Temporal Pattern of Pre-Shooting Psychophysiological States in Elite Athletes: A Probabilistic

Approach

Maurizio Bertolloa,b

, Claudio Robazzaa,b

, Walter Nicola Falascaa,b

, Massimiliano Stocchia, Claudio

Babilonic,d

, Claudio Del Percioe, Nicola Marzano

f, Marco Iacoboni

g, Francesco Infarinato

e, Fabrizio

Vecchioh, Cristina Limatola

g,i, & Silvia Comani

a,l,m

aBIND - Behavioral Imaging and Neural Dynamics Center, Chieti, Italy

bDepartment of Human Movement Science, University of Chieti-Pescara, Italy

cDepartment of Biomedical Sciences, University of Foggia, Foggia, Italy

dSan Raffaele Hospital, Cassino, Italy

eIRCCS San Raffaele Pisana, Roma, Italy

fIRCCS SDN, Napoli, Italy

gDepartment of Physiology and Pharmacology, University of Rome “Sapienza”, Roma, Italy

hAssociation for Biomedical Research “Fabenefratelli” (AFaR), Roma, Italy

iIRCCS Neuromed, Pozzilli (IS), Italy

lDepartment of Neuroscience and Imaging, University of Chieti-Pescara, Italy

mVilla Serena Hospital, Città S. Angelo, Pescara, Italy

Date of submission of the revised manuscript (second revision): September 22, 2011

Corresponding author:

Maurizio Bertollo

BIND - Behavioral Imaging and Neural Dynamics Center

Via dei Vestini, 33

66100 Chieti Scalo (CH) Italy

Telephone: +39 0871 3554052

Fax: +39 0871 3554043

E-mail address m.bertollo@unich.it

*Identification page plus Title (This is the only part of submission where names and affiliations are entered)

Research Highlights

Time course of arousal and vigilance were investigated using an IZOF approach

Emotion and physiological mechanisms were associated with shooters’ performance

The approach was effective in the assessment of psycho-physiological indices

*Highlights

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 1

Temporal Pattern of Pre-Shooting Psychophysiological States in Elite Athletes: A Probabilistic

Approach

Date of submission of the revised manuscript (second revision): September 22, 2011

*Manuscript plus title (anonymous -- no names, affilitions, or any detail that would reveal author's idenity should appear here )Click here to view linked References

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 2

Abstract

Objective: Studies on the temporal pattern of affect and psycho-physiological indices in shooting

were limited to a group analysis of data based on a performance approach. In contrast, the IZOF-

based probabilistic approach provides a feasible methodology to study within-individual patterns of

a performer’s states. The time course of physiological data before performance previously had not

been investigated within the probabilistic methodology framework. The main purpose of our study

was to examine the value of the probabilistic approach in the assessment of the time course of

physiological indicators of arousal/activation and vigilance during the period preceding the shot, in

comparison with the performance-based method.

Design: Longitudinal assessment of psycho-physiological data and performance outcomes was

conducted on eight elite pistol shooters in a controlled setting.

Results: Our findings showed that the use of the probabilistic method to analyse physiological

parameters (skin conductance and heart rate) was more effective than the performance-based

method to describe the physiological mechanisms associated with shooters’ performance.

Conclusions: The probabilistic method enabled us to better discern the contribution of

arousal/activation and vigilance to optimal and non-optimal performance in elite shooters, thereby

providing a sharper representation of the temporal pattern of performers’ states before shooting.

From an applied perspective, we believe that the adoption of the probabilistic approach can help

athletes become aware of the subtle variations occurring in their psychophysical states during the

preparatory period preceding the shot and not only at the moment of shot release.

Keywords: Shooting; Activation; Vigilance; Skin Conductance; Heart Rate; IZOF model

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 3

Temporal Pattern of Pre-Shooting Psychophysiological States in Elite Athletes: A Probabilistic

Approach

Shooting performance requires a focus of attention on the target as well as full control of

coordination between postural activity and arm raising (Hillman, Apparies, Janelle, & Hatfield,

2000). Tremayne and Barry (2001) proposed that both attentional processing and motor preparation

are involved in skilled pistol shooting. A few years later, Guillot and colleagues (Guillot, Collet,

Dittmar, Delhomme, Delemer, & Vernet-Maury, 2005; Guillot, Collet, Molinaro, & Dittmar, 2004)

suggested that the control of emotional load also is needed for an effective performance, in

particular immediately before the trigger pull.

Shooting is characterised by a globally static posture with forearm and hand movements of

relatively small amplitude and rate. Therefore, physiological correlates of shooting performance

could be easily and reliably investigated by various electrophysiological methods (Goodman,

Haufler, Shim, & Hatfield, 2009). During the last two decades, the psychophysiological activation

effects on shooting performance have been evaluated through neuro-vegetative indices to define the

range of values compatible with optimal performance (Guillot et al., 2004, 2005; Guillot, Collet,

Dittmar, Delhomme, Delemer, & Vernet-Maury, 2003; Kontinnen & Lyytinen, 1992; Kontinnen,

Lyytinen, & Viitasalo, 1998; Tremayne & Barry, 2001; Vaez-Mousavi, Hashemi-Masoumi, &

Jalali, 2008). However, different perspectives of investigation have often led to highly variable

terminology, such as arousal, alertness, vigilance and attention. For example, Hardy, Jones, and

Gould (1996), drawing on the work of Pribram and McGuiness (1975), proposed defining arousal

as referring to the organism’s immediate response to new stimuli or input, and activation as a

complex multidimensional state reflecting the organism’s anticipatory readiness to perform, which

can be altered by changes in arousal. In contrast, Barry, Clarke, McCarthy, Selikowitz, and Rushby

(2005) used “arousal” to refer to the current energetic state, and “activation” to refer to the

recruitment of arousal for task execution. Using yet another perspective, Guillot and colleagues

(2003) defined activation as a set of processes required to improve the aptitude of an organism to

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 4

process information and to carry out an action. In this framework, activation is a relatively non-

specific concept. The same authors, drawing on Tremayne and Barry’s (2001) conceptualisation,

considered vigilance as more related to behavioural aspects that involve sustained readiness to

detect and respond to environmental changes. Vigilance is, therefore, an active performance-related

process that involves perception of stimuli and information processing. This last preparatory

attentional state is associated with anticipated cognitive-perceptual or behavioural activity (Barry,

1988) that is independent of arousal/activation.

Despite the recognised integrative nature of the physiological processing occurring during

activation and vigilance, Tremayne and Barry (2001), suggested a useful operational one-to-one

correspondence between some physiological indices and the related psychophysiological processes.

In particular, they proposed that arousal/activation can be assessed by Skin Conductance (SC) level,

whereas vigilance can be estimated through Heart Rate (HR). Indeed, SC level is a transient change

in the electrical properties of the skin and indicates the state of arousal or activation through the

activity of sympathetic cholinergic neurons at the level of eccrine dermal sweat glands (Barry &

Sokolov, 1993). HR is an indirect measure of the autonomic function of the nervous system, which

is considered in relation to arousal and activation: cardiac signal pattern during the few seconds

prior to shooting has been the most commonly used autonomic nervous system physiological index

related to mental processes (Guillot at al., 2003).

The early Lacey and Lacey (1980) studies on the directional variations of cardiac

functioning related to behaviour, led these authors to develop the stimulus intake-rejection

hypothesis: stimulus intake (focused attention) would be associated with cardiac deceleration

(bradychardia), while stimulus rejection (cognitive function excluding distracting or aversive

environmental stimuli) would be accompanied by cardiac acceleration (tachycardia). From another

perspective, Obrist (1981) has provided a different explanation of the relationship between motor

preparation and HR decrease. According to Obrist, HR variation is not a direct correlate of

attention, but an indirect effect of motor activity reduction. Indeed, several studies have reported a

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 5

systematic pattern of cardiac deceleration immediately before the trigger pull in precision sport

(Guillot et al., 2005; Kontinnen et al., 1992, 1998; Landers, Han, Salazar, Petruzzello, Kubitz, &

Gannon, 1994; Robazza, Bortoli, & Nougier, 1998), whereas only a few studies have found cardiac

acceleration. For instance, Hatfield, Landers, and Ray (1987), observed HR increase in elite rifle

marksmen, and Salazar, Landers, Petruzzello, Hans, Crews, and Kubitz (1990) found HR increase

in archers during five inter-beat intervals (corresponding to 3-4 seconds) preceding the arrow

release.

The investigation of the temporal patterns of psycho-physiological indices has generally

used a traditional “performance-based” categorisation approach in the comparison of novice and

expert groups, or best and worst performances. For example, Tremayne and Barry (2001) classified

the shots scoring 10 (a bull’s eye) as “best shots”, and the shots scoring less than 10 as “worst

shots”, whereas Kontinnen et al. (1998) used a score of 9 as their reference. It therefore appears that

this traditional perspective has some limitations, a main one being that categorising performance on

the basis of the score distribution of the sample does not explain performance dynamics at the

individual level.

While psycho-physiological research has substantiated the relationship between

psychomotor efficiency and physiological activity (Hatfield & Hillman, 2001), individually oriented

prospective and retrospective reports have contributed to the current understanding of how emotions

influence sporting performance (see Hanin, 2007; Robazza, 2006). A multimodal assessment has

been advocated to measure affect, including self-reports, behavioural observations, and

physiological recordings (Lang, 2000).

Vaez-Mousavi, Barry, and Clarke (2009) have recently recognised the potential value of

studying individual differences within a multidimensional framework such as Hanin’s Individual

Zones of Optimal Functioning (IZOF) theory (Hanin, 2004, 2007). The IZOF model, which has

received increasing attention in sport psychology, conceptualises emotion-related states in terms of

the five inter-dependent dimensions of form, content, intensity, context, and time (Hanin, 2000,

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 6

2007). Form, content, and intensity dimensions describe the structure of individual experiences and

meta-experiences (i.e., beliefs about expected effects of emotions on performance), while context

and time dimensions are related to the dynamics of individual experiences. In our study, we

examined affect, HR, and SC as components of the form dimension manifested in intensity and

hedonic valence (content dimension) in a practice context, prior to and during performance (time

dimension). The IZOF model employs the notion of “in-out of the zone” as a criterion to evaluate

current emotion intensity and to predict individually good or bad performance.

According to this theoretical framework, Kamata, Tenembaum, and Hanin (2002) have

proposed a new probabilistic interpretation of the affect-performance link. They have improved the

original IZOF methodology by introducing a probabilistic approach, in order to provide a

computational procedure to reliably determine the lower and upper boundary levels of the IZOF and

the related probabilistic curve thresholds. The implementation of this methodology has led to the

identification of probability-based zones of intensity as ranges of affective and physiological states,

within which each individual is assigned a probability to perform at a particular level (e.g., optimal,

moderate, poor) (Cohen, Tenenbaum, & English, 2006; Golden, Tenenbaum, & Kamata, 2004). The

probabilistic method enables the assessment of individual zones of intensity related to specific

performance levels that can easily implement introspective (such as emotional report) and objective

data (such as HR and SC level).

Recent studies have revealed that unique probability-based zones of intensity related to

performance can be determined in various sport settings such as collegiate golf (Cohen et al., 2006),

collegiate tennis (Golden et al., 2004), and archery (Johnson, Edmonds, Carlos Moraes, Medeiros

Filho, & Tenenbaum, 2007). Edmonds, Mann, Tenenbaum, and Janelle (2006) have incorporated

both perceived affectivity (i.e., arousal and pleasantness) and physiological measures (i.e., HR and

SC) to define individual zones for car racers. They found that perceived and objective measures of

arousal can be used simultaneously to define individual zones of functioning for performers

competing in a simulated car racing task. In another work, Medeiros Filho, Moraes, and Tenenbaum

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 7

(2008) have examined real-time affective and physiological patterns of optimal and non-optimal

performances of three high-level archers. All studies using the probabilistic methodology have

examined introspective affective measures (i.e., arousal and pleasantness) and/or physiological

measures (i.e., HR and SC) detected prior to performance, whereby each affective or physiological

data point was associated with a performance score. However, the time course of affective or

physiological data in the few moments preceding the event has not been investigated previously.

In summary, studies on the temporal pattern of emotional, psychophysical, psycho-

physiological indices in shooting were limited to a group analysis of data, with the aim of

comparing novice vs. expert or worst vs. best performance outcomes. This “performance-based”

between-individuals methodology overlooks performance dynamics at the individual level. In

contrast, the IZOF-based probabilistic approach provides a feasible methodology to study within-

individual patterns of a performer’s states. However, the time course of physiological data before

performance has not been investigated within the probabilistic methodology framework. Therefore,

the main purpose of our study was to assess the value of the probabilistic approach in studying the

time course of physiological indicators of arousal/activation and vigilance (SC and HR respectively)

during the period preceding the shot. The same psychophysical measures were used in previous

studies (e.g., Edmonds et al., 2006). We expected that the probabilistic method would permit us to

discern the contribution of arousal/activation and vigilance to optimal and non-optimal performance

in elite shooters better than the performance-based approach, thereby providing a sharper

representation of the temporal pattern of performers’ states before shooting.

Method

Participants

Eight elite athletes (the Italian Olympic air pistol shooters team, including two women) were

recruited for this study. The elite air pistol shooters have been regularly competing in national and

international tournaments; they had been practicing pistol shooting for more than 10 years and for at

least five times a week. The mean participants’ age was 29.2 years (1.6 SD, range: 21–45 years). All

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 8

shooters were right-handed as measured by the Edinburgh Inventory (mean 71.3%, SD 4.8%), and

gave their informed consent according to the Declaration of Helsinki. They were free to withdraw

from the study at any time. The procedure was approved by the local Institutional Ethics Committee

(University of Chieti, Italy).

Experimental Procedures

All participants performed a total of 60 air-pistol shots in 2 separate recording blocks (with

an inter-block interval of about 10 min). Shooters were required to execute the same number of

shots as in a competitive event. Sixty shots were deemed sufficient to estimate the probability-based

zones in shooting, as recommended by Johnson, Edmonds, Kamata, and Tenenbaum (2009). The

performers were free to relax between two consecutive shots and to shoot when they felt ready

(with an average inter-shot interval of about 1 min). The motivational level during training was high

because of the internal competition to qualify for the Olympic Games. The distance between the

shooter and the target was 10 m and the diameter of the target was 6 cm, according to the

international shooting competition rules (http://www.issf-

sports.org/theissf/rules/english_rulebook.ashx). The score obtained with each shot was

automatically recorded with electronic scoring targets, and simultaneously with an optical shooting

simulator unit (SCATT company, Russia) that also permitted the trajectories of the pistol to be

recorded on the target during the pointing phases.

The following physiological measurements were simultaneously performed:

1. Skin conductance level was measured using two Ag/AgCl electrodes attached to the fingertips

of the second and third finger of the non-dominant hand. The electrodes were connected to the

acquisition system (Powerlab 16/30, ADInstruments, Australia) via a ML 116 amplifier that was

developed in accordance with the recommendations by Venables and Christie (1980). The

electrodes were positioned 10 minutes before the experimental session as suggested by

Boucsein (1992).

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 9

2. The cardiac signal was recorded with a sampling frequency of 1 KHz using a piezoelectric pulse

transducer attached on the first finger of the non-dominant hand of the athlete, and directly

connected to the acquisition system (Powerlab 16/30, ADInstruments, Australia).

Furthermore, a device based on acoustic technologies (cardio-microphone and PowerlabVC

16/30, ADInstruments, Australia) was used to define the shooting instant. All signals monitored by

means of Labchart software (v7.1, ADInstruments. Australia) were acquired with a sampling

frequency of 1 kHz.

Following the tutorial by Johnson et al. (2009), the athletes were told to pay attention to

their affective states just before each shooting phase and to score them in terms of intensity on a

modified 11-point Borg scale, which has been successfully used in psychophysical studies (Borg,

2001) as well as to investigate emotions (Hanin & Syrjä, 1996). The verbal anchors of the scale,

developed to avoid floor and ceiling effects, were 0 = nothing at all, 0.5 = very, very little, 1 = very

little ,2 = little, 3= moderately, 5 = much, 7 = very much,10 = very, very much, 11 = maximal

possible. Hedonic tone scores were also assessed on the same Borg scale. Scores ranged from -11

(extremely dysfunctional or unpleasant) to +11 (extremely functional or pleasant). Athletes were

asked to: (1) score their feelings in terms of intensity, and (2) score their hedonic tone (i.e., pleasant

or unpleasant). This procedure has been used in previous studies, before and during performance

(Pellizzari, Bertollo, & Robazza, 2011; Robazza, Pellizzari, Bertollo, & Hanin, 2008).

Data Pre-processing

All signals recorded with the acquisition system were exported as Matlab files for data

processing. The timing of each shot was identified by means of the conjoint use of two triggers: the

cardio-microphone signal and a manual trigger, the latter consisting of notes taken by the

experimenters during data acquisition. Once the timing of all shots was identified, we selected, prior

to each shot, a time window during which the physiological data could be processed. For

comparison purposes, those time windows had to be of the same duration for all athletes. The length

of the time windows (12 seconds) was defined on the basis of the features of the recordings during

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 10

the inter-shot intervals. Indeed, between one shot and the next, the athlete moved to load the pistol

for the next shot, and those movements introduced strong artefacts and a high level of noise in the

recorded signals. Only when the athlete was ready for the next shot, did those interferences

disappear, permitting us to collect signals useful for further processing. By analysing the entire set

of signals recorded for all athletes, we found that a period of 12 seconds was the longest interval

preceding the shot during which no interference affected the recordings. This interval also

corresponded to the minimum time required for shooting, from pistol elevation to shot, across all

participants and shots. In order to analyse the physiological data, we further needed to normalise the

size of the physiological data with respect to the number of heartbeats considered prior to each shot.

For that purpose, we calculated, for all athletes, the number of heartbeats included in the time

windows preceding the shots on the basis of the detection of the peaks of ventricular depolarisation

(R peaks).The number of heartbeats included in the 12-second time window preceding each shot

varied across athletes depending on their heart rate (range: 12-17 heartbeats).

As a result, for each athlete we had a three-dimensional matrix (time, signal and shot

number) for each physiological variable, namely, HR and SC. Instantaneous HR was determined as

the inverse of the RR interval duration (in ms), calculated as the interval between two subsequent R

peaks, detected with an error of ± 1 ms. For normalisation purposes, it was necessary to have, for

each athlete and for each period preceding a shot, the same number of HR values and SC level

estimates. Therefore, we calculated an average SC level value for each RR interval, and obtained

the same number of HR values and SC level estimates for each athlete and for each time window

preceding the shot. Those values were used for subsequent statistical analysis and the application of

the probabilistic approach.

Data Analysis

Two different data processing approaches were used and compared: the “performance-

based” method of affect, HR, and SC level categorisation, and the probabilistic method of affect,

HR, and SC level categorisation.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 11

Performance-based approach of affect, HR, and SC level categorisation. Three

performance levels were derived for each participant based on their individual shooting scores. The

ordered distribution of scores was divided into three levels defined by tertiles, and labelled as

optimal, moderate, and poor performance. Each category contained approximately a third of the

data. A symbolic representation of the objective performance scores was adopted and performance

scores were categorised as follows: optimal scores were labelled as 2, moderate as 1, and poor as 0.

On the whole sample (N = 476 valid shots), optimal performance ranged from 10.3 to 10.9 (n = 159,

M = 10.6, SD = .15), moderate performance from 9.6 to 10.4 (n = 157, M = 10.14, SD = .16), and

poor performance from 7.4 to 10.0 (n = 160, M = 9.51, SD = .43). Significant differences emerged

on the shooting scores across the three performance categories (F2, 473 = 568.98, p < .001, ηp2 = .71;

Scheffé post-hoc, all ps < .001). Performance level was used as the independent variable to examine

affective states in preparation for the shot. In particular, a multivariate analysis of variance

(MANOVA) was executed to assess differences of affect and hedonic tone levels across the three

performance categories in the whole sample. Performance level was also used to investigate the

time course of HR and SC level during the 12-second time window preceding the shot. Repeated

measures analyses of variance (RM-ANOVA) 3 × 12 (performance level × assessment) were

carried out for this purpose. The Greenhouse-Geisser adjustment to the degrees of freedom was

applied to compensate for violations of sphericity assumptions.

Probabilistic method of affect, HR, and SC level categorisation. A probabilistic approach

was adopted to derive five performance level categories. The trichotomized performance data

derived from the performance-based method were re-coded to identify each poor and moderate

performance as occurring with a level of affective states (intensity and hedonic valence), HR, or SC

level either greater than or less than the level of HR or SC that occurred during optimal

performances. This procedure recognises the likelihood that some poor and moderate performances

are associated with levels of affective states, HR, or SC above or below the levels associated with

optimal performance (Johnson et al., 2009). This procedure also involved isolating affect, hedonic

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 12

valence, HR, and SC levels during the optimal performances (OP) for each athlete. The mean

intensity of these optimal performances was then independently calculated for affect and hedonic

valence in preparation for the shot, and for HR and SC level as assessed during the period preceding

the shot. Data points associated with moderate performances were then separated into two

categories: (a) moderate performances with an affective state, hedonic valence, HR, or SC level

below the mean level of the respective measures related to optimal performances (moderate/below:

Mo/B), and (b) moderate performances with an affective state, hedonic valence, HR, or SC level

above the mean level of the respective measures found in optimal performances (moderate/above:

Mo/A). A similar procedure was used to categorise data points associated with poor performances,

thereby leading to two additional categorisation of HR and SC level (poor/below: P/B, and

poor/above: P/A).

Differences in affect and hedonic tone levels of the five performance categories were

checked separately in the whole sample through analysis of variance (ANOVA). RM-ANOVAs 5 ×

12 (performance level × assessment) were performed to examine the temporal pattern of HR and SC

level. The Greenhouse-Geisser adjustment to the degrees of freedom was applied to compensate for

violations of the sphericity assumptions.

Individual zones of functioning. A series of logistical ordinal regressions (LORs) were

performed for each performer, as described in Kamata et al.’s methodology (2002). After

classifying data points (shot series) into one of the five performance levels previously identified

(P/B, Mo/B, OP, Mo/A, and P/A), LOR was performed on each of the four affect- and

physiological-performance pairs: (a) affect intensity-performance level; (b) hedonic tone level-

performance level; (c) HR-performance level, and (d) SC-performance level. HR and SC values

entered in the LOR analyses were those recorded during the12 seconds before the shot. These

analyses resulted in the regression coefficients needed to create performance probability curves.

Regression coefficients were then pasted into spreadsheets required to create probability curves,

which are available from Kamata et al. (2002) and Johnson et al. (2009).

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 13

Results

Performance-based Method of Affect, HR, and SC Level Categorisation

MANOVA results of affect and hedonic tone intensity scores for the three performance

categories were not significant, Wilks’ λ = 1.00, F4, 944 = .34, p = .853. However, RM-ANOVA 3 ×

12 (performance level × assessment) yielded significant results on the main effect of assessment for

both HR (F3.84, 1813.81 = 17.66, p < .001, ηp2 = .04, power = 1.00), and SC level (F2.22, 1049.56 = 76.59,

p < .001, ηp2 = .14, power = 1.00) (see Figures 1 and 2). No additional significant effects were

observed.

Probabilistic Method of Affect, HR, and SC Level Categorisation

ANOVA demonstrated significant differences on performance levels for both affect

intensity scores (F4, 471 = 49.17, p < .001, ηp2 = .30, power = 1.00) and hedonic tone intensity scores

(F4, 471 = 4.71, p < .001, ηp2 = .04, power = .95). Moderate levels of affect (M = 4.51, SD = 1.61)

and hedonic valence (M = 1.82, SD = 3.64) were associated with optimal performance. In

accordance with Yerkes and Dodson’s (1908) inverted-U model, poor and moderate performances

occurred when affect and hedonic valence levels were either above or below the levels associated

with optimal performance.

RM-ANOVAs 5 × 12 (performance level × assessment) showed significant main effects for

HR (performance level, F4, 471 = 4.82, p < .001, ηp2 = .04, power = .96; assessment, F3.85, 1811.39 =

18.47, p < .001, ηp2 = .04, power = 1.00) and SC level (performance level, F4, 471 = 45.04, p < .001,

ηp2 = .28, power = 1.00; assessment, F2.24, 1052.62 = 70.38, p < .001, ηp

2 = .13, power = 1.00).

Performance level × assessment interaction effects were shown for HR, F15.38, 1811.39 = 2.85, p <

.001, ηp2 = .02, power = 1.00, but not for SC level (see Figures 3 and 4). Additional analyses were

performed to establish whether the different findings obtained through the two methods of affect,

HR, and SC level categorisation relied on actual differences or were an artefact due to the different

levels of performance classification (i.e., three levels for the “performance-based” method and five

levels for the probabilistic method). Data from P/B and Mo/B performances were aggregated into

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 14

one category and data from P/A and Mo/A performances were aggregated into another category,

thereby leading to three performance levels (i.e., optimal, poor/moderate below optimal, and

poor/moderate above optimal). Again, ANOVA yielded significant differences by performance

levels for both affect intensity scores (F2, 473 = 97.85, p < .001, ηp2 = .29, power = 1.00) and hedonic

tone intensity scores (F2, 473 = 9.15, p < .001, ηp2 = .04, power = .98). Furthermore, RM-ANOVAs 3

× 12 (performance level × assessment) and the Greenhouse-Geisser adjustment showed significant

main effects for HR (performance level, F2, 473 = 8.52, p < .001, ηp2 = .04, power = .97; assessment,

F3.84, 1818.09 = 16.42, p < .001, ηp2 = .03, power = 1.00) and SC level (performance level, F2, 473 =

88.50, p < .001, ηp2 = .27, power = 1.00; assessment, F2.22, 1051.64 = 76.21, p < .001, ηp

2 = .14, power

= 1.00), and performance level × assessment interaction for HR, F7.69, 1818.09 = 4.30, p < .001, ηp2 =

.02, power = 1.00. These findings substantiate the differences existing between the performance-

based method and the probabilistic method.

Individual Performance Zones Differences

A performer’s probability curves for HR is illustrated in Figure 5. The Y axis represents the

probability of a specific performance level. As shown in this example, the shooter’s performance

levels include: (a) a poor performance associated with a HR level below and above the mean

optimal HR (P/B and P/A); (b) a moderate performance associated with a HR level below and

above the mean optimal HR (Mo/B and Mo/A); and (c) an optimal performance (OP). In the

example given in Figure 5, the athlete’s optimal HR was about 93.5 bpm, with an optimal range

between 90 and 96 bpm. Within this HR range, the athlete has the highest probability of performing

optimally, while simultaneously having a lesser probability of performing moderately or poorly.

Optimal levels and lower and upper boundaries of affective states, HR, and SC were identified for

each participant, as indicated by the vertical lines drawn within the probability curves (see Figure

5). A large variability of optimal levels and ranges of affective states, HR and SC across

participants emerged, as shown from the values given in Table 1.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 15

Discussion

The relation between performance, arousal/activation, and vigilance in precision sport and

shooting has been investigated with both nomothetic (Hatfield et al., 1987; Kontinnen et al., 1992,

1998; Laders & Boutcher, 1986; Tremayne & Barry, 2001) and idiographic approaches (Guillot et

al., 2003; Robazza et al., 2003). Within the idiographic approach, Hanin’s (2007) IZOF model is

one of the most useful frameworks developed in sport psychology. The probabilistic method,

subsequently developed by Kamata and colleagues (2002), has been used to strengthen the ability of

the IZOF model to discriminate between different types of performance, and has been shown to be

effective in analysing both affect (emotion and pleasure) and the related physiological parameters

during task execution (Edmonds, 2006).

In our study, affect intensity and hedonic tone level were used as indicators of the affect

load before each shooting phase. SC level was used as an indicator of arousal/activation during the

shooting phase, whereas HR was used as an indicator of vigilance during the pre-shot period, in

agreement with Tremayne and Barry (2001). We then compared the effectiveness of the

performance-based and probabilistic methods to categorise the performance of elite shooters, using

a multimodal assessment approach to measure affect and physiological responses. This multimodal

approach served as a conceptual basis for the qualitative and quantitative analyses of the structural

and functional relationship between affective states and performance quality, while providing

probabilistic estimations (Edmonds et al., 2006). Particular attention was devoted to evaluating the

discriminative power of the physiological parameters traditionally used to typify activation and

vigilance, SC level and HR respectively, when they are estimated not only at the instant before the

shot, but also during the preparatory period preceding the shot.

Since affect is known to be a multidimensional construct (Gould & Udry, 1994; Hardy et al.,

1996), it becomes unlikely that any two individuals could share the same level and intensity range

of affect and psychophysical responses with respect to a particular performance task. This notion is

corroborated by previous findings demonstrating that arousal or activation and vigilance are

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 16

manifested at multiple subjective and physiological levels (Lazarus, 2000; Vallerand & Blachard,

2000). Indeed, our results showed that each athlete had an individual zone of optimal functioning,

specific for affect and physiological parameters, as shown in Table 1. For this reason, the

performance approach based on the differences between best and worst performance seems to be

less useful in understanding the complexity of the mechanisms underlying performance. Indeed it

fails to differentiate correctly between optimal and non-optimal zones even when a within-subject

analysis is performed. Conversely, the probabilistic approach emphasises the within-individual

dynamics of perceived states based on various degrees of performance quality. This approach,

which takes the individual in relation to his or her own performance as the unit of analysis, permits

one to detect the subtle variations in the intensity of affective and physiological states associated

with different levels of performance (i.e., poor, average, and good), and to convey these paired

observations into probabilistic curves.

In order to better highlight the difference in efficacy of the two compared methods, it may

be useful to discuss their discrepancies and/or similarities with regard to each measure of affect and

each physiological parameter. When using the performance-based method, we found no differences

of affect and hedonic tone in optimal, poor, and moderate performance, although some differences

in SC level and HR appeared. Conversely, when we used the probabilistic approach we found

significant differences in affect intensity and hedonic tone, as well as in SC level and HR, as a

function of performance. These findings may be interpreted as an indication that activation is

manifested at both an individual’s psychological and physiological levels, as previously suggested

by Lazarus (2000) and Vallerand and Blanchard (2000). These results are in line with the contention

that athletes have to reach and maintain idiosyncratic optimal performance zones for best

achievement.

Findings regarding the physiological parameters during the period before the shot

demonstrate that the probabilistic method improves the ability to discriminate between the

physiological mechanisms underlining the different types of performance (poor, moderate, and

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 17

optimal). The time course patterns of SC level obtained using the performance-based method were

in agreement with previous findings and showed a decrease of SC intensity that might be explained

by a reduced arousal/activation (Guillot et al., 2003; Tremayne & Barry, 2001). The probabilistic

method identified different intensity levels in the SC patterns for poor and moderate performance,

both above and below the optimal zone of functioning. In fact, the slope of the SC time course was

the same for all conditions (poor, moderate, and optimal), but SC intensity was significantly

different across performance conditions. The slope of the time course of a given variable indicates

the rapidity with which this variable changes over time. In our case, we interpret the similarity of

the SC slopes in all conditions as a consequence of the tonic characteristic of SC, which usually

undergoes a very slow drift over time (Boucsein, 1992). From an applied perspective, athletes

should become aware of the effects on performance of the different SC intensities, and maintain a

specific level of activation in order to be more effective.

With regard to the physiological indicator of vigilance, we found, by means of the

performance-based method, a significant HR decrease of about 6-7 seconds before the shot, in

agreement with previous evidence by Guillot et al. (2003) and Tremayne and Barry (2001). These

findings are also in agreement with those of Boutcher and Zinsser on golf putting (1990), Robazza

et al. (1998) on archery, and Guillot et al. (2003, 2004, 2005), Konttinnen et al. (1998), Tremayne

and Barry (2001) on shooting, whereas they differ from those of Hatfield et al. (1987) on shooting,

and Salazar et al. (1990) on archery, who found cardiac acceleration, which they explained as a

response to the physical demands of their experimental requirements. Congruent with these results

recorded in the literature, we found that cardiac decelerations associated with optimal performance

started about 3 seconds earlier than those preceding poor or moderate performance. These results

are not in line with those by Guillot et al. (2004) in elite pentathletes. It is reasonable to assume that

these differences depend on the level of the athletes; indeed, pentathletes enrolled for the study by

Guillot et al. (2003, 2004) were not at the top level of shooting athletes as were those who

participated in the present study and in that by Tremayne and Barry (2001). Nevertheless, we found

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 18

a smoother HR decrease in optimal performance than in moderate and poor performance, which

differs from the findings of Tremayne and Barry. We speculate that our result may be an indication

of a modulation of simultaneous attentional and kinaesthetic control of the body. During the

concentration phase, shooters may focus their attention on the control of each part of their body to

reduce all irrelevant movements (Hillman et al., 2000). Indeed, it is important for the shooter to

modulate the external focus of attention while keeping a kinaesthetic imagery and internal

representation of action (Wulf, 2007). Therefore, a shooter’s attentional focus on both the external

and kinaesthetic stimuli would result in a quite stable HR that is associated with optimal

performance.

By means of the probabilistic method, we can further highlight not only the absolute average

variations of HR, the expression of different levels of vigilance, but also the different tendency of

HR during the preparatory period preceding the shot as a function of performance. The temporal

trend shown in Figure 3 suggests that optimal performance is associated with a moderate level of

cardiac deceleration. It can be hypothesised that remarkable cardiac decelerations imply attentional

focus directed toward bodily and proprioceptive symptoms for postural, balance and motor control,

whereas small cardiac decelerations are related to an attention that is more focused on the external

stimuli, namely on the target. Such a hypothesis is based on the evidence that the rate of cardiac

deceleration, quantified by means of the slope of the time course of HR, changes considerably about

6-7 seconds before the shot, with a significant interaction between performance and time before the

shot. It is worth underlining that these HR slope variations could not be appreciated with the

performance-based method of analysis. Indeed, the pattern of cardiac deceleration associated with

the performance above the optimal activation level was steeper than that associated with the

performance below the optimal level. This finding could be due to different behaviours: one related

to stimulus intake and therefore to focusing attention, and another related to stimulus rejection and

maybe to other cognitive processes such as decision-making at the moment of shot release.

However, about 3 seconds before the shot there was another change in the HR slope that was not

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 19

revealed by the performance-based method. Specifically, we observed a tachycardia in the

moderate/below performance level that is in agreement with the results by Hatfield et al. (1987) in

shooting and by Salazar et al. (1990) in archery. This tachycardia could be explained by the

engagement on the target and by movement planning and execution, as well as by cognitive load

such as the decision-making before shooting.

We may conclude that the use of the probabilistic method of analysis of psychological data

(i.e., affect and hedonic tone) and physiological parameters (such as SC level and HR) seems to be

more effective than the performance-based method in describing the psychophysiological

mechanisms associated with shooters’ performance. Indeed, the probabilistic method permits one to

appreciate the subtle variations of pre-performance affect and those occurring in both SC level and

HR during the preparatory period preceding the shot until shot release.

Conclusions

The probabilistic method was successfully applied to the study of the time-course of psycho-

physiological indices in elite shooters, thereby enabling us to differentiate the contribution of

arousal/activation and vigilance to both optimal and non-optimal performance. From an applied

perspective, we believe that the adoption of the probabilistic approach can help athletes to become

aware of the subtle variations occurring in their psychophysical states during the preparatory period

preceding the shot and not only at the moment of shot release. Future studies might examine the

effectiveness of the probabilistic approach in predicting performance and how frequently the

probability-based zones of intensity of psychophysical variables need to be (re)estimated during the

season to improve the predictive power of this assessment methodology. Upcoming research may

also use the probabilistic method to investigate the multidimensional mechanisms underpinning

arousal/activation and vigilance at a central level. This might be achieved by quantifying neuro-

physiological measures of cortical arousal/activation and vigilance, in line with Boucsein’s (1993)

triple-activation model describing brain monoaminergic influences underlying psycho-physiological

reactions. Additional hypotheses based on both the inverted U and the U relations, as suggested by

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 20

Vaez Mousavi et al. (2008, 2009), might be examined to deepen our understanding of the links

among arousal/activation, vigilance and performance in elite shooters.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 21

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Table 1

Individual’s Optimal Levels (OP), Lower Boundary (LB), and Upper Boundary (UB) of Affective States, HR (in bpm), and SC (in µs)

Affect intensity Hedonic tone HR SC

Shooter LB OP UB LB OP UB LB OP UB LB OP UB

1 2.05 3.72 5.28 5.03 6.42 7.82 71.64 76.76 81.88 12.87 23.52 37.25

2 3.06 4.02 5.00 -2.68 -0.25 3.08 96.09 122.45 155.18 10.47 12.33 14.38

3 2.00 3.05 5.64 -4.21 0.73 2.30 83.87 89.42 97.00 16.25 19.45 23.52

4 5.00 5.46 5.92 3.95 4.45 4.98 90.39 93.36 96.19 12.14 16.02 19.67

5 2.39 3.64 4.64 -1.59 0.25 2.44 76.91 81.03 84.91 16.94 21.03 25.48

6 5.01 5.98 5.98 -4.98 -4.09 -3.08 79.76 81.45 83.39 16.13 21.93 27.94

7 2.61 3.64 4.61 1.45 4.00 5.00 71.02 73.55 76.17 20.83 23.46 26.47

8 5.52 6.39 7.30 1.94 3.76 5.37 72.89 77.11 81.78 19.88 24.68 29.28

Mean 3.46 4.49 5.55 -0.14 1.91 3.49 80.32 86.89 94.56 15.69 20.30 25.50

Min 2.00 3.05 4.61 -4.98 -4.09 -3.08 71.02 73.55 76.17 10.47 12.33 14.38

Max 5.52 6.39 7.30 5.03 6.42 7.82 96.09 122.45 155.18 20.83 24.68 37.25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 28

Figure 1. Mean heart rate profiles for optimal, moderate, and poor performance. Heart rate is

expressed in bpm, and time to shot in s.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 29

Figure 2. Mean SC profiles for optimal, moderate, and poor performance. SC is expressed in µs and

time to shot in s.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 30

Figure 3. Mean heart rate profiles for optimal performance (OP), and moderate/below (Mo/B),

moderate/above (Mo/A), poor/below (P/B), and poor/above (P/A) optimal performance. Heart rate

is expressed in bpm, and time to shot in s.

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Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 31

Figure 4. Mean SC profiles for optimal performance (OP), and moderate/below (Mo/B),

moderate/above (Mo/A), poor/below (P/B), and poor/above (P/A) optimal performance. SC is

expressed in µs and time to shot in s.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Running head: TEMPORAL PATTERN OF PRE-SHOOTING STATES 32

Figure 5. Heart rate probability curves of a shooter showing the probability of: (a) a poor

performance associated with an heart rate level below the mean optimal heart rate (P/B); (b) a

moderate performance associated with an heart rate level below the mean optimal heart rate

(Mo/B); (c) an optimal performance (OP); (d) a moderate performance associated with an heart rate

level above the mean optimal heart rate (Mo/A); and (e) a poor performance associated with an

heart rate level above the mean optimal heart rate (P/A). Heart rate is expressed in bpm.

Figure(s)Click here to download high resolution image

Figure(s)Click here to download high resolution image

Figure(s)Click here to download high resolution image

Figure(s)Click here to download high resolution image

Figure(s)Click here to download high resolution image

1

Response to Reviewers We thank the Reviewers for the constructive suggestions intended to improve the paper. We have revised our manuscript addressing all the comments. A point-by-point list of the revisions made is given below. For the sake of clarity, the Reviewer’s comments are reported in italics. Reviewer #1 GENERAL COMMENT: I'm quite happy about the way the authors addressed my general and specific comments. Now the paper reads better and the authors are to be commended on the good job they did. However, I'm still convinced that the proposed definition of the term Individual Affect-related Performance Zones (IPAZ) is still flawed. It needs to be either explained properly or dropped altogether. Reply: We thank you very much for your appreciation of our work. According to your suggestion we have dropped the term Individual Affect-related Performance Zones throughout the paper. Specific comments: 1. Page 6/ lines 26-35: . The implementation of this methodology has led to the identification of Individual affect-related Performance Zones (IAPZs) as ranges of affective and physiological state intensities, within which each individual is assigned a probability to perform at a particular level (e.g., optimal, moderate, poor) ." . In fact, Kamata et al (2002) proposed a computational procedure to identify "probability-based zones of intensity" related to specific performance (outcome) levels (p.19/53-54). Reply: Please see above to the reply to the general comment. 2. In Figure 5, there is HR intensity ranging from 87 to 101 bpm and probability curves of achieving each of the five levels of performance outcomes. The athlete's optimal HR level is about 93.5 bpm with the probability of about 0.45. Probability-based optimal zone of HR intensity is between 90 and 96 bpm (with related probabilities ranging from 0.46 (93.5) to 0.30 (90 & 96 bmp). Here probability curves are claimed to specify the intensity zones for all five performance levels. How then do the individual affect-related performance zones (IPAZ) fit in here? Reply: As explained above, we have dropped the term Individual Affect-related Performance Zones throughout the paper and changed to “probability-based zones of intensity”, “individual zones of intensity related to specific performance levels”, “probability-based zones of intensity related to specific performance levels”, and “individual zones of functioning”. Therefore, the issue of explaining or fitting data into the notion of IAPZ is no longer a problem. 3. A paragraph on page 6 (lines 36-50) needs re-writing for clarity. Reply: The sentence has been modified as follows: “The probabilistic method enables the assessment of individual zones of intensity related to specific performance levels that can easily implement introspective (such as emotional report) and objective data (such as HR and SC level).” 4. p.16/29-32 Probabilistic curves can't be termed poor, moderate, and optimal. These terms are the labels for performance levels. By the way, what are moderate and optimal performance categories? I would argue that more natural for your shooters would be individually good, average, and poor performances.

*Response to Reviewers

2

Reply: The sentence has been modified as follows: “This approach … permits one to detect the subtle variations in the intensity of affective and physiological states associated with different levels of performance (i.e., poor, average, and good)…” Reviewer #2: GENERAL COMMENT The questions raised by this reviewer was adequately addressed in the new version of the manuscript. Only minor corrections are listed below. Reply: We thank you very much for your appreciation of our first revision. Specific comments: 1. In pg. 6, just a minor correction: "prospectives and retrospective reports", where prospective is the correct form. Reply: correction done.

2. In pg. 14, line 17 - "yielded significant results on assessment main effect for both" the word "of" is missing after assessment. Reply: correction done.