Introduction:

Understanding how fatigue impacts performance during middle distance running events has traditionally been seen through a mechanistic lens (Coyle, 1994). Dating back to work by AV Hill, scientists have looked at performance being limited via catastrophic failure at the muscular level (Noakes, 2012). Previously, researchers (Tucker & Noakes, 2009) focused on tasks to failure or fixed pace tasks, to ascertain how fatigue occurred and influenced performance. More recently, an ecological approach has been utilized, with the realization that performance does not occur in a controlled vacuum, but instead mainly consists of a freely paced task in which the subject’s success is the result of their decisions made during that task (Renfree, Martin, Micklewright & St Clair Gibson, 2014). Over the past decade, this has led to the evaluation of pacing strategies to understand this decision making process.

Pacing, which is the distribution of effort via altering speed over the course of a race, during middle and long distance races has been described as a battle between expected and actual experiences and modulated by the degree of drive and motivation (Williams, 2014). Current theories start with a race template where an expectation of different physiological and psychological phenomenon is created. As the athlete gets into the race, this expected outcome is compared to the actual experience. The experience, which is up for debate, may include both the tracking of internal physiological phenomenon and external phenomenon such as distance covered, splits, competitors, and so forth. It is this combination of internal and external information, which helps determine current state. Marcora and Staino (2010) suggested that this information is essentially coalesced and represented by Rating of Perceived Exertion, while others believe it’s more of an algorithmic approach (Renfree et al., 2014).

A runner’s speed is then adjusted based on this comparison between expected and actual experience (Noakes, 2012). While there is debate on the exact mechanisms, and whether this process occurs consciously, subconsciously, or some combination of both, the general process is similar (Renfree et al., 2014; Marcora & Staino, 2010). According to Renfree, this comparison combined with a risk versus benefit calculation results in a feeling of positive or negative affect, which then is used to influence pace adjustment. In Marcora et al (2010) psychobiological model, instead of relying on conscious or subconscious feeling, a comparison of expected RPE changes versus actual RPE changes at that moment are used to influence the pacing strategy.

This approach of looking at pacing as a cognitive decision making process has largely resulted in investigation using deception studies in which select variables, such as knowledge of speed, distance, and time, are either enhanced or taken away to examine their effects on pacing and performance (Williams et al., 2014a). For example, Mauger, Jones & Williams (2009) manipulated the subjects’ knowledge of the end point of the a 4km cycling test to show that, with multiple trials subjects can adjust to this lack of information. There have been many investigations looking at manipulating subject’s knowledge of endpoint, timing, and intensity as evidenced by Williams et al (2014) a thorough review of the subject.

Through these investigations, researchers have begun to narrow down the impact that deception has on performance. For example, in their review Williams et al (2014a) found that out of 31 studies using deception, 10 showed performance enhancement effects, with these mainly coming from deception of intensity, while manipulating distance and time have altered pace but not performance Contrasting this, psychological manipulation via manipulating performance expectancy have largely been shown to increase performance (Lohse & Sherwood, 2011; McKay, Lethwaite & Wulf, 2012; Stoate, Wulf & Lethwaite, 2012). This evidence points to the idea that deception can effectively be used to understand the dynamics of pacing and performance. By evaluating the impact that manipulating the aforementioned variables have on pacing, what information matters can start to be understood.

While this approach has merit, what is being followed is an isolationist approach that looks at the impact a specific variable has on the pacing paradigm. This is useful for understanding how a variable may be used in a race situation, however it creates a false constraint that is usually not present. The issue with this approach, as Williams et al. outlined in their review, is that these methods lack ecological validity (2014a). There are very few athletic competitions or even exercise modalities in which the constraints of the tasks are not known. Put simply, no one lines up for a race without knowing the distance they are to race.

To combat some of these shortcomings, we can see it’s the actual decisions (to cover a surge, to speed up or slow down halfway through the race, when to kick) that are made throughout a performance which define performance. Up until this point we’ve focused on the global view of performance and the pacing without knowing what causes those critical decisions to be made. Given the dynamic environment of racing, with variability of the pace nearing 5% in experience runners (Cottin, Papelier, Durbin, Koralsztein & Billat, 2002)., as opposed to the stable even pace concept that prevails in popular coaching literature, the decisions made to adjust pace and distribute energy are likely crucial for performance.

Instead of using a cognitive based approach, we can follow Smits, Pepping & Hettinga’s (2014) approach and use a more ecological based approach, utilizing the concept of affordances. The concept of affordances refers to the idea that our interaction with the environment elicits action possibilities and can even invite certain actions to be performed (Withagen, de Poel, Araujo & Pepping, 2012). For instance, if in a steeplechase race when we approach a hurdle, the hurdle invites fully jumping over it without touching it, jumping on top of it with a light touch of the barrier, hopping over it with light assistance from using our hands on top of the barrier to stabilize ourself, or a myriad of other choices. Which action is selected depends on this interaction between the obstacle the environment provides (i.e. it’s height, length, etc.) and our own action capabilities (i.e. given our fatigue can we fully jump it). By utilizing the concept of affordances, a better understanding to the decision making process can be had

Affordances:

The concept of affordances was developed by Gibson (1966), in which he investigate the concept of how perception and action interact in the environment. Up until Gibson, a cognitive approach of indirect perception was taken, in which agents picked up information in the environment and needed cognition to interpret. The cognitive approach required an interpretation to assign meaning to any perception. It can be seen as a linear approach in which we move from picking up a perception, having our minds interpret that perception and then making a decision based on the information in a very sequential fashion. In this approach, the environment only provides information that has no meaning until one is assigned to it by our own internal representations. In essence it was a serial and linear process in which perception was interpreted and then acted upon based on that interpretation.

Contrasting this approach, Gibson developed the idea of direct perception. Instead of seeing perception as information that needed an outside agent to provide meaning, in ecological psychology, the concept is that perception is in fact direct, in that objects provided opportunities for action on their own (Withagen et al., 2012). These opportunities for action were termed affordances. Differing from the cognitive approach, the concept of going from perception to action was seen as something that occurs in conjunction and parallel. This way perception of the environmental information and corresponding action are developed simultaneously, so that action selection and specification are defined as the same process (Smits et al., 2014). The classic example is that when seeing a chair, it affords sitting, standing on it, throwing,, or perhaps breaking depending on our action capabilities. If a person is in need of a place to sit versus if they are need of a way to reach the ceiling to change a light bulb, that same chair will invite different actions.

This parallel simultaneous processing means that multiple action possibilities are often prepared at once. In one theory, multiple potential actions are continuously prepared for and compete for selection (Cisek, 2007). The ultimate selection of an action depends on the continual search for evidence that simultaneously occurs. As these biasing factors toward a particular decision accumulate, selection of a decision is refined. Ultimately, it is this interaction between what actions an environment affords, what biasing factors push us towards, and our own capabilities to perform such actions that influence our decision making.

When we look at athletic competition, this concept of affordances has largely been researched using traditional “ball” sports (Fajen, Riley & Turvey, 2009). In American football, a running back attempting to find and run through a gap in his offensive line can be seen as a situation in which multiple affordances, or oppurtunities for action may present themselves. He may be presented with multiple affordances and the question becomes what decision does he make. The number of affordances depends on the ability of our running back to have the capabilities to perceive them and then the action capabilities to follow through. An experienced and skilled performer is likely to pick up more opportunities for action and may be able to see more gaps in the line, while our novice may simply have a single affordance presented due to his capabilities and inability to detect further options.

For an endurance athlete, the concept of recognizing gaps in a line might not seem readily transferable, but when a race is broken down into the many decisions or non-decisions to alter pace, the idea of finding the right affordance becomes paramount. Pacing is the distributing of our energy across the entire race distance with a goal of getting to the finish line just as we have maximally spent our energetic reserves for that race. Having too much left or spending our reserves to early both results in suboptimal performance. Therefore, when faced with action possibilities, selection or non-selection of the correct ones influences this delicate balance of reaching the finish line in an optimal state of fatigue and energy distribution.

This distribution of energy at any given moment can be seen as a prediction of our future state. The accuracy of this prediction will reflect how successful our race is likely to be. If in our current state we perceive that we can maintain the pace for the remainder of the race quite easily, then that might invite a decision to increase the speed. However, if our prediction was inaccurate in a negative direction, then we will likely suffer from fatigue and slow down before we reach the finish line. This is why the accuracy and degree of coupling between our perception abilities is paramount to success. The more accurate match between our current abilities and the expected changes that the affordance provides, the more likely we will have a low degree of mismatch between when we experience exhaustion and complete the race.

In terms of affordances, this means that it is not only is important to be able to perceive action possibilities, but it is important to act upon the correct one, which in terms of performance would be the one that minimizes the degree of mismatch between exhaustion and completion of the race. If the wrong affordance is selected, then this mismatch will likely be high. What we’re left with is the issue of selecting from these competing affordances.

At any moment we could be bombarded with multiple affordances and the corresponding action depends on what one is selected. This process of selection is referred to as the affordance-competition hypothesis (Cisek, 2007; Fajen et al., 2009; Smits et al., 2014). The theory goes that for any given situation, we have multiple affordances competing simultaneously. Which one is selected is determined by biasing factors influencing the selection. Biasing factors can almost be seen as small pieces of evidence that slightly push or pull towards a particular action. As an example, in a race, the raising of the shoulder of a competitor in front of our runner might bias him to prepare to pass that competitor, as the raising of the shoulders could be seen as an external signal of fatigue and possibly slow down. In that, the relationship between the environmental property and the action capabilities of the person combine to determine selection (Cisek, 2007). What we are left with is a continual process of potential actions that are prepared while “evidence” for selecting or not selecting is continually gathered (Smits et al., 2014).

Therefore, in athletic performance, the key determinants are twofold. First we have the ability of attunement so that we are able to pick out affordances (Fajen et al., 2009). Secondly, we’re left with ones action capabilities that allow for completion of the affordances (Fajen et al., 2009; Deschamps, Hug, Hodges & Tucker 2014). After all, an affordance may be present, but if we are not attuned to the large gap beginning to open up on the inside of the track because we are biased towards looking for gaps on the outside of the pack where they traditional are, then it does no good to our runner. Similarly, if he does not have the action capabilities, in terms of speed to run through that gap, then it also would be of no use.

While the concept may have been used traditionally in “ball” sports, it’s clear to see how this could apply to middle and long distance events. Decision making during a paced competition, such as a 1,500m race on the track, can be seen in terms of the affordance-competition hypothesis. Throughout a race there is a continual bombardment of affordances, which present the opportunity to act. These may range from closing or preventing gaps from opening up in the pack, counteracting a move from the leader mid race, or deciding when is the opportune time to use our final sprint based on this interaction between fatigue and distance remaining. Whether we speed up, slow down, or counter our competitors moves, depends on this combined integration of recognizing affordances.

Perception Capabilities

In order for an affordance to invite behavior, the affordance obviously has to be present and secondly the actor needs to be able to perceive it. When using performance as the backdrop, the question becomes what are the factors that impact our affordance recognizing capabilities, or in other words our perception, and secondly, how big of an impact do they have? While the majority of the research subsequently presented has not looked at these concepts through an affordance lens, we can still glean information on what is important by looking at research that manipulates our perceptual information.

 

Visual

The obvious choice for altering perception would be to alter visual information. In particular, a concept called optic flow is pertinent in regards to sport performance. Optic flow refers to the sense of movement one has while passing through an environment (Gibson, 1950). Research has shown that taking away or altering this visual information can affect subsequent perception abilities. For example, Profitt, Stefanucci, Banton & Epstein (2003) found that after walking on a treadmill with no optic flow information, subjects perceived distances to be further away than when they were provided with optic flow when walking on the treadmill.

Parry et al. (2012, 2014) ran a series of experiments in which they altered optic flow while running and cycling. In Parry, Chinnasamy & Micklewright’s initial study (2012), they took cyclists and had them perform a 20km self paced time trial on an ergometer. Following this, they had the cyclists complete three more 20km trials matching their initial trials power output. During the three trials the cyclists watched a video of scenery passing that was either matched to the speed they were cycling, playing at a speed 15% slower, or 15% faster than they were going so that a mismatch between visual information of optic flow and speed was achieved. What they discovered was that optic flow influences RPE, in that a slower optic flow lowered RPE and that even with being told to match power output from the earlier trial, cyclists increased their power output during the slower optic flow condition.

This finding that showed optic flow had an impact on not only perception of effort, but also on pacing. To follow this up Parry and Micklewright (2014) investigated optic flow’s impact on running using 5k time trials. Instead of attempting to control for pace, the authors chose to let the trials be self-paced with no feed.htmlback about how fast they were running or how much distance they had covered. What they found was that with slower optic flow, the perception of distance covered slowed, while with a fast optic flow the opposite occurred. In other words, when the participants though they had completed 5km, they were on average 423.5m shorter during the fast optic flow, and 1067m further than 5km during the slow optic flow state.

This research demonstrates the large impact that optic flow has on perception abilities and action capability prediction. If simply manipulating optic flow can alter the predictability of distance estimation, which is a factor that we use to decide our pacing strategy, then one has to question the transferability of much of the pacing and fatigue research which is performed in the lab with no optic flow (Williams et al, 2014a). Simply relying on studies that use stationary ergometers or treadmills might influence the decision making process during performance; as one of the key variables, optic flow, from which information on pacing is ascertained, is artificially constrained

Auditory:

Another perceptual item that can influence the perception of affordances is auditory feed.htmlback (Sigrist, Rauter, Reiner & Wolf 2013; Oliver & Kreger-Stickles, 2006; Eriksson & Bresin, 2013). When competing, athletes are exposed to an array of auditory stimuli ranging from hearing their own and competitors breathing and movement (i.e. footfalls) to external yelling of splits, coaching instructions, and spectator support..

There has been an array of research looking at auditory cues impact on factors such as running cadence or economy, using devices such as metronomes (Eriksson & Bresin, 2013; Oliver & Kreger-Stickles, 2006). Heiderscheit, Chumanov, Michalsi, Wille and Ryan (2011) found that using metronomes set at various percentages of the runners’ preferred stride rate influenced mechanical loading at the knee and hip joints. Similarly, in a pilot test, Eriksson et al. (2013) found positive evidence that vertical displacement during the running stride could be influenced by auditory feed.htmlback.

However, to the author’s knowledge, there have only been a few studies looking at external auditory stimuli that occurs in a simulated competitive environment and evaluated its influence on performance For instance, the impact of using music to manipulate performance has been investigated, but since in the majority of competitive situations using things like headphones are not permitted, it’s not a practical performance manipulator that tells us much about how auditory information impacts decision making (Terry, Karageorghis, Saha & D’Auria, 2012)

In one study investigating the effect of auditory information on performance, they found that giving false verbal split information did not impact performance (Beedie, Lane & Wilson, 2012). However, there was a reduced metabolic cost during the false positive feed.htmlback situation and a change in emotional state in response to that feed.htmlback. The individuals cited lower anxiety and gloominess with higher happiness and calmness. The emotional impact of hearing good or bad news, whether it’s splits that are faster or slower than expected could therefore have an impact on decision making ability as we shall see.

In one study (Bood, Nijssen, van der Kamp & Roerdink, 2013), they investigated the impact of music or a metronome on time to exhaustion during a treadmill run. What they discovered was that both motivational music and a metronome improved time to exhaustion, albeit most likely through different mechanisms. The music led to reduced RPE likely through it’s motivational properties, while the metronome led to a higher degree of coupling between the runners pace and their cadence. The metronome allowed for the most consistent maintenance of cadence throughout the trial, which could potentially influence running economy.

Along similar lines, Hoffmann, Torregrosa and Bardy (2012) found a coupling between breathing rate and a metronome during a cycling task. While not a performance test, they found a reduction in energy expenditure when this coupling occurred. While not directly tested, it’s possible that the hearing of the footfalls of competitors could have a metronome like effect pulling people towards the same cadence and breathing rhythm that the pack in a competitive race is using. For this reason, investigating the impact that more ecological sounds, such as footfalls, have during performance seems pertinent.

 

Competitors

Competitions in track and field involve multiple competitors on the track at once fighting for position and involve an amount of tactical acumen. Dating back to the 19th century, Triplett (1898) found that having competitors improved performance more so when paced during a cycling performance test than during an unpaced one.

More recently, researchers (Williams et al., 2014b) have investigated the role of competitors mostly using simulated avatars. In two separate studies using a 4,000m trial in one and a 16.1km cycling trial in another, performance was improved when subjects faced an avatar that was set at a faster speed than each subject’s baseline (Stone et al, 2012; Williams, et al, 2014b). In both cases, the change in performance was attributed to greater anaerobic energy use in the later portion of the race. This lends credence to the idea that competing against someone likely allows for accessing a deeper reserve.

While the use of an avatar no doubt captures the internal representation of competing against an individual, the ecological value is lost as well as the external aspects that a competitor brings. When looking at these studies, they address the issue of competing on overall performance and explain what changes on the physiological side, but the details of why this occurs are lost.

 

Attunement

While understanding how the interaction of the aforementioned constraints impact performance, when looking at the coupling of perception and action, one needs to be able to pick out affordances from the environment. To do so, as described previously, necessitates having the action capability to enact the affordance plus the attunement to be able to pick it up in the environmental array (Fajen et al., 2009). Therefore, what affordances are correctly picked up and ultimately selected, are in part determined by our ability to be attuned to them.

One way to analyze what is important is to see what experts do compared to novices. A study looking at expert climbers demonstrated that they were more attuned to the properties of the climbing surface that were functional, as opposed to the novices who focused more on the structural components (Boschker, Bakker & Michaels, 2002).

Similarly, a review by Fajen et al. (2009) found that novices tend to rely on information that doesn’t directly impact the specification of movement, while experts pick out information that directly impacts action .In other words, the research demonstrates that experts had a better capacity to pick out what actually mattered in completing the performance.

It’s not only a difference between elites and novices or in simply picking up the information either. In looking at elite versus sub-elite cricketers, Muller, Abernethy and Farrow (2006) found that there was a difference in the ability and speed of information pick up between these two groups. This and similar studies, led Yarrow, Brown and Krakauer (2009) in a review to conclude that elite athletes are better able to pick up affordance possibilities and the biasing information to select them earlier than non-elites.

Another interesting finding is that novices tend to rely on what could be best called a strict template (Fajen, 2009). For example, in a study by Smith, Flach, Dittman and Stanard (2001), they found that in a simulated ball hitting task, that novices relied on a very narrow range of information of the ball speed and size as it approached to predict collision . In other words, if either speed or size was shifted to a degree, their accuracy in predicting collision decreased. With training, however, subjects shifted attention to variables that allowed for a more general and flexible model to predict collision more accurately. The ones who demonstrated successful transfer and learning relied on a wider range of optimal margin, as opposed to a linear margin where they looked for a particular angle and rate of expansion in their visual field. Greater investigation into the concept is required to determine if the idea transfers to the pre-race template that is so important in pacing.

While there are few studies comparing experts and novices in terms of attunement in endurance or self paced events, we can still derive information from the aforementioned research. There is a likelihood that an expert runner would have a broader based race template that allows for successful completion of a race in a variety of different manners based on tactics, pace, and so forth, where a novice might only see success in a narrow range of race possibilities. In other words, the degree of mismatch between perceived and actual race template would be less. Additionally, expert runners are likely to be able to pick up cues of fatigue earlier. Although, anecdotally East African runners often display a more reckless racing style, risking “blow up” in races more so than their American or European components (R. Canova, personal communications). This could be a result of motivation overriding race template or affordance recognition as the reward is greater than the risk.

 

Manipulating attention/fatigue.

In addition to looking what factors experts are attuned to, we can look at how attunement changes with manipulating variables. While not directly measured, we can use psychology studies using focus of attention to help us understand what people are paying attention to.

One such study found that the presence of competitors shifted attention from internal to external (Williams et al., 2014a). This makes intuitive sense in that we have to shift focus to what is going on around us in the presence of competition. It also begs the question of whether lab paced tests which are often trials against one self shifts performance dynamics as the focus of attention is more internal than it would be in the outside world. It’s possible that individuals use competitors presences to further refine their own perception of affordances. A competitor could serve as a base of comparison, as it is quite regular that athletes make comments about listening to competitors breathing patterns and realizing that their own were more relaxed, thus giving them confidence, and perhaps evidence, that could enhance performance or influence their decision to speed up Therefore shifting attunement and bias towards different affordances than we would experience in actual competition.

It also seems that as pain or fatigue increase they have a pulling or distracting effect that shifts people from an external or dissociative state towards an associative one (Lohse, 2011). This seems like it would have a negative effect on external affordance recognition and performance. Lohse et al. (2011) also showed that having an external focus of attention increased fatigue resistance on an endurance wall sit task. While affordances are thought to be picked up both consciously and subconsciously, a shifting of attention either due to external factors such as competitors or internal factors such as pain, likely will shift our perception abilities. Once again, this points to a constantly changing climate of perception and action as a race unfolds. With this shifting of race conditions, it is imperative that our own internal representation of current fatigue versus distance remaining also shifts. With each change in conditions, the degree of speed and accuracy of assessing our own action capabilities will likely play a role in our end performance. In essence, the goal is to accurately assess our energy given the distance remaining with each new perturbation of the race and environment that occurs.

 

Action Capabilities

The other half of the equation when determining the magnitude of ones response or indeed whether one acts, is if there are action capabilities to support such action. The ability to complete such affordance determines whether one will be biased towards specifying it or not. Research has shown that humans are accurate at predicting their own capabilities in tasks ranging including reaching, grasping and jumping (Deschamps et al., 2014). In the world of running, for example, if one perceived a large shift in pace during the later portion of the race a selection for acting up that might not occur if one does not have the capabilities, such as an anaerobic reserve present, to do so. Although not in an athletic realm, Fajen (2005) showed how rapid recalibration of action capabilities occurs when he had subjects perform a braking task. Subjects were put through a computer driving simulation where they controlled braking through pulling of a joystick back. By manipulating the strength of the braking without the subjects knowing, they were able to show that people recalibrated following the change in the braking strength, even when visual feed.htmlback was taken away so that the participants didn’t know the result of the task. What this tells us is that action capabilities can be adjusted without requiring direct outcome feed.htmlback.

Knowing one’s action capabilities is in line with current theories on pacing which state that we create a pre-race template and then compare our current state to our expected state (Renfree et al., 2014; Noakes, 2012). Seen through the lens of affordances, what we are doing is assessing our current action capabilities and recalibrating them throughout the race.

 

Capabilities evolve over the race:

One way to conceptualize our change in capabilities is by using a concept called the Hazard score (De Koning, 2011). The Hazard score is the product of your momentary RPE and the fraction of the competition remaining. The concept is used to demonstrate the role that effort and perceived race distance remaining interact to determine pacing decisions. In practical terms, as we get closer to the finish, there is potentially less danger, so we can reach higher levels of effort, knowing that we are almost done. As we make it closer to the finish of the race, our Hazard score drops, and the reins are loosened so that a phenomenon like the end spurt or ‘kick’ can occur. In their own work De Koning et al. (2011) found that Hazard scores predicted changes in pace in simulated competition, where if the Hazard score was too high, participants tended to slow down, while if it was too low, they increased speed.

Based on this concept and the ‘endspurt’ phenomenon, we know that distance can change our action capability assessments, but often, the make or break decisions are made during the middle of the race when hazard levels, and corresponding fatigue levels, are high. After all, if we were to watch a race, almost anyone can summon up the energy to increase speed and have the perception of sprinting the last 100m, but it’s from far enough away from the finish to introduce some ‘hazard’ and uncertainty in which the race starts to develop.

From this standpoint, we can see that making an accurate assessment of ones action capabilities is paramount to success. As demonstrated by the aforementioned study by Parry and Micklewright (2012), with the manipulation of visual information people can under or overestimate the distance covered by significant portions. If one is trying to time maximum exhaustion to occur at the finish line, which one could argue is the objective of pacing, then any calibration error could impact performance.

Since decisions are made under fatigue, stress, and high Hazard levels, it is prudent to understand how emotional states can impact capability assessment. Graydon, Linkenauger, Techman and Proffitt (2012) showed that anxiety caused subjects to underestimate a wide range of abilities including reaching, grasping, and passing. They demonstrated that during stressful conditions the reserve, or the gap between actual capabilities and perceived abilities, is updated to minimize risk. Similarly, Deschamps et al. (2014) found that injecting a substance that caused a painful reaction in one leg reduced the subjects estimation of hopping performance in both legs. This is an interesting finding because it shows the global effect pain can have, as estimation and actual hopping performance was reduced in the leg that was not injected. This global effect might explain why during the same race distance, athletes often explain different limitations, whether it is a burning sensation in a particular muscle like the quads, exasperated breathing, or a number of other limitations. Depending on what the athlete is attuned to, that perception, such as increased breathing rate, could have global effects on perceived action capabilities.

Further demonstrating the link between stress and action capabilities, researchers (Daviaux, Mignardot, Cornu, & Deschamps, 2014) investigated the effects of the stress of 24 hours of sleep deprivation had on perceived action capabilities of stepping over a bar. Following sleep deprivation, participants showed a decrease in their estimation of how high of a bar they could step over, despite actual performance actually being equal to their non-sleep deprived state. Similarly demonstrating a link between stress, pain, and perception abilities researchers (Tabor, Catley, Gandevia, Thacker & Mosely, 2013) found that administrating a painful shock altered how close the participants judged a switch that could eliminate that pain.

Lastly, Witt found that those in chronic pain tended to overestimate the distance to walk to a target (2009). These findings led Deschamps et al. (2014: Pg 271.e6) to state that “individuals perceive the environment in terms of the costs of acting within it,”. Tying this to prior research which showed there was an emotional reaction to external feed.htmlback, such as split calling, it can be seen that the world of affordance recognition and action capabilities can be biased by a myriad of factors (Beedie et al., 2012).

Based on these findings it can be seen that as stress and pain change, action capabilities are adjusted. Given that competing in events like the 1,500m on the track are generally considered very painful (Mauger & Hopker, 2012) and bring along a large degree of stress and anxiety both before and during the race (O’Connor, Carda & Graf, 1991), it seems logical that action capabilities during a race would quickly change. Given that anxiety can increase with the degree of importance of the competition and performance goal (Jones, Swain & Cale, 1990), one has to question whether our pre-race action capability assessment is accurate or if it’s skewing because of anxiety is one of the things that could cause underperformance.

Before leaving this concept, the ability to assess competitor’s action capabilities has received recent interest. A number of research studies looking at assessing the accuracy in which we can predict other people’s affordance abilities has ranged from 10% all the way down to less than 1% error levels (Fajen et al, 2009). For example Stroffgen, Gorday, Sheng and Flynn (1999) found that individuals could accurately predict others maximum preferred sitting height within 10% error for both short and tall people. In another series of experiments Ramenzoni, Riley, Davis, Shockley and Armstrong (2008) demonstrated that subjects were able to perceive thers maximum reach-with-jump height to within an 8% error. Additionally, in a follow up experiment in the same study they found that simply by viewing an individual walking, subjects could use this information to accurately assess that individuals jumping to reach an object ability. This demonstrates that there is a degree of transference between assessing others action capabilities using similar, but distinctly different movements. Assessing the abilities of your competitors is important in competitor. Consider the scenario where a competitor surges mid-race. One has to immediately assess whether a competitor will be able to sustain that surge and if so for how long, before deciding whether to follow.

To the author’s knowledge, there have been no studies looking at the dynamic nature of assessing one’s action capabilities during a middle or long distance race. Although anecdotally, the ability to judge ones own capacities as well as those of their competitors, can be inferred by the degree of slowdown in the 2nd half of a race, as it’s obvious that people miscalculated their capacity to make it to the finish line. Having coached at all levels, it might explain why on the track, you see larger slowdowns in the younger High School aged athlete when compared to the professional level. The degree of mismatch between effort distribution and actual action capabilities may be larger in less experienced runners. Adding credence to this concept, Micklewright et al .(2012) found that younger children tended to employ a pacing strategy that elicited a faster start followed by a progressive slowing of the pace, on a running task that took approximately 4 minutes to complete. In contrast to this, older children used a U-shaped pacing strategy with a slightly faster start, a slowing in the middle, but an increase in speed during the last 20% of the race. This shows the evolution of a pacing strategy towards a more optimal one. As Noakes, Lambert, and Hauman (2008) showed that in 32 world record 1,500m races (which would take around 3:30 to complete) the pacing including a slightly faster first and last lap, with the middle two laps being slightly slower.

 

Conclusion

Using an affordance based model to understand decision making during middle distance events leaves several questions unanswered. Firstly, athletes are making decisions under heavy fatigue, stress, and high hazard levels. This means that affordance recognition and action capability assessment dynamically changes throughout the race, in that the constraints, which might bias us towards specification of an action early on, differ from those that bias us later on. What these biasing factors are and how they change remains largely unexplored.

Secondly, researchers have investigated the use of a pre-race template off of which to base expectations of increases of effort, which is similar to an initial assessment of action capabilities (St. Clair-Gibson et al., 2005). This is done using prior knowledge and experience and has been found to be a more potent decider in pacing strategy than physiological feed.htmlback (Albertus et al., 2005). However, there has been little assessment of how these action capabilities are updated and changed over the course of a race and to what degree the various perceptual contributors, such as optic flow, contribute to this assessment..

Additionally, knowing the impact that affordances has on performance, one has to question whether we lose valuable information and change the dynamics of exercise by doing performance tests on an indoor device like a treadmill versus outside. We obviously eliminate variables such as optic flow, which we’ve seen can influence fatigue and perception. As we saw with when competitors were there or not, this shifts attention to an internal focus. Which would mean a greater reliance on internal physiological feed.htmlback, which runs contrary to what St. Clair-Gibson (2005) found as important when looking at fatigue and performance.

Finally, tying items together, pacing has been used as a way to investigate out how fatigue manifests itself and what decisions are made throughout a race. While it gives a good overarching view of what is going on, we miss the actual decisions that are being made. Using the coupling of perception and action, we can look at these decisions through the lens of affordances and start to identify what actually matters.

 

Albertus Y, Tucker R, Gibson ASC, Lambert EV, Hampson DB, Noakes TD. Effect of Distance feed.htmlback on Pacing Strategy and Perceived Exertion during Cycling. Med Sci Sports Exerc. 2005; 37(3):461–8.

Beedie, C. J., Lane, A. M., & Wilson, M. G. (2012). A possible role for emotion and emotion regulation in physiological responses to false performance feed.htmlback in 10 mile laboratory cycling. Applied psychophysiology and biofeed.htmlback, 37(4), 269-277.

Bood, R. J., Nijssen, M., van der Kamp, J., & Roerdink, M. (2013). The power of auditory-motor synchronization in sports: enhancing running performance by coupling cadence with the right beats. PloS one, 8(8), e70758.

Boschker, M. S. J., Bakker, F. C., & Michaels, C. F. (2002). Memory for the functional characteristics of climbing walls: perceiving affordances. Journal of Motor Behavior, 34(1), 25–36. doi:10.1080/00222890209601928

Cisek, P. (2007). Cortical mechanisms of action selection: the affordance competition hypothesis. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 1585–1599. doi:10.1098/rstb.2007.2054

Cottin, F., Papelier, Y., Durbin, F., Koralsztein, J. P., & Billat, V. L. (2002). Effect of fatigue on spontaneous velocity variations in human middle-distance running: use of short-term Fourier transformation. European Journal of Applied Physiology, 87(1), 17–27. doi:10.1007/s00421-002-0582-8

Coyle, E. F. (1994). Integration of the physiological factors determining endurance performance ability. Exercise and sport sciences reviews, 23, 25-63.

Daviaux, Y., Mignardot, J. B., Cornu, C., & Deschamps, T. (2014). Effects of total sleep deprivation on the perception of action capabilities. Experimental brain research, 232(7), 2243-2253.

De Koning, J. J., Foster, C., Bakkum, A., Kloppenburg, S., Thiel, C., Joseph, T., … Porcari, J. P. (2011). Regulation of Pacing Strategy during Athletic Competition. PLoS ONE, 6(1), e15863. doi:10.1371/journal.pone.0015863

Deschamps, T., Hug, F., Hodges, P. W., & Tucker, K. (2014). Influence of Experimental Pain on the Perception of Action Capabilities and Performance of a Maximal Single-Leg Hop. The Journal of Pain, 15(3), 271.e1–271.e7. doi:10.1016/j.jpain.2013.10.016

Eriksson, M., & Bresin, R. (2010). Improving running mechanics by use of interactive sonification. Proceedings of ISon, 95-98.

Fajen, B. R. (2005). The scaling of information to action in visually guided braking. Journal of experimental psychology: human perception and performance, 31(5), 1107.

Fajen, B. R. (2007). Rapid recalibration based on optic flow in visually guided action. Experimental Brain Research, 183(1), 61–74. doi:10.1007/s00221-007-1021-1

Fajen, B. R., Riley, M. A., & Turvey, M. T. (2009). Information, affordances, and the control of action in sport. International Journal of Sport Psychology, 40(1), 79.

Gibson JJ. The Perception of the Visual World. Boston: Houghton Mifflin; 1950. pp.131–6.

Gibson, J. J. (1966). The senses considered as perceptual systems. Boston,

MA: Houghton Mifflin.

Graydon, M. M., Linkenauger, S. A., Teachman, B. A., & Proffitt, D. R. (2012). Scared stiff: the influence of anxiety on the perception of action capabilities. Cognition & Emotion, 26(7), 1301–1315. doi:10.1080/02699931.2012.667391

Heiderscheit, B. C., Chumanov, E. S., Michalski, M. P., Wille, C. M., & Ryan, M. B. (2011). Effects of step rate manipulation on joint mechanics during running. Medicine and science in sports and exercise, 43(2), 296.

Hoffmann, C. P., Torregrosa, G., & Bardy, B. G. (2012). Sound stabilizes locomotor-respiratory coupling and reduces energy cost. PloS one, 7(9), e45206.

Jones, G., Swain, A., & Cale, A. (1990). Antecedents of multidimensional competitive state anxiety and self-confidence in elite intercollegiate middle-distance runners. The Sport Psychologist, 4(2), 107-118.

Lohse KR, Sherwood DE. Defining the focus of attention: effects of attention on perceived exertion and fatigue. Front Psychol. 2011;2;332.

Mauger, A., & Hopker, J. (2012). The pain of exercise-insights in to the role of pain on exercise regulation and performance. J. Sports Med. Doping Stud, 2, 1-2.

Mauger, A. R., Jones, A. M., & Williams, C. A. (2009). Influence of feed.htmlback and prior experience on pacing during a 4-km cycle time trial. Medicine and Science in Sports and Exercise, 41(2), 451–458. doi:10.1249/MSS.0b013e3181854957

Marcora, S. M., & Staiano, W. (2010). The limit to exercise tolerance in humans: mind over muscle? European Journal of Applied Physiology, 109(4), 763–770. doi:10.1007/s00421-010-1418-6

McKay B, Lewthwaite G, Wulf G. Enhanced expectancies improve performance under pressure. Front in Psychol. 2012;3(8):1–5

Micklewright, D., Angus, C., Suddaby, J., St Clair, G. A., Sandercock, G., & Chinnasamy, C. (2012). Pacing strategy in schoolchildren differs with age and cognitive development. Medicine and science in sports and exercise, 44(2), 362-369.

Muller, S., Abernethy, B. & Farrow, D. How do world- class cricket batsmen anticipate a bowler’s intention? Q. J. Exp. Psychol. (Colchester) 59, 2162–2186 (2006).

Noakes, T. D. (2012). Fatigue is a Brain-Derived Emotion that Regulates the Exercise Behavior to Ensure the Protection of Whole Body Homeostasis. Frontiers in Physiology, 3. doi:10.3389/fphys.2012.00082

Noakes, T. D., Lambert, M. I., & Hauman, R. (2009). Which lap is the slowest? An analysis of 32 world mile record performances. British journal of sports medicine, 43(10), 760-764.

O’connor, P. J., Carda, R. D., & Graf, B. K. (1991). Anxiety and intense running exercise in the presence and absence of interpersonal competition. International journal of sports medicine, 12(4), 423-426.

Oliver, N., & Kreger-Stickles, L. (2006, November). Enhancing exercise performance through real-time physiological monitoring and music: a user study. In Pervasive Health Conference and Workshops, 2006 (pp. 1-10). IEEE.

Parry, D., Chinnasamy, C., & Micklewright, D. (2012). Optic flow influences perceived exertion during cycling. Journal of Sport & Exercise Psychology, 34(4), 444–456.

Parry, D., & Micklewright, D. (2014). Optic flow influences perceived exertion and distance estimation but not running pace. Medicine and Science in Sports and Exercise, 46(8), 1658–1665. doi:10.1249/MSS.0000000000000257

Proffitt DR, Stefanucci J, Banton T, Epstein W. The role of effort in perceiving distance. Psychol Sci. 2003;14(2):106–12.

Ramenzoni, V., Riley, M. A., Davis, T., Shockley, K., & Armstrong, R. (2008). Tuning in to another person’s action capabilities: perceiving maximal jumping-reach height from walking kinematics. Journal of Experimental Psychology: Human Perception and Performance, 34(4), 919.

Renfree, A., Martin, L., Micklewright, D., & St Clair Gibson, A. (2014). Application of Decision-Making Theory to the Regulation of Muscular Work Rate during Self-Paced Competitive Endurance Activity. Sports Medicine, 44(2), 147–158. doi:10.1007/s40279-013-0107-0

Stoffregen, T. A., Gorday, K. M., Sheng, Y. Y., & Flynn, S. B. (1999). Perceiving affordances for another person’s actions. Journal of Experimental Psychology: Human Perception and Performance, 25(1), 120.

Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013). Augmented visual, auditory, haptic, and multimodal feed.htmlback in motor learning: A review. Psychonomic bulletin & review, 20(1), 21-53.

Smith, M. R., Flach, J. M., Dittman, S. M., & Stanard, T. (2001). Monocular optical constraints on collision control. Journal of Experimental Psychology: Human Perception andPerformance, 27, 395-410

Smits, B. L. M., Pepping, G.-J., & Hettinga, F. J. (2014). Pacing and Decision Making in Sport and Exercise: The Roles of Perception and Action in the Regulation of Exercise Intensity. Sports Medicine, 44(6), 763–775. doi:10.1007/s40279-014-0163-0

Spencer, M. R., & Gastin, P. B. (2001). Energy system contribution during 200-to 1500-m running in highly trained athletes. Medicine and science in sports and exercise, 33(1), 157-162.

St Clair Gibson A, Goedecke JH, Harley YX, Myers LJ, Lambert MI, Noakes TD, et al. Metabolic setpoint control mechanisms in different physiological systems at rest and during exercise. J Theor Biol. 2005;236(1):60–72.

Stoate I, Wulf G, Lewthwaite R. Enhanced expectancies improve 
movement efficiency in runners Enhanced expectancies improve 
movement efficiency in runners. J Sports Sci. 2012;30(8):37–41.

Stone, M. R., Thomas, K., Wilkinson, M., Jones, A. M., Gibson, A. S. C., & Thompson, K. G. (2012). Effects of Deception on Exercise Performance: Implications for Determinants of Fatigue in Humans. Medicine & Science in Sports & Exercise, 44(3), 534–541. doi:10.1249/MSS.0b013e318232cf77

Tabor, A., Catley, M. J., Gandevia, S., Thacker, M. A., & Moseley, G. L. (2013). Perceptual bias in pain: A switch looks closer when it will relieve pain than when it won’t. PAIN®, 154(10), 1961-1965.

Terry, P. C., Karageorghis, C. I., Saha, A. M., & D’Auria, S. (2012). Effects of synchronous music on treadmill running among elite triathletes. Journal of Science and Medicine in Sport, 15(1), 52–57. doi:10.1016/j.jsams.2011.06.003

Triplett, N. (1898). The dynamogenic factors in pacemaking and competition. The American journal of psychology, 9(4), 507-533.

Tucker, R., & Noakes, T. D. (2009). The physiological regulation of pacing strategy during exercise: a critical review. British Journal of Sports Medicine, 43(6), e1-e1.

Williams, E. L., Jones, H. S., Sparks, S., Marchant, D. C., Micklewright, D., & Mcnaughton, L. R. (2014a). Deception Studies Manipulating Centrally Acting Performance Modifiers: A Review. Medicine & Science in Sports & Exercise, 46(7), 1441–1451. doi:10.1249/MSS.0000000000000235

Williams, E. L., Jones, H. S., Andy Sparks, S., Marchant, D. C., Midgley, A. W., & Mc Naughton, L. R. (2014b). Competitor presence reduces internal attentional focus and improves 16.1km cycling time trial performance. Journal of Science and Medicine in Sport. doi:10.1016/j.jsams.2014.07.003

Withagen, R., de Poel, H. J., Araújo, D., & Pepping, G. J. (2012). Affordances can invite behavior: Reconsidering the relationship between affordances and agency. New Ideas in Psychology, 30(2), 250-258.

Witt, J. K., Linkenauger, S. A., Bakdash, J. Z., Augustyn, J. S., Cook, A., & Proffitt, D. R. (2009). The long road of pain: chronic pain increases perceived distance. Experimental Brain Research, 192(1), 145–148. doi:10.1007/s00221-008-1594-3

Yarrow, K., Brown, P., & Krakauer, J. W. (2009). Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nature Reviews Neuroscience, 10(8), 585–596. doi:10.1038/nrn2672

 

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A Different Way to Look at Fatigue: An Ecological Psychology Framework

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