Proficient brain for optimal performance: the MAP model perspective

Introduction

This article is based on the distinction between performance effectiveness and processing efficiency (Eysenck & Calvo, 1992; Eysenck et al., 2007), and concerns the functioning of athletes during different types of optimal and suboptimal performance as conceived in the multi-action plan (MAP) model (Bortoli et al., 2012). Performance effectiveness has been defined as the quality of performance (e.g., the shooting scores). Processing efficiency regards the relationship between performance effectiveness and the use of resources or effort. According to Eysenck & Derakshan (2011), “…processing efficiency is high when performance effectiveness is high and use of resources is low and it is low when performance effectiveness is low but use of resources is high” (p. 956). However, this definition is not always tenable. Applied and theoretical studies observing the interaction between performance effectiveness and processing efficiency have shown that elite athletes can function in different modes when experiencing optimal performance (Bortoli et al., 2012; Bertollo et al., 2013; Carson & Collins, 2016; Furley, Schweizer & Bertrams, 2015; Swann et al., 2016). Athletes are able to attain effective performances not only when they execute in automatic, fluent, and flow-like states, but also when they proficiently exert some effort to cope with distressful situations. Proficient athletes, indeed, are able to achieve performance goals or results with a high degree of certainty, minimum energy expenditure, and minimum movement time in different states and conditions (Magill & Anderson, 2014). Proficiency is an individual’s capability of achieving a particular and complex task by switching effectively between an automated and a more controlled execution according to the task and situational demands.

According to this view, two qualitatively different modes of processing, namely, automatic and controlled, have been recently proposed in the sport science field drawing on existing theories in general psychology (see Furley, Schweizer & Bertrams, 2015). Both types of performance are optimal, but Type 1 (fluent, automatic, and procedural) autonomous processing would be initiated and completed in the presence of relevant triggering conditions, whereas Type 2 (competent, regulated, and declarative) controlled processing would rely on working memory capacity to deal with novel problems, uncommon situations or unexpected events.

Referring to the performance effectiveness and processing efficiency distinction, optimal performance can be achieved in conditions of either efficient processing (i.e., fluent performance and low attentional control and/or effort) or effortful processing (i.e., high attentional control and/or effort). In others words, proficient athletes can complete the task successfully using both efficient and effortful types of processing (Wilson, 2008). In the sport domain, flow-like states in which athletes perform fluently are rare. Thus, athletes need to use their proficiency, developed through a large amount of deliberate practice (Ericsson, 2007), to maintain high performance levels and cope with distressful situations.

The MAP model

To advance the knowledge on the unique psychophysiological and behavioural mechanisms underlying optimal and suboptimal performance states among high-skilled athletes, Bortoli and colleagues (2012) have developed the MAP intervention model. The model is embedded within the IZOF framework (Hanin, 2007), and fits well with the ACT hypothesis that anxiety will typically impair processing efficiency more than performance effectiveness. It also concurs with dual-process theories that describe and explain the dual modes of processing of athletes, namely, automatic and controlled (see Furley, Schweizer & Bertrams, 2015). In this view, Type 1 automatic processing allows for the fast and effortless execution of behavioural responses, whereas Type 2 controlled processing is well equipped for dealing with novel problems or unexpected events. Furthermore, the MAP model is rooted in a motoric perspective, in which skill establishment is related to aspects governing motor control. From this viewpoint, skill establishment concerns the structures/representations of the movement and refers to the level and consistency of automaticity in the execution of different movement components (Carson & Collins, 2016).

According to the MAP model (Bortoli et al., 2012), performance experience can be depicted along two different axes: one represents the performance level per se, and the other represents the amount of control over the task. According to this interaction, this performance experience plane can be classified into four performance Types: optimal-automatic (Type 1), optimal-controlled (Type 2), suboptimal-controlled (Type 3), and suboptimal-automatic (Type 4; see Fig. 1). In Type 1 performance (flow condition) the athlete is self-confident and in control of the situation, and is able to direct his or her physical and mental energies toward the accomplishment of a task. This type of processing is independent of available working memory capacity. The movement is effortless, autonomous, and consistent and attentional control systems play a role of supervision. Both performance effectiveness and processing efficiency are high. Stress, fatigue, unexpected events, or performance issues could disrupt performance. These unfavourable conditions can lead to Type 3 performance, typified by an excessive reinvestment in controlled processing and an overload of working memory, which undermines movement fluidity and automaticity (Maxwell, Masters & Eves, 2000; Oudejans et al., 2011). Both performance effectiveness and processing efficiency are low. Finally, Type 2 performance state is characterized by high performance effectiveness and low processing efficiency (i.e., effortful processing and involvement of working memory; Eysenck & Derakshan, 2011), while Type 4 performance is typified by ineffective performance, inefficient processing, and no reliance on working memory.

Research findings on the MAP model have shown that different physiological (e.g., skin conductance responses, breathing rate, and heart rate) and behavioural markers (e.g., kinematic patterns) underlie the four types of performance states (Bertollo et al., 2013; Filho et al., 2015) and focus of attention can affect performance (Bertollo et al., 2015). There is also initial evidence that different neural patterns are associated with the four performance types foreseen in the MAP model (Comani et al., 2014; Di Fronso et al., 2016). Specifically, Comani et al. (2014) observed that optimal-automatic performance (i.e., Type 1) states among shooters were characterized by lower alpha power in the central, contralateral parietal, and occipital areas (at shot release), in agreement with the neural efficiency hypothesis (i.e., global decrease in cortical activity). Conversely, optimal-controlled performance (i.e., Type 2) was characterized by an increased alpha power activity in the frontal and occipital areas.

Method

Results

Descriptive statistics for the behavioural data for each performance type as derived from the interaction of optimal/suboptimal and automatic/controlled performance dimensions are summarized in Table 1.

A 4 × 3 (performance types × time) repeated measures analysis of variance (RM-ANOVA) was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on approximately 90 shots for each participant in three different frequency bands.

Theta ERD/ERS

A series of RM-ANOVAs 4 × 3 (performance × time) on the ERD/ERS theta band yielded significant effects on performance, time, and the interaction performance by time. Only the significant results are reported in Table 2.

Table 2:

Significant results of the RM-ANOVAs 4 × 3 (performance × time) on the ERD/ERS theta band.

Theta ERD/ERS
Variable	Electrodes	Degrees of freedom	F	p	${\eta }_p^2$
Performance	Fpz	3,27	5.338	.005	.372
Performance	FC2	3,27	4.230	.014	.320
Performance	CP2	3,27	4.054	.016	.311
Performance	CP6	3,27	3.222	.038	.264
Time	F7	2,18	5.005	.018	.357
Time	FC1	2,18	5.317	.015	.371
Time	FC5	2,18	7.316	.004	.410
Time	T7	2,18	7.888	.003	.467
Time	Cz	2,18	4.054	.035	.311
Time	CP1	2,18	8.858	.001	.496
Time	CP5	2,18	3.972	.037	.306
Performance × Time	Fz	6,54	2.033	.047	.204
Performance × Time	CP6	6,54	2.680	.023	.229

DOI: 10.7717/peerj.2082/table-2

Figure 2 illustrates the grand averages of the scalp topographical distributions of the ERD/ERS amplitudes in the theta band for each performance type. The theta ERD/ERS was mapped during three pre-shot periods: from −3 s to −2 s, from −2 s to −1 s, and from −1 s to shot release (t = 0).

Given our interest in performance comparison, we performed a post-hoc analysis for performance and performance by time interaction. Fisher post-hoc analyses for the significant results in theta band are reported in Supplemental Information.

Low alpha ERD/ERS

A series of RM-ANOVAs 4 × 3 (performance × time) on the ERD/ERS low alpha band yielded significant effects on performance and time. The significant results are reported in Table 3. No differences on the performance by time interaction were found.

Table 3:

Significant results of the RM-ANOVAs 4 × 3 (performance × time) on the ERD/ERS low alpha band.

Low alpha ERD/ERS
Variables	Electrodes	Degrees of freedom	F	P	${\eta }_p^2$
Performance	F8	3,27	3.279	.040	.261
Performance	FC2	3,27	5.105	.006	.358
Time	Fp1	2,18	3.969	.048	.306
Time	Fpz	2,18	4.635	.044	.340
Time	F7	2,18	8.905	.002	.497
Time	F3	2,18	3.767	.042	.295
Time	F4	2,18	8.809	.002	.495
Time	F8	2,18	3.845	.040	.299
Time	FC5	2,18	8.879	.002	.497
Time	FC1	2,18	4.504	.025	.334
Time	T7	2,18	5.544	.028	.381
Time	C3	2,18	9.101	.001	.503
Time	Cz	2,18	6.124	.009	.405
Time	Cp5	2,18	6.193	.008	.408
Time	Cp1	2,18	10.926	.001	.548
Time	Oz	2,18	5.842	.011	.394
Time	O2	2,18	5.554	.013	.382

DOI: 10.7717/peerj.2082/table-3

In Fig. 3 we plotted the grand averages of the scalp topographical distributions of the ERD/ERS amplitudes in the low alpha frequency band for each performance type. The low alpha ERD/ERS was mapped at three pre-shot periods: from −3 s to −2 s, from −2 s to −1 s, and from −1 s to shot release (t = 0). Similiarly to the theta band results, we performed the post hoc analysis for performance and performance by time interaction. Fisher post-hoc analyses for the significant results in low alpha band are included in Supplemental Information.

High alpha ERD/ERS

A series of RM-ANOVAs 4 × 3 (performance × time) on the ERD/ERS high alpha band yielded significant effects on performance, time, and the performance by time interaction. The significant results are reported in Table 4.

Table 4:

Significant results of the RM-ANOVAs 4 × 3 (performance × time) within subjects on 432 the ERD/ERS high alpha band.

High alpha ERD/ERS
Variables	Electrodes	Degrees of freedom	F	p	${\eta }_p^2$
Performance	Fp1	3,27	3.570	.026	.284
Performance	F8	3,27	3.038	.046	.252
Performance	P3	3,27	3.721	.023	.292
Time	Fp1	2,18	5.460	.029	.378
Time	Fp2	2,18	4.811	.040	.348
Time	F7	2,18	4.670	.049	.342
Time	F4	2,18	8.354	.002	.481
Time	Fc5	2,18	8.186	.002	.476
Time	Fc2	2,18	4.846	.020	.350
Time	T7	2,18	4.813	.021	.348
Time	C3	2,18	4.689	.022	.343
Time	Cz	2,18	4.322	.029	.329
Performance × Time	Fp1	6,54	2.968	.033	.248
Performance × Time	Fz	6,54	2.401	.039	.211
Performance × Time	F8	6,54	3.196	.009	.262
Performance × Time	C3	6,54	3.473	.005	.224
Performance × Time	Cp1	6,54	2.401	.039	.278

DOI: 10.7717/peerj.2082/table-4

Figure 4 shows the grand averages of the scalp topographical distributions of the high ERD/ERS amplitude in the alpha frequency band for each performance type. The high alpha ERD/ERS was mapped during three pre-shot periods: from −3 s to −2 s, from −2 s to −1 s, and from −1 s to zero time.

Similarly to the previous results, we performed a post-hoc analysis for performance and performance by time interaction. Fisher post-hoc analyses for the significant results in the high alpha band are included in Supplemental Information.

Discussion

In the present study, we used a mobile EEG device to analyse the cortical activity underlying different types of optimal and suboptimal performance states in the framework of the MAP model. Assessing brain activity using mobile EEG data collection enables evaluation of neural efficiency during performance in actual settings (Park, Fairweather & Donaldson, 2015). Our findings showed that elite athletes used different types of functioning to achieve optimal performance, sometimes typified by effort, attentional control, and use of resources higher than in flow-like automatic performance (Bortoli et al., 2012; Carson & Collins, 2016; Furley, Schweizer & Bertrams, 2015). This outcome is in line with the view that “the processing inefficiency caused by the disruption of the inhibition and shifting functions of the central executive system does not necessarily lead to decrements in performance effectiveness … by using compensatory or alternative processing strategies” (Wilson, 2008, p. 195). Similar behaviour has been observed when people have to cope with affordances that are not always appropriate in a particular situation, and therefore need to be inhibited by higher cognitive functions located in the frontal lobes (Furley, Schweizer & Bertrams, 2015). In brain studies, ERD has been associated with higher involvement of cognitive processes (in particular working memory), and reflects anticipatory attention (Klimesch, 2012; Nussbaumer, Grabner & Stern, 2015).

In the sport science domain, there is emerging evidence suggesting that the neural efficiency hypothesis does not fully account for elite athletes’ brain activity (Vecchio, Del Percio & Babiloni, 2012). Vecchio and colleagues have argued that information processing in the cognitive-motor cortical systems of elite athletes is more complex than that predicted by the “neural efficiency” hypothesis. In experts, some neural networks within the cortex might reflect the “neural efficiency” (e.g., default mode network and relaxation), whereas other networks might reflect other mechanisms (e.g., task request, attentional demands, or affective demands). In cognitive neuroscience, Poldrack (2015) recently raised doubts about efficiency as a useful concept to explain expert performance. He proposed that the common usage of the concept of “efficiency” in cognitive neuroscience is equally vacuous. In general, the term is used to describe situations where performance appears similar but activation is greater for one group (e.g., novices, which are usually labelled as “less efficient”).

In discussing the brain studies associated with superior performance, we adopted the idiosyncratic framework of the MAP model to test different processing modes underpinning different types of performance. In this framework, we hypothesized to integrate the neural efficiency hypothesis within a broader concept, namely, the neural proficiency hypothesis of superior performance, in which athletes’ brain waves are modulated by the individual’s effort to maintain a high performance level by switching proficiently between Type 1 and Type 2 performance states. Proficiency is an active and qualitative process that involves the interaction between the efficiency of human processing and the efficacy of the performance, and it is not only the ratio between the input and the output of the biomechanical system to reach the outcome. A proficient brain is the ensemble of the neural features that define the state of being adequate or well qualified both physically and psychologically for the task. This implies that efficient processing and inefficient processing (measured through cortical activity) during performance are modulated by the degree of effort and by the attentional demands of the control systems involved in the task. This is mirrored in the neural activity found in optimal (Type 1 and Type 2) and suboptimal (Type 3 and Type 4) performance states, as found in earlier preliminary case studies with elite athletes (Comani et al., 2014; Di Fronso et al., 2016). Our findings are also in line with a study on movement-related cortical activity as a function of expertise and pressure (Cooke et al., 2014), in which a greater reduction in EEG high-alpha power during preparation for action was deemed to reflect more resources being devoted to response programming. Indeed, both cortical excitatory and inhibitory factors can contribute to elite skilled performance during different types of good and poor performances. This most likely depends on the different degree of attentional requests on the task and the high anxiety levels usually found in competition that can undermine automaticity (Hatfield et al., 2013). In this situation, a Type 2 processing, well equipped for dealing with novel problems, is more appropriate (Furley, Schweizer & Bertrams, 2015).

In agreement with prior studies demonstrating that the event-related changes of the cortical desynchronization/synchronization (ERD/ERS) can capture differences in the brain activity of elite athletes during optimal and suboptimal performances (Comani et al., 2014; Del Percio et al., 2009a; Del Percio et al., 2009b; Di Fronso et al., 2016). In the present study we investigated the event-related changes of cortical activity (ERD/ERS) in the theta and alpha bands in association with different performance states. ERS gathers a similar state of neuronal assemblies resulting in summation of postsynaptic potentials (synchrony) due to similarity of neuronal states. Conversely, a dissimilar state of neuronal assemblies results in differential assignment of neurons and de-synchrony or changing power or amplitude (ERD). These two types of brain configurations are an expression of the two classical types of performance: good and poor (Cooke, 2013; Del Percio et al., 2009a; Del Percio et al., 2009b; Hatfield & Kerick, 2007). In effect, the cortical patterns displayed in Figs. 2, 3 and 4 confirm that neural desynchronization (i.e., ERD) is mainly associated with Type 3 performance (suboptimal-controlled). However, ERS activity was not only associated with optimal-automated performance (Type 1), but also associated with suboptimal-automated performance (Type 4). In addition, during optimal-controlled performance (Type 2) cortical activity showed greater ERD activity in different areas and frequency bands compared to optimal-automated performance, contrary to the neural efficiency hypothesis, which states that more synchronization would appear in association with good performance. However, when we observed a more diffused and evident ERD activity in the brain (i.e., when athletes were unable to maintain a consistent execution associated with economy of effort), we simultaneously observed a decline toward a suboptimal-controlled performance (Type 3). This pattern is more evident in the theta band and it involves the right parietal areas, in agreement with the evidence that (1) theta activity is also present at parietal sites in the high- vs. low-error condition (Arrighi et al., 2016), and that (2) integration of visual and spatial information involves both frontal and parietal areas (Morgan et al., 2013).

The ability to achieve performance goals with a high degree of certainty, and not only minimum energy expenditure, is paramount for high performance in sport. Thus, athletes need to use different strategies to maintain or achieve good performance (Bortoli et al., 2012; Furley, Schweizer & Bertrams, 2015; Swann et al., 2016). These strategies, that may involve the engagement of working memory, require more effort than during optimal-automatic performance experiences (Type 1). Processing efficiency tends to decrease during this type of effective performance (Type 2). Indeed, Type 2 performance of our shooters was typified by a dissimilar state of neuronal assemblies during active task engagement, resulting in differential assignment of neurons and desynchronized theta/alpha power. Specifically, we found a significant desynchronization in the visuo-spatial and visuo-motor attentional networks (Fpz Fc2 Cp2 Cp6) in the theta band during Type 3 performance, whereas the other types of performance were still synchronized. These findings also suggest that more sustained attention on the target associated with top-down processes was necessary during execution (Kao, Huang & Hung, 2013; Missonnier et al., 2006). This is apparent from the results of the performance by time interaction in which we found significant ERD in the frontal midline theta (Fz) during the second before shot release as well as de-synchronization in parietal areas (Cp6) two seconds before shot release, consistent with findings from other studies (Chuang, Huang & Hung, 2013; Doppelmayr, Finkenzeller & Sauseng, 2008; Kao, Huang & Hung, 2013).

Previous studies have found that neural networks engaged in the control of action are dynamically reorganized depending on whether the task calls for volitional control or is performed automatically (see Matsuzaka, Akiyama & Mushiake, 2013). Specifically, the prefrontal cortex and anterior striatum were shown to exhibit task-related activity modulation (i.e., synchronization) under the former but not the latter condition (i.e., desynchronization). In other words, neural networks for movement control were optimized, being regulated by smaller scale neural circuits. This view is in line with the overall cortical synchronization on the task observed on Type 1 and Type 4 performances and with the desynchronization observed on Type 2 and Type 3 performances. In particular, the higher ERD during Type 3 performance is evident in Fig. 2 (Theta ERD/ERS). On the other hand, theta ERS reflected the activation of neural networks involved in allocation of attention related to target stimuli rather than working memory (Missonnier et al., 2006). Finally, the ERS activity of Type 4 performance can be explained with the lower level of control of the action exerted by the participants during this type of suboptimal performance (see Table 1).

Moreover, we can hypothesize that the ERD/ERS pattern found in Type 1 performance is likely related to a “default mode network” functioning, proper to autonomous skills and goal-relevant attentional focus when approaching shot release (Doppelmayr, Finkenzeller & Sauseng, 2008), similar to what was found in golfers and basketball free throwers (Chuang, Huang & Hung, 2013; Kao, Huang & Hung, 2013). Excessive reinvestment in controlled processing undermines automaticity, and is related to higher cortical activity in the right parietal and frontal areas (Broyd et al., 2009), which is evident during Type 3 performance. Gentili and colleagues (2011) showed that with practice humans have the capacity to adapt their movements when challenged with novel demands or task requirements. They found a cortical adaptation of frontal temporal and parietal regions and a progressive idling of cortical rhythms in a learning paradigm of a visuo-motor task (Gentili et al., 2011). They suggested that the update of motor plans in response to incoming sensory information could be mirrored in theta band oscillation. There is also growing evidence that theta oscillations are linked to error monitoring (Cavanagh, Cohen & Allen, 2009; Gevins & Smith, 2000; Luu, Tucker & Makeig, 2004; Trujillo & Allen, 2007; Yordanova et al., 2004). Error monitoring could explain the difference we obtained in the maps for optimal (Type 1 and 2) and suboptimal (Type 3 and 4) performance.

Findings in Alpha ERD/ERS (Figs. 3 and 4) concur with the evidence indicating relationships between: (1) low Alpha power and general cortical arousal, and (2) high Alpha power and task-relevant attentional processing (Babiloni et al., 2008; Hatfield et al., 2013; Pfurtscheller & Lopes da Silva, 1999). Indeed, we observed a significant difference among the different types of performance, particularly in FC2 site (see Supplemental Information), with ERD patterns (+1.12%) in low Alpha band for Type 3 performance. Thus, higher levels of general cortical arousal were associated with suboptimal performance states. In Type 2 performance cortical activity was synchronized on the task (−2.78%). ERS was more evident in Type 4 (−6.18%) and Type 1 performances (−9.14), indicating a low level of arousal and a minimal conscious control typical of flow-like behaviour (Bortoli et al., 2012; Jackson & Csikszentmihalyi, 1999). FC2 is one of the electrodes that represent the area where attentive processes and control on core components of action can be integrated for the control of goal-directed and stimulus-driven attention processes (Corbetta & Shulman, 2002).

Furthermore, the increase in the neuronal predictive power for action that was observed in the Fp1, F8, and P3 for high alpha band indicates that the optimization of neural networks can occur qualitatively in the form of enhanced proficiency of the neuronal representation of action. It is related to the visuo-spatial network that involves the switches between the performance types reflected in the activity of fronto-parietal areas during a few seconds preceding the shot release (i.e., Fpz, Fz, F8, Cp1; Matsuzaka, Akiyama & Mushiake, 2013). Moreover, the involvement of the de-synchronization of the contralateral sensory-motor area (i.e., C3) is an expression of the control of the right finger/hand (Pfurtscheller & Lopes da Silva, 1999). The results are also in agreement with the findings by Sauseng and colleagues (2005), who suggested that a shift of attention selectively modulates excitability of the contralateral posterior parietal cortex and that this posterior modulation of alpha activity is controlled by prefrontal regions.

In summary, the findings of the present study provided support to the construct of neural proficiency in sport performance that underlies Type 1 and Type 2 performance states as conceptualized in the MAP model (Bortoli et al., 2012), as well as in dual-process theories (Furley, Schweizer & Bertrams, 2015) and ACT (see Wilson, 2008). According to this view, neural proficiency is associated with Type 1 and Type 2 performance states, and mirrors the behaviour of elite athletes when they use efficient or effortful processing to deal with task demands. This is in agreement with the literature reviewed by Klimesch (2012), who highlighted how alpha ERS and ERD reflect inhibition and release from inhibition, respectively. On the other hand, theta ERS reflects top-down processes related to working memory and attention (Doppelmayr, Finkenzeller & Sauseng, 2008).

From a neurophysiological perspective, successful goal-directed actions require the individuals to flexibly adapt their behaviour to deal with performance problems or environment changes, beyond correct action selection, planning, and execution (Ullsperger, Danielmeier & Jocham, 2014). Performance optimization is ensured by a continuous monitoring of a wide network of brain areas (such as the posterior medial frontal cortex), which detects and evaluates deviations of actual from predicted states.

In conclusion, our findings provide evidence of distinct cortical processes underlying the four types of performance states derived from the interaction between performance and attention control levels. However, our methodological approach did not enable us to distinguish specific cortical networks among the four types of performance. Further investigation using the tools of graph theory (Bullmore & Sporns, 2009) and dynamic functional connectivity (Hutchison et al., 2013) within the framework of the MAP model, could provide a better understanding of the functional connectivity and interactions among default mode, dorsal and ventral attention network, sensory motor network, and visual network, typically involved in shooting performance. The investigation of ERD/ERS patterns after shot release could provide important information for a better understanding of the dynamics of brain oscillations during recovery from a highly demanding task execution.

From an applied point of view, our findings can address neurofeedback training to enhance performance in closed skill sports. Our findings can also help in choosing the proper areas to apply transcranial electrical stimulation (Paulus, 2011) in order to facilitate cortical plasticity, influencing the membrane potential of neurons and modulating spontaneous firing rates. Indeed, during sport performance, the brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales.

Supplemental Information