''group'' factor for AED value was not significant. Therefore, the effect of meditation on AED cannot be explained by the decrease of reaction time. In addition, the most important difference between meditators and non-meditators was not the overall accuracy or task response time, but the difference in AED when searching for an error in self-related and non-self-related sentences. This effect did not depend on the tasks response time. Also, the statistical significance of this effect increased when controlling for age and sex. Overall, the most significant behavioral effect of meditation did not depend neither on the general response time, nor on general quality of task performance, nor sex, nor age of the participants.

It has been shown that the recognition of grammatical errors in sentences depends on their emotional coloring. Sentences with emotionally negative signals usually induce a slower error recognition response, with worse accuracy scores when compared to emotionally positive or neutral sentences (Ayusheeva et al., 2018). In our results, it can be observed that in non-meditators the greatest delay and decline in quality of task performance happens in sentences containing aggression towards others. It could be interpreted as an indicator that the description of aggressive behavior of other people causes the most negative emotions, which is shown in behavioral data of a linguistic task performance.

The effect of meditation consists in the general acceleration of performance of a linguistic task for all categories of emotional (but not neutral) sentences and in reduction of differences in the TPT between anxiety and aggression related sentences. This correlates with the assumption that meditation improves voluntary control over own emotions (Aftanas and Golosheykin, 2005; Marchand, 2012; Kasala et al., 2014). Our result could be interpreted as an indicator that meditators reduce the extent of negative perception of sentences about aggression that improves their control over execution of a grammatical task.

Other behavioral effects of meditation are connected with the alignment in AED for sentences about self and non-self. This effect happens due to improvement (in comparison with non-meditators) in accuracy in sentences about non-self with maintaining the accuracy for self. Differences in the TPT for the sentences about one-self and others in meditators remain the same, as well as in non-meditators. These results could be hypothetically interpreted based on data about cross-cultural differences in the neuronal processes underlying an assessment of self and non-self. It is known that in people who originate from individualistic (so-called ''western'') cultures, a significantly stronger activation of the medial prefrontal cortex was associated when thinking about self rather than non-self, including when thinking about relatives (Kelley et al., 2002). In contrast to this, in people from collectivistic (''oriental'') cultures the thinking about self causes the same levels of activation of the prefrontal cortex as when promoted to think about relatives (Zhu et al., 2007; Wang et al., 2012). According to the self-report of our participants, the purpose of the Buddhist meditational practices is subjectively realized as ''a dissolution of consciousness in the Universe.'' As meditators aspire to reach this goal, the differences between themselves and others are perceived as an illusion that needs to be eliminated. It is possible to speculate that the lack of differences in accuracy of recognition of sentences about self and others in meditators is an indicator reflecting the effect of meditation on their consciousness. On the other hand, all of our participants (including nonmeditators) consider the Buddhist community of Russia and Kazakhstan to belong to oriental, rather than to western culture. Moreover, the differences in TPT for sentences about self and non-self were still present in the meditators group. Another possible explanation for the effect of meditation is the general decrease in negative emotions that improves attention to those sentences which cause the most negative reaction in nonmeditators. Such an assumption is in agreement with the results received when comparing reactions to anxiety and aggression related sentences.

Amplitude of frontal P300 peak is one of the most frequently noted neurophysiological markers of meditation (Cahn and Polich, 2009; Atchley et al., 2016; Telles et al., 2019). In the study of Vipassana meditation, it was revealed that dynamics of P300 together with theta and alpha spectral power differ in people with various durations of meditation practice (Kakumanu et al., 2019). Our ERP results showed the differences at the level of brain activity between meditators with long-term experience both from non-meditators and intermediate group of meditators. In long-term meditators the amplitude of P300 peak in all frontal and left temporal cortical regions was increased in comparison with other groups. At the same time, in all groups of participants higher amplitude of P300 in left frontal and temporal regions correlated with faster task solution for all categories of sentences. Frontal amplitude of the P300 peak is a well-known correlate of voluntary-controlled attention (Heinze et al., 1999). Our findings could be interpreted as the indicator reflecting the general improvement of ability to concentrate attention as a result of long-term meditation. It is known, that the right fronto-temporal areas are activated under processing of unconscious negative emotion (Satto and Aoki, 2006). Therefore, a decrease in the P300 amplitude in meditators with shorter experience can be hypothetically interpreted as an indicator of a general decrease of sensitivity to negative emotions.

The differences revealed in behavior may have several interpretations. Probably, it is a direct effect of meditation practice on self-assessment behavior. Another possible reason of revealed behavioral effects is differences in the personality traits of the participants. In our study, intergroup differences in the Big-Five factor markers were not identified. However, some of psychological properties, not covered by the Goldberg's inventory, could exist between non-meditators and meditators before the latter began to practice meditation. These properties can have influence on recognition of self-related sentences.

It is necessary to notice that the differences in brain activity between the long-term meditators and other groups have no direct relation to the differences in the behavioral values. First of all, the behavioral differences have been found in the intermediate meditators as well, whereas the ERP differences were detected only in long-term meditators. Besides, ERP differences among the experimental groups did not concern the differences in reactions of the sentences about anxiety and aggression or about one-self and others, and were revealed for the reactions to all sentences, including the neutral ones. Respectively, in the present study we did not find the indicators of brain activity which are connected with the changes in perception of one-self and others, observed in behavioral assessment. On the contrary, our ERP findings reflect the long-term effect of meditation, which is shown at the perception of any speech-related information, including unemotional information about inanimate objects. A more detailed comparison of various behavioral and neurophysiological effects of meditation is a topic for our future research.

### LIMITATIONS

1. Although the groups of participants were balanced by sex and age to the best of our ability, inside of all groups there was significant age variability. The age range was between 20 and 66 years old: it was connected to age heterogeneity in groups of meditators. Besides, the researched samples were not few in number of participants. Our hypotheses about the effects of meditation on behavior and brain activity should be tested in larger groups.

## CONCLUSION

Meditative practice changes a perception of emotional coloring of written speech. Meditators have an increase in behavioral control of recognition of emotionally negative sentences about aggression, which is reflected in an increase in speed of performance of grammatical tasks. Meditation changes a perception of information about one-self and others. The differences in a recognition of sentences about one-self and others, which are characteristic of non-meditators, have not been observed in meditators. Effect of long-term meditation on brain activity is an increase in amplitude of P300 peak of ERP in frontal and left temporal cortical areas, which correlates with the reduction of the TPT. Revealed in this study is that ERP-effects of meditation are not specific to the emotional category of a sentence, and are not directly connected with processes of assessment of one-self or others.

### REFERENCES

# DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article.

# ETHICS STATEMENT

The study was approved by the Institute of Physiology and Basic Medicine ethics committee. All applicable subject protection guidelines and regulations were followed in the conduct of the research in accordance with the Declaration of Helsinki. The study aim was explained to all participants and they signed the informed consent.

## AUTHOR CONTRIBUTIONS

ASav designed the study; organized scientific expedition to Baikal, participated in EEG data collection, undertook the statistical analysis, and wrote the first draft of the manuscript. ST and KS participated in EEG and behavioral data collection and analysis, and gave feedback to participants. YM is responsible for communication with the participants and leaders of the lamaistic Buddhism community of Russia, organization of scientific expedition, and control of ethical aspects of the study. ASap, SL, GKo and GKn participated in EEG and behavioral data analysis. All authors have read and approved the final manuscript.

### FUNDING

The part of this work related to the data collection and processing was supported by the Russian Foundation of Basic Research [RFBR; Российский Фонд Фундаментальных Исследований (РФФИ) ] under Grant No. 18-29-13027. Elaboration of experimental model was supported by the Russian Science Foundation (RSF) under Grant No 17-18-01019. The publication of this article was supported by the project ''Investigation, analysis and complex independent expertise of projects of the National technological initiatives, including the accompanying of projects of ''road map'' ''NeuroNet,'' which is executed within the framework of the state assignment No. 28.12487.2018/12.1 of the Ministry of Science and Higher Education of the Russian Federation.

among Yakuts and Russians during the recognition of emotionally colored verbal stimuli,'' in Proceedings of the 11th International Conference Bioinformatics of Genome Regulation and Structure \Systems Biology, BGRS\SB 2018 124–126 (Novosibirsk, Russia: IEEE). doi: 10.1109/CSGB.2018.85 44755

**Conflict of Interest**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Savostyanov, Tamozhnikov, Bocharov, Saprygin, Matushkin, Lashin, Kolpakova, Sudobin and Knyazev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Cognitive Fitness Framework: Towards Assessing, Training and Augmenting Individual-Difference Factors Underpinning High-Performance Cognition

#### Eugene Aidman1,2,3 \*

1Land Division, Defence Science & Technology Group, Edinburgh, SA, Australia, 2School of Psychology, The University of Sydney, Sydney, NSW, Australia, 3School of Biomedical Sciences & Pharmacy, University of Newcastle, Newcastle, NSW, Australia

#### Edited by:

Benjamin Cowley, University of Helsinki, Finland

#### Reviewed by:

Simonetta D'Amico, University of L'Aquila, Italy Annette Kluge, Ruhr University Bochum, Germany Leonard Zaichkowsky, Boston University, United States

\*Correspondence: Eugene Aidman<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognitive Neuroscience, a section of the journal Frontiers in Human Neuroscience

Received: 12 September 2019 Accepted: 19 December 2019 Published: 14 January 2020

#### Citation:

Aidman E (2020) Cognitive Fitness Framework: Towards Assessing, Training and Augmenting Individual-Difference Factors Underpinning High-Performance Cognition. Front. Hum. Neurosci. 13:466. doi: 10.3389/fnhum.2019.00466 The aim of this article is to introduce the concept of Cognitive Fitness (CF), identify its key ingredients underpinning both real-time task performance and career longevity in high-risk occupations, and to canvas a holistic framework for their assessment, training, and augmentation. CF as a capacity to deploy neurocognitive resources, knowledge and skills to meet the demands of operational task performance, is likely to be multifaceted and differentially malleable. A taxonomy of CF constructs derived from Cognitive Readiness (CR) and Mental fitness (MF) literature maps into phases of operational cycle from foundational to advanced, mission-ready and recovery. Foundational cognitive attributes, such as attention, executive control and co-action, were hypothesized to be trainable at the initial Cognitive Gym phase. More advanced training targets at the CR phase included stress and arousal regulation, adaptability, teamwork, situation awareness (including detection, sense-making and prediction) and decision making (de-biasing and confidence calibration). The mission-ready training phase is focused on tolerances (to sleep loss, monotony, pain, frustration, uncertainty) and resistance (to distraction, deception or manipulation). Operational Augmentation phase relies on support tools such as decision aids and fatigue countermeasures, while the Recovery phase employs reflexive (e.g., mindfulness), and restorative practices (e.g., nutrition and sleep hygiene). The periodization of cognitive training in this cycle is hypothesized to optimize both real-time cognitive performance and the resilience that enables life-long thriving. One of the most promising avenues of validating this hypothesis is by developing an expert consensus on the key CF ingredients and their relative importance in high-performance settings.

Keywords: cognitive fitness, task performance, operational readiness cycle, RDoC domains cognitive functioning, measurement, trainability

# INTRODUCTION

Performance psychology is a rapidly expanding field that is of growing significance to a wide range of occupations—from competitive sport and performing arts to first responder and military professions. These user groups share a common focus on striving for superior performance in challenging tasks under stressful conditions, and on effective recovery to enable repeat performance across the lifespan. The physical and psychological factors contributing to task performance are tightly interconnected and go beyond mere ''wellness'' (i.e., the absence of pathology). They include, apart from knowledge and skills, a range of ''capacity'' factors, such as strength, endurance, and flexibility, that is best summarized by the concept of ''fitness.'' This article introduces the concept of Cognitive Fitness (CF) as a capacity to deploy neurocognitive resources underpinning the execution of goal-directed action and proposes a hypothetical set of its ingredients. Similar to physical fitness (PF), CF enables the application of knowledge, skills and attitudes (KSA) in generating task performance. The ingredients of PF are well established, with robust measurement protocols for muscular strength, aerobic/anaerobic endurance and range of motion/joint flexibility (Jeffreys and Moody, 2016), as well as validated training interventions such as strength and conditioning, cardiovascular fitness, or high-intensity interval training.

The meaning of psychological fitness is less clear, with this widely used term referring to diverse characteristics ranging from ''character strengths and assets'' (Cornum et al., 2011; Vie et al., 2016) to ''resources that provide protection against the development of mental disorders'' (Wesemann et al., 2018). The concept of mental fitness (MF) has emerged in the mental health and positive psychology literature (McCarthy, 1964; Seligman, 2008) to promote a positive and proactive notion of mental health. The MF literature is focused on identifying protective factors, such as cognitive flexibility, implicated both in the prevention of mental illness and in the promotion of flourishing (Keyes, 2007; Robinson et al., 2015). MF is also critical in the world of work, especially in high-stakes occupations where cognitive lapses can undermine the performance of complex socio-technical systems, while individual's and teams' superior capacity to sense, think, decide and act is widely seen as conferring critical performance advantages (Baker and Phillips, 2000; Bowers and Cannon-Bowers, 2014; Fletcher and Wind, 2014; Herzog and Deuster, 2014; Ahn and Cox, 2016; Bogga, 2017).

The growing literature on cognitive readiness (CR; Foster, 1996; Morrison and Fletcher, 2002; Grier, 2011; O'Neil et al., 2014) has developed extensive conceptual models of factors contributing to sustained professional performance in complex, dynamic, and unpredictable environments (Bolstad et al., 2006; Grier, 2012). In particular, CR has been construed as a comprehensive set of predictors—both distal and proximal—of cognitive performance by the military personnel in complex missions facing agile, near-peer opposition (Kluge and Burkolter, 2013; Sotos, 2019). The key components of CR include: (1) trainable skills, knowledge and attitudes (KSAs); (2) dynamic functional states; and (3) stable, trait-like characteristics ranging from cognitive ability to working memory and learning styles (Grier, 2012; Mason and McQuade, 2013). CF (Aidman, 2017) corresponds to the cognitive element of this latter CR component. Despite the lack of causal cognitive mechanisms explained by the CR construct (Crameri et al., 2019) it has been instrumental in stimulating the development of measurement frameworks for assessing individuals' and teams' fluctuating capacity for operational task performance (Fatkin and Patton, 2008; Grier et al., 2012) and evaluating training interventions to improve it (Kluge and Burkolter, 2013; Peña and Brody, 2016). The explanatory power of the CR construct can be enhanced by establishing its connections to causal factors, such as arousal regulation, discomfort tolerance and inhibitory control, underpinning an individual's performance in cognitively demanding tasks. These fundamental, biologically traceable dimensions of cognitive functioning have been well established in clinical neuroscience (Cuthbert and Insel, 2013; Appelbaum, 2017). Their incremental predictive validity for the assessment of psychopathology (e.g., Yücel et al., 2019) and mental health in nonclinical populations (Carcone and Ruocco, 2017) indicates that these broad domains of cognitive functioning can potentially underpin CR as well. The concept of CF (Aidman, 2017) was introduced to examine these causal connections between CR and cognitive factors underpinning mental health, to bridge the gap between the CR and MF literature, and to develop a more tractable and systemic approach to the assessment and training of high-performance cognition. CF is focused on the ''capacity'' component of CR—as distinct from its ''knowledge, skill and expertise'' (KSE) components. The aim of the current article is to develop a working definition of CF, by drawing on the ''why,'' the ''what'' and the ''how-to'' questions from the cognitive training and readiness literatures, and connecting them to the broad, biologically traceable domains of cognitive functioning developed in clinical neuroscience. A hypothesized set of constituent elements of CF is then articulated in a Cognitive Fitness Framework (CF2), followed by a discussion of its potential applications in the areas of assessment, training periodization and operational augmentation. Finally, a research agenda is suggested to improve CF2 and the measurement tools supporting it.

### COGNITIVE TRAINING: FROM "HOW TO" TO "WHAT" AND "WHY"

MF has been defined as a set of malleable attributes that can develop through regular practice—analogous to physical training (Robinson et al., 2015). How trainable are the elements of CF is an empirical question. The growing literature on cognitive training has accumulated promising evidence of its effectiveness in areas such as visuospatial training in developmental populations (Boccia et al., 2017) and memory training for older adults (Gross et al., 2012; although see Redick, 2019; for a broader analysis of the latter).