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Psychology of Sport and Exercise 14 (2013) 612e621

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Psychology of Sport and Exercise

journal homepage: www.elsevier .com/locate/psychsport

Differential effects of task-specific practice on performance in a simulated penaltykick under high-pressure

Martina Navarro a,b,*, Nelson Miyamoto a, John van der Kamp b,c, Edgard Morya a,d,Geert J.P. Savelsbergh b,e, Ronald Ranvaud a

aDepartment of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, BrazilbResearch Institute Move, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlandsc Institute of Human Performance, University of Hong Kong, Hong Kongd Edmond and Lily Safra International Neuroscience Institute of Natal, Natal, Brazile Institute for Biomedical Research into Human Movement and Health, Manchester Metropolitan University, Manchester, UK

a r t i c l e i n f o

Article history:Received 7 May 2012Received in revised form26 January 2013Accepted 5 March 2013Available online 14 March 2013

Keywords:Penalty kickAttentional control theoryPoint of no returnHigh-pressureTimingIndividual differences

* Corresponding author. University of São Paulo, 15Paulo, SP 05508-900, Brazil.

E-mail addresses: m.navarro@vu.nl, martina.navar

1469-0292/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.psychsport.2013.03.004

a b s t r a c t

Objectives: The current study investigated to what extent task-specific practice can help reduce theadverse effects of high-pressure on performance in a simulated penalty kick task. Based on theassumption that practice attenuates the required attentional resources, it was hypothesized that task-specific practice would enhance resilience against high-pressure.Method: Participants practiced a simulated penalty kick in which they had to move a lever to the sideopposite to the goalkeeper’s dive. The goalkeeper moved at different times before ball-contact.Design: Before and after task-specific practice, participants were tested on the same task both under low-and high-pressure conditions.Results: Before practice, performance of all participants worsened under high-pressure; however,whereas one group of participants merely required more time to correctly respond to the goalkeepermovement and showed a typical logistic relation between the percentage of correct responses and thetime available to respond, a second group of participants showed a linear relationship between thepercentage of correct responses and the time available to respond. This implies that they tended to makesystematic errors for the shortest times available. Practice eliminated the debilitating effects of high-pressure in the former group, whereas in the latter group high-pressure continued to negatively affectperformance.Conclusions: Task-specific practice increased resilience to high-pressure. However, the effect was afunction of how participants responded initially to high-pressure, that is, prior to practice. The results arediscussed within the framework of attentional control theory (ACT).

� 2013 Elsevier Ltd. All rights reserved.

In sports, it is well-known that the pressure faced by athletesduring competition, particularly when the stakes are high, canimpair performance (Baumeister, 1984; Beilock & Carr, 2001;Masters, 1992). An example par excellence is the penalty kick inassociation football, which often decides the outcome of impor-tant football matches (Armatas, Yiannakos, Papadopoulou &Galazoulas, 2007). Although the kicker clearly has the upperhand, during international Championships they fail to convert aconsiderable portion of kicks (20e35%, see Morya, Bigatão, Lees, &Ranvaud, 2003). The relatively low success rate is commonly

24 Av. Prof. Lineu Prestes, São

ro@usp.br (M. Navarro).

All rights reserved.

attributed to the high-pressure that kickers face in the penaltysituation (Jordet, Elferink-Gemser, Lemmink, & Visscher, 2007;Jordet & Hartman, 2008; Jordet, Hartman, Visscher & Lemmink,2007).

Recent work has used attentional control theory (ACT: Eysenck.Derakshan, Santos & Calvo, 2007) to explain the adverse effects ofhigh-pressure on sporting performance (Nibbeling, Daanen,Gerritsma, Hofland, & Oudejans, 2012; Nieuwenhuys & Oudejans,2011; Oudejans, 2010; Oudejans & Pijpers, 2009; Wilson, Vine, &Wood, 2009b), including penalty kicks (Navarro et al., 2012;Noël & van der Kamp, 2012; Wilson, Wood, & Vine, 2009a; Wood& Wilson, 2010). ACT assumes that an athlete’s attentional controlreflects the interactive contributions of two attentional systems.The first is a topedown goal-directed system that controls atten-tion based on expectations, knowledge and goals. The second is a

1 In fact, this prevented Navarro et al. (2012) from deriving the PNR for thisgroup. With participants performing reliably below chance, the mathematical pointhalfway between perfect and chance performance is not meaningful.

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621 613

stimulus-driven system that controls attention in a bottom-upfashion, attention being directly guided by salient or conspicuousstimuli (Corbetta & Shulman, 2002). The basic claim of ACT is thatin high-pressure situations the integrity of the goal-directedattentional system is reduced. Firstly, this may result in an in-crease in the mental effort invested by the goal-directed system tomaintain focus on task-relevant information. This adversely affectsthe system’s efficiency, but preserves performance outcome untilthe attentional resources are outstripped. Anxiety may also affectthe integrity of the goal-directed system to such an extent that itseffectiveness comes into play, resulting in an increased influenceof the stimulus-driven system. As a consequence, the focus ofattention increasingly shifts towards conspicuous or distractingstimuli, such as internal worries or threat-related information,thereby drawing attention away from pertinent task-relevant in-formation. Wilson et al. (2009a; see also Wood & Wilson, 2010)tested predictions of ACT by measuring gaze patterns of partici-pants taking penalty kicks in a laboratory under conditions of low-and high-pressure. To induce high-pressure a monetary prize wasawarded and a leader board with the scores was circulated amongthe participants. Wilson et al. (2009a) reported that under high-pressure, penalty kickers gazed significantly longer towards thegoalkeeper, which resulted in more centralized shots within thegoalkeeper’s reach (see also Bakker, Oudejans, Binsch, & van derKamp, 2006). Thus, consistent with ACT, in the high-pressurecondition attention became more stimulus-driven, and kickerswere more likely to focus on the threat-inducing and conspicuousgoalkeeper. Wood and Wilson (2010) showed that this wasparticularly evident when the goalkeeper waved his arms beforethe shot.

Recently, Navarro et al. (2012) provided additional evidence infavour of high-pressure perturbing the interaction between thegoal-directed and stimulus-driven systems, but in a differentmanner than proposed by Wilson et al. (2009a). These authorsused a computer-based simulated penalty kick task to investigatehow high-pressure affects the point of no return (PNR), that is,the moment in time beyond which a kicker is unable to makereliable decisions in response to the goalkeeper dive (Morya,Ranvaud & Machado Pinheiro, 2003; van der Kamp, 2006). Thisexperimental protocol presented participants with a computerscreen that showed a goalmouth and three dots, which repre-sented goalkeeper, ball and kicker. The ‘kicker’ moved upwards,towards the stationary ‘ball’, while higher up the ‘goalkeeper’, inthe middle of the goalmouth, moved randomly to the right or tothe left at different times before the ‘kicker’ contacted the ‘ball’.Participants were instructed to tilt a lever to the right or to theleft, exactly at the moment the ‘kicker’ superposed the ‘ball’ andin the direction opposite to the goalkeeper movement. To inducehigh-pressure, the task was performed in front of a loud partic-ipative audience that watched performance projected on a largescreen. The low-pressure condition was conducted in a smallisolated booth. Navarro et al. reported that for one group ofparticipants the PNR (defined as the instant at which the prob-ability that the ball is directed to the opposite side of the ‘goal-keeper’s’ movement is halfway between chance performance[i.e., 50% correct] and perfect performance [i.e., 100% correct])shifted under the high-pressure relative to the low-pressurecondition. Participants needed more time (i.e., 228 ms versus316 ms in the low- and high-pressure conditions respectively) tochoose the correct direction. In a second group of participants,high-pressure caused an altogether different change. Counter-intuitively, when the ‘goalkeeper’ moved very briefly before the‘kicker’ superposed the ‘ball’, providing very little time for theparticipant to respond, these participants systematically directedthe ‘ball’ to the same side as the ‘goalkeeper’ (rather than

performing on chance level, as the first group did).1 Navarro et al.(2012) proposed two distinct scenarios to explain these differenteffects of high-pressure, both in line with ACT. They proposedthat for the first group, the high-pressure decreased the effi-ciency of the goal-directed attentional system, which would haveslowed down performance (i.e., shifting the PNR to earlier mo-ments of the run-up). For the second group, high-pressureresulted in a degradation of the integrity (or even a break-down) of the goal-directed system, resulting in attention beingcontrolled by the stimulus-driven system. This led participants toautomatically, but wrongly, tilt the lever to the same side that the‘goalkeeper’ moved.

The current study employed this computer-based simulatedpenalty task to investigate whether task-specific practice canmoderate the observed effects of high-pressure, that is, whetherpracticing the penalty task under low-pressure can help maintainthe efficiency and/or effectiveness of the goal-directed attentionalsystem under high-pressure. In an attempt to reverse the adverseeffects of high-pressure, researchers have explored different in-terventions. One intervention method is to accustom participantsto the pressure inducing situation, for instance by have thempractice under increased levels of pressure, although not neces-sarily at the same levels as encountered in competition (Beilock &Carr, 2001; Nieuwenhuys & Oudejans, 2011; Oudejans & Pijpers,2009). A second method seeks to increase automaticity of taskperformance. It is well-known that with practice, a task, or at leastsome of it components, automatises such that less conscious orgoal-directed control is required for task execution (Abernethy,Masters, Maxwell, van der Kamp & Jackson, 2007). This freeing ofattention can already be achieved by repetitive practice of the taskor one its essential components (Brown, 1998, 2008; Brown &Bennett, 2002), and may be accelerated by limiting the amount ofdeclarative task-relevant knowledge that is accumulated duringpractice (Jackson & Farrow, 2005; Masters., 1992). In the presentstudy, we aimed to reverse the adverse effects of high-pressure byincreasing task automaticity through task-specific practice. Thischoice was based on research that shows that a spatial, non-temporal task (e.g., pursuit motor tracking) may interfere withthe execution of a temporal, non-spatial task (e.g., pressing a mousebutton every 5 s) as shown by the production of less accurate andconsistent time judgements (Brown, 1998). Brown and Bennett(2002: Brown, 1998, 2008) found that practice of the spatial, non-temporal task attenuates the interference. Indeed, dual taskprobes indicated that task-specific practice reduced the amount ofresources required to perform the non-temporal task (Brown &Bennett, 2002).

In the current study, participants practiced the computer-basedsimulated penalty task (Morya, Bigatão, et al., 2003; Morya,Ranvaud, et al., 2003) in eight sessions over a period of threeweeks to automatize task execution. In order to investigatewhether task-specific practice can indeed moderate the negativeeffects of high-pressure, participants were tested on the simulatedpenalty task before and after practice under high-pressure in thepresence of a large participative audience (Navarro et al., 2012).The task required the participants to tilt a lever in the directionopposite to the displacement of the ‘goalkeeper’ exactly at themoment the ‘kicker’ superposed the ‘ball’. During practice,augmented feedback was provided about timing errors (i.e.,whether the lever was tilted too early or too late). It was presumedthat, next to improvements in the direction of the response, task-

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621614

specific practice would induce improvements in coincidencetiming, that is, tilting the lever closer to the exact moment that the‘kicker’ and ‘ball’ superposed2 In line with the attenuation-effect(Brown & Bennett, 2002), we presumed that practice wouldautomatize the simulated penalty task, or at least some of itscomponents, thereby freeing more attentional resources for thegoal-directed system. Consequently, we hypothesized that task-specific practice would allow for increased effort by the goal-directed system under higher-pressure, reducing the increase inthe time needed for the participants to respond to goalkeepermovement (i.e., smaller increase in PNR). Additionally, task-specific practice may avert the breakdown of the goal-directedsystem under high-pressure, preventing participants from un-willingly tilting the lever to the same side as the goalkeepermovement.

Methods

Participants

Fifteen right-handed male undergraduate students (mean age19.8 years, SD ¼ 4.2) that were enrolled in Sports Science orPhysical Education curricula volunteered to practice and performthe simulated penalty task in low- and high-pressure conditions.They were all relatively experienced sportsmen, and had normal orcorrected-to-normal vision. Local ethical committee approval wasobtained prior to testing, and participants provided informedconsent before taking part.

Apparatus

The protocol used in the current experiment was the computer-based simulated penalty kick task developed by Morya, Bigatão,et al. (2003) and Morya, Ranvaud, et al. (2003); see also Navarroet al. 2012). The software MEL Professional 2.01 (Psychology Soft-ware Tools, PST Inc., Pittsburgh, PA) generatedwhite visual stimuli ona black background on the screen. Three lines represented the postsand the cross bar of a goalmouth, while three dots represented agoalkeeper (within the goalmouth area), a ball and a kicker. In eachtrial, the ‘kicker’ moved vertically upwards towards the stationary‘ball’, located at the centre of the display, at a speed of 4.7 cm s�1. In90%of the trials the ‘goalkeeper’ eithermoved to the left or to the rightat the same speed of 4.7 cm s�1. The ‘goalkeeper’ remained stationaryin the remaining 10% of trials. ‘Goalkeeper’ sideward motion started51, 102, 153, 204, 255, 306, 357, 408, or 459 ms before the ‘kicker’ and‘ball’ were superposed. Participants responded to the stimuli bymanually tilting a vertical lever to the right or to the left, tipping offoptical sensors connected to the game port. Participants satwith theireyes centrally placed .57 m in front of a 1700 computer screen (60 Hz),resting their head against an adjustable chin and forehead support.

During the pre and posttests a Suunto T3 heart rate monitor wasused to measure heartrate, and saliva samples were collected andstored to measure cortisol levels using a commercial radioimmu-noassay (Hellhammer, Wüst, & Kudielka, 2009). Participants’ pro-pensity to be stressed and participants’ subjective perceptions of

2 In spite of the obvious differences between this simulation and in situ penaltykicking, there is some analogy between precision in coincidence timing in thesimulation, which requires focussing on the ‘ball’, and the importance of focussingon the ball in situ. Real penalty kicks require accurate and forceful contact betweenthe kicking foot and the ball. Recent work (Wood & Wilson, 2010) shows thatshortly before ball-contact, the better performing penalty takers focus attention tothe ball, suggesting that directing attention to the ball (rather than the goalkeeper)is a pertinent factor for successful penalty kicking, just like it is in the presentcomputer-based simulated penalty kick task.

the situational stress were obtained using IDATE (Biaggio &Natalício, 1979; Gorenstein & Andrade, 1996), which is BrazilianPortuguese version of the STAI (i.e., the State -Trait Anxiety In-ventory, Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). TheIDATE consists of 20 questions addressing trait anxiety and 20questions regarding state anxiety on a 4-point ranking scale.

Design

The experiment consisted of a pretest (including a low-pressureand a high-pressure condition, the sequence of which was counter-balanced across participants), a 3-week practice period (2e3 sessionsperweek, total of 8 sessions under low-pressure; cf. Brown&Bennett,2002) and a posttest (including a low-pressure and a high-pressurecondition, with the low-pressure condition always being conductedfirst). The measurements for pretest and posttest and the practicesessions took place on different days. The low-pressure posttest tookplace the day immediately following thefinal practice session and thehigh-pressure posttest the next day. Participants faced 100 trialsduring each test and practice sessions, 10 trials for each of nineavailable time intervals (ATI, time between ‘goalkeeper’ motion and‘kicker-ball’ contact) in randomized order. Participants were instruc-ted to tilt the lever at the exact moment the ‘kicker’ superposed the‘ball’, which always occurred 1352 ms after trial onset, to the sideopposite of the ‘goalkeeper’motion. Trials for which participants hada temporal error larger than 42ms (i.e., tilting the lever 42ms beforeor after the ‘kicker’ and ‘ball’ were superposed) were replaced untilparticipants had performed 100 trials with the required temporalaccuracy. Participants were told that failing to comply with thesetemporal constraints would be equivalent to missing the goalmouthaltogether. These temporal errorswereflaggedwith amessage on thescreen that they had tilted the lever too early or too late.

Pressure manipulation

To induce high-pressure, we followed the protocol used byNavarro et al. (2012, see also Baumeister & Steinhilber, 1984). Par-ticipants performed the task in a large lecture hall in front of a loudparticipative audience that watched their performance projectedon a large screen (1.90 � 2.00 m). The audience was composed ofmore than 80 classmates of the participants, who were enrolled intwo different curricula (i.e., Physical Education and Sports Science).The audience was encouraged to openly support or boo the par-ticipants according to curriculum in which they were enrolled. Incontrast, in the low-pressure condition, participants performed thetask alone in a small, dimly lit isolated booth to favour maximumconcentration on the task.

Procedure

Each participant performed the pretest, posttest and practicesessions individually. The pretest and posttest started with in-structions to the participants about the experiment, who thencompleted the STAI. The heart rate monitor was then attached, andparticipants warmed up with 10 familiarization trials of the simu-lated penalty task. They then performed the 100 test trials. Imme-diately after completion of the test and 45 min later, samples ofsaliva were collected again. As a consequence of experimenter error,the heartrate and samples of saliva, however, were not collected forthe low-pressure condition of the posttest. Measurements in thetest sessions were conducted at the same time of the day between14:00e16:00 h to avoid confounds with circadian cortisol variations.

The eight practice sessions took place under low-pressure in theisolated booth, and participants were simply required to performthe task with the objective of trying to improve performance. No

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621 615

saliva samples were collected, nor was heart rate registered in thesepractice sessions.

Data analysis

We first classified participants into two groups based on theanalysis procedures in Navarro et al. (2012). These authors foundthat under high-pressure participants showed differences as to themodel that best described the relationship between the percentageof responses to the correct side and ATI (i.e., available time interval).For some participants, the best fit was obtained with a standardlogistic model, whereas for other participants a simple linear modelbetter fitted the data (see also Introduction). Similarly, the currentparticipants were classified in two groups, according to the r-valuesof the logistic and linear fits for the relation between the percentageof responses to the correct side (i.e., tilting the lever to the sideopposite to which the ‘goalkeeper’ moved) and ATI in the high-pressure conditions. The choice of a logistic fit followed standardpsychophysical procedure (Regan, 2000). The logistic model pre-sumes that as a function of stimulus characteristic (i.e., in the pre-sent study, the available time before response, ATI) there is a broadresponse threshold. Above the threshold, participants’ responses areaccurate (i.e., in our case 100% responses to the correct side), whilebelow the threshold participants’ responses are random (i.e., whichin our case corresponds to 50% responses to correct side), indicatingthat participants are simply guessing.Mathematically, the thresholdis defined as the stimulus value (i.e., the ATI) that corresponds withthe point half way through the transition from random (i.e., 50%responses to correct side) to perfect performance (i.e., 100% re-sponses to correct side) and is derived from fitting a logistic modelthrough the data (for an overview of this procedure, see Regan,2000). In the present study, the ATI corresponding to the half waypoint (i.e., 75% responses to the correct side) is taken to represent theminimum time needed to respond to the ‘goalkeeper’movement, orPNR (Morya, Bigatão, et al., 2003; Morya, Ranvaud, et al., 2003;Navarro et al., 2012; van der Kamp, 2006). Reiterating, Navarro et al.(2012) found that under high-pressure a logistic model was not al-ways a good fit: for some participants a linear model betterdescribed the relationship between ATI and percentage of correctresponses. Hence, for each participant in the present experiment,the r-values for the logistic and linear model were compared in thehigh-pressure condition. Participants who showed higher r-valuesfor the logistic model were assigned to the ‘logistic group’, partici-pants who showed higher r-values for the linear model wereassigned to the ‘linear group’. Subsequently, the r-values for modelfits were submitted to a 2(group: logistic, linear) by 2(model: lo-gistic, linear) by 2(test: pretest, posttest) by 2(condition: low-pressure, high-pressure) ANOVA with repeated measures on thelast three factors to determine whether practice affected the bestfittingmodel. In the case of a violation of the sphericity assumption,HuyneFeldt corrections to the degrees of freedom were applied.Post-hoc comparisons were carried out with t-tests using the Bon-feronni correctionprocedures (p< .05). The size of anyeffect presentwas evaluated using partial eta squared (h2p).

We then examined differences in PNR. However, because PNRcannot be obtained meaningfully from a linear model (and becausethe linear model only provided a better fit of the data in high-pressure, see below), we performed this analysis in two steps.First, we compared the PNR of both groups only in the low-pressureconditions by submitting it to a 2(group: logistic, linear) by 2(test:pretest, posttests) ANOVA with repeated measures on the last fac-tor. In addition, we assessed changes in PNR for the logistic grouponly as a function of practice and condition by submitting the PNRto a 2(condition: low-pressure, high-pressure) by 2(test: pretest,posttest) ANOVA with repeated measures on both factors.

We then analysed temporal errors. To this end, we first countedthe number of replaced trials (i.e., trials in which the lever wasmoved 42 ms before or after the kickereball superposition). For theremaining valid 100 trials, we calculated the constant error (i.e., thesigned temporal error, with minus signs indicating an earlyresponse and plus signs indicating delayed responses). These var-iables were submitted to separate 2(group: logistic, linear), by2(condition: low-pressure, high-pressure) by 2(test: pretest, post-test) ANOVA with repeated measures on the last two factors.

Finally, we assessed whether the pressure inducing protocol waseffective or not in increasing anxiety in the high-pressure conditionsrelative to the low-pressure conditions. Since there is a high vari-ability in cortisol production across the population (Hellhammeret al., 2009), the salivary cortisol levels were individually normal-ized by calculating a percentage difference score between absolutevalues of the measurement taken immediately after completion ofthe task and absolute values of the measurement taken 45min aftertask completion (relaxation period; see Gunnar, Talge, & Herrera,2009; Abbott et al., 2003). This percentage difference in cortisollevel, the average heart rate and the IDATE state-anxiety scores weresubmitted to separate 2(group: logistic, linear) by 3(test-condition:pretest low-pressure, pretest high-pressure, posttest high-pressure)ANOVAs with repeated measures on the last factor. Finally, theIDATE trait-anxiety scores were compared by an independent t-testbetween groups.

Results

Model fits

Visual inspection of the individual graphs depicting the per-centage of responses to the correct side as function of ATI suggestedthat for the low-pressure conditions a logistic model best describedthe data for all participants. In the high-pressure conditions,however, several of the individual graphs followed a model with alinear fit to the data (Navarro et al., 2012). These differences werenot altered by practice. More specifically, under the high-pressurecondition eight participants performed according to the logisticmodel and were thus classified as ‘logistic group’ (see Fig. 1aeb),whereas the remaining seven participants performed according tothe linear model (Table 1) and were therefore classified as ‘lineargroup’ (Fig. 2aeb). The subsequent ANOVA revealed significanteffects in r-values for model, F(1,14) ¼ 6.26, p < .05, h2p ¼ .36, group,F(1,14) ¼ 5.53, p < .05, h2p ¼ .33, model by group, F(1,14) ¼ 10.4,p < .01, h2p ¼ .48, condition by test, F(1,14) ¼ 33.2, p < .001, h2p ¼ .75,condition by test by group, F(1,14) ¼ 6.30, p < .05, h2p ¼ .36 andmodel by condition by group F(1,14) ¼ 4.76, p ¼ .05, h2p ¼ .30. Yet,these effects were all subordinate to a significant model by groupby condition by test interaction, F(1,14) ¼ 13.18, p < .05, h2p ¼ .52.Post-hoc comparison indicated that in both groups in the low-pressure condition the logistic model provided a better fit thanthe linear model both before and after practice. This was also foundfor the high-pressure conditions for the logistic group; yet, thelinear model showed a better fit for the linear group in the high-pressure condition both before practice and after practice. Impor-tantly, this indicates that practice did not result in qualitativechanges in the relationship between percentages of responses tothe correct side and ATI (as would have been the case if the goal-directed system were to have regained control over the stimulus-driven system).

Point of no return

Based on the fitted logistic model, we next determined the PNR(i.e., the ATI that corresponds to the 75% point on the logistic

Fig. 1. Percentage of correct responses as a function of available time for the logistic group in (a) low-pressure and (b) high-pressure conditions. Continuous line refer to per-formance before practice and dashed lines refer to performance after practice (Note that practice fully reversed the effect of high-pressure on the PNR).

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621616

curve, see Fig. 1aeb, and 2a). However, the PNR for the lineargroup in the high-pressure conditions could not be obtained,because the logistic model did not provide an appropriatedescription of the data. The ANOVA comparing the PNR of bothgroups under low-pressure before and after practice did not reveala main effect for group, F(1,14) ¼ 3.23, p ¼ .10, h2p ¼ .23, test,F(1,14) ¼ .18, p > .05, h2p ¼ .02, nor for the group by test interaction,F(1,14) ¼ 1.05, p ¼ .33, h2p ¼ .09. Hence, the PNRs in the low-pressure conditions before and after practice did not changesignificantly, both for the linear group (i.e., before: M ¼ 280 ms,SD ¼ 52.1, and after: M ¼ 270 ms, SD ¼ 16.8) and for the logisticgroup (i.e., before: M ¼ 236 ms, SD ¼ 32.4 and after: M ¼ 259 ms,SD ¼ 15.5). A second ANOVA, comparing the PNR only for thelogistic group, revealed a significant main effect for condition,F(1,14) ¼ 7.34, p < .05, h2p ¼ .55, and for the condition by testinteraction F(1,14) ¼ 35.2, p < .001, h2p ¼ .85. The main effect fortest F(1,14) ¼ 2.66, p > .05, h2p ¼ .31, was not significant. Post-hoccomparisons indicated a shorter PNR in the low-pressure(M ¼ 236 ms, SD ¼ 32.4) than in the high-pressure condition

Table 1Mean (and Standard Deviation) of logistic and linear model r-values for logistic and line

Low-pressure before High-pressure before

Logistic model Linear model Logistic model Linear m

Logistic group .82 (.09) .75 (.14) .79 (.09) .64 (.10)Linear group .83 (.30) .70 (.24) .65 (.10) .79 (.10)

before practice (M ¼ 323 ms, SD ¼ 16.1). After practice, however,PNR in high-pressure condition (M ¼ 240 ms, SD ¼ 19.4) haddecreased to the same value as in the low-pressure condition(M ¼ 259 ms, SD ¼ 51.2). In other words, for the logistic group thepractice sessions fully reversed the negative effect of high-pres-sure on the PNR (as would be expected if the goal-directed systemhad gained more attentional resources).

Temporal errors

As can be seen in Fig. 3, the number of replaced trials in the low-pressure condition gradually decreased during practice sessions.Moreover, the number of replaced trials also decreased from pretestto posttest in the high-pressure condition, but nonethelessremained higher than in the low-pressure condition. The ANOVAon the number of replaced trials revealed main effects for condi-tion, F(1,14) ¼ 45.69, p < .001, h2p ¼ .80, and test F(1,14) ¼ 39.41,p < .001, h2p ¼ .78, but not for group, F(1,14) ¼ .26, p > .05, h2p ¼ .02.In addition, significant interactions were found for test by group,

ar groups under low- and high-pressure conditions before and after practice.

Low-pressure after High-pressure after

odel Logistic model Linear model Logistic model Linear model

.92 (.06) .79 (.09) .73 (.13) .64 (.16)

.91 (.04) .79 (.07) .63 (.20) .71 (.22)

Fig. 2. Percentage of correct responses as a function of available time for the linear group in (a) low-pressure and (b) high-pressure conditions. Continuous lines refer to per-formance before practice, dashed line refer to performance after practice (Note that under high-pressure, practice did not induce changes in performance).

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621 617

F(1,14)¼ 5.77, p< .05, h2p ¼ .34 and condition by test F(1,14)¼ 20.27,p < .05, h2p ¼ .65, which were all subordinate to a significantinteraction for group by condition by test, F(1,14) ¼ 6.50, p < .05,h2p ¼ .37. Post-hoc comparisons indeed pointed to a decrease in the

Fig. 3. Number of replaced trials (i.e., timing errors) for the logistic and linear groups beforegroups high-pressure condition increased the number of replaced trials before and afterconditions).

number of replaced trials with practice both in the high- and in thelow-pressure condition; yet, for the linear group the differencebetween pretest and posttest in the low-pressure condition, did notreach significance.

, during and after practice in the low- and high-pressure conditions (Note that for bothpractice. Practice lowered the number of replaced trials equally for both groups and

Fig. 4. Constant errors (ms) (and standard error) for the logistic and linear groups before, during and after practice in the low- and high-pressure conditions (Note that for bothgroups there’s a trend in slightly delayed responses).

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621618

Fig. 4 shows how the constant error changes from pretest toposttest in the low-pressure and in the high-pressure conditions.The change in constant error points to the lever being tilted later inthe high-pressure than in the low-pressure condition, and later inthe posttest than in the pretest. This was confirmed by main effectsfor condition, F(1,14) ¼ 10.90, p < .01, h2p ¼ .49, and testF(1,14) ¼ 7.23, p < .05, h2p ¼ .39. There was no main effect for group,F(1,14) ¼ 3.94, p > .05, h2p ¼ .26, nor were there any interactions.

Stress measures

Table 2 shows cortisol level, heartrate and STAI scores in thedifferent experimental conditions. These indicate higher stress inboth high-pressure conditions than in the low-pressure conditionin the pretest (at least increase in cortisol level and heart rate).Accordingly, for the percentage difference in cortisol level, theANOVA revealed a significant main effect for test-condition,F(2,28) ¼ 5.72, p ¼ .01, h2p ¼ .34, but not for group F(2,28) ¼ .76,p > .05, h2p ¼ .06. Post-hoc comparisons indicated that for bothgroups the difference in cortisol level in the high-pressure condi-tions of both the pretest and posttest was significantly higher than

Table 2Mean (and Standard Deviation) of cortisol levels (% increase), heartrate (beats permin�1) and state and trait anxiety scores for logistic and linear groups under low-and high-pressure conditions before and after practice.

Group Before practice After practice

Low-pressure High-pressure High-pressure

Cortisol level (%) Logistic 2 (9) 28 (17) 20 (14)Linear 5 (8) 39 (20) 23 (13)

Heartrate (beatsper min�1)

Logistic 66 (16) 85 (8) 96 (15)Linear 77 (4) 110 (11) 109 (15)

State-anxiety scores Logistic 42 (5) 45 (5) 46 (7)Linear 46 (2) 46 (5) 48 (3)

Trait-anxiety scores Logistic 47 (2) 46 (3) e

Linear 45 (3) 46 (4) e

in pretest low-pressure condition, but not different from eachother. Similarly, for the heart rate significant effects were found fortest-condition, F(2,28) ¼ 33.34, p < .001, h2p ¼ .75, and groupF(2,28) ¼ 15.59, p < .01, h2p ¼ .58, but not for the group by testecondition interaction. Post-hoc comparisons indicated that for bothgroups the high-pressure conditions provoked higher levels ofstress than in the pretest low-pressure condition, even after prac-tice. Further, the linear group had higher heart rates than the lo-gistic group. Finally, the ANOVA for the state-anxiety scores did notshow a significant effect of test-condition, F(2, 28) ¼ 1.5, p > .05,h2p ¼ .12, nor for group, F(2,28) ¼ .11, p> .05, h2p ¼ .01, and also trait-anxiety scores did not differ between groups (t(14) ¼ .41, p > .05).

Discussion

The present study investigated whether task-specific practice,which presumably increases automatization, can help preventperformance decrements observed as a result of high-pressure byNavarro et al. (2012). The present study confirmed the detrimentaleffects of increased pressure published by these authors. Basically,in dealing with high-pressure condition, participants showed oneof two responses.3

Participants of the logistic group (so labelled, because the rela-tion between the percentage of response to the correct side and ATIshowed the typical S-shape of a standard logistic model underhigh-pressure) showed a shift in the PNR under high-pressure,indicating that the minimum time they needed to correctlyrespond to the ‘goalkeeper’s’ movement was increased. Interpret-ing these results within the ACT framework (Eysenck, Derakshan,

3 In fact, Navarro et al. (2012) discerned a third group. Participants of this thirdgroup also showed a logistic curve under both low- and high-pressure; however,they did not show a difference in PNR between low- and high-pressure conditions.This absence of shift in PNR was attributed to a negligible increase in anxietyamong these participants. We did not observe this pattern among the currentparticipants.

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621 619

Santos, & Calvo, 2007), Navarro et al. (2012) argued that this mayreflect a decline in the efficiency of the goal-directed attentionalsystem, which either results from a decrease in attentional re-sources or an increase in effort to maintain performance underhigh-pressure.

On the other hand, participants that made up the linear group(so called because the relationship between the percentage ofresponse to the correct side and ATI was linear under high-pres-sure) were prone to systematically commit errors under high-pressure. That is, when time pressure was rigorous (i.e., the ‘goal-keeper’ moved very late) they showed a clear bias to the same sideas the ‘goalkeeper’, rather than random behaviour. In terms of ACT,this may be understood as the goal-directed system becomingineffective in inhibiting the automatic stimulus-driven system(Navarro et al., 2012).

Most significantly, however, the present study demonstratesthat task-specific practice to enhance task automatization haddifferent effects on the two groups under high-pressure, but notunder low-pressure. However, before discussing the differentialeffects of high-pressure after practice, it is important to brieflydiscuss the relationship between practice and automatization oftask execution. The simulated penalty kick task requires that twoconditions be fulfilled for participants to be successful: they musttilt the lever to the side opposite to ‘goalkeeper’ movement andthey must do this at the right time. Presumably, both aspects of thetask share the same attentional resources. Brown (1998), (2008),and Brown and Bennett (2002) demonstrated that for this type oftask the so-called attenuation-effect may occur. That is, practicingone task component (e.g., timing) reduces its attentional demands,thereby increasing the availability of resources for a second taskcomponent (e.g., spatial). Accordingly, participants practiced thesimulated penalty kick task with feedback on timing to attenuatethe attentional task demands, at least for the temporal componentof the task. It must be noticed, however, that we did not directlyassess whether the practice indeed resulted in the freeing ofattentional resources. It would be important for future work toverify this contention, for example, by implementing dual taskprobes during practice (see e.g., Brown & Bennett, 2002). For now,we propose as most likely explanation for our results that practiceindeed automatized task execution and hence resulted in makingmore attentional resources available (see also Abernethy, Maxwell,Masters, van der Kamp, & Jackson, 2007).

ACT holds that situations of high-pressure call for a higherfraction of available attentional resources. Yet, if the attentionalresources are limited, then a point is reached at which supply be-comes insufficient and performance suffers. In line with theattenuation effect, practicing the simulated penalty kick task withfeedback on timing would reduce the attentional demands for thisaspect of the task, freeing resources for the spatial components ofthe task. This would allow for the allocation of more resourcesunder high-pressure to decide on the side to direct the ‘ball’. Inother words, practice would increase the efficiency of the goal-directed system relative to pre-practice.

The logistic group in the low-pressure condition indeedbenefited from practicing the temporal aspects of the task (i.e.,less replaced trials and smaller constant error), pointing toautomatization. Under high-pressure the same timing improve-ment occurred. Although practice did not lead to improvement inPNR (i.e., minimum time needed to respond correctly) in low-pressure, in high-pressure it did bring the PNR to low-pressurelevels. Apparently, the additional resources that were freed dur-ing practice allowed the goal-directed system to more fully focuson moving the lever in the direction opposite to the ‘goalkeeper’,such that the stimulus-driven system could be inhibited moreeffectively.

For the linear group, however, practice effects were less obvious.Although there were clear indications that during practice theseparticipants altered timing (i.e., they also slightly delayed theirresponse), they did not showa significant decrement in the numberof temporal errors under low-pressure (In fact, we cannot rule outthat this is simply a ‘ceiling effect’, the number of errors alreadybeing very small before practice). Under-high pressure, however,timing significantly improved from pretest to posttest. Intriguingly,even after practice the performance of the linear group was stillaffected by high-pressure. With little time available, the partici-pants were still inclined to systematically commit errors, that is, tomove the lever to the same side as the ‘goalkeeper’. It thus seemedthat the goal-directed system remained unable to inhibit thestimulus-driven system. The main hypothesis to explain this lack ofimprovement would be that the goal-directed system did not haveaccess to sufficient additional attentional resources, necessary toinhibit the stimulus-driven system. This may be due to insufficientimprovements in automatization following practice (e.g., there wasno significant decrease in the number of temporal errors). More-over, counteracting a breakdown of the goal-directed system underhigh-pressure may require the freeing of a considerable larger poolof resources than merely improving its efficiency (cf. the logisticgroup). Alternatively, it might be argued that, given their relianceon the stimulus-driven system, practice may have further increasedthe speed of this system among the participants of the linear group,making it evenmore difficult for the goal-directed system to inhibitthe stimulus-induced response. Yet, this explanation is unlikely fortwo reasons. First, the stimulus-driven system is an automatizedsystem, which would require huge amounts of additional practiceto increase the degree of automatization even further. In addition, itwould also follow that the percentage of errors would increase,which was not the case. Finally, heart rate measures also suggestedthat levels of anxiety were somewhat higher among participants ofthe linear group compared to participants of the logistic group,although the increases in heart rate from the low- to the high-pressure conditions were of similar magnitude (i.e., 40%). To shedlight on these issues, further investigations of longer and/or morespecific training strategies are needed, to examine why the linearand logistic groups responded differently to practice.

Considering the effects of practice, especially the delay inresponding to ‘goalkeeper’ movement in both the low- and high-pressure conditions, it may be useful to make a comparison withprior reports on quiet eye practice (see e.g., Behan & Wilson, 2008;Causer, Holmes, & Williams, 2011; Harle & Vickers, 2001; Vickers &Williams, 2007; Williams, Vickers, & Rodrigues, 2002; Wilson &Wood, 2011). Quiet eye (QE) refers to a long final fixation on arelevant target prior to the execution of the critical phase ofmovement (Vickers, 2007). In the simulated penalty kick task, thiswould be equivalent to the duration of the final fixation on thegoalkeeper prior to tilting the lever. Vickers (1996) proposes thatQE reflects the organization of visual attention to optimize controlof the movement, while minimizing environmental distraction.Hence, the longer the QE, the better the performance. Thus, practiceof QE may not only improve task-relevant gaze control, but alsoenhance attentional control under high-pressure (Vine & Wilson,2010; 2011). In the current task, the logistic group might havelearnt to slightly delay their response, enabling them to focuslonger on the ‘goalkeeper’, which, analogous to QE, improved theability to inhibit the stimulus-driven system, that is, to better useinformation to aim to the side opposite ‘goalkeeper’movement. Yet,others have argued that longer fixations on the goalkeeper (relativeto the target location and/or ball) may actually impede perfor-mance (e.g., Wilson et al., 2009a; Wood & Wilson, 2010). Possibly,this is not simply due to a longer fixation, but a combination withthe inability to inhibit the prepotent stimulus-driven response.

M. Navarro et al. / Psychology of Sport and Exercise 14 (2013) 612e621620

Participants of the linear group may have encountered the sameproblem, since they seem to more heavily rely on the stimulus-driven system (already before practice). It is important for futureresearch to evaluate these conjectures using gaze recordings.

There are obvious issues on how relevant the present design andprocedures are for an actual penalty kick, not the least of which isthat the participants did not kick a ball, but moved a lever inresponse to events presented on a computer screen. Having saidthis, however, we do think that the present study does informfurther research of penalty kicking in low- and high-pressure sit-uations. Previous work has identified two penalty kick strategies(see Morya, Bigatão, et al., 2003; Morya, Ranvaud, et al., 2003; vander Kamp, 2006). Of these two strategies, the keeper-independentstrategy, during which the penalty taker disregards the goal-keeper‘s action, has been recommended (e.g., Van der Kamp, 2011).Yet, it is not unlikely that penalty kickers often attempt to take intoaccount the goalkeeper actions (i.e., keeper-dependent strategy) tokick the ball to the side opposite to the goalkeeper dives (see alsoNavarro, van der Kamp, Ranvaud, & Savelsbergh, 2013). The presentstudy suggests that penalty takers should only adopt this strategy ifthey are able to inhibit stimulus-driven responses. The presentstudy shows that there are differences in the capability for a penaltytaker to learn to do this, which may be related to the innate ten-dency to rely on the stimulus-driven rather than on the goal-directed system.

In sum, the current study is the first to experimentally assess thedegree to which practice can reduce the adverse effects of high-pressure on the minimum time needed to respond to goalkeepermovement. Before practice, all participants showed significantperformance decrements under high-pressure; they eitherrequired more time to respond to the goalkeeper or were unable toinhibit stimulus-driven responses. In one group of participants,practice (i.e., with the aim to automatize task performance ratherthan directly addressing anxiety) minimized the debilitating effectsof high-pressure; they needed an equal amount of time to respondin low- as in high-pressure. Presumably, practice (and augmentedfeedback on temporal errors) had provided them with the addi-tional attentional resources to increase spatial goal-directed effortsunder high-pressure. In the second group of participants, however,the improvements during practice were insufficient to improveperformance. They remained unable to inhibit automatic responseswhen the available time was short. In sum, our results providestrong support for the contention that task-specific practice canhave a major influence in improving performance in penalty kicksunder high-pressure. However, there are large differences betweengroups of individuals that might be related to initial tendencies forattentional control.

Role of the funding source

We are grateful for financial support from the National Counselof Technological and Scientific Development (CNPq; grant 140765/2008-5 and grant 480996/2009-1) and the Coordination for theImprovement of Higher Level Personnel (CAPES; grant 3024/10-5).

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