Time to inhibit: P300 amplitude differences in individuals with high and low temporal efficiency

Abstract

Temporal processing and inhibitory control are closely interconnected. This study investigated whether individuals of high and low temporal efficiency display different behavioral and neural patterns while performing an electrophysiological Go/No-Go task. Individuals with lower temporal processing had significantly poorer behavioral performance of the task—slower reaction times to Go stimuli, greater number of omissions, and lower stimulus detectability (lower d-prime value)—than the high temporal efficiency group. Additionally, participants with low temporal efficiency had significantly lower P300 response to correct inhibitions (No-Go stimuli) compared to those with high temporal efficiency. Furthermore, the greater amplitude of the difference wave (No-Go vs Go) in the high temporal efficiency group may suggest superior efficacy of response inhibition and attention control processes in comparison to the low temporal efficiency group. These findings highlight significant differences in inhibitory control at both behavioral and neural levels in young adults differing in temporal processing efficiency.

Introduction

Temporal processing and executive functions are closely intertwined (Brown et al. 2013; Pouthas and Perbal 2004). This relation between temporal processing and executive functions can be explained by resource theory (Brown 2008; Brown et al. 2015; Brown and Perreault 2017). The theory postulates that temporal processing is analogous to any other executive function. It further suggests that temporal and executive processes share the same set of attentional resources (Brown and Merchant 2007). In our paper, we focus on the relationship between temporal processing and one of the core executive functions—inhibitory control.

Inhibitory control permits the individual to resist distractions, block out irrelevant information, and suppress automatic responses (Diamond 2013). These abilities are essential for effective decision-making and performance in a range of contexts (Hasher et al. 2007; Brown and Perreault 2017). One of the most frequently employed methodologies for evaluating inhibitory control is the Go/No-Go task (Raud et al. 2020). In our study, we implemented the electrophysiological Go/No-Go paradigm (Gomez et al. 2007), in which, in addition to monitoring behavioral performance, we were able to investigate neural patterns while performing this task. In this context, the P300 component is often examined—especially the P3b subcomponent, given the necessity for a voluntary reaction to rare stimuli (Polich 2003). The P300 is a positive event-related potential associated with changes in stimulus context and is observed ~300 to 600 ms after stimulus onset. The P300 amplitude reflects the response of the nervous system during the processing of incoming information (Sur and Sinha 2009). Greater P300 amplitude is associated with better cognitive information processing (Polich 2007). In contrast, a reduced amplitude is often observed when one is distracted or mentally fatigued (Zhao et al. 2012), suggesting a reduced capacity and efficiency of cognitive information processing. In this study, we investigated the potential difference in neuronal response in an inhibitory control Go/No-Go task related to temporal processing.

Temporal processing refers to our ability to perceive and process information as well as the patterning of our behavior in time (Mauk and Buonomano 2004). This ability is reflected in our temporal efficiency, which affects the performance of various cognitive functions. Temporal processing in the millisecond time range enables the perception of succession, i.e., the ordering of stimuli presented in rapid sequence. This requires identifying particular incoming stimuli and then perceiving their order. This capacity is essential for the effective execution of a Go/No-Go task (Marika et al. 2015). Several paradigms have been developed for evaluating individual temporal processing efficiency (Fostick and Babkoff 2013, 2022); however, one of the most frequently employed is the temporal-order judgment (TOJ) paradigm, which we use in this study. The participants’ temporal efficiency was indexed by their temporal-order threshold value, which represents the minimum interval required to identify the before–after temporal relation of the presented stimuli (Bao et al. 2013, 2014). A shorter temporal-order threshold value is indicative of higher temporal efficiency (HTE), whereas a longer temporal-order threshold value is indicative of lower temporal efficiency (LTE) (Szelag et al. 2018; Fostick et al. 2019). In our study, we implemented spatial and spectral TOJ paradigms, in which paired sounds were presented in rapid succession with varying interstimulus intervals. In the spatial task, two 1-ms clicks were presented to one ear at a time. In the spectral task, two 10-ms tones (400 and 3000 Hz) were presented to both ears. Participants were instructed to indicate the correct order of presented sounds in the spatial task (left to right or right to left) and the spectral task (high to low or low to high). As each task engages different nontemporal perceptual strategies that affect performance, our previous studies have demonstrated that consistent performance across both the spatial and spectral TOJ tasks provides the most reliable indicator of temporal efficiency (Szymaszek et al. 2009; Fostick et al. 2019; Stanczyk et al. 2023).

Previous research shows that people differ in their level of temporal efficiency, which influences their cognitive functioning (Stauffer et al. 2012; Szelag et al. 2022). Lower temporal efficiency often corresponds with poorer cognitive functioning in terms of working memory (Choinski et al. 2020; Jablonska et al. 2020), executive function (Nowak et al. 2016), and language capacity (Oron et al. 2015; Choinski et al. 2023). The deterioration of temporal efficiency can also be seen in a variety of mental and cognitive conditions (Toplak et al. 2006; Vatakis and Allman 2015; Choinski et al. 2023). It is worth noting that people with these conditions frequently exhibit impaired inhibitory control, which serves to confirm their interrelation. Specifically, problems with inhibitory control have been linked to poor temporal processing and difficulties in time estimation in children and adults with various developmental conditions, including attention deficit hyperactivity disorder (Rubia et al. 2009), autism (Pavlína et al. 2018), learning disabilities (Grinblat and Rosenblum 2016), and cerebral palsy (Cabezas and Carriedo 2019). Furthermore, in older individuals, temporal information processing efficiency is linked to individual differences in inhibition, with deterioration in both functions occurring concurrently with age.

The majority of the abovementioned research on temporal processing and inhibitory control has focused on clinical groups, thus in our study we explored these relationships within the young healthy adult population. The objective of this study was to examine whether participants with high versus low temporal efficiency (indicated by consistent high or poor performance in spatial and spectral TOJ tasks) display different patterns of electrophysiological activity in an inhibitory control Go/No-Go task.

Materials and methods

Participants

Two groups of participants were tested: a High Temporal Efficiency group (HTE, n = 33) who demonstrated consistent high performance in the spatial and spectral TOJ task and a Low Temporal Efficiency group (LTE, n = 33) who exhibited consistently poorer performance in both spatial and spectral TOJ tasks. Participants were categorized based on the median values derived from the initial sample of 84 participants (spatial task: Me = 38 ms; spectral task: Me = 61 ms, please see description of both spatial and spectral TOJ paradigms in Introduction section). Specifically, participants with temporal-order threshold values below or equal to these medians in both tasks were assigned to the HTE group, while those with temporal-order threshold values above or equal to the medians in both tasks were assigned to the LTE group. Participants with mixed outcomes (i.e., temporal-order threshold values above/below the median in one task and below/above in the other) were excluded from further analysis, resulting in a final sample size of 66 participants. The taxonomy of classification was described in detail in our previous publication (Krystecka et al. 2024). Characteristics of HTE and LTE as well as temporal-order threshold values for particular groups are presented in Table 1. HTE and LTE groups significantly differed in spatial (U = 1; P < 0.001; r = 0.858) and spectral (U = 0; P < 0.001; r = 0.86) TOJ task performance.

Participants were recruited based on the following inclusion criteria: age between 20 and 27 yr, right-handed, native Polish speakers, no neurological or psychiatric disorders, no formal musical education (as musicians display better temporal efficiency, compared to non-musicians; Rammsayer and Altenmüller 2006; Rammsayer et al. 2012), normal hearing levels (verified by pure-tone screening audiometry; Audiometer MA33, MAICO), and normal nonverbal intelligence (measured with the Polish version of Raven’s Standard Progressive Matrices) (Jaworowska and Szustrowa 2000).

This study was in line with the Declaration of Helsinki and was approved by the Bioethics Committee of the Nicolaus Copernicus University, Collegium Medicum in Bydgoszcz (permission no. KB 289/2019). All participants provided written informed consent prior to the study.

Procedure

The HTE and LTE groups performed the electrophysiological Go/No-Go task. The assessment took place in a quiet laboratory room in the Nencki Institute of Experimental Biology, Polish Academy of Sciences. Participants sat in a comfortable chair with their chins placed on a chin rest to restrict head movement. Stimuli were displayed on a 22″ monitor screen, located ~80 cm from the participant.

Electrophysiological Go/No-Go task

The visual Go/No-Go task was designed using Presentation software, version 14.9. The experimental stimuli comprised two distinct black shapes: a triangle (Go stimulus) and an upside-down triangle (the No-Go stimulus), presented on a gray background at the center of the screen. The participants were instructed to press a key on a response box (Cedrus RB-834; Cedrus Corporation, San Pedro, USA) when the triangle (Go stimulus) appeared and inhibit their reaction when the upside-down triangle (No-Go stimulus) appeared. The task was divided into five blocks, each consisting of 60 stimuli (75% Go stimuli and 25% No-Go stimuli). The order of stimuli was pseudorandomized such that three No-Go trials could not appear in a row. The stimuli were displayed for 300 ms with two interstimulus intervals of 600 and 900 ms. Before the main task, participants did a practice session to learn the task and instructions. The practice session consisted of 20 stimuli with feedback given about correctness.

Data acquisition

EEG data were recorded from 64 scalp Ag/AgCl active electrodes (EasyCap; ActiCAP, Brain Products, Germany) placed according to the 10-20 international system from the BrainVision Recorder v.1.10 software (Brain Products, Germany). The recorded signal was filtered online in the band 0.1 to 250 Hz with a sampling frequency of 5,000 Hz. The reference electrode was on the nose. The impedance of each electrode was kept <15 kΩ.

E‌EG preprocessing

Offline analysis was performed using the open-source EEGLAB toolbox (Delorme and Makeig 2004). A preprocessing pipeline was developed following Delorme’s recommendations (Delorme 2023). Data were resampled to 500 Hz and a 0.5-Hz high-pass filter was applied; signals were referenced to the average of all channels, followed by electrode line noise detection and interpolation (threshold: 4 SD), clean_rawdata channel correlation removal (threshold: 0.8 correlation), clean_rawdata Artifact Subspace Reconstruction rejection (threshold: 20), and Independent Component Analysis followed by ICLabel with probability thresholds of 80% for muscle artifacts and 90% for eye artifacts, which were then removed. The data were then divided into sections based on the Go and No-Go stimuli. Single-trial EEG data sections were taken from −200 to 800 ms after the stimulus and corrected using a 200-ms baseline. The remaining epochs were averaged for each condition, considering only correct hits (Go stimulus) or correct rejections (No-Go stimulus). The number of included No-Go trials ranged from 38 to 74 (on 75 trials, x ± SD = 58 ± 9). Three subjects (LTE group) were excluded from further analysis due to noisy EEG signals.

Statistical analyses

IBM SPSS Statistics 28 was used to calculate all behavioral data. The normality of the behavioral data distribution was tested with Shapiro–Wilk tests. All variables except d-prime were not normally distributed, and therefore the nonparametric Mann–Whitney U test was used to compare between HTE versus LTE differences in reaction time, hits/omissions, correct rejections, and false alarms. For d-prime, which met the test assumptions (normal distribution, homogeneity of variance), an independent-sample t-test was performed. The d-prime measure was calculated based on signal detection theory (Stanislaw and Todorov 1999), which has been found to be sufficiently unbiased to be the “best” measure of psychophysical performance. It is essentially a standardized score and is calculated as the difference between the (Gaussian) standard scores for the false alarm rate and the hit rate. A value of d-prime = 5 indicates perfect performance; a value of d-prime = 0 indicates chance (“guessing”) performance.

Electrophysiological data were analyzed using MNE-Python (Gramfort et al. 2014). For the analysis and data visualization, custom scripts were used based on the Borsar and Pylabianca (Magnuski et al. 2023) GitHub repositories. Cluster-based permutation tests (Maris and Oostenveld 2007) with correction for multiple comparisons (1,000 permutations) or the cluster entry threshold P < 0.05 were used for all electrophysiological analyses. Nonparametric statistical tests were chosen for within-group and between-group comparisons primarily because they deal well with the problem of multiple comparisons (Groppe et al. 2011). This is extremely important in the case of multidimensional data, such as in this study, where the data matrix consisted of 63 individuals assigned to groups (33 in HTE and 30 in LTE), encompassing 63 electrodes and 500 time points ranging from −0.2 ms before stimulus onset to 800 ms post-stimulus onset. The abovementioned matrix was created separately for Go and No-Go stimuli. First, our analyses focused on between-group analysis separately for Go and No-Go stimuli. Next, the level of discrimination between Go and No-Go stimuli was examined for HTE and LTE groups separately. A significant within-group difference in reactions to Go versus No-Go stimuli prompted us to conduct further analysis. In order to achieve this, the data matrix was reduced to the time range of interest, which reflected the greatest discrepancy between Go and No-Go stimuli (300 to 450 ms) and selecting the electrodes that best represented these differences for stimuli in both groups (F1, F2, Fz, FC2, FC1, C2, Cz, C1). By subtracting No-Go and Go stimuli, a difference wave for HTE and LTE was obtained, which reflected the efficacy of response inhibition and attention control processes (Chong et al. 2008).

Results

Between-group differences in behavioral performance on the Go/No-Go task

Significant differences between HTE and LTE groups were observed for hits/omissions (U = 289; P = 0.004; r = 0.36) and reaction time (U = 250; P = 0.001; r = 0.42; Fig. 1A) for Go stimuli, as well as for d-prime (t(61) = 2.187; P = 0.031; d = 0.55; Fig. 1B). The HTE group exhibited a lower number of omissions, faster reaction times to Go stimuli, and a higher d-prime value in comparison to the LTE group. The differences in other parameters, such as correct rejections and false alarms, were not statistically significant (Table 2).

Between-group differences in electrophysiological activity of the Go/No-Go task

Cluster-based permutation analyses for No-Go stimuli

The analysis revealed a difference between groups in the time range from 310 to 500 ms following stimulus onset in the form of a positive cluster with P = 0.031 (Fig. 2). The HTE group exhibited significantly higher P300 amplitude for No-Go stimuli compared to the LTE group (Fig. 3A).

Cluster-based permutation analyses for Go stimuli

The analysis did not reveal any significant differences between the HTE and LTE groups in their responses to Go stimuli, with the lowest cluster having P = 0.225 (Fig. 4A).

Within-group differences in electrophysiological activity of the Go/No-Go task

Cluster-based permutation analyses separately for HTE and LTE

Examination of the differences in cortical response to No-Go versus Go stimuli separately for HTE and LTE revealed significant within-group differences. In HTE, a significant difference (No-Go vs Go) was observed within two clusters (positive and negative) with P-values of 0.002 and 0.006, respectively, in the time range from 300 to 500 ms (Fig. 5A, C). In LTE, a significant difference (No-Go vs Go) was observed in a single significant positive cluster with a P-value of 0.046 within the time range of 340 to 500 ms (Fig. 5B, D).

Within-group differences in response to Go and No-Go stimuli revealed significantly larger and stronger clusters in HTE compared to LTE, indicating a greater difference in response to No-Go compared to Go stimuli in this group (Fig. 5E, F). Thus, it seems reasonable to verify further the differences in the strength of responses to Go and No-Go stimuli between HTE and LTE groups.

Between-group differences in electrophysiological activity of the Go/No-Go task

Cluster-based permutation analyses for the difference wave

We analyzed the difference between No-Go versus Go stimuli for HTE and LTE. This analysis was narrowed down to 8 electrodes (F1, F2, Fz, FC2, FC1, C2, Cz, C1) and 51 time points, ranging from 300 to 450 ms. We demonstrated that the difference in amplitude between Go and No-Go stimuli was significantly greater in HTE than in the LTE group (P = 0.018, Fig. 6C). This difference is evident across electrodes C1, Cz, FC1, FC2, Fz, F2, and F1 for the range from 320 to ~400 ms.

Summary of results

This study revealed significant differences between the HTE and LTE groups in both behavioral performance and electrophysiological activity during the Go/No-Go task. The HTE group exhibited better behavioral performance of the Go/No-Go task—that is to say, fewer omissions, faster reaction times to the Go stimuli, and better stimulus detectability as indicated by a higher d-prime value compared to the LTE group. Furthermore, cluster-based permutation analyses revealed a significant difference in P300 amplitude in response to No-Go stimuli, with the HTE group showing a significantly higher amplitude in the 310- to 500-ms time range compared to the LTE group. However, no significant differences were found between groups in response to Go stimuli. Furthermore, within-group comparison revealed that in both HTE and LTE groups, No-Go stimuli generated significantly greater amplitude compared to Go stimuli, especially in the frontocentral location. Additionally, the HTE group presented a significant decrease in amplitude at peripheral electrodes in the perception of No-Go stimuli. Due to the noticeable within-group differences in perception of Go versus No-Go stimuli, a difference wave was calculated for each group. The results revealed that the difference wave (No-Go vs Go) in the HTE group was significantly higher compared to the LTE group, particularly for the frontal electrodes.

Discussion

The present study examined the relationship between temporal efficiency and inhibitory control. Different patterns of brain activity were identified in the electrophysiological Go/No-Go task in the high and low temporal processing efficiency groups. Our findings indicated a significantly higher P300 response to the inhibitory (No-Go) stimulus in the high temporal efficiency group (HTE) compared to the low temporal efficiency group (LTE; Fig. 3). These differences included a frontocentral topography with a leftward shift in the time range of 310 to 500 ms post-stimulus (Fig. 2). The peak of the greatest P300 potential activity, and thus the largest differences between groups, occurred 330 to 350 ms after the stimulus onset. Previous studies have indicated that a higher P300 amplitude (as was observed in the HTE group; Fig. 3) may be indicative of better cognitive functioning, particularly in the context of attention and information processing (Abu Hasan et al. 2016; Kaiser et al. 2020; Didem et al. 2022). The frontocentral topography of the P300 component is typical for this type of inhibitory task. The observed stronger left-sided differences between the HTE and LTE may reflect either higher left-sided involvement in millisecond temporal perception (Nani et al. 2019) or greater left-hemispheric dominance in No-Go stimulus perception in HTE than in LTE (Barry et al. 2018). There were no significant differences between groups in response to the Go stimuli (Fig. 4).

Frontocentral regions are critical for both temporal processing and inhibitory control. Improved temporal processing is associated with increased activation in lateral prefrontal and striatal regions (Wiener et al. 2014). Similarly, improved inhibitory control is associated with altered neural activation patterns, particularly in the inferior frontal gyrus and dorsolateral prefrontal cortex (Berkman et al. 2014). The effective neural connectivity between these regions of the brain is ensured by dopamine. Low levels of dopamine in these regions are associated with poorer executive control, as well as difficulties with temporal processing (as observed in the LTE group) (Mitchell et al. 2018). A number of studies have confirmed the effect of dopamine agonists and dopamine-related genetic factors on behavioral differences in temporal processing in healthy controls (the dopamine clock hypothesis) (Wiener et al. 2011; Balcı 2014). Individuals with dopamine neurotransmission deficits in regions of the frontal cortex involved in temporal processing are particularly prone to difficulties perceiving time at an accelerated rate (Blum et al. 2008, 2015). Consequently, individuals with such temporal disturbances are at a higher risk of developing issues related to substance abuse, such as alcohol and drug dependency (Conner et al. 2010). Moreover, several clinical conditions involving dopaminergic dysfunction are characterized by impaired temporal processing, such as stimulant abuse (Wittmann et al. 2007) and Parkinson’s disease (Jones et al. 2008).

It is worth noting that the divergence in the cerebral response to the No-Go stimulus between the HTE and LTE groups was not behaviorally manifested in a different number of correct inhibitions, as both groups exhibited a similar percentage of correct rejections (Table 2). However, there were other behavioral differences between these groups that may account for this phenomenon. A significant difference was observed between HTE and LTE in the number of omissions of Go stimuli and the calculated d-prime (Table 2, Fig. 1B). An individual’s d-prime provides an indication of their capacity to identify signals (McNicol 2004). This indicates that the LTE group had a significantly lower sensitivity to rapidly changing Go versus No-Go stimuli, thus we may suggest that they adopted a strategy of more frequent omissions to avoid erroneous button-presses. A study examining the psychometric properties affecting the results of the Go/No-Go task found that a reduction in stimulus presentation time and the use of more complex stimuli simultaneously resulted in a significant decrease in the number of true hits on Go trials (Rezvanfard et al. 2016). Consequently, the rapid presentation time and high similarity between Go and No-Go stimuli in our study may have also significantly impeded the ability to distinguish between signals and noise in the LTE group.

The hypothesis regarding lower sensitivity to stimuli in the LTE group appears to be supported by the within-group analysis of neuronal responses to Go versus No-Go stimuli. The analysis in the HTE group revealed significant differences between the perception of No-Go versus Go stimuli, reflected in two highly significant clusters. The positive cluster indicated a significantly greater response in the frontocentral regions and a significantly reduced response in the peripheral regions during the perception of No-Go stimuli compared to Go stimuli (Fig. 5A, C, E). The same analysis for the LTE group revealed a much smaller difference. A single positive cluster in the frontocentral region, approaching the threshold of statistical significance, distinguished the No-Go stimuli from the Go stimuli in this group (Fig. 5B, D, F). The difference in response amplitudes to Go versus No-Go stimuli can be attributed to various factors, such as the processing characteristics, inhibitory mechanisms, and attentional processes involved in each type of stimulus. Studies have shown that response inhibition (to No-Go stimuli) is more cognitively demanding than response activation (to Go stimuli). Furthermore, No-Go stimuli required a longer response time for processing, indicating increased cognitive effort (Gao et al. 2017).

In order to determine whether the electrophysiological response to Go versus No-Go stimuli differed significantly between HTE and LTE, we compared the difference in brain activity (calculated as difference wave between No-Go and Go stimuli) within the 300- to 450-ms time window. The results demonstrated that the HTE group exhibited a significantly greater difference wave in comparison to the LTE group, particularly in the frontal regions and to a lesser extent in the central regions (Fig. 6B). The observed outcome may be indicative of divergences in the perception of stimuli, as evidenced by a notable discrepancy in the calculated d-prime. Furthermore, the dissimilarities in P300 amplitude between Go versus No-Go stimuli can be regarded as an indicator of the efficacy of response inhibition and attention control processes (Chong et al. 2008). Consequently, the larger the differential wave in HTE (Fig. 6A, C), the greater the involvement of control mechanisms and the better the ability to inhibit responses in comparison to the LTE group.

In the Go/No-Go task, participants were required to press a key as rapidly as possible when a triangle appeared on the screen, thus reaction time in this task is a reliable indicator of information processing speed (Rigoli et al. 2021). Our results demonstrated that HTE exhibited significantly faster reaction times to Go stimuli compared to the LTE group (Table 2, Fig. 1A). This finding may indicate that the Go stimuli were processed more effectively and that motor responses were faster in the HTE group. This may also reflect greater efficiency in attention and concentration, resulting in better suppression of distractions and focus on the relevant aspects of the stimulus in this group of participants (Zomeren and Brouwer 1994; Littman and Takács 2017). Prolonged reaction times have been observed in patients with cognitive impairments, traumatic brain injuries, dementia, or mental illnesses. Nevertheless, it is essential to acknowledge that the reaction time observed in the Go/No-Go task can be influenced by a complex interplay of cognitive, physiological, and task-related factors (Rigoli et al. 2021).

This study has some limitations that may limit the generalizability of the findings, such as its small sample size. However, it was of the utmost importance for us to select participants of high and low temporal efficiency because, as we know from previous experience, consistent performance across both the spatial and spectral TOJ tasks provides the most reliable indicator of temporal efficiency. Our division of the sample into discrete groups resulted in a lack of continuous data, preventing us from making inferences about the type and strength of the relationship between temporal processing and inhibitory control.

The present study demonstrated that individual differences in temporal processing are reflected in P300 amplitude dynamics during inhibitory processes in young adults. The underlying mechanisms responsible for this outcome can be attributed to several different processes, including the efficacy of response inhibition, attention control, and temporal efficiency. However, the biological process that exerts the most significant impact on these processes is the functioning of the dopaminergic system and the efficiency of neural connectivity between the brain regions involved in the task. This raises the question of whether it is possible to improve one’s functioning in terms of temporal efficiency, inhibitory ability, and attention control. Following the maxim that prevention is better than cure, young individuals with low temporal processing efficiency may find it encouraging that interventions such as temporal processing training have shown promise for improving executive functions. By enhancing the precision of temporal processing, especially for short intervals, these interventions may positively impact executive functions including inhibitory control (Szelag and Skolimowska 2014; Xia et al. 2020). The efficacy of temporal information processing (TIP) training in facilitating cognitive enhancement has been evidenced so far in individuals with aphasia (Choinski et al. 2023), children with language impairments (Szymaszek et al. 2009), and older adults (Szelag and Skolimowska 2014). However, the potential of TIP training in young adults remains unproved. TIP constitutes an essential component of human cognition because many cognitive functions, such as language, attention, memory, motor control, and planning, are characterized by specific temporal dynamics. This foundational role of TIP suggests that temporal training could have a positive impact on daily functioning in young adults and enhance their overall well-being by strengthening the underlying temporal framework that supports these critical cognitive processes. This study makes a novel contribution to the field of cognitive neuroscience by examining in healthy young adults the differences in inhibitory control associated with temporal processing efficiency.

Acknowledgments

We thank Aleksandra Bartnik and Adrianna Zając for their technical assistance during the data collection phase.

Author contributions

Klaudia Krystecka (Formal analysis, Investigation, Methodology, Resources, Visualization, Writing—original draft, Writing—review & editing), Magdalena Stanczyk (Investigation, Resources, Writing—original draft), Mateusz Choinski (Methodology, Writing—review & editing), Elzbieta Szelag (Funding acquisition), and Aneta Szymaszek (Conceptualization, Methodology, Project administration, Software, Supervision, Writing—original draft, Writing—review & editing).

Funding

Supported by the National Science Centre (Narodowe Centrum Nauki, NCN), Poland, grant no. 2018/29/B/HS6/02038.

Conflict of interest statement: The authors declare no conflicts of interest.

Data availability

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

References