To Focus or Not to Focus: Is Attention on the Core Components of Action Beneficial for Cycling Performance?

Abstract

We conducted a counterbalanced repeated measure trial to investigate the effect of different internal and external associative strategies on endurance performance. Seventeen college-aged students were randomly assigned to three experimental conditions to test the notion that different attention-performance types (optimal Type 1, functional Type 2, and dysfunctional Type 3) would influence endurance time on a cycling task. Specifically, Type 1 represented an effortless and automatic, “flow-feeling” attentional mode. Type 2 referred to an associative focus directed at core components of the task. Type 3 represented an attentional focus directed at irrelevant components of the task. Participants completed three time-to-exhaustion-tests while reporting their perceived exertion and affective states (arousal and hedonic tone). Results revealed that Type 1 and Type 2 attentional strategies, compared to Type 3 strategy, exerted functional effects on performance, whereas a Type 3 strategy was linked to lower performance, and lower levels of arousal and pleasantness. Applied implications are discussed.

Full text

110
applied research
The Sport Psychologist, 2015, 29, 110 -119
http://dx.doi.org/10.1123/tsp.2014-0046
© 2015 Human Kinetics, Inc.
To Focus or Not to Focus: Is Attention on the Core
Components of Action Beneficial for Cycling Performance?
Maurizio Bertollo and Selenia di Fronso
University “G. d’Annunzio” of Chieti-Pescara
Edson Filho
University of Central Lancashire
Vito Lamberti and Patrizio Ripari
University “G. d’Annunzio” of Chieti-Pescara
Victor Machado Reis
University of Trás-os-Montes and Alto Douro
Silvia Comani, Laura Bortoli, and Claudio Robazza
University “G. d’Annunzio” of Chieti-Pescara
We conducted a counterbalanced repeated measure trial to investigate the effect of different internal and external asso-
ciative strategies on endurance performance. Seventeen college-aged students were randomly assigned to three experi-
mental conditions to test the notion that different attention-performance types (optimal Type 1, functional Type 2, and
dysfunctional Type 3) would inuence endurance time on a cycling task. Specically, Type 1 represented an effortless
and automatic, “ow-feeling” attentional mode. Type 2 referred to an associative focus directed at core components of
the task. Type 3 represented an attentional focus directed at irrelevant components of the task. Participants completed
three time-to-exhaustion-tests while reporting their perceived exertion and affective states (arousal and hedonic tone).
Results revealed that Type 1 and Type 2 attentional strategies, compared with Type 3 strategy, exerted functional effects
on performance, whereas a Type 3 strategy was linked to lower performance, and lower levels of arousal and pleasantness.
Applied implications are discussed.
Keywords: attentional focus, cycling, fatigue, endurance, multi-action plan model
Bertollo, di Fronso, Comani, Bortoli and Robazza are with
the Behavioral Imaging and Neural Dynamics Center, Dept.
of Medicine and Aging Sciences, University “G. d’Annunzio
of Chieti-Pescara, Chieti, Italy; Edson Filho is with School
of Psychology, University of Central Lacashire, Preston, UK;
Lamberti and Ripari are with the of Dept. of Clinical and Experi-
mental Science, University G. d’Annunzio Chieti-Pescara,
Chieti, Italy; Machado is with the School of Life Sciences and
the Environment, University of Trás-os-Montes and Alto Douro,
Vila Real, Portugal. Address author correspondence to Maurizio
Bertollo at m.bertollo@unich.it.
There is general agreement about the importance of
studying how different attentional strategies inuence
performance in sport and exercise settings (Basevitch
et al., 2011; Blancheld, Hardy, de Morree, Staiano, &
Marcora, 2014; Connolly & Tenenbaum, 2010; Hutchin-
son & Tenenbaum, 2007; Razon et al., 2010; for a review,
see Brick, MacIntyre, & Campbell 2014). In this regard,
previous research has shown that one’s ability to self-
regulate attentional focus (e.g., attentional exibility)
is associated with the ability to sustain exertive effort in
endurance tasks (for a review, see Tenenbaum, 2005). To
perform optimally, athletes must be able to employ differ-
ent attentional strategies to control external and internal
distracters, while focusing on body and task-relevant cues
(Tenenbaum, 2001, 2005).
Attentional Strategies
for Endurance Performance
Early research suggested that there are primarily two
coping strategies that can be used to enhance performance

To Focus or Not to Focus 111
TSP Vol. 29, No. 2, 2015
in endurance tasks (a) “association” and (b) “dissocia-
tion” (Weinberg, Smith, Jackson, & Gould, 1984). Asso-
ciation occurs when people monitor their body sensations
(e.g., respiration rate, body temperature, muscle pain
and tightness), while reminding themselves to relax
and modify stride and pace to secure greater running
economy. Indeed, elite performers monitor their body
sensations more effectively than their less accomplished
counterparts (Raglin & Wilson, 2008). Dissociation
occurs when individuals ignore pain, fatigue, or boredom
by directing their attention outwards or by focusing on
pleasant stimuli (e.g., listening to music; Weinberg et
al., 1984). In this regard, Schücker and colleagues found
that external attentional focus has a signicant impact on
physiological performance measures of running economy
(Schücker, Anheier, Hagemann, Strauss, & Völker, 2013).
Association and dissociation are examples of atten-
tion control, which is a topic of great interest to sport
and exercise psychologists. In essence, associative and
dissociative strategies represent two distinct cognitive
styles that indicate where individuals allocate attention
to improve adjustment to a physical task (Tenenbaum,
2005). This initial distinction between the two broad
categories of attention focus (as association and disso-
ciation) was introduced by Morgan and Pollock (1977),
and has since oriented research on attentional focus and
physical effort (Hutchinson & Tenenbaum, 2007; Stan-
ley, Pargman, & Tenenbaum, 2007; Stevinson & Biddle,
1998). According to Morgan and Pollock, association is
an internal attentional style used by people to monitor
sensorial input while performing a physical task. Disso-
ciation pertains to any cognitive strategy used to divert
attention away from internal sensations and toward
external distractions. Schomer (1986) differentiated
associative and dissociative strategies by discussing the
presence of “task-related” and “task-unrelated” thoughts.
Specically, associative thoughts are related to the task at
hand (e.g., bodily sensations, performance instructions,
and pace monitoring), while dissociative thoughts are
not relevant to the task (e.g., reective activity thoughts,
problem solving).
Stevinson and Biddle (1998) argued that a dichoto-
mous treatment of attentional focus was inherently sim-
plistic, and therefore proposed a two dimensional model
considering: (a) the direction of attention (internal or
external), and (b) task relevance (relevant or irrelevant).
Internal strategies allow an individual to monitor his/her
internal states while making appropriate psychophysi-
ological adjustments to accommodate pain and effort.
Conversely, external strategies allow the performer to
shift attention to exterior events, thus reducing percep-
tions of exertion. Task-relevant thoughts involving an
internal focus (e.g., physical sensations) are classied as
internal association, whereas task-relevant thoughts with
an external focus (e.g., pacing) are labeled external asso-
ciation. Similarly, task irrelevant thoughts with an internal
focus (e.g., daydreams) were categorized as internal dis-
sociation and task-irrelevant thoughts with an external
focus (e.g., scenery) as external dissociation. Drawing on
Stevinson and Biddle’s (1998) classication, Brick et al.
(2014) suggested that the associative dimension should
also include active self-regulation (i.e., thoughts related
to cadence, pacing, technique, strategy, or maintaining a
relaxed state) and internal sensory monitoring.
Tenenbaum’s (2005) effort-related model added the
notion that associative and dissociative focus depends on
workload intensity. In particular, Tenenbaum observed
that people may intentionally switch their attentional
focus, between associative and dissociative strategies,
under low workload intensities. However, when physi-
cal symptoms of exertion reach a threshold upon which
attention exibility (i.e., ease of switching back and forth
between dissociative to an associative pattern) is compro-
mised, a nal switch from a dissociative to an associative
focus occurs (e.g., increased somatic awareness, and
pain; see Connolly & Tenenbaum, 2010; Hutchinson &
Tenenbaum, 2007; Stanley et al., 2007). In this regard,
endurance athletes have reported that focusing on internal
(association) cues is functional for performance unless
they feel very tired and distressed, when an associative
strategy is viewed as dysfunctional and unnatural (Mas-
ters & Ogles, 1998).
It is important to note that research on the relation-
ship between performance and attentional focus (as asso-
ciative or dissociative) has produced conicting results.
While some scholars have found a linkage between higher
ratings of perceived exertion (RPE) and an associative
focus (Baden, McLean, Tucker, Noakes, & Gibson,
2005), others observed a linkage between higher RPE
and a dissociative focus (Beaudoin, Crews, & Morgan,
1998; Brewer, Van Raalte, & Linder, 1996). Furthermore,
there is also literature suggesting no differences in perfor-
mance as a function of attentional focus (Harte & Eifert,
1995; Weinberg et al., 1984). Despite these conicting
results, there is a general agreement that the optimiza-
tion of attentional control may produce signicant gains
in endurance performance and external focus strategies
can also be benecial to performance beside the internal
ones (Schücker et al., 2013). The purpose herein was to
investigate the effect of different internal and external
associative strategies on endurance performance in
cycling, using the multi-action plan (MAP) model as a
theoretical basis for our experimental protocol.
The Multi-Action Plan Model
The recently proposed MAP model is based on the notion
that different attentional strategies lead to different per-
formance states, namely optimal and less than optimal
(Bertollo, Bortoli, Gramaccioni, Hanin, Comani, & Roba-
zza, 2013; Bortoli, Bertollo, Hanin, & Robazza, 2012).
Specically, an automatic attentional focus (Type 1) has
been linked to optimal performance in sports. However,
an attentional focus directed at a core component of a
given action (Type 2) has also been associated with func-
tional performance, which is dened within the individual
zones of optimal functioning (IZOF) framework as an

112 Bertollo et al.
TSP Vol. 29, No. 2, 2015
individual’s effective recruitment and use of available
resources for optimal achievements (Hanin, 2007). In
contrast, over-controlled attentional focus has been found
to lead to dysfunctional performance in sports. Previous
research based on the MAP model revealed that atten-
tional focus moderates performance quality in self-paced
tasks (i.e., rie and pistol shooting). In effect, Bortoli et al.
(2012) observed that four performance categories result
from different attentional strategies. These performance
states are: (a) Type 1, optimal performance, character-
ized by an automatic (“ow-feeling” like) attentional
mode and pleasant-functional emotions; (b) Type 2,
functional performance, typied by an associative focus
directed at core components of a given task/action and
pleasant or unpleasant-functional emotions; (c) Type
3, dysfunctional performance, characterized by a focus
directed at irrelevant components of a given task/action
and unpleasant-dysfunctional emotions; and (d) Type 4,
poor performance, typied by a markedly irrelevant focus
and pleasant-dysfunctional emotions.
The MAP model was developed to orient applied
interventions aimed at reaching and maintaining maxi-
mal performance in presence of distress, fatigue, and
distracting situations. In detail, the MAP model is con-
ceptualized as a function of distinct performance levels
(i.e., optimal or suboptimal) and attentional demands
(i.e., automatic or controlled), thus establishing four
performance categories: Optimal-automatic (Type 1),
optimal-controlled (Type 2), suboptimal-controlled
(Type 3), and suboptimal-automatic (Type 4). Indeed,
these four types of performance have been found to rely
on specic psychophysiological patterns, including skin
conductance levels, respiration rate, and fronto-occipital
and interfrontal coherence in the alpha band (Bertollo et
al., 2013; Comani et al., 2014). Of note, perceived arousal
and pleasantness levels have also been found to predict
performance in endurance tasks in general (Hanin, 2007),
and in respect to the MAP model’s performance categori-
zation in particular (Bertollo et al., 2013). In fact, arousal
and pleasantness underlie the notion of core affect, thus
inuencing one’s ability to perform a given task (Russell
& Weeks, 1994; Russell, Weiss, & Mendelsohn, 1989).
Hence, in the current study, we were also interested in
assessing whether the cyclists’ core affect (i.e., arousal
and hedonic tone) patterns would differ in regards to the
MAP model categories.
It is also important to note that the MAP model is
idiosyncratic in nature, thus assuming that one’s strate-
gies and behaviors during performance are unique. In
essence, the MAP model posits that individuals who
focus on their idiosyncratic core components of action
in conditions of distress or fatigue are likely to consis-
tently attain high performance levels. In a recent study,
for instance, cyclists identied “pedaling rate” as a core
component of action linked to endurance performance
(Comani et al., 2014). To this extent, Bortoli et al.
(2012) suggested that an appropriate focus on one or a
few core components of the action helps performers to
self-regulate by maintaining optimal action tendencies.
In contrast, reinvesting attention on body feelings (e.g.,
muscular tension, muscular stiffness, and pain) in the
attempt to control the whole action tends to increase the
likelihood of performance breakdown (Masters & Max-
well, 2008). According to the MAP model, individuals
can reach functional performance levels by directing
their attention on the core components of the action (i.e.,
using action-centered strategies) and/or optimizing their
emotional states (i.e., using emotion-focused strategies).
The Present Study
We investigated the effect of different internal and exter-
nal associative strategies on endurance performance.
Drawing on the MAP model assumptions, we hypoth-
esized that participants in a Type 1 performance condition
would experience optimal performance and a “ow state”
typied by pleasant affect, while externally concentrat-
ing on pacing. Type 2 performance situation, in which
participants’ attention was directed internally on the core
component of the cycling action, was expected to result
in a functional state and be accompanied by pleasant or
unpleasant affect. Finally, Type 3 performance condition
was predicted to augment individual’s fatigue sensations
and cause a suboptimal performance state because of
excessive focus on muscle feelings and pacing. Type 4
performance, characterized by unfocused attention and
poor performance, was not considered in the current study
because it is less relevant to the development of applied
guidelines for performance improvement in sports.
Method
Design
Based on the performance states delineated in the MAP
model, we conducted a counterbalanced repeated mea-
sure trial to investigate the effect of different internal and
external associative strategies on endurance performance.
This is congruent with the importance of testing the
MAP model assumptions in sport modalities other than
self-paced sports (e.g., dart throwing, pistol shooting),
especially endurance sports in which attentional control
and performance are intrinsically related. In particular,
we tested participants in time-to-exhaustion trials during
cycling, while collecting psychological markers of fatigue
and affect (i.e., RPE, hedonic tone and arousal).
Participants
A priori power analysis (effect size = .50, power of .95,
and an alpha level of .05) was used to determine the
sample size (N = 18). To detect a moderate effect size
(see Cohen, 1988), we recruited 21 college-aged students.
Four students discontinued participation from the experi-
ment due to either personal or health reasons. Accord-
ingly, seventeen students (5 women and 12 men, Mage =
24.3 years, SD = 4.9 years) completed the experimental

To Focus or Not to Focus 11 3
TSP Vol. 29, No. 2, 2015
protocol, consisting of four visits to an exercise physiol-
ogy laboratory. All seventeen volunteers participated reg-
ularly in different physical activities of low or moderate
intensity. Assessment at baseline revealed that the tness
level of participants was generally low (men: V
.O2max M
= 39.03 ml kg-1 m-1, SD = 15.64, power peak output M
= 231.54, SD = 69.01; women: V
.O2max M = 26.31 ml
kg-1 m-1 SD = 5.32, power peak output M = 118.57, SD =
13.91). After being briefed on the general purpose of the
study, the participants agreed to participate and signed
a written informed consent. The study was conducted in
accordance with the declaration of Helsinki and received
approval from the local university ethics committee.
Measurements
Ratings of Perceived Exertion (RPE). RPE was
measured through a CR-10 Scale ranging from “0” (no
effort) to “•” (maximal sustainable effort). The verbal
anchors were: 0 = nothing at all, 0.5 = extremely weak,
1 = very weak, 2 = weak, 3 = moderate, 5 = strong, 7 =
very strong, 10 = extremely strong, • = absolute maximum.
No verbal anchors were used for 4, 6, 8 and 9. Of note,
the CR-10 Scale is instrumental in diminishing ceiling
effects as its ratings are linearly related to various physi-
ological parameters such as V
.O2max, lactate, and heart
rate (Borg, 1998).
Affect Grid. This is a single-item scale designed to
quickly assess core affect along the dimensions of
pleasure-displeasure and sleepiness-arousal (Russell et
al., 1989). In our study, the participants were asked to
place a single “X mark” on the 9 × 9 grid, which columns
represent pleasantness and arousal scores. Hence, both
the pleasure-displeasure and sleepiness-arousal can range
from 1 to 9.
Manipulation Check Questionnaire. The participants
were asked to rate, using a 10-point frequency scale with
anchors 1 (never) and 10 (always), one of the following
questions: “How often did you focus your attention on
the metronome?” (Type 1 performance condition), “How
often did you focus your attention on your feet to maintain
individual RPM pacing?” (Type 2 performance condi-
tion), and “How often did you focus your attention on
the tension of your muscles and body or fatigue?” (Type
3 performance condition).
Procedures
Four visits to the laboratory were planned, with intervisit
intervals of 48–72 hr. Two trained scholars collected the
data. Data collection occurred in a quiet (no music playing
and no other people allowed in the laboratory) and safe
environment to ensure the comfort of the participants.
During the rst session, the cycle ergometer was set-up
and adjusted to each participant’s needs. The participants
used the same cycle ergometer set-up during the subse-
quent visits. They were allowed to ask questions at any
time during the study.
Incremental Test. During the rst visit to the laboratory,
participants received standard instructions about the use
of the Borg CR-10 RPE scale (Borg & Borg, 2001) and
the affect grid (Russell et al., 1989). They also performed
an incremental test to determine their anaerobic threshold
or second ventilatory threshold (VT2). Specically, after
a warm-up (4 min cycling at 25 W), V
.O2 and V
.
CO2 were
measured using an incremental protocol on a Monark
Cycle Ergometer (939 E). Heart rate, V
.O2 and V
.
CO2
were continuously monitored during the exercise using
a Schiller CS 200 system. VT2 was determined through
the V-Slope method (Wasserman, Stringer, Casaburi,
Koike, & Cooper, 1994). Pedal rate was set at 70 rpm
(rpm) and the workload power output was initially set at
25 W. Subsequently, the power output was incrementally
increased by 25 W every 2 min until exhaustion. After
the completion of the incremental test, the participants
were given a 20-min rest period. After this period, par-
ticipants were asked to pedal at VT2 + 5% for 10 min
to identify their preferred pedaling rate (PPR), while
familiarizing themselves with the study’s procedures.
Overall, this initial assessment indicated that participants
were not trained athletes but rather recreational exercis-
ers and, in some instances, arguably unt individuals.
Lastly, the participants were assigned to three different
experimental conditions to be undertaken separately
during the subsequent three meetings. These conditions
required that the participants kept their focus of attention
on either: (a) a metronome that reproduced their PPR
(external associative strategy) aimed at eliciting a Type
1 performance state, which is typied by movement
automaticity and optimal-pleasant affect; (b) their PPR
(i.e., internal associative strategy on pacing representing
the core component of action) aimed at eliciting a Type 2
performance condition typied by focused attention on
the relevant aspects of action and pleasant or unpleasant
affect; or (c) muscle fatigue feelings and difculties in
maintaining pacing (dysfunctional associative strategy on
internal feelings) aimed at inducing Type 3 dysfunctional
performance state and unpleasant affect (see Figure 1).
Time-to-Exhaustion Test at Individual Constant Load.
During the subsequent visits to the laboratory, participants
were assigned to one of the three experimental conditions,
each dened in a random order and occurring on differ-
ent days. They performed a time-to-exhaustion test, at
individual VT2 power intensity, while reporting their RPE
and affective states. Time-to-exhaustion was determined
as either (a) the maximum interval in which the par-
ticipants could maintain exercise intensity (VT2 + 5%),
and/or (b) the moment in which participants’ reached
volitional exhaustion. The rst criterion, in particular,
was established to prevent excessive lengthening of the
experimental condition, especially with well-trained
individuals. Individual VT2 and PPR of each participant
was set during test. After a resting period (no movement)
of 2 min, and a warm-up period of 4 min on the cycle
ergometer at individual power (calculated as the 40–50%
of the individual V
.O2max), the participants performed a

114 Bertollo et al.
TSP Vol. 29, No. 2, 2015
constant load until exhaustion at their individual power
(i.e., VT2 + 5% with a PPR) while maintaining their PPR.
After exhaustion, the participants engaged in an active
recovery period of 4 min (at the same power used during
warm-up) followed by a resting period of 2 min (no move-
ment). RPE and affect grid scores were collected in the
last 5 s of every 1 min period throughout the entire test.
The manipulation check questionnaire was administered
at the end of data acquisition to verify adherence to the
experimental conditions.
Statistical Analysis
Analysis of variance with repeated measures (RM-
ANOVA), with an alpha level set at .05, was computed to
compare participants’ time-to-exhaustion test, RPE, and
affective scores across the three experimental conditions.
Bonferroni post hoc test was used to identify potential
differences among the three experimental conditions.
Furthermore, condition × time RM-ANOVAs were
performed for RPE and affect grid scores at 0% (rst
minute), 25%, 50%, 75%, and 100% (last completed
minute) in the time-to-exhaustion test. This is aligned
with the importance of measuring temporal changes in
affective states in general (Hanin, 2007), and in fatigue
in particular (Blancheld et al., 2014). These iso-times
were measured at the selected time-points, thus allow-
ing for the identication of affective (i.e., arousal and
pleasantness) and perceptual (RPE) changes throughout
the exhaustion tests. Specically, iso-time values for
0% corresponded to the values for the rst full minute
of each time-to-exhaustion test. The value of iso-time
at 100% was dened as the shortest time-to-exhaustion
accomplished by each individual over their three tests.
The minute 100% iso-time was divided by two to obtain
the value corresponding to 50% iso-time (see Blancheld
et al., 2014). The 25% and 75% iso-times were derived
accordingly.
Results
Manipulation Check
Manipulation check results showed that participants
adhered satisfactorily to the experimental conditions.
During the Type 1 performance condition, response rat-
ings ranged from 5 to 9, which corresponded to an adher-
ence frequency from often to almost always (M = 6.82,
SD = .87). In the Type 2 performance state, the response
ratings ranged from 6 to 9 (very often to almost always;
M = 7.29, SD = .89), whereas in the Type 3 performance
state the response ratings ranged from 7 to 9 (M = 7.82,
SD = .74). The levels of arousal, affect, and perception of
effort were also analyzed before the time-to-exhaustion
test. RM-ANOVA results on the affect grid and RPE data
collected during the rest and warm-up phases revealed no
differences among the experimental conditions before the
time-to-exhaustion test in regards to arousal level, F(2,
32) = 0.89, p = .41, hedonic tone, F(2, 32) = 2.32, p =
.11, and RPE, F(2, 32) = 0.76, p = .48.
Experimental Manipulation
Descriptive Analysis. Descriptive statistics for all
measures are reported in Table 1. High standard deviation
scores on time-to-exhaustion indicate large individual
differences in the efcacy of the Type 1 and Type 2
strategies. Two examples of idiosyncratic trends for RPE
Figure 1 — The multi-action plan (MAP) model as applied to the experimental cycling conditions.

To Focus or Not to Focus 11 5
TSP Vol. 29, No. 2, 2015
during the entire experimental phase are presented in
Figure 2. Panel A shows the RPE trend of a cyclist who
reached best performance using Type 1 performance
strategy. On the other hand, panel B shows the RPE
trend of a cyclist who sustained a longer time in cycling
performance through Type 2 performance strategy. Both
participants showed poorer performance under the Type
3 performance condition.
Inferential Analysis. In RM-ANOVA, the assumption
of sphericity was violated, and thus the Greenhouse-
Geisser correction was applied to the degrees of freedom
for subsequent F statistic calculation. RM-ANOVA
on overall scores showed differences across the three
experimental conditions in regard to the duration of the
time-to-exhaustion test, F(1.81, 29.03) = 11.41, p < .01,
ηp2= .41, power .98. Bonferroni post hoc test showed
differences in the duration of time-to-exhaustion test
between Type 1 (M = 18.35 min) and Type 3 (M = 14.12
min) performance states (p < .01), and between Type 2 (M
= 17.65 min) and Type 3 (M = 14.12 min) performance
states (p < .01).
To explore the effect of internal and external asso-
ciative strategies on RPE, based on the MAP model
assumptions, we calculated the slope of RPE for the
time-to-exhaustion test. Subsequently, we performed a
RM-ANOVA on the overall slope scores to explore the
impact of RPE, and affect trend during task. Results
revealed differences among the three experimental
conditions on the slope scores of RPE during time-to-
exhaustion-test, F(1.67, 26.86) = 4.01, p = .03, ηp2 =
.21, power .62. In particular, Bonferroni post hoc test
showed difference between Type 1 (M = 0.67) and Type
3 (M = 0.88) performance states (p < .01) and Type 2 (M
= 0.73) and Type 3 (M = 0.88) performance states (p =
.04). No differences were found with regards to the RPE
slope during the recovery period, and scores of arousal
and hedonic tone.
Condition × iso-time RM-ANOVAs on RPE, arousal,
and hedonic tone were also performed. Specically, we
compared the three experimental conditions over iso-time
for RPE. Results revealed an effect on condition, F(1.89,
30.24) = 462.46, p < .01, ηp2 = .97, power 1.00, on time,
F(2.32, 37.13) = 74.29, p < .01, ηp2 = .82, power 1.00,
and in the interaction between condition and time, F(2.78,
44.53) = 6.57, p = .01, ηp2 = .29, power .95. These dif-
ferences are graphically depicted in Figure 3 (Panel A),
in which higher RPE levels are more evident for Type 3
performance state at the 75% and 100% iso-times.
Furthermore, when comparing the three experimen-
tal conditions over iso-time on Arousal level, we found
an effect on condition, F(1.34, 21.55) = 10.47, p < .01,
Table 1 Descriptive Statistics of Physiological,
Performance, and Affective Data
Variable M
SD
VT2-V
.O2 (ml kg-1 m-1) 23.08 9.88
VT2-Power (Watt) 138.82 54.84
VT2-HR 142.06 24.74
PPR (rpm) 75.41 15.06
Time to exhaustion (min)
Type 1 performance 18.35 6.93
Type 2 performance 17.65 6.52
Type 3 performance 14.12 5.95
Arousal
Type 1 performance 5.44 1.89
Type 2 performance 5.90 1.70
Type 3 performance 5.56 1.90
Hedonic tone
Type 1 performance 5.25 1.88
Type 2 performance 5.33 1.72
Type 3 performance 4.87 1.59
Note. VT2 = second ventilatory threshold; V
.O2 = oxygen consumption;
HR = heart rate; PPR = preferred pedaling rate.
Figure 2 — Individual trends of RPE during the time-to-exhaustion-test. Panel A shows an athlete who reached best performance
using internal associative, Type 2 performance strategy. Panel B shows an athlete who reached best performance using external
associative, Type 1 performance strategy.

116 Bertollo et al.
TSP Vol. 29, No. 2, 2015
ηp2 = .39, power .93, time, F(3.11, 39.49) = 3.80, p <
.01, ηp2 = .34, power .99, and in the interaction between
condition and time, F(3.47, 55.60) = 6.57, p < .01 ηp2 =
.19, power .83. These differences can be seen in Figure 3
(Panel B), in which lower levels of arousal accompanied
Type 3 performance throughout the entire test. Finally,
signicant differences were observed for hedonic tone
on condition, F(1.55, 24.80) = 21.64, p < .01, ηp2 = .57,
power 1.00, and time, F(2.52, 40.85) = 4.92, p < .01,
ηp2 = .23, power .84, but not for the interaction (Figure
3, Panel C).
Discussion
We conducted a counterbalanced repeated measure trial
to investigate the effect of different internal and external
associative strategies on endurance performance. Based
on the MAP model, we differentiated among an exter-
nal focus associated with a uent performance (Type
1), an internal focus linked to optimal performance
regulation (Type 2), and an internal focus hindering
automatic execution of motor performance (Type 3).
Results reinforced the notion that fatigue is a natural
psychobiological process related to attentional focus
and motivation (Blancheld et al., 2014; Schücker et al.,
2013). Specically, both external (Type 1) and internal
(Type 2) associative attention strategies, in comparison
with internal attentional focus (Type 3), were found to
be functional, whereas intense internal attentional focus
(Type 3) was found to be dysfunctional to performance.
Of note, the functional effects of Type 1 and Type 2 per-
formance states observed herein have also been identied
in shooters (Bertollo et al., 2013; Bortoli et al., 2012).
Furthermore, ndings pertaining to Type 3 performance
states are consistent with the reinvestment hypothesis,
in which automatic movements are disrupted when indi-
viduals try to consciously monitor and over control both
feedback input (e.g., feelings of fatigue) and movement
execution (Masters & Maxwell, 2008).
Our results showed that cyclists were able to reach
and maintain optimal performance when using an external
associative strategy by focusing attention on metronome
(Type 1 performance). Similarly, the cyclists were able
to perform optimally when using an internal associative
strategy with attention focused on the core component
of the action (i.e., PPR, Type 2 performance). However,
when the cyclists focused their attention on internal and
irrelevant features of the task (e.g., disruption of PPR or
muscle tension), they performed poorly (dysfunctional
performance). These results suggest that in the absence
of ow-like performance states, individuals may still
perform well by adopting a Type 2 strategy. Therefore,
applied sport and exercise psychologists should assist
individuals in identifying and focusing on their (idio-
syncratic) core components of action linked to functional
performance patterns. In essence, by learning how to
focus on core components of action individuals may be
able to perform well while ignoring unpleasant feelings
of fatigue (e.g., muscle pain).
Our ndings pertaining to Type 2 performance are
in agreement with the notion that a functional internal
focus (internal association) may also lead to functional
performance experiences. To this extent, Masters and
Ogles (1998) noted that an internal associative strategy
is neither dysfunctional nor unnatural. Accordingly,
individuals may benet from internal associative strate-
gies, especially under high workload intensities when the
ability to switch between association and dissociation is
compromised (Tenenbaum, 2005). In this regard, it is
important to note large interindividual differences among
the cyclists, with some individuals being remarkably
unt/sedentary. The differences were in the intensity,
variability, and magnitude of the cyclists’ subjective and
psychophysiological recordings. Overall, these results are
in accordance with the individuality principle and reect
the idiosyncratic nature of maximal performance in sports
(Hanin, 2007). In effect, some individuals may perform
better using external strategies while others experience
maximal endurance performance when adhering to
internal strategies (Bortoli et al., 2012). In fact, although
applying different methodological approaches various
scholars have emphasized the importance of identifying
Figure 3 — Panel A, panel B, and panel C show RPE, arousal and hedonic tone levels at 0%, 25%, 50%, 75%, and 100% iso-time during the
time-to exhaustion test in the three experimental conditions.

To Focus or Not to Focus 11 7
TSP Vol. 29, No. 2, 2015
individual differences of performance-related states
(Bortoli et al., 2012; Filho, Moraes, & Tenenbaum, 2008).
There is also a general agreement on the importance of
identifying the fundamental variables and mechanisms
linked to peak performance in sports (Hanin, 2007).
Although internal strategies may be functional from
an idiosyncratic standpoint, our nomothetic (group level)
analysis echoed the notion that (a) external attentional
focus is best for performance gains in economy of effort
(Schücker et al., 2013), and (b) associative-dissociative
dimension is the main determinant of RPE (Stanley et al.,
2007). In this regard, exergaming technology can be used
to create “dissociative environments” (e.g., gym, physical
therapy clinics) aimed at diverting attention from feel-
ings of fatigue. In addition to gaming technology, sport
and exercise psychologists may use bioneurofeedback
multimedia modalities to help people to divert attention
away from unpleasant fatigue sensations (Perry, 2012).
We also measured core affect throughout the time-to-
exhaustion-tests. The results revealed that affective value
means (arousal and hedonic tone) did not differ across
conditions. However, when comparing iso-times across
conditions, we observed differences for both arousal and
pleasantness trends. Thus, the relationship between iso-
time and RPE was found to be inuenced by attentional
strategies, with an internal dysfunctional associative
focus leading to lower levels of arousal and pleasant-
ness. The fact that Type 3 performance was associated
with lower levels of pleasure and arousal, suggests that
focusing on fatigue feelings is related to energy demo-
bilization. Indeed, Hanin (2007) posited that optimal
performance is likely to occur when energy matches
task demands (i.e., energy matching hypothesis). The
ow-feeling theory also reects the notion that optimal
performance is likely to occur when one’s psychosocial
skills are “a good match” for a given challenging task
(Csikszentmihalyi & Csikszentmihalyi, 1993). It is also
important to note that individuals have different arousal
and hedonic tone levels linked to optimal and less than
optimal performance (Robazza, Pellizzari, Bertollo, &
Hanin, 2008). In this regard, Hanin (2007) has noted
that there is interindividual variability in the intensity
and content of idiosyncratic functional and dysfunctional
affective states.
Overall, three main conclusions derive from our
study: (a) both internal and external attention strategies,
namely Type 1 and 2 performance states, can exert func-
tional effects on performance compared with attentional
focus on feelings of fatigue (Type 3 performance); (b)
internal attention can be functional if the attentional
focus is directed toward the core component of action
(Type 2 performance) rather than on feelings of fatigue
(Type 3 performance); and (c) attentional focus on feel-
ings of muscle fatigue leads to poor performance and
low pleasant states and arousal levels (thereby causing
energy demobilization) during high intensity exercise
(iso-time > 50%). These conclusions are congruent with
the MAP model performance categorization, particularly
with the notion that individuals can perform well when
directing their attentional focus to the core components
of action related to a given exertive task. From a broader
theoretical standpoint, these ndings support a top down
psychobiological account of endurance fatigue (Marcora,
2009) in which exhaustion is viewed as a volitional choice
inuenced by psychological factors (e.g., attentional and
motivational strategies) rather than a process determined
by afferent feedback from the muscular and cardiovascu-
lar systems. In fact, this new psychobiological model has
been seen as an alternative to the traditional peripheral
afferent feedback framework (i.e., inhibitory feedback
triggered by increased concentration of metabolites
such as lactate and urea; Gandevia, 2001) in trying to
explain exhaustion in humans (Blancheld et al., 2014;
Marcora, 2009).
It is also important to acknowledge the limitations
of our study in attempt to better orient future research
efforts. First, it is difcult to induce Type 1, ow-like
performance in both laboratory and ecological settings
(Csikszentmihalyi & Csikszentmihalyi, 1993; Hanin,
2007). Peak performance experiences are rare, and thus
pose a challenge to scholars and practitioners interested
in its antecedents and outcomes. Longitudinal assessment
may be an alternative to this limitation, as it allows for
the recording of a larger data set with a correspondingly
larger sample of peak-performance records (Filho et al.,
2008). The use of a mixed-method approach, involv-
ing the measures of core affect used in this study and
surveys on ow and qualitative assessments, may allow
the researcher to triangulate participants’ perceived ow
states with bio- and neuro-feedback data, thus yielding
a more comprehensive assessment of peak performance
experiences in laboratory settings. A second limitation
of our study is related to our convenience sample, which
was comprised primarily of college students and novices.
In this regard, caution should be taken in generalizing
the present ndings across endurance sports (e.g., road
cyclists), given that participants of this study were mostly
sedentary individuals. Comparing sedentary individuals
with elite athletes (i.e., the expert-novice paradigm), and
elite athletes among themselves (i.e., the expert perfor-
mance approach) may reveal the nomological network
pertaining to the attention-performance linkage in endur-
ance and motor tasks. Third, it could be argued that the
lack of a control group may limit the generalizability
of our ndings. However, we opted by a counterbal-
anced design because a true neutral effect is somewhat
unrealistic in sport and exercise psychology contexts.
In this regard, extant empirical evidence suggests that
attention exibility is ultimately compromised in time-
to-exhaustion trials (for a review see Tenenbaum, 2005).
Notwithstanding, future studies could consider alternative
experimental protocols to advance research on exertion.
Finally, although a manipulation check conrmed the
validity of our experimental protocol, the participants
did not receive training on attention control to reach and
maintain Type 1 and Type 2 performance states. Hence,
the participants’ ability to adeptly control their attentional
focus might not have been ideal. Future research should

118 Bertollo et al.
TSP Vol. 29, No. 2, 2015
test the effect of systematized mental skills training on
individuals’ ability to increase the likelihood of optimal-
automatic performance (Type 1), focus on their core
components of action (Type 2), and prevent dysfunctional
performance states (Type 3).
Experimental trials testing the MAP model predic-
tions in regards to other mental processes and skills (e.g.,
imagery, self-talk, and goal setting) are also warranted.
Qualitative retrospective reports are important in the
study of experts’ mental processes, including meta-
emotional and meta-cognitive fatigue and performance
experiences (Hanin, 2007). Moreover, the implementation
of the IZOF probabilistic methodology may generate
more information regarding the idiosyncratic nature of
optimal performance states, as previously demonstrated
in other studies (e.g., Bertollo et al., 2012; Filho et al.,
2008). Studying perceived exertion through multisenso-
rial approaches (e.g., audio and scent) may continue to
advance our understanding of the mechanisms underpin-
ning fatigue in humans.
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