The contributions of executive functions to decision-making in sport

ABSTRACT

In sports, when the game situations become complex, untypical, and uncertain, making good decisions requires controlled processing, i.e., executive functions to regulate thought and action. The current study aimed at comprehending the effects of three core executive functions on decision-making in sport. In study 1, 86 volleyball players (49 experts and 37 novices) completed a routine blocking decision task as well as three core executive function tasks assessing updating, inhibition and shifting. Results showed that the effects of executive functions on blocking decision accuracy were moderated by sports level, with significant predictivity observed only for experts. In study 2, 32 expert players engaged in a dual-task version of the blocking decision task. In this study, only updating was correlated with decision accuracy. Study 3, involving 26 experts, introduced interference during the decision task. The findings highlighted a closer, albeit not statistically significant, relationship between decision accuracy and inhibition compared to updating. Collectively, these findings demonstrated the nuanced yet crucial roles of executive functions in decision-making in sport, underscoring more efficient cognitive processing among higher-level athletes and the different roles served by each executive function.

KEYWORDS:

• Executive functions
• updating
• inhibition
• shifting
• decision-making in sport

As competition level upgrades, technical and physical abilities become insufficient to determine sport performance, while expertise in decision-making becomes crucial (Ashford et al., 2021; Gleeson & Kelly, 2020). In sport contexts, decision-making refers to the process whereby athletes choose what they will do to achieve a given objective contingent on what is happening in the game (Raab et al., 2019).

The decision-making process is formed of three levels from automated to conscious according to the decision maker’s familiarity with the situation and their relevant knowledge: (i) simple match, (ii) diagnose the situation and (iii) evaluate a course of action (Klein et al., 2010). “Simple match” refers to a case where the decision maker perceives the situation as typical, as the information available is provided in an obvious fashion and matches their initial expectations of the situation, leading to an automated decision response. “Diagnose the situation” represents a case where the available information is presented in an atypical fashion, making it necessary for the decision maker to clarify the goals, cues, and expectations to restore typicality and make decision. “Evaluate a course of action” means a more complex scenario where the information available must be assessed deliberately and the decision maker must run rapidly mental simulations to consider consequences that may emerge in taking each viable option. The decision-making can be placed on a continuum that spans from bottom-up automatic processing to top-down controlled processing depending on the cognitive effort involved (Ashford et al., 2021). Factors such as the complexity of situation (Raab, 2003), its typicality (Macquet, 2009) and the relevance of contextual priors (Levi & Jackson, 2018) dictate the positioning of a decision lies within this continuum (Ashford et al., 2021). In straightforward, typical, and predictable scenarios, the bottom-up automatic processing is sufficient. Conversely, in complex, atypical, and uncertain situations, the top-down controlled processing becomes indispensable. Executive functions (EFs) refer to a series of top-down mental processes which regulate thoughts and actions when automatic processing, instinct or intuition is insufficient or impracticable, necessitating paying and concentrating attention (Diamond, 2013). Given that EFs happen to be the core mechanisms of top-down processing, they might play important roles in decision-making, especially in nonroutine situations. As opponents’ tactics become more flexible and varied, the decision-making contexts become increasingly intricate. At this moment, simple matching is insufficient to make the appropriate decision. Instead, the top-down controlled processing is necessary, which means EFs are required to diagnose game situation or to evaluate the course of action. This assertion is supported by previous functional magnetic resonance imaging (fMRI) studies, which found significant activity in frontal cortex, implicated with EFs (Heyder et al., 2004; Kim et al., 2011; Lütcke et al., 2009), when players anticipated the landing point of shuttlecock (Wright et al., 2011) and badminton stroke direction (Wright et al., 2010). Meanwhile, research, involving decision under risk conditions, have demonstrated that individuals with superior EFs were more adept at making favourable decision (Marquez-Ramos et al., 2023) and that concurrent EF tasks could disrupt decision performance (Starcke et al., 2011). These findings suggested that decision-making tapped into the rational-analytical system, which necessitates the involvement of EFs.

There are three postulated core EFs: updating and monitoring of working memory representations (updating), inhibitory control (inhibition), and mental set shifting (shifting) (Diamond, 2013; Miyake et al., 2000). Updating involves the dynamic encoding of information, not merely mechanically storing it in working memory. It entails the continual integration of new, task-relevant information while mitigating interference from outdated or irrelevant data (Diamond, 2013; Miyake et al., 2000). Inhibition refers to deliberately resisting strong internal propensity or external interference by controlling attention, emotions, or behaviours to focus on current task and take suitable action (Diamond, 2013; Miyake et al., 2000). Shifting encompasses the ability to flexibly switch back and forth between tasks, operations, or mental sets, which makes it possible to transform own behavioural pattern and idea according to changes of environment to adapt and take advantage of the ever-changing world (Diamond, 2013; Miyake et al., 2000). The three core EFs are interdependent (Diamond, 2013; Miyake et al., 2000). Updating underpins inhibition by concentrating on current working memory content, thereby reducing the likelihood of inhibitory error (Diamond, 2013). Inhibition, in turn, supports updating by curtailing internal and external disruptions, accordingly, recombining ideas and facts in new and creative ways (Diamond, 2013). Shifting builds on both updating and inhibition, necessitating the suppression of prior thoughts or actions and the assimilation of new information or thought patterns into working memory (Diamond, 2013). Additionally, these foundational EFs give rise to higher-order functions such as planning, reasoning, and problem-solving (Diamond, 2013).

EFs are crucial for the athletes’ success, especially for elite athletes (Montuori et al., 2019). In tennis, for example, EF wasn’t only correlated with key performance metrics such as total points won, 1st serve points won, and shot error rate in a single match (Ishihara, Kobayashi et al., 2018), but could also predict players’ prefecture rankings 18 months later (Ishihara et al., 2018). Furthermore, targeted training focused on the inhibition function over a six-day period has been shown to decrease the ratio of volley misses in tennis players (Ducrocq et al., 2016). Similarly, Ducrocq et al. (2017) indicated that a 10-day regimen concentrating on the updating function improved tennis players’ volley performance under pressure. In soccer, EFs have been linked with the number of goals (Vestberg et al., 2017), number of assists (Vestberg et al., 2020) and game intelligence (Scharfen & Memmert, 2021; Vestberg et al., 2020). Researchers also found that EFs could predict soccer players’ goals and assists over subsequent seasons (Vestberg et al., 2012). Lundgren et al. (2016) identified a correlation between the shifting function and ice hockey players’ athletic performance in the following season. It is notable that all of sports mentioned above belong to open-skill sports which require reacting quickly in response to dynamically changing, unpredictable and externally-paced environment, by contrast there are few studies involving closed-skill sports, in which the sport environment is relatively highly consistent, predictable, and self-paced for athletes (Singer, 2000). It’s because the open-skill sports involve higher cognitive demands, but the closed-skill sports can benefit more of automatised schemes and skills (Singer, 2000). Therefore, in open-skill sports, successful performance invariably requires athletes to adeptly regulate thoughts and actions through EFs. Moreover, the relationship between sport performance and EFs differs based on the athletes’ level of expertise. Vaughan and Laborde (2021) found a moderating effect of basketball players’ athletic level on the relationship between EF and free-throw performance. Specifically, higher athletic levels involved a stronger relationship. Hagyard et al. (2021) demonstrated that EF was associated with self-reported and coach-assessed performance in open-skill sports, with this association being more pronounced in higher-level athletes. Hagyard et al. (2021) argued that the moderating effect of sports level might be caused by competition anxiety depleting novices’ attentional resources and thereby disrupting their EFs. However, elite athletes, through prolonged competitive participation, may have developed enhanced coping mechanisms with competition anxiety, thus mitigating its detrimental impact on EFs. We propose an alternate explanation: the differences between novices and elites might arise from differential success in transferring domain-general cognitive functions to sport-specific cognitive skills. It is argued that expertise could promote cognitive transfer, as expert athletes exhibit superior ability in exploring and perceiving information that specifies affordances compared to novices (Oppici & Panchuk, 2022; Rosalie & Müller, 2012). Previous fMRI studies examining the effect of expertise on cortical activation during sport anticipation found badminton experts displayed greater activations in the frontal cortex, while novices showed stronger responses in the occipital cortex, which indicated a greater reliance on primary sensory processing over advanced controlled processing in novices but reverse in experts (Wright et al., 2010, 2011). This disparity might explain the differential relationships between sport performance and EFs across various levels of athletic expertise, which supports our cognitive transfer failure hypothesis.

Although numerous studies have explored the relationships between EFs and sport performance, the precise roles of EFs in this context remain a topic of debate (Kalén et al., 2021). An essential confounding variable that requires careful consideration when examining this relationship is health-related factors, such as physical or physiological ability (Furley et al., 2023). For instance, aerobic fitness is associated with successful sport performance. However, the majority of studies have either neglected to report the level of aerobic fitness or have not statistically controlled for it (Furley et al., 2023). As an effective predictor of sport performance, decision-making is affected hardly by physical or physiological ability. Hence, exploring the effects of EFs on decision-making in sport can shed light on the broader relationship between EFs and performance. In situations where the game environment is clear, typical, and predictable, automatic simple match allows players to make decision fast and accurately. However, athletes typically operate in complex and dynamic environments, necessitating quick responses to unpredictable and externally-paced changes (Singer, 2000). Hence, the top-down controlled processing is necessary to diagnose game situations and to evaluate courses of action, underscoring the need for well-developed EFs. While direct research exploring the relationships between EFs and decision-making in sport is limited, previous studies highlighting significant activation in frontal cortex during sport anticipation (Wright et al., 2010, 2011) and the significance of EFs in risk decision (Marquez-Ramos et al., 2023; Starcke et al., 2011) suggest that EFs might play crucial roles in decision-making in sport. Furthermore, the observed greater activation in the frontal cortex among sport experts (Wright et al., 2010, 2011), along with the moderated relationship between EFs and sport performance by sports level, implies that the impact of EFs on decision-making in sport may also be influenced by the athlete’s level of expertise.

Consequently, in this research, we explored the correlations between EFs and decision-making in volleyball (an open-skill strategic sport) by investigating blocking decision. In study 1, we explored the relationships between EFs and blocking decision performance under routine condition, with an emphasis on how these relationships might be influenced by the athletes’ level of expertise. Given that athletes in competitive settings are often required to process multifarious information and handle multiple tasks simultaneously while contending with various internal and external disruptions, our investigation extended to the impact of EFs on blocking decision in conjunction with parallel task in study 2 and under conditions of interference in study 3. In consideration of the different effects of the three core EFs, we hypothesised that the updating function would be more critical in scenarios involving parallel tasks, whereas the inhibitory function would have a heightened influence under conditions of interference.

Study 1: EFs and blocking decision in volleyball

In volleyball, a blocker’s duty involves tracking the number and position of opposing attackers as well as the trajectory of the ball, in order to anticipate the direction of the attack and effectively block or at least slow down the ball, aiding the team’s defensive efforts (Ficklin et al., 2014; Fleddermann & Zentgraf, 2018). This complex task likely requires robust EFs to regulate thought and action. Hence, we selected the blocking decision in volleyball as a representative measure of decision-making in sport. Drawing from studies on cortical activity during sport anticipation and the influence of EFs on sport performance, we hypothesised that players with superior EFs would exhibit enhanced performance in blocking decision. Furthermore, we posited that the impact of EFs on blocking decision would be moderated by the athletes’ competitive level, with greater effects observed in higher-level players.

Method

Participants

Forty-nine expert volleyball players from China Volleyball League (male 22, female 27; age = 19.41 ± 2.483 years; experience = 7.06 ± 3.058 years) and thirty-seven novices who were undergraduate students majoring in physical education (male 22, female 15; age = 22.41 ± 1.979 years; experience = 3.78 ± 2.110 years) took part in the study. All participants possessed normal or corrected-to-normal vision and normal colour vision.

Materials

Blocking decision task

The temporal-occlusion paradigm was used to assess decision performance, where game video clips were presented and occluded before a critical decision point (e.g., Ward et al., 2013). The clips, sourced from China Volleyball League (in which participants had not competed), were filmed from behind the service area, simulating a player’s perspective. Each clip began with the moment when the defensive team hitting the ball over the net and was occluded at the point when the opposing setter was going to contact the ball but without any wristwork. In each trial, the video clip was presented between a 500-ms fixation cross and a three-option picture (see Figure 1). Participants were required to determine the optimal blocking position (left, centre, or right) within 3000 ms when the three-option picture appeared. The next trial would begin if participants made decision. The task consisted of 36 trials (6 trials for practice). The dependence measures were response accuracy and average reaction time (RT) of all trials. The RT measured in millisecond.

Figure 1. Sequence of events in a trial in blocking decision.

N-back task

The 2-back task (Braver et al., 1997) was employed to measure the updating function. Participants were asked to identify if a letter from a set of six possible letters (E, G, Q, R, T, and W) matched the letter presented two trials prior. The task comprised 38 test trials and 14 practice trials, with response accuracy as the dependent measure.

Stroop task

Stroop task (MacLeod, 1991) was utilised to assess the inhibition function. In the task, when one of four possible words (“red”, “green”, “blue” and “yellow”) emerged, participants were asked to report the ink colour (one of red, green, blue and yellow), which could either match or conflict with the word’s meaning. The task involved 64 test trials (with a mix of 16 congruent and 48 incongruent stimuli) and 16 practice trials. All participants achieved high accuracy on this task (accuracy = 0.93 ± 0.073). The dependence measure was the inhibition RT, calculated as the average RT difference between congruent and incongruent trials.

More-odd shifting task

More-odd shifting task (Salthouse et al., 1998) was conducted to evaluate the shifting function. Participants had to categorise digits (ranging from 1 to 9, excluding 5) based on their size (more or less than 5) when displayed in black, or their parity (odd or even) when displayed in green. The task was structured into three blocks. The initial block consisted of 32 target trials, supplemented by 8 practice trials, with all digits displayed in black. This was mirrored in the second block, where digits were presented in green. The third block, designed to evaluate shifting efficiency, comprised 128 target trials and 16 practice trials, featuring an equal distribution of black and green digits. Here, participants were required to alternate between the two categorisation strategies. The primary metric for assessing shifting function was the “shifting cost”, calculated as the average RT differential between trials in the final block and those in the initial two blocks.

Procedure

The study was approved by the ethics committee of Beijing Sport University and was performed in full compliance with the Declaration of Helsinki. All participants were provided written informed consent before involvement. The sequence began with participants undertaking the blocking decision task, followed by the three EF tasks. The order of the three EF tasks was counterbalanced. All tasks were presented using E-prime 2.0 software on a 14-inch laptop computer.

Statistical analyses

The performances of the two groups (expert and novice) on the decision task and EF tasks were compared using t-test for independent samples. Pearson’s product moment correlation was applied to assess the relationships between decision performance and three EFs within each group. To explore the moderating effect of sports level on these relationships, hierarchical regression analyses were performed. The dependent variable was decision accuracy, with individual EF and sports level as predictor variables in the first step and their interaction (EF multiplied by sports level) introduced as an additional predictor in the second step.

Results

Differences between groups in decision-making and EFs

Elite players demonstrated significantly higher decision accuracy compared to novices, as shown in Table 1. However, there were no significant differences between the groups in terms of average decision-making RT and the three EFs.

Table 1. Descriptive data and difference between two groups on the decision-making and EFs.

	N	M ± SD
		DM-ACC	DM-RT	2-back	stroop	more-odd shifting
Novice	37	0.49 ± 0.102	904.98 ± 287.650	0.76 ± 0.170	57.90 ± 69.704	342.36 ± 126.059
Elite	49	0.63 ± 0.095	882.91 ± 323.251	0.76 ± 0.156	49.12 ± 61.178	358.62 ± 148.717
Total	86	0.57 ± 0.119	892.41 ± 306.874	0.76 ± 0.161	52.90 ± 64.734	351.63 ± 138.871
t		−6.40***	0.33	−0.16	0.62	−0.54

Note. DM = decision-making; ACC = accuracy; RT = average RT of all trials.

*** p < 0.001 (two-tailed).

Relationship between decision-making and EFs

In the novice group, no significant correlations were observed between decision-making performance and the EFs (Table 2). In contrast, the elite group showed significant correlations between decision accuracy and each of the EFs (Table 2).

Table 2. Correlations between decision-making performance and EFs in Study 1.

	Novice					Elite
	DM-ACC	DM-RT	2-back	Stroop	More-odd shifting	DM-ACC	DM-RT	2-back	Stroop	More-odd shifting
DM-ACC	1					1
DM-RT	−0.06	1				−0.29*	1
2-back	−0.08	0.22	1			0.35*	−0.28	1
Stroop	0.13	−0.26	0.05	1		−0.38**	0.13	−0.16	1
More-odd shifting	0.12	−0.15	0.08	0.33*	1	−0.43**	0.11	0.11	0.36*	1

Note. DM = decision-making; ACC = accuracy; RT = average RT of each trial.

* p < 0.05 (two-tailed); ** p < 0.01 (two-tailed).

Moderating effect of sports level on the relationship between decision-making and EFs

Decision accuracy, updating and sports level

The hierarchical regression analysis revealed that updating accuracy and sports level collectively accounted for 34.2% of the variance in decision accuracy (F (2, 83) = 21.59, p < 0.001). The interaction effect of updating accuracy and sports level was significant (changes in R² = 0.032, changes in F (1, 82) = 4.18, p = 0.04), indicating a moderation by sports level (Table 3). For elite players, the slope of the regression line for updating accuracy was significantly positive (t = 2.57, p = 0.01), suggesting a stronger relationship in this group, unlike the novice group where the slope was not significant (t = −0.50, p = 0.62).

Table 3. Hierarchical regression analysis with sports level and updating accuracy as predictor variables.

	variable	β	t	p
Step 1	Sports level	0.57	6.41	0.000
	Updating accuracy	0.12	1.35	0.182
Step 2	Sports level	0.57	6.54	0.000
	Updating accuracy	−0.07	−.54	0.590
	Sports level × updating accuracy	0.26	2.04	0.044

Note. Dependent variable: decision-making accuracy.

Decision accuracy, inhibition RT and sports level

Similarly, inhibition RT and sports level explained 34.0% of the variance in decision accuracy (F (2, 83) = 21.41, p < 0.001). The interaction term was significant (changes in R² = 0.044, changes in F (1, 82) = 5.83, p = 0.018), indicating a moderating effect of sports level (Table 4). About the slope for the simple regression line representing inhibition RT, it was significantly negative (t = −2.81, p = 0.007) for elite group, whereas it was not significant for novices (t = 0.75, p = 0.46).

Table 4. Hierarchical Regression Analysis with Sports Level and Inhibition RT as Predictor Variables

	variable	β	t	p
Step 1	Sports level	0.57	6.32	0.000
	Inhibition RT	−0.11	−1.26	0.213
Step 2	Sports level	0.57	6.53	0.000
	Inhibition RT	0.10	0.81	0.420
	Sports level × inhibition RT	−0.30	−2.41	0.018

Note. Dependent variable: decision-making accuracy.

Decision accuracy, shifting cost, and sports level

Shifting cost and sports level together accounted for 35.6% of the decision accuracy variance (F (2, 83) = 22.91, p < 0.001). The interaction effect was also significant (changes in R²= 0.043, changes in F (1, 82) = 5.83, p = 0.018), suggesting the moderation by sports level (Table 5). The slope for shifting cost was significantly negative for elite players (t = −3.26, p = 0.002) but not for novices (t = 0.72, p = 0.48).

Table 5. Hierarchical regression analysis with sports level and shifting cost as predictor variables.

	variable	β	t	p
Step 1	Sports level	0.58	6.60	0.000
	Shifting cost	−0.17	−1.90	0.062
Step 2	Sports level	0.58	6.72	0.000
	Shifting cost	0.12	0.79	0.430
	Sports level × shifting cost	−0.35	−2.41	0.018

Note. Dependent variable: decision-making accuracy.

Discussion

The study’s analysis of blocking decision performance among volleyball players of varying expertise revealed that experts exhibited higher accuracy than novices without compromising response speed. This superiority can be attributed to experts’ extensive professional knowledge related to the sport, accrued through years of training (Afonso et al., 2012; Evans et al., 2012; Lex et al., 2015), which enhances their capacity to retrieve relevant information in memory representation and engage in more accurate intuitive processes (Musculus, 2018), option generative processes (Basevitch et al., 2020) and recognition of contextual priors (Levi & Jackson, 2018). Experts’ proficiency in using early segmental motion cues within the opponent’s movement pattern, as opposed to less skilled players who rely more on later-occurring object flight information (Farrow et al., 2005; Müller & Abernethy, 2012). Experts could cluster higher order cues together into one source of information (Johnston & Morrison, 2016), so that they prefer to perceive information chunks but discrete information (Ashford et al., 2021), thus facilitating superior decision-making.

Our findings corroborate the established link between sport performance and EFs (Ishihara, Kobayashi et al., 2018; Ishihara et al., 2018; Lundgren et al., 2016; Vestberg et al., 2012; 2017; 2020). Open-skill sports like volleyball require high cognitive demands (Singer, 2000), necessitating the involvement of EFs for efficient regulation of thoughts and actions. In volleyball, blockers must continuously load the ever-changing location information of teammates and opponents into working memory (updating), and resist distraction from flashlight or inappropriate instructions from coach (inhibition), and furthermore fast switch blocking strategies according to opponents’ attacking tactics (shifting). Although automatic processing suffices in clear and predictable game situations, complex and dynamic contexts necessitate top-down controlled processing for appropriate decision-making. This requirement is supported by previous fMRI studies showing significant frontal cortex activation during sport anticipation (Wright et al., 2010; 2011). Interestingly, while EFs were not predictive of blocking decision accuracy in novices, they were crucial for experts. This moderating effect of sports level aligns with previous research involving sport performance (Hagyard et al., 2021; Vaughan & Laborde, 2021). This did not mean for novices the game situations were simple and then EFs were not required, but because of cognitive transfer failure from EFs to decision-making. The decision process always happens instantaneously, so players must limit the cognitive load (Petiot et al., 2021). Experts are able to efficiently allocate cognitive resources to discern temporal relationships among various information features, reflecting advanced controlled processing (North & Williams, 2008; Weigel et al., 2015). In contrast, novices tend to focus more on details, indicative of primary sensory processing. This cognitive disparity between experts and novices is further evidenced by fMRI studies showing distinct patterns of cortical activation in anticipation of badminton (Wright et al., 2010, 2011). Our results indicated that although novices’ EFs were not significantly inferior to those of experts, their EFs did not correlate with blocking decision accuracy, pointing to a cognitive transfer failure from EFs to decision-making. This perceptual-cognitive difference in decision-making between different levels of athletes extends beyond expertise in knowledge, and it encompasses experts know to reorganise and refine their knowledge representation based on the game situation (Elferink-Gemser et al., 2010; Kannekens et al., 2011). Experts leverage their EFs to facilitate decision-making, whereas lower-level players do not, highlighting a more efficient cognitive processing among high-level athletes compared to their lower-level counterparts.

Study 2: EFs and blocking decision in volleyball with parallel task

In sport competitions, players are often required to process various streams of information or handle multiple tasks simultaneously to make optimal decision. For instance, they need to simultaneously keep track of teammates, opponents, and the ball. The dual-task paradigm is a typical research approach to examine the capacity to manage multiple information streams or tasks concurrently. High skill in coping with cognitive and motor performance in dual-task situations is considered a hallmark of elite-level proficiency (Amico & Schaefer, 2022; Chen et al., 2015; Fleddermann & Zentgraf, 2018; Gabbett et al., 2011; Schaefer & Amico, 2022; Schaefer & Scornaienchi, 2022). In such circumstances, EFs are crucial to regulate concurrent information or tasks. Traditional dual-task studies that combine a cognitive task with a motor task are undoubtedly influenced by participants’ physical and physiological abilities. Decomposing the decision process into its component parts, such as tracking teammates’ positions, opponents’ passing routes, and the ball’s trajectory, can be challenging. Therefore, in study 2, we introduced a parallel cognitive task, facial expression recognition task, during the blocking decision task to examine the effects of EFs on processing multiple information streams or tasks simultaneously. This facial expression recognition task was designed to simulate information or task needed to process simultaneously during decision, such as teammates’ positions, opponents’ passing routes or the trajectory of ball, to increase players’ cognitive load. Based on the effect of updating on cognitive processing, it might be more responsible for identifying relevant information as much as possible or performing multiple tasks simultaneously in a given period of time. So, we hypothesised a stronger correlation between updating and decision performance compared to the other two EFs.

Method

Participants

Approximately one week after completing study 1, the expert volleyball players were invited back. Thirty-two expert players (male 12, female 20; age = 18.53 ± 2.286 years) took part in the study.

Materials

Blocking decision task with parallel task

The setup was similar to study 1, but with an addition: during the last 500 ms of each clip, a face exhibiting an expression would flash on the screen. Participants were required to recognise the facial expression within 2000ms after making their blocking position (Figure 2). The task comprised 36 trials (including 6 practice trials) with distinct clips from those used in study 1. The dependent measures were decision accuracy, face recognition accuracy and a weighted accuracy calculated by the product of decision accuracy and parallel task accuracy. E-prime 2.0 was used for administering the task on a 14-inch laptop computer.

Figure 2. Sequence of events in a trial in blocking decision with parallel task.

Statistical analyses

Relationships between dual-task performances and three EFs were assessed using Pearson’s product moment correlation.

Results

The results, as illustrated in Table 6, reveal a notable trend: only the updating function showed a significant correlation with dual-task performances. Specifically, updating displayed a closer correlation with the composite index of dual-task performance, known as weighted accuracy, compared to its correlation with decision accuracy alone (t = 1.85, df = 29, p < 0.05, one-tailed). Neither inhibition nor shifting functions exhibited significant correlations with dual-task performances.

Table 6. Correlations between decision-making performance and EFs in Study 2.

	M ± SD	DM-ACC	FR-ACC	W-ACC	2-back	Stroop	More-odd shifting
DM-ACC	0.53 ± 0.099	1
FR-ACC	0.80 ± 0.105	0.14	1
W-ACC	0.42 ± 0.105	0.85**	0.64**	1
2-back	0.74 ± 0.164	0.39*	0.49**	0.55**	1
Stroop	52.78 ± 49.272	−0.22	−0.02	−0.19	−0.34	1
More-odd shifting	379.49 ± 115.572	−0.25	0.30	−0.01	0.06	0.13	1

Note. DM = decision-making; ACC = accuracy; FR = face recognition; W = weighted.

* p < 0.05 (two-tailed); ** p < 0.01 (two-tailed).

Discussion

The finding that only updating significantly associated with blocking decision performance in a dual-task condition aligns with our hypothesis. This suggests that updating is responsible for identifying relevant information as much as possible or performing multiple tasks simultaneously during decision. The stronger correlation of updating with weighted accuracy than with decision accuracy alone further confirms the hypothesis.

The dual-task paradigm posits that simultaneous tasks compete for a shared pool of cognitive resources (Navon & Gopher, 1979; Wickens, 2002). When the cognitive demands of these tasks exceed individual’s capacity, performance on one or both tasks can deteriorate (Strobach et al., 2015). EFs, particularly updating, play a crucial role in coordinating these simultaneous tasks (Erickson et al., 2007; Low et al., 2009; Szameitat et al., 2002). Updating is tasked with capturing key features of relevant information as much as possible and prioritising information representation in working memory based on current condition. In our dual-task scenario, the facial expression recognition task simulates information or task needed to process simultaneously during decision, such as teammates’ positions, opponents’ passing routes or the trajectory of ball. So, this additional facial information was regarded as the relevant information or task with the goal and increased players’ cognitive load during decision. A robust updating function enables players to effectively coordinate the parallel task and other relevant decision-making information. It allows for flexible and efficient prioritisation and processing of information within the given timeframe. Therefore, superior updating correlates with higher accuracy in decision-making under dual-task conditions. In the context of volleyball, the ability to simultaneously capture teammates’ positions, opponents’ passing routes and the trajectory of ball is crucial for blockers in determining their blocking position. Enhanced updating allows for better coordination of cognitive resources among multiple pieces of information, dynamically encoding them to facilitate appropriate decision-making. This becomes increasingly vital as opponents’ tactics grow more complex, such as more potential spikers, which means blockers must track more opponents simultaneously. Hence, the stronger correlation between updating and weighted accuracy compared to decision accuracy alone reflects the critical role of updating in managing cognitive load in complex, multi-faceted decision-making scenarios.

Study 3: EFs and blocking decision in volleyball under interferences

Athletes are often challenged to maintain focus on performance amidst various external and internal disturbances. External disruptions could stem from crowd noise, flashes of light, or movements from officials, while internal disturbances might arise from coaches’ misguided instructions or players’ own preconceived notions about opponents’ tactics. Effective allocation of attention resources in such scenarios, safeguarded by EFs, is essential. Furley and Memmert (2012) found basketball players with higher working memory capacity excelled in focusing on tactical decision-making while blocking out auditory distraction (the first name of every subject) and ice hockey players with higher working memory capacity were more successful at adapting their tactical decision-making according to the situation instead of relying on prepotent inappropriate tendency (inappropriate tactical instructions from coach). In study 3, we explored the impact of EFs on athletes’ ability to resist distractions during decision by introducing both external and internal disturbances. A flashing face was used to make external visual disturbance, which simulated the disturbance from flashlight or judges’ movement to players in game. And referring to Furley and Memmert (2012), the inappropriate tactical instruction from coach was used for causing internal inappropriate dominant propensity. Based on the effect of inhibition on cognitive processing, it might be more responsible for resisting all kinds of unrelated information. So, we hypothesised that under the distraction condition, the correlation between inhibition and decision performance would be stronger than with other EFs.

Method

Participants

About one week after completing study 2, expert volleyball players were invited back for Study 3. Twenty-six expert players (male 12, female 14; age = 21.19 ± 1.833 years) took part in the study.

Materials

Blocking task under distraction

The task was modelled after the format used in study 1, but with added distractions. A face would flash during the last 500 ms of each clip, representing an external visual disturbance. This was followed by a 1000 ms prompt where the “coach” provided a recommendation on the blocking position, though these suggestions were provided by investigator with only 33.33% accuracy. Participants then had to decide the blocking position (Figure 3). The task consisted of 36 trials (6 practice trials), featuring different clips from those in Studies 1 and 2. The dependent measure was decision accuracy. The task was presented via E-prime 2.0 on a 14-inch laptop.

Figure 3. Sequence of events in a trial in blocking decision under distraction.

Statistical analyses

The relationships between decision performance and the three EFs were assessed using Pearson’s product moment correlation.

Results

As shown in Table 7, decision accuracy exhibited significant correlations with both updating and inhibition functions. However, there was no significant correlation with the shifting function. While the correlation between decision accuracy and inhibition was stronger than that with updating, this difference was not statistically significant (t = 0.39, df = 23, p > 0.05, one-tailed).

Table 7. Correlations between decision-making performance and EFs in Study 3.

	M ± SD	DM-ACC	2-back	Stroop	More-odd shifting
DM-ACC	0.61 ± 0.105	1
2-back	0.81 ± 0.139	0.39*	1
Stroop	41.99 ± 76.009	−0.48*	−0.16	1
More-odd shifting	346.22 ± 167.540	−0.26	−0.01	0.47*	1

Note. DM = decision-making; ACC = accuracy.

* p < 0.05 (two-tailed).

Discussion

In the condition involving distractions, both inhibition and updating were significantly linked to the blocking decision performance, while shifting did not show a significant relationship. The correlation of blocking decision performance with inhibition was stronger than with updating, although not significantly. These results partially support our hypothesis, indicating that both inhibition and updating are crucial in managing blocking decision under interference, with inhibition potentially playing a slightly more prominent role.

Interference stimuli and inappropriate internal propensities compete for cognitive resources with relevant information and matching internal template required for decision-making (Navon & Gopher, 1979; Strobach et al., 2015; Wickens, 2002). When these disturbances significantly occupy cognitive resources, they can impair task performance. This is evident in scenarios where inappropriate tactical instructions from coach lead to suboptimal decision-making (Furley & Memmert, 2010; 2012). Such instructions create an internal propensity to decide in a manner that may not align with the actual situation, leading to response competition and conflict. EFs are crucial in resisting both distractive stimuli and inappropriate instructions, ensuring the allocation of cognitive resources to relevant information and suitable internal templates. Inhibition enables players to selectively focus on targets and voluntarily ignore unrelated stimuli (Theeuwes, 1991), as well as to override prepotent response tendencies (Anderson & Levy, 2009). Therefore, players with better inhibition can more effectively block out irrelevant visual distractions and resolve competing response tendencies, thereby enhancing their performance in blocking decision under both external and internal disturbances. Updating plays a supportive role in inhibiting external and internal distractions by determining which information should be represented in working memory according to the situation (Lehto, 1996) and reinforcing the focus on that (Diamond, 2013; Miyake et al., 2000). This focus reduces the likelihood of loading interference stimuli and inappropriate internal propensities into working memory. While inhibition directly resists external distraction and internal tendencies, updating provides indirect support, potentially explaining why the correlation with decision performance was somewhat stronger for inhibition than updating.

General discussion

The aim of this series of studies was to investigate the effects of three core executive functions on decision-making in sport, specifically focusing on blocking decision in volleyball. We found that the sports level moderated the influence of EFs on blocking decision performance, with only elite players demonstrating a significant association between their EFs and decision accuracy. Athletes’ behaviours are guided by two different types of processing: automatic and controlled (Evans & Stanovich, 2013; Furley et al., 2015). Automatic processing, effortless and not reliant on working memory, is triggered by the current situation. In contrast, controlled processing requires conscious allocation of cognitive resources and depends heavily on working memory. With extensive training, athletes develop responses that become part of their problem representation, hardly requiring controlled processing. Thereupon, decision-making is based on internalised knowledge structures and is straightforward, relying more on automatic processing (Araújo et al., 2019; Klein et al., 2010). However, game situations are often complex and unpredictable, challenging the adequacy of automatic processing alone (Furley et al., 2015; Toner & Moran, 2014). In such dynamic and flexible game environments, athletes need to employ controlled processing to diagnose situations and evaluate courses of actions, particularly in response to unconventional tactics (Araújo et al., 2019; Klein et al., 2010). EFs, as key mechanisms of controlled processing, are necessary when automatic processing is incompetent. However, the associations between EFs and decision-making performance was not observed in novices, potentially due to a cognitive transfer failure from EFs to decision-making. Elite players’ excellent blocking decision performance stem not just from internalised knowledge structures (automatic processing) but also from regulating thoughts and actions through EFs (controlled processing). Elite athletes could flexibly alternate between two different types of processing (Furley et al., 2015; Nyberg, 2014; Toner & Moran, 2014), which suggests more efficient cognitive processing in elite players compared to their lower-level counterparts during decision-making.

Through three studies, we found each of the three core EFs played distinct roles in decision-making in sport. Updating was key in loading relevant information into working memory and coordinating multiple tasks simultaneously. For blockers, superior updating prompts them to track teammates, opponents, and the ball concurrently, becoming increasingly critical with more potential opponent spikers. Inhibition involves voluntarily ignoring both external and internal distractions, helping blockers resist external disturbances, such as flashing lights and judges’ movement, as well as internal disruptions, such as inappropriately rehearsing and instructions from coaches or stereotypes about opponent tactics. Shifting, on the other hand, is crucial in adapting to changing game environments and strategies. For blockers, better shifting prompts them to flexibly and rapidly switch between different potential blocking targets to make final blocking decision according to opponent’ ever-changing tactics. Notably, updating was consistently significantly associated with decision performance across all situations, affirming theories that three core EFs are interdependent and support each other in sport field (Diamond, 2013; Miyake et al., 2000). Updating could dynamically guide athletes’ attention, acting as a central mechanism of attention allocation in goal-directed behaviour (Furley & Memmert, 2013; Furley & Wood, 2016). In the interference situation, intense focus on relevant information and on appropriate internal propensity reduces the likelihood of inhibitory errors. However, shifting was significantly correlated with decision performance only in routine situation, likely because it builds on updating and inhibition (Diamond, 2013). When these two are sufficient to make an optimal decision, shifting may not be necessary. In sum, blockers need EFs to make optimal blocking decision. This series of studies not only underscores the importance of these cognitive functions in sport but also contributes to a deeper understanding of how elite athletes process information and make decision under various game conditions.

Limitations

There are some limitations in our study. Firstly, in study 2, a task involving item position recognition may have been more ecologically valid than facial expression recognition, aligning closer to the spatial demands of volleyball. Secondly, in study 3, blending external disturbances with internal dominant propensity limited our ability to differentiate between attention inhibition and cognitive inhibition mechanisms. Thirdly, the absence of a specific task to assess the role of shifting in decision-making means that our understanding of its impact is based more on theoretical speculation than empirical evidence.

Future directions

While this study focused on the effects of three core EFs on blocking decision in volleyball, the generalisability of these findings to other sports remains uncertain. Future research should extend the examination of EFs’ effects on decision-making across various sports. Exploring the connection between EFs, decision-making in sport, and overall sport performance could help validate the hypothesis that EFs influence decision processes, which in turn impact performance. Additionally, following Zelazo and Müller (2002) classification of EFs into “Cool” (cognitive self-regulation) and “Hot” (emotional-motivational) components, future studies should investigate the influence of “Hot” EFs on decision-making in sport and examine how both “Cool” and “Hot” EFs collectively affect this process.

Conclusion

This study has delved into the intricate relationships between the three core EFs and blocking decision performance in volleyball. We have found that expert players’ advantages in decision-making stem not only from internalised knowledge structures but also from their more efficient cognitive processing capabilities. Experts effectively employ EFs to enhance their decision-making, a proficiency less evident in lower-level players. Additionally, the three core EFs demonstrate distinct roles within the realm of decision-making in sport, with updating emerging as a potentially central mechanism. In conclusion, effective decision-making in sport necessitates the involvement of EFs.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by Fundamental Research Funds for the Central Universities, BeijingSport University [grant number 2016ZD004], Fundamental Research Funds for Central Universities, BeijingSport University [grant number 2018ZD009] and Open Funding Project of National Key Laboratory of Human Factors Engineering [grant number 614222202062211].