Differential Analysis of Heart Rate Variability in Repeated Continuous Performance Tests Among Healthy Young Men

Chung-Chih Hsu; Tien-Yu Chen; Jia-Yi Li; Terry B. J. Kuo; Cheryl C. H. Yang

doi:10.30773/pi.2024.0251

Psychiatry Investig > Volume 22(2); 2025 > Article

Hsu, Chen, Li, Kuo, and Yang: Differential Analysis of Heart Rate Variability in Repeated Continuous Performance Tests Among Healthy Young Men

Original Article

Psychiatry Investigation 2025;22(2):148-155.

Published online: February 17, 2025

DOI: https://doi.org/10.30773/pi.2024.0251

Differential Analysis of Heart Rate Variability in Repeated Continuous Performance Tests Among Healthy Young Men

Chung-Chih Hsu^1,^2,^3,^*

, Tien-Yu Chen^1,^2,^4,^*

, Jia-Yi Li^2,⁵

, Terry B. J. Kuo^1,^2,^6,⁷

, Cheryl C. H. Yang^1,^2,^6,⁸

¹Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan

²Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

³Division of Psychiatry, Armed Force Hualien Hospital, Hualien, Taiwan

⁴Department of Psychiatry, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei, Taiwan

⁵Department of Health and Leisure Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan

⁶Department of Education and Research, Taipei City Hospital, Taipei, Taiwan

⁷Tsaotun Psychiatric Center, Ministry of Health and Welfare, Nantou, Taiwan

⁸Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

Correspondence: Cheryl C. H. Yang, PhD Brain Research Center, Sleep Research Center, and Institute of Brain Science, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei 11221, Taiwan Tel: +886-2-2826-7058, Fax: +886-2-2827-3123, E-mail: cchyang@nycu.edu.tw

Correspondence: Terry B. J. Kuo, MD, PhD Tsaotun Psychiatric Center, Ministry of Health and Welfare, No. 161, Yu-Pin Road, Tsaotun Township, Nantou 54249, Taiwan Tel: +886-49-2550800, Fax: +886-49-2561240, E-mail: tbjkuo@nycu.edu.tw

^* These authors contributed equally to this work.

Received August 9, 2024 Revised October 27, 2024 Accepted November 20, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC).

Methods

We recruited 31 men aged 20-35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0.

Results

Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II.

Conclusion

A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Keywords: Executive function, Heart rate variability, Parasympathetic nervous system

INTRODUCTION

Executive function, a fundamental domain of attention, encompasses top-down control processes that regulate behavior and facilitate adaptation to situational changes [1]. This domain includes critical components such as inhibitory control (IC), decision-making, working memory, and set shifting. Among them, the IC serves as the core component [2]. There are various tests employed to assess IC, with the Continuous Performance Test (CPT) being a notable tool and widely used clinically [3-5]. The CPT requires participants to respond to sequences of signals presented at varying intervals, a process that primarily evaluates IC and related impairments [6-9].

IC is linked to the prefrontal cortex and the autonomic nervous system (ANS), encompassing both the sympathetic (SNS) and parasympathetic nervous systems (PNS). This relationship is elaborated in the neurovisceral integration model, which articulates the interplay between the prefrontal cortex and ANS in top-down regulation [10-12]. Furthermore, both the polyvagal theory and the neurovisceral integration model emphasize collaboration between the prefrontal cortex and PNS across various hierarchical structures [12-14].

Heart rate variability (HRV) serves as a non-invasive indicator of the ANS, and its parameters have been identified as biomarkers of top-down self-regulation. The high-frequency (HF) component of HRV related to the IC has been explored in numerous studies [11,15]. It has been reported that the repeated stimuli over a one-week interval produce physiological variations in heart rate [16]. Moreover, an earlier study involving a 90-minute task of correcting transcription errors repeated five times daily over 3 consecutive days revealed a decrease in the mean RR interval and an increase in the low-frequency (LF) component of HRV, which was inconsistent with the previous studies. However, it remains uncertain whether these outcomes stem from prolonged testing fatigue or direct effects of task execution on different days [17]. It is hard to accurately determine the variations in HRV during the CPT measurements taken on different days for the lack of momentary analysis.

In our study, we analyzed the HRV parameters during relatively short-term executive tasks spaced 1 week apart. To capture moment-to-moment HRV fluctuations, we employed a standard 5-minute HRV time-window approach, with each window overlapping the next by 4 minutes, thereby creating 1-minute intervals between measurements. This methodology enabled us to assess (1) the involvement and correlation of the PNS with IC throughout the executive process and (2) the impact of repeated task execution on HRV dynamics during the task.

METHODS

Participants

This study was approved by the Institutional Review Board of the National Yang Ming Chiao Tung University (IRB: YM 108115EF). The authors assert that all procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and the Declaration of Helsinki of 1975, as revised in 2008. All participants were university or graduate students. We recruited 31 healthy men with body mass index between 18.5-28 kg/m². They had never received the Conners’ Continuous Performance Test-II (CCPT-II) before. The study protocols were explained in detail to the participants, and their basic information was obtained after they provided written informed consent to participate. By querying participants, we excluded individuals who were receiving treatment for conditions such as cancer, renal or cardiovascular diseases, diabetes, depression, anxiety, psychotic disorders, or any condition related to substance withdrawal or use disorders. Participants were instructed to take a 10-minute break in a quiet room where the temperature was maintained at 25°C by an air conditioner. Then we attached the electrocardiogram (ECG) patch to obtain continuous ECG data on the day consent was obtained (Day 1). After a 5-minute ECG recording, the participants performed a complete 14-minute test. The participants were asked to sit still for 1 minute after the test. The ECG was recorded throughout the entire process, which was repeated on day 7.

CCPT-II

The CCPT-II is a widely used assessment tool for measuring the IC of the EF and is known for its internal consistency and test-retest reliability [18]. This test, conducted using CCPT-II software, spans 14 minutes and features alphabetic characters displayed for 250 ms each. Participants are seated before a computer in a quiet room, responding to the rapidly changing stimuli by pressing or not pressing the space bar for “nonX” and “X” signals, respectively. The CCPT-II software evaluates omissions, commissions, and detectability (d’) at the conclusion of the test [19]. Among these parameters, d’ serves as a psychophysical indicator of attentive executive performance, derived mathematically from signal detection theory [20-22] and widely applied across various conditions [23-25].

HRV

HRV parameters were primarily analyzed using both time-and frequency-domain methods, derived from 5-minute ECG signal windows. The significance of these parameters has been comprehensively reviewed in recent literature [26,27]. Among the time-domain HRV parameters, we measured the mean RR interval and standard deviation of NN intervals (SDNN) for each time window. The SDNN is posited to be more indicative of PNS activity when assessed over a short-term resting condition [26]. In the frequency-domain analysis of HRV, we examined the HF power (0.15-0.40 Hz), which is associated with PNS modulation and respiratory influences, also referred to as the respiratory band. Additionally, we analyzed the LF power (0.04-0.15 Hz), which was considered to reflect a combination of SNS and PNS activities in the past. However, recent evidence suggests that this is primarily due to baroreflex mechanisms in resting conditions [28-31]. We applied a logarithmic transformation to the HF and LF power data to correct potential skewness in their distributions [32].

HRV measurement

Throughout the test, the ECG was continuously recorded, with the participants maintaining a steady sitting position without significant movement. They were instructed to breathe naturally, avoiding both hyperventilation and paced breathing during the study [33]. An HRV analyzer (WG-103A, wegene) was used to acquire and process the ECG signals, employing an 8-bit analog-to-digital converter at a sampling rate of 256 Hz, enabling real-time ECG signal analysis, the fast Fourier transformation was applied to convert the data from the stationary RR intervals into the power spectrum of the frequency domain subsequently, as previously outlined [32]. To track immediate HRV fluctuations during repeated CCPT-II sessions, we employed a 5-minute time window approach. Each window overlapped the subsequent one by 4 minutes, thereby creating a 1-minute interval between consecutive time windows (as shown in Figure 1) [34]. The HRV value from the initial time window was established as baseline (t0). Additionally, we selected the data from the 14th minute time-window (t14) as the reactive HRV during CCPT-II. All the HRV measurements were included in Supplementary Table 1.

Statistical analysis

Statistical analyses were performed using the SPSS Statistics 20.0 (IBM Corp.). The Shapiro-Wilk test did not show a significant deviation from normality in the distribution of the investigated values. We employed one-way analysis of variance (ANOVA) to assess whether there were any significant differences among the tests for the CCPT-II parameters. Additionally, repeated measures ANOVA with Bonferroni correction was utilized to evaluate if there were significant differences among the tests for the HRV parameters. We evaluated the fluctuation of the HRV parameters (including the RR, SDNN, and both the logarithmically transformed LF and HF) using the 5-minute time-window with 1-minute interval. The difference between the corresponding HRV parameters and baseline (t0 time window) was analyzed using GEE. The correlation between the CCPT-II and HRV parameters in the entire process was obtained using the mean HRV data to represent the general HRV value in the entire CCPT-II process. To explore the association between HRV and executive function, we calculated the correlation coefficients between the mean HRV and HRV at the t0 and t14 time-windows, and the difference between the t0 and t14 time-windows during each CCPT-II. The above correlation coefficients with the corresponding CCPT-II parameters were calculated by Pearson’s correlation and all corrected with the participants’ age. Significance was tested at a 95% confidence interval. p<0.05 indicated statistical significance.

RESULTS

The demographic data and HRV metrics are presented in Table 1. There were no significant differences in CCPT-II performance and the corresponding HRV parameters across the repeated tests. This lack of variance suggests stability or homeostasis in cardiac autonomic modulation throughout repeated CCPT-II sessions.

Figure 2 illustrates the HRV parameters during each CCPT-II, highlighting the temporal patterns of HRV metrics in each time window. The analysis revealed significant differences in HRV parameters compared with the baseline time-window (t0) as follows: SDNN showed a significant decrease at t4 and from t6 to t14 in the first CCPT-II, with similar changes in the second CCPT-II. Low-frequency power with logarithm (lnlf) significantly decreased from t5 to t13 in the first CCPT-II and from t5 to t14 in the second CCPT-II that hinted the baroreflex might involve in the CCPT-II process. Neither the RR interval nor high-frequency power with logarithm (lnHF) at each time-window exhibited significant deviations from their respective baseline values at t0 time window.

In Table 2, we presented the mean HRV parameters for each CCPT-II session and their correlations, as determined using Pearson’s correlation coefficient corrected by the participants’ age. There was no significant correlation between the HRV and the CCPT-II parameters at the baseline (t0) in both tests. Notably, the mean SDNN, LF, and HF demonstrated a significant positive correlation with detectability (d’) in the first CCPT-II session (p=0.012 for SDNN, p=0.045 for lnLF, and p=0.032 for lnHF).

To elucidate the relationship between momentary HRV and d’ during each CCPT-II session, we computed the correlation coefficients between CCPT-II parameters and HRV at t14 and the difference baseline (t0), as detailed in Table 2. Notable correlations were predominantly observed in the first CCPT-II at different time windows: d’ showed a positive correlation at t14 time window with SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031); d’ showed a positive correlation by the difference between t14 and the baseline with lnLF (R=0.444, p=0.044). In the second CCPT-II, no HRV parameters showed significant correlation with CCPT-II parameters at different time-window.

DISCUSSION

This study is the first to meticulously track momentary HRV fluctuations during repeated CCPT-II sessions using a continuous 5-minute time-window approach with a 1-minute interval. We observed a significant correlation between lnHF and detectability (d’) in the first CCPT-II session. Furthermore, momentary changes in lnLF showed a significant decrease compared to baseline in both CCPT-II sessions, whereas lnHF did not exhibit significant variations. These findings are discussed in the context of their implications for understanding HRV dynamics in CCPT-II.

This study has several noteworthy findings. First, lnHF reflects cardiac vagal control mediated by the central autonomic network. The dynamic modulation of IC in response to external stimuli can be inferred from the observed increase or decrease in vagal control. This relationship illustrates how IC adaptively responds to environmental challenges, as indicated by changes in lnHF [10,35]. This adaptive modulation was further elucidated using a recently proposed “vagal tank” model, which suggested that IC in response to external stimuli induces changes in cardiac vagal control. According to this model, reduced vagal withdrawal during cognitive tasks indicates more effective self-regulation. This concept highlights the nuanced interplay between IC and autonomic responses, particularly in the context of cognitive demand [36-39]. In our study, lnHF did not exhibit significant changes compared to baseline during both CCPT-II sessions (Figure 2), suggesting few phasic fluctuations in vagal control during CCPT-II. We regard this stability in lnHFs as indicative of the PNS-efficient regulatory IC rather than potential fatigue during CCPT-II [40,41].

Second, prior research conducted by Siennicka et al. [42] demonstrated an association between higher resting HRV and attentional maintenance, although not explicitly with attentive performance. Importantly, their study collected HRV data exclusively in a resting state without considering HRV dynamics during task engagement. Indeed, the assessment of attentive executive performance is more accurate based on HRV measurements during task execution than at rest. In our study, we utilized d’ as an indicator of IC during the CCPT-II. A distinctive feature of this study was continuous HRV monitoring during the test. This approach allowed for a deeper understanding of the role of HRV in the assessment of IC, a core component of EF. Our results indicate significant correlations between both mean SDNN and mean lnHF with d’, aligning with the neurovisceral integration model and supporting previous assertions regarding the involvement of the PNS in regulating EF [10,43-45]. In this study, we furthered these assertions to IC in the first CCPT-II.

Third, our study found no significant correlation between lnHF and d’ in the second CCPT-II session, a finding that, at first glance, appears inconsistent with Thayer’s integrated neurovisceral model. This model typically posits a close relationship between vagal activity (as indicated by lnHF) and IC. The lack of correlation in the second session prompts further examination and suggests potential nuances in the model’s applicability across different contexts or repeated testing scenarios of IC [10,46]. Thayer’s model emphasizes that the prefrontal cortex is a key neural component in the regulation of goaldirected IC. However, prefrontal cortex is not critical for the performance under the repetition of likely tasks [47]. Indeed, IC is not only directed by the prefrontal cortex, but also the presupplementary motor area of cortex and the inhibitory level of cortex that attributed to the activation of cortical inhibitory neurons [48,49]. This suggests that involvement of the prefrontal cortex alone may not fully explain the observed correlations (or lack thereof) between repeated IC test and PNS activity. Our study put further evidence for the need to consider a broader neural network beyond the prefrontal cortex to thoroughly understand IC [50-53].

In summary, our study posits the following viewpoints: 1) there is efficient regulation of PNS in CCPT-II process; 2) the significantly PNS correlates with IC is only in the first CCPT-II; and 3) the relationship between IC and the PNS derived from repeated tests must considers the influence of broader neural networks other than the prefrontal cortex alone.

Limitations

This study had several limitations. This study was limited by its small sample size and narrow demographic range and focused on a specific age group and sex. This choice is necessitated by the sensitivity of HRV to age and hormonal fluctuations. Despite these constraints, the use of continuous HRV analysis, which is a more data-intensive and complex method than traditional approaches, underscores the value of our research. HRV was chosen for its clinical practicality and objective nature over other physiological measures such as blood pressure, respiration, or pupillometry. Additionally, the absence of blood pressure measurements necessitates a more cautious interpretation of baroreflex data. It is crucial to recognize that the CCPT-II is designed to assess IC over a brief period. Consequently, further studies are essential to explore the longitudinal relationships between IC and HRV, as well as the interactions between baroreflex and other facets.

Conclusion

Our study revealed that PNS modulation, as indicated by lnHF, efficiently modulated IC. PNS significantly correlated with IC only during the initial CCPT-II session. The lack of a significant correlation between HRV parameters and IC in subsequent sessions suggests that repeated tasks may involve broader neural networks beyond the prefrontal cortex. This study contributed to a deeper understanding of the complex relationship between the PNS and IC, highlighting the need to further explore this intricate interplay.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2024.0251.

Supplementary Table 1.

Comparison of demographic data and variables of the study in different intervention times

pi-2024-0251-Supplementary-Table-1.pdf

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

Cheryl C.H. Yang receives a grant (112W32101) from the Brain Research Center, National Yang Ming Chiao Tung University and a grant (MOST 110-2629-B-A49A-501) from the National Science and Technology Council of Taiwan. All remaining authors have declared no conflicts of interest.

Author Contributions

Conceptualization: Chung-Chih Hsu, Tien-Yu Chen. Data curation: Chung-Chih Hsu, Terry B. J. Kuo. Methodology: Cheryl C. H. Yang. Project administration: Cheryl C. H. Yang. Resources: Terry B. J. Kuo. Software: Chung-Chih Hsu. Visualization: Jia-Yi Li. Writing—original draft: Chung-Chih Hsu. Writing—review & editing: Tien-Yu Chen.

Funding Statement

This study was supported by the Tri-Service General Hospital Research Foundation (TSGH-D-113147 and TSGH-D-114144).

Acknowledgments

None

Figure 1.

HRV was analyzed through a continuous approach, employing 5-minute ECG time-windows. Each time-window overlapped the subsequent one by 4 minutes, resulting in a 1-minute interval between time-windows. The dot plot displayed at the bottom illustrates the dispersion of RR intervals at the relevant time points. ECG, electrocardiogram; HRV, heart rate variability; CCPT-II, Conners’ Continuous Performance Test-II.

Figure 2.

Analysis parameters of heart rate variability from the t0 in each CCPT-II by GEE. The data is represented by the value derived from each minute time-window. *Denotes the significant difference at each time-window in comparison with t0 time-window in the first CCPT-II; ^†Denotes the significant difference at each time-window in comparison with t0 time-window in the second CCPT-II. p<0.05 indicates significance. RR, mean R-R interval of electrocardiogram; SDNN, standard deviation of NN intervals; lnLF, low-frequency power with logarithm; lnHF, high-frequency power with logarithm; CCPT-II, Conners’ Continuous Performance Test-II.

Table 1.

Comparison of demographic data and variables of the study in different intervention times (N=31, male only)

Variables	1st CCPT-II	2nd CCPT-II	p
CCPT-II parameters
Omission	2.10±3.83	2.84±11.6	0.737
Commission	16.50±8.25	14.30±7.61	0.274
Detectability	0.621±0.415	0.738±0.396	0.259
HRV (time-window)
t0 (baseline)
RR (ms)	770±112	758±105	0.689
SDNN (ms)	68.5±24.7	70.0±22.3	0.822
lnLF [ln(ms)²]	6.81±0.71	6.98±0.87	0.438
lnHF [ln(ms)²]	6.05±1.05	6.18±1.22	0.678
t14
RR (ms)	769±124	759±107	0.669
SDNN (ms)	53.1±23.7	54.8±22.8	0.940
lnLF [ln(ms)²]	6.25±1.03	6.41±0.98	0.461
lnHF [ln(ms)²]	6.05±1.06	6.08±1.08	0.882
t14-t0
RR (ms)	14.5±52.1	7.8±61.4	0.693
SDNN (ms)	1.09±2.99	0.10±4.83	0.382
lnLF [ln(ms)²]	-0.43±0.76	-0.54±0.67	0.614
lnHF [ln(ms)²]	0.15±0.52	-0.06±0.72	0.270
t(mean)
RR (ms)	750±184	769±106	0.909
SDNN (ms)	58.5±22.5	56.7±22.8	0.951
lnLF [ln(ms)²]	6.14±1.36	6.45±1.04	0.833
lnHF [ln(ms)²]	5.92±1.51	6.14±1.26	0.858

The participants’ age was 23.3±2.05 years. Values are presented as mean±SD. The CCPT-II parameters between 1st and the 2nd CCPT-II were analyzed by one-way ANOVA. The HRV values between the tests were compared using repeated measure ANOVA with Bonferroni correction. p<0.05 indicates significance. CCPT-II, Conners’ Continuous Performance Test-II; HRV, heart rate variability; RR, mean R-R interval in electrocardiogram; SDNN, standard deviation of NN intervals; lnLF, low-frequency power with logarithm; lnHF, high-frequency power with logarithm; SD, standard deviation; ANOVA, analysis of variance

Table 2.

Correlation between the HRV at different time-window and the CCPT-II parameters of each CCPT-II corrected with age

Time-window	HRV parameters	Omission	Commission	Detectability
t0	RR
	1st	0.185	0.177	0.129
	2nd	0.079	0.159	-0.086
	SDNN
	1st	0.005	-0.096	0.266
	2nd	0.154	0.125	-0.014
	lnLF
	1st	0.177	0.013	-0.129
	2nd	0.157	0.199	-0.014
	lnHF
	1st	0.074	-0.148	0.294
	2nd	-0.060	0.274	-0.197
t14	RR
	1st	0.271	0.039	0.327
	2nd	0.246	0.107	-0.055
	SDNN
	1st	0.073	-0.388	0.578^*
	2nd	0.050	-0.104	0.044
	lnLF
	1st	0.254	-0.284	0.493^*
	2nd	0.047	-0.138	0.096
	lnHF
	1st	0.068	-0.298	0.432^*
	2nd	-0.016	-0.042	-0.039
t14-t0	RR
	1st	-0.036	-0.226	0.363
	2nd	0.295	-0.019	0.071
	SDNN
	1st	0.072	-0.304	0.413
	2nd	-0.390	0.159	-0.296
	lnLF
	1st	0.124	-0.238	0.444^*
	2nd	-0.162	-0.224	0.103
	lnHF
	1st	-0.158	0.101	-0.024
	2nd	-0.108	-0.231	0.134
t (mean)	RR
	1st	-0.049	-0.278	0.285
	2nd	0.223	-0.095	-0.102
	SDNN
	1st	0.075	-0.277	0.474^*
	2nd	0.044	-0.046	-0.007
	lnLF
	1st	0.219	-0.137	0.390^*
	2nd	-0.059	-0.012	0.017
	lnHF
	1st	0.016	-0.271	0.400^*
	2nd	-0.053	-0.129	-0.154

^* p<0.05.

RR, mean R-R interval in electrocardiogram; SDNN, standard deviation of NN intervals; lnLF, low-frequency power with logarithm; lnHF, high-frequency power with logarithm; CCPT-II, Conners’ Continuous Performance Test-II; HRV, heart rate variability

REFERENCES

1. Diamond A. Executive functions. Annu Rev Psychol 2013;64:135-68.

2. Kang W, Hernández SP, Rahman MS, Voigt K, Malvaso A. Inhibitory control development: a network neuroscience perspective. Front Psychol 2022;13:651547

3. Aerdker S, Feng J, Schöner G. Habituation and dishabituation in motor behavior: experiment and neural dynamic model. Front Psychol 2022;13:717669

4. Scionti N, Cavallero M, Zogmaister C, Marzocchi GM. Is cognitive training effective for improving executive functions in preschoolers? A systematic review and meta-analysis. Front Psychol 2020;10:2812

5. Gunnerud HL, Ten Braak D, Reikerås EKL, Donolato E, Melby-Lervåg M. Is bilingualism related to a cognitive advantage in children? A systematic review and meta-analysis. Psychol Bull 2020;146:1059-1083.

6. Areces D, Rodríguez C, García T, Cueli M, González-Castro P. The influence of state and trait anxiety on the achievement of a virtual reality continuous performance test in children and adolescents with ADHD symptoms. J Clin Med 2021;10:2534

7. Mesquita C, Nazar BP, Pinna CM, Rabelo B, Serra-Pinheiro MA, Sergeant J, et al. How can continuous performance test help to assess inattention when mood and ADHD symptoms coexist? Psychiatry Res 2016;243:326-330.

8. Borgatti R, Piccinelli P, Montirosso R, Donati G, Rampani A, Molteni L, et al. Study of attentional processes in children with idiopathic epilepsy by Conners’ continuous performance test. J Child Neurol 2004;19:509-515.

9. Kozak K, Lucatch AM, Lowe DJE, Balodis IM, MacKillop J, George TP. The neurobiology of impulsivity and substance use disorders: implications for treatment. Ann N Y Acad Sci 2019;1451:71-91.

10. Thayer JF, Hansen AL, Saus-Rose E, Johnsen BH. Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Ann Behav Med 2009;37:141-153.

11. Holzman JB, Bridgett DJ. Heart rate variability indices as bio-markers of top-down self-regulatory mechanisms: a meta-analytic review. Neurosci Biobehav Rev 2017;74:233-255.

12. Smith R, Thayer JF, Khalsa SS, Lane RD. The hierarchical basis of neurovisceral integration. Neurosci Biobehav Rev 2017;75:274-296.

13. Porges SW. The polyvagal perspective. Biol Psychol 2007;74:116-143.

14. Sørensen L, Wass S, Osnes B, Schanche E, Adolfsdottir S, Svendsen JL, et al. A psychophysiological investigation of the interplay between orienting and executive control during stimulus conflict: a heart rate variability study. Physiol Behav 2019;211:112657

15. Hilgarter K, Schmid-Zalaudek K, Csanády-Leitner R, Mörtl M, Rössler A, Lackner HK. Phasic heart rate variability and the association with cognitive performance: a cross-sectional study in a healthy population setting. PLoS One 2021;16:e0246968

16. Bradley MM. Natural selective attention: orienting and emotion. Psychophysiology 2009;46:1-11.

17. Takahashi M, Arito H. Effects of single and repeated cognitive tasks on autonomic balance as observed by an analysis of R-R intervals. Eur J Appl Physiol Occup Physiol 1996;72:316-322.

18. Shaked D, Faulkner LMD, Tolle K, Wendell CR, Waldstein SR, Spencer RJ. Reliability and validity of the Conners’ continuous performance test. Appl Neuropsychol Adult 2020;27:478-487.

19. Homack S, Riccio CA. Conners’ continuous performance test (2nd ed.; CCPT-II). J Atten Disord 2006;9:556-558.

20. Wixted JT. The forgotten history of signal detection theory. J Exp Psychol Learn Mem Cogn 2020;46:201-233.

21. Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 2016;19:165-171.

22. Liu NH, Chiang CY, Chu HC. Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors (Basel) 2013;13:10273-10286.

23. Ballard JC. Assessing attention: comparison of response-inhibition and traditional continuous performance tests. J Clin Exp Neuropsychol 2001;23:331-350.

24. Epstein JN, Erkanli A, Conners CK, Klaric J, Costello JE, Angold A. Relations between continuous performance test performance measures and ADHD behaviors. J Abnorm Child Psychol 2003;31:543-554.

25. Fasmer OB, Mjeldheim K, Førland W, Hansen AL, Syrstad VE, Oedegaard KJ, et al. Linear and non-linear analyses of Conner’s continuous performance test-II discriminate adult patients with attention deficit hyperactivity disorder from patients with mood and anxiety disorders. BMC Psychiatry 2016;16:284

26. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health 2017;5:258

27. Pham T, Lau ZJ, Chen SHA, Makowski D. Heart rate variability in psychology: a review of HRV indices and an analysis tutorial. Sensors (Basel) 2021;21:3998

28. Rahman F, Pechnik S, Gross D, Sewell L, Goldstein DS. Low frequency power of heart rate variability reflects baroreflex function, not cardiac sympathetic innervation. Clin Auton Res 2011;21:133-141.

29. Goldstein DS, Bentho O, Park MY, Sharabi Y. Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes. Exp Physiol 2011;96:1255-1261.

30. Heathers JA. Everything Hertz: methodological issues in short-term frequency-domain HRV. Front Physiol 2014;5:177

31. Laborde S, Mosley E, Thayer JF. Heart rate variability and cardiac vagal tone in psychophysiological research - recommendations for experiment planning, data analysis, and data reporting. Front Psychol 2017;8:213

32. Kuo TB, Lin T, Yang CC, Li CL, Chen CF, Chou P. Effect of aging on gender differences in neural control of heart rate. Am J Physiol 1999;277:H2233-H2239.

33. Song HS, Lehrer PM. The effects of specific respiratory rates on heart rate and heart rate variability. Appl Psychophysiol Biofeedback 2003;28:13-23.

34. Zhang H, Zhu M, Zheng Y, Li G. Toward capturing momentary changes of heart rate variability by a dynamic analysis method. PLoS One 2015;10:e0133148

35. Berthoud HR, Neuhuber WL. Functional and chemical anatomy of the afferent vagal system. Auton Neurosci 2000;85:1-17.

36. Laborde S, Raab M. The tale of hearts and reason: the influence of mood on decision making. J Sport Exerc Psychol 2013;35:339-357.

37. Laborde S, Raab M, Kinrade NP. Is the ability to keep your mind sharp under pressure reflected in your heart? Evidence for the neurophysiological bases of decision reinvestment. Biol Psychol 2014;100:34-42.

38. Thayer JF, Ahs F, Fredrikson M, Sollers JJ 3rd, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 2012;36:747-756.

39. Laborde S, Mosley E, Mertgen A. Vagal tank theory: the three Rs of cardiac vagal control functioning - resting, reactivity, and recovery. Front Neurosci 2018;12:458

40. Wekenborg MK, Schwerdtfeger A, Rothe N, Penz M, Walther A, Kirschbaum C, et al. Determining the direction of prediction of the association between parasympathetic dysregulation and exhaustion symptoms. Sci Rep 2022;12:10648

41. Chen Y, Liu M, Zhou J, Bao D, Li B, Zhou J. Acute effects of fatigue on cardiac autonomic nervous activity. J Sports Sci Med 2023;22:806-815.

42. Siennicka A, Quintana DS, Fedurek P, Wijata A, Paleczny B, Ponikowska B, et al. Resting heart rate variability, attention and attention maintenance in young adults. Int J Psychophysiol 2019;143:126-131.

43. Forte G, Favieri F, Casagrande M. Heart rate variability and cognitive function: a systematic review. Front Neurosci 2019;13:710

44. Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology 2022;47:72-89.

45. Magnon V, Vallet GT, Benson A, Mermillod M, Chausse P, Lacroix A, et al. Does heart rate variability predict better executive functioning? A systematic review and meta-analysis. Cortex 2022;155:218-236.

46. Thayer JF, Lane RD. A model of neurovisceral integration in emotion regulation and dysregulation. J Affect Disord 2000;61:201-216.

47. Durstewitz D, Vittoz NM, Floresco SB, Seamans JK. Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 2010;66:438-448.

48. Aron AR. The neural basis of inhibition in cognitive control. Neuroscientist 2007;13:214-228.

49. Wessel JR, Anderson MC. Neural mechanisms of domain-general inhibitory control. Trends Cogn Sci 2024;28:124-143.

50. Blok LER, Boon M, van Reijmersdal B, Höffler KD, Fenckova M, Schenck A. Genetics, molecular control and clinical relevance of habituation learning. Neurosci Biobehav Rev 2022;143:104883

51. Suess PE, Porges SW, Plude DJ. Cardiac vagal tone and sustained attention in school-age children. Psychophysiology 1994;31:17-22.

52. Cabeza R, Nyberg L. Neural bases of learning and memory: functional neuroimaging evidence. Curr Opin Neurol 2000;13:415-421.

53. Balleine BW, O’Doherty JP. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology 2010;35:48-69.