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Motivational Effects of Enhanced Expectancies for Motor Learning in
Individuals with High and Low Self-Efficacy
Saeed Ghorbani1* & Andreas Bund2
1. Department of Physical Education and Sport Science, Aliabad Katoul Branch, Islamic Azad
University, Aliabad Katoul, Iran
2. Institute of Applied Educational Sciences, University of Luxembourg, Esch-s.-Alzette L-
4365, Luxembourg, Luxembourg
How to cite: Ghorbani, S., & Bund, A. Motivational Effects of Enhanced Expectancies for
Motor Learning in Individuals with High and Low Self-Efficacy. Perceptual & Motor Skills,
https://doi.org/10.1177/0031512519892390.
Abstract
Previous research has shown that enhanced expectancies can foster a person’s motivational
state and facilitate motor learning. However, the effects of enhanced expectancies on
motivational state and subsequent motor learning in individuals with varied motivational states
(e.g., self-efficacy; SE) are not well understood. The present study examined the effects of
enhanced expectancies on motivational state and motor learning in individuals with high and
low SE by manipulating the type of knowledge of results (KR). We selected 60 participants
from among 251 male undergraduate students, based on their SE level, and assigned them to
four groups: (a) Good Performance KR & High-SE, (b) Poor Performance KR & High-SE, (c)
Good Performance KR & Low-SE, and (d) Poor Performance KR & Low-SE. We asked
participants to throw beanbags at a target with their non-dominant hand during an acquisition
phase (10 blocks of six trials each) and during subsequent retention and transfer tests. During
acquisition, the Good-KR groups received KR from their three most accurate trials in each
training block whereas the Poor-KR groups received KR from their three poorest attempts in
each block. We measured accuracy scores and changes in SE as dependent variables. Results
showed that KR from good trials, relative to KR from poor trials, led to better accuracy scores
during acquisition, retention and transfer tests, independent of participants’ initial SE levels.
Moreover, KR on good trials enhanced SE in the Good-KR & Low-SE group and kept SE at a
high level in the Good-KR & High-SE group during acquisition and on the retention and
transfer tests. These results provide support for the OPTIMAL theory of motor learning, and
we discuss results in terms of the potential effects of positive feedback on motor performance
in professional athletes.
Keywords: Enhanced expectancies, self-efficacy, the OPTIMAL theory
Introduction
In recent years, research has shown that providing learners with positive performance
feedback can enhance learners’ expectancies and subsequently facilitate motor performance
and learning (Ghorbani, 2019; Gonçalves, Cardozo, Valentini, & Chiviacowsky, 2018; Lessa,
Tani, Chiviacowsky, 2018; Saemi, Wulf, Varzaneh, & Zarghami, 2011; Saemi, Porter, Ghotbi-
Varzaneh, Zarghami, & Maleki, 2012). Enhancing learners’ performance expectancies is
considered a motivational factor in the OPTIMAL (optimizing performance through intrinsic
motivation and attention for learning) theory of motor learning (Wulf & Lewthwaite, 2016),
whereby learners’ positive expectancies are operationalized by the self-efficacy (SE) construct.
SE is generally defined as the belief in one's ability to succeed in specific situations or to
execute a task (Bandura, 1977).
A common research method for enhancing learners’ motivational states is to provide
them knowledge of results (KR) from their more accurate (i.e., good) trials versus their less
accurate trials (i.e., poor). KR from good trials has been shown to lead to higher SE beliefs and,
thereafter, to more effective learning (Badami, VaezMousavi, Wulf, & Namazizadeh, 2011,
2012; Saemi et al., 2011, 2012). For example, in a golf-putting task, Badami et al., (2011, 2012)
found that KR from good trials led to significantly better motor learning and higher levels of
perceived competence and self-confidence than did KR from poor trials. In addition, Saemi et
al., (2011, 2012) demonstrated that KR from good trials led to higher SE and improved motor
learning. Chiviacowsky and Wulf (2007) found no significant differences in throwing beanbags
at a target with the non-dominant hand during an acquisition phase, but, on delayed retention,
KR from good trials resulted in significantly better performance than KR from poor trials.
Chiviacowsky, Wally, and Borges (2009) replicated these results with older adults.
Accordingly, Wulf and Lewthwaite (2016) proposed that motivational factors mediate
observed learning benefits following positive feedback (e.g., KR from good trials).
While previous research has identified this mediating role of enhanced motivational
state associated with in the benefits of positive performance KR on motor learning (Saemi et
al., 2011, 2012; Wulf, Chiviacoswsky, Cardozo, 2014), these prior studies did not address how
learners whose sense of self-efficacy varied might differentially respond to good or poor
performance KR. Previous studies (Badami et al., 2011, 2012; Saemi et al., 2011, 2012)
generally ignored the role of a priori differences in motivational state among individual
participants. But, if motivational state mediates the relationship between positive performance
feedback and motor learning, then a priori individual differences in motivational state must be
considered. Therefore, present study sought to examine the good and poor performance KR
effects on motivational state and motor learning in individuals who began motor learning with
high versus low SE levels. We hypothesized that individuals with high and low SE would differ
in their retention and transfer performance as a result of receiving of good or poor performance
KR. We used a 2 × 2 factorial design to compare individuals who reported either a high or a
low level of SE and who then randomly assigned them to good and poor performance KR,
creating four experimental groups: (a) Good KR & High-SE, (b) Poor KR & High-SE, (c) Good
KR & Low-SE, and (d) Poor KR & Low-SE. We used the motor learning task and procedure
in studies by Chiviacowsky and Wulf (2007) and Chiviacowsky et al., (2009).
Method
Participants
This research protocol was developed and performed in accordance with the
Declaration of Helsinki and was approved by the university institutional review board.
Furthermore, all participants gave written informed consents.
First, we tested a large sample (N = 251) of male undergraduate students (bachelor
students of various disciplines who took part in first course of physical education) to determine
their self-reported SE levels. All participants were right-handed and had no prior experience
with the motor task. Before trying the motor task, participants rated themselves with respect to
their expected performance at it – throwing a beanbag with the non-dominant arm to hit a 100-
point target on one of six trials. They used a rating scale ranging from 10 (not confident) to 100
(absolutely confident). We assigned students with low SE self-rating scores (between 10 and
20) to the Low-SE group and students with SE scores (between 90 and 100) to the High-SE
group, yielding a final participant sample of 60 male student (M age = 21.35, SD = 1.86 years),
30 in each SE group. We then randomly allocated these participants to separate groups that
would receive good performance KR and poor performance KR, creating four experimental
groups: (a) Good KR & High-SE (n = 15), (b) Poor KR & High-SE (n = 15), (c) Good KR &
Low-SE (n = 15), and (d) Poor KR & Low-SE (n = 15).
Motor Task
The motor task was similar to the task used by Chiviacowsky and Wulf (2007) and
Chiviacowsky et al., (2009). The participants were asked to throw beanbags with the non-
dominant arm at a target on the ground. The distance between the participant and the center of
the target was three meters. Concentric circles around the target with radiuses of 10, 20, 30, 40,
50, 60, 70, 80, 90, and 100 cm served to determine the accuracy of the throws. If the beanbag
landed in the center of the target, then the score was 100. If it landed in one of the other circles,
then the score was 90, 80, 70, 60, 50, 40, 30, 20, or 10 points, respectively. Finally, if it landed
outside the target, then a score of 0 was recorded (Chiviacowsky & Wulf, 2007).
Procedure
The participants were tested individually on two consecutive days. Prior to data
collection, the participants were given general information about the experimental procedure
and asked to complete a questionnaire that asked for information such as age, laterality, and
previous experiences with the motor task. Finally, the participants were given brief instructions
about the beanbag-throwing task, which consisted of holding the beanbag with the non-
dominant hand and throwing it at the target. During the acquisition phase, participants
performed 10 blocks of six trials each, and one day later, they completed the retention and
transfer test, consisting of 10 trials each without KR. In the transfer test, the distance to the
target was changed from 3 to 4 meters. The participants were given six seconds to execute each
throw. The participants were allowed to look at the target before each block, but during the
acquisition, retention, and transfer phases, they were prevented from viewing the outcomes by
wearing opaque swimming goggles. At the end of each acquisition block, participants in the
Good-KR & High-SE and Good-KR & Low-SE groups received KR from their three most
accurate trials in that block, whereas those in the Poor-KR & High-SE and Poor-KR & Low-
SE groups received KR from their three poorest attempts in that block. All participants had
been told that, at the end of each block, they would be given KR about three throws, but they
had not been told whether the KR would refer to their best or worst attempts (Chiviacowsky &
Wulf, 2007). KRs from all three trials were written on a board and provided to the participant
for fifteen seconds. It included the number of attempts, the direction, and the score of the
landing relative to the center of the target. That is, if the beanbag landed in the upper part of
the target, then a plus sign was added to the throwing score (e.g., +50). Conversely, if the
beanbag landed in the lower part of the target, then a minus sign was presented before the
throwing score (e.g., -50). Therefore, KR included information about the throw’s distance from
the center of the target, as well as information about the direction of the error. We measured
time with a digital chronometer. During the experimental procedure, prior to each acquisition
block and before the retention and transfer tests, all participants completed the same SE scale
that had been previously used to select the participants with high and low SE levels.
Statistical Analysis
We averaged accuracy scores in the first acquisition block across all six trials and
analyzed as baseline performance in a 2 (SE: High vs. Low) × 2 (KR: Good vs. Poor) analysis
of variance (ANOVA). We further averaged accuracy scores in the acquisition phase across a
10 blocks of six trials each in a 2 (SE: High vs. Low) × 2 (KR: Good vs. Poor) × 10 (Block)
repeated measures ANCOVA including first Block scores as a covariate. Accuracy scores on
the single block retention and transfer tests were averaged across all 10 trials and analyzed in
a 2 (SE: High vs. Low) × 2 (KR: Good vs. Poor) ANCOVA including first Block scores as a
covariate. Initial (baseline) SE scores and SE scores on the retention and transfer tests were
analyzed in a 2 (SE: High vs. Low) × 2 (KR: Good vs. Poor) ANOVA. Finally, SE scores in
the acquisition phase were analyzed in a 2 (SE: High vs. Low) × 2 (KR: Good vs. Poor) × 10
(Block) repeated measures ANOVA. When there were significant group differences, we
calculated partial eta squared (η2) as the effect size. Moreover, we used simple linear regression
analyses to determine possible associations between the SE and accuracy scores during
acquisition and on the retention and transfer tests. First, the SE scores at baseline was used as
predictors of performances in the first block, and the SE scores obtained before the retention
and transfer tests were used as predictors of performance on the retention and transfer test,
respectively. Second, performance scores from the first block were used as predictors of SE in
second block, and performance scores from the last block were used as predictors of SE taken
before the retention test. Shapiro-Wilk test was used to examine normality of accuracy scores
at baseline. For all analyses, we set the statistical significance level a p < 0.05.
Results
Accuracy Scores
The results of Shapiro-Wilk test showed that the accuracy scores at baseline were
normally distributed (p = .641). The analysis of the first block showed no significant main
effects for KR or SE and there was no significant KR × SE interaction at baseline.
During the acquisition phase, there was a main effect for KR, F (1, 55) = 51.99, p ≤
.001, η2 =.48. Moreover, there was a significant Block × KR × SE interaction, F (8, 440) =
2.152, p < .05, η2 =.03. However, the main effects of Block as well as SE were not significant.
As shown in Figure 1, the Good-KR groups achieved higher accuracy scores than the Poor-KR
groups. No other interactions were significant.
The data from the retention test indicated a main effect of KR, F (1, 55) = 20.03, p ≤
.001, η2 = .26, with the Good-KR groups showing better throwing accuracy than the Poor-KR
groups. There was no significant main effect of SE and no significant interaction. On the
transfer test, there were significant main effects for KR and SE, F (1, 55) = 27.29, p ≤ .001, η2
= .33, and F (1, 55) = 9.52, p ≤ .01, η2 = .14, respectively. There was no significant KR × SE
interaction. With regard to KR, the Good-KR groups had higher accuracy scores compared
with the Poor-KR groups. The main effect of SE occurred because of the higher accuracy scores
of the performance of the Good-KR & Low-SE group (see Figure 1).
Figure 1. Accuracy scores across the acquisition phase and on the retention and transfer tests for all groups. GH:
KR-Good&High-SE, PH: KR-Poor&High-SE, GL: KR-Good&Low-SE, PL: KR-Poor&Low-SE groups. AQ:
Acquisition phase, RET: Retention test, TRA: Transfer test.
Self-Efficacy Scores
An analysis of the first block showed no significant main effect for KR. However, the
main effect for SE was statistically significant, F (1, 56) = 3705.68, p ≤ .001, η2 = .98. The KR
× SE interaction was not significant. Confirming the pre-study group assignment, participants
in the High-SE conditions reported much higher baseline SE scores than those in the Low-SE
conditions.
During the acquisition phase, there were significant main effects for Block, F (9, 504)
= 16.81, p ≤ .001, η2 =.23, for KR, F (1, 56) = 103.28, p ≤ .001, η2 =.64, and for SE, F (1, 56)
= 124.42, p ≤ .001, η2 =.69. Moreover, all of the interactions including the Block × KR, Block
× SE, KR × SE, and Block × KR × SE interactions were significant (each p ≤ .01). Here,
participants in the Good-KR groups had higher average SE scores than those in other groups.
Moreover, participants in the Good-KR & Low-SE group showed SE increases and those in
the Good-KR & High-SE group showed no SE changes. In addition, participants in the Poor-
50
60
70
80
90
100
AQ1 AQ2 AQ3 AQ4 AQ5 AQ6 AQ7 AQ8 AQ9 AQ10 RET TRA
Accuracy Scores
GH PH GL PL
KR & High-SE group showed SE decreases and those in the Poor-KR & Low-SE group showed
slight SE increases. Overall, participants reported increases in their SE scores during the
acquisition phase following exposure to the Good-KR conditions and decreases in SE scores
following exposure to the Poor-KR conditions during the acquisition phase.
Finally, analyses of the results from retention and transfer tests demonstrated significant
main effects for KR, F (1, 56) = 17.87, p ≤ .001, η2 = .24, and F (1, 56) = 31.32, p ≤ .001, η2 =
.35, respectively, and for SE, F (1, 56) = 6.02, p = .017, η2 = .09, and F (1, 56) = 8.73, p = .005,
η2 = .13, respectively. No significant interactions were observed. On both tests, participants in
the Good-KR groups reported higher SE levels than those in the Poor-KR groups. Moreover,
participants in the High-SE groups reported higher SE scores at retention and transfer than
those in the Low-SE groups; for example, the Good-KR & High-SE group had higher SE scores
than the Good-KR & Low-SE group, and similarly, the Poor-KR & High-SE group had higher
SE scores than the Poor-KR & Low-SE group.
Figure 2. SE scores across the acquisition phase and on the retention and transfer tests for all groups. HG: High-
SE&KR-Good, HP: High-SE&KR-Poor, LG: Low-SE&KR-Good, LP: Low-SE&KR-Poor groups. AQ:
Acquisition phase, RET: Retention test, TRA: Transfer test.
10
20
30
40
50
60
70
80
90
100
AQ1 AQ2 AQ3 AQ4 AQ5 AQ6 AQ7 AQ8 AQ9 AQ10 RET TRA
Self-Efficacy Scores
GH PH GL PL
Regression Analyses
SE scores at baseline did not significantly predict performances in the first block, but
performance in first block significantly predicted SE before the second block, F (1, 58) = 8.16,
p = .006, Adjusted R2 = .108, β = .351. Performance in the last block also significantly predicted
SE before the retention test, F (1, 58) = 4.60, p = .036, Adjusted R2 = .058, β = .271. SE scores
before the retention test significantly predicted performance on the retention test, F (1, 58) =
11.44, p ≤ .001, Adjusted R2 = .15, β = .406, and, SE scores before the transfer test significantly
predicted performance on the transfer test, F (1, 58) = 8.22, p = .006, Adjusted R2 = .109, β =
.352.
Discussion
Previous research has clearly demonstrated that KR from well-performed (good) trials
versus KR from poorly performed (poor) trials enhances motivational state and motor learning
in novice performers (Badami et al., 2011, 2012; Saemi et al., 2011, 2012). However, no
research has yet investigated what role the learner`s initial motivational state plays in
responsiveness to KR type. The present study was designed to investigate this issue. Our results
replicated earlier findings that, during acquisition, KR from good trials resulted in significantly
better performance accuracy than KR from poor trials. Similarly, KR from good trials, relative
to KR from poor trials, led to significantly better accuracy on the retention test. Participants´
initial SE levels had no mediating influence on this beneficial effect of positive feedback on
motor performance and learning on acquisition and the retention test. However, on the transfer
test, the Good-KR & Low-SE group had higher accuracy scores than the other three groups.
These results are in line with previous studies in which enhanced expectancies benefitted motor
performance and motor learning (Ávila, Chiviacowsky, Wulf, & Lewthwaite, 2012; Badami et
al., 2012; Chiviacowsky & Wulf, 2007; Chiviacowsky et al., 2009; Ghorbani, 2019; Gonçalves
et al., 2018; Grand, Daou, Lohse, & Miller, 2017; Lessa et al., 2018; Montes, Wulf, & Navalta,
2018; Ong, & Hodges, 2018; Pascua, Wulf, & Lewthwaite, 2015; Saemi et al., 2011, 2012;
Stoate, Wulf, Lewthwaite, 2012; Wulf et al., 2014), but they extend these findings to suggest
a particular benefit for individuals with low self-efficacy ratings.
In addition, our results showed that during acquisition, KR from good trials did not
(further) increase SE of participants in Good-KR & High-SE group (see Figure 2), as these
participants simply maintained their initially high level of SE during acquisition. On the other
hand, KR from good trials significantly enhanced Se for participants in the Good-KR & Low-
SE group during acquisition. During the second half of the acquisition phase (i.e., Blocks 6-
10), these low SE learners showed a similar SE level to learners in the Good-KR & High-SE
group (see Figure 2). Both of these Good-KR groups reported higher SE on the retention and
transfer tests than the Poor-KR groups. These results are in line with previous research showing
good-KR benefits (Badami et al., 2011, 2012; Saemi et al., 2011, 2012) and provide support
for the OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016), positing that KR from
good trials leads to increases in motivational state that might partially explain better motor
learning. Moreover, our results revealed that, for learners who reported high baseline SE
(presumably indicating high motivation), KR from good trials was associated with continued
high self-reported SE. This finding has important implications for the role of positive feedback
on the improved motor performance of professional athletes with high levels of SE.
OPTIMAL theorists assume that enhanced expectancies facilitate motor learning by
making dopamine available for memory consolidation and neural pathway development, thus
contributing to efficient goal-action coupling by preparing the motor system for task execution
(Wulf & Lewthwaite, 2016). In this way, the enhanced expectations of our low Se participants
and the maintained high expectancies of our high SE participants following good performance
KR may have led to more effective retention and transfer performance.
Of note, we found that KR from poor trials was associated with decreased SE during
acquisition for participants in the Poor-KR & High-SE group. These findings are in line with
previous research (Saemi et al., 2012) showing that poor performance reduces SE for
individuals with high baseline SE, perhaps explaining the lower performance of this group on
the retention test. Surprisingly, KR from poor trials slightly increased SE during acquisition in
the Poor-KR & Low-SE group, possibly because the specific data in their poor performance
KR showed them to be performing slightly better than the severely poor performance they
expected, as indicated by the nature of our SE rating scale. At the beginning of the study, these
learners rated their SE as 10 or 20, while their “poor” performance KR indicated that poor
performance beanbag throws were close to the scores from good trials (e.g., 50, 60). This might
have inspired the learners in this group to increase their confidence ratings in comparison to
their very low SE scores at the beginning of the study. Interestingly, at the end of the acquisition
phase, there were no significant differences in the SE scores of the two Poor-KR groups,
reflecting the significant gains of the low SE participants, even when exposed to this Poor-KR
condition.
According to the regression analysis, there were reciprocal effects between
participants’ performance and SE. Performance in the first and last acquisition blocks predicted
SE scores in the second block and before the retention test, respectively; and SE scores just
before the retention and transfer tests predicted performance on these tests. These results are in
accordance with Wulf et al.’s (2014) results and provide further support for predictions from
the OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016), again showing that
enhanced expectancies mediated motor learning KR benefits.
Among this study’s limitations, we used participants’ SE as both an independent
variable (with which to select participants) and as a dependent variable (to measure the effects
of positive feedback on motivational state). However, because we aimed to study how learners
with variant SE responded motivationally to positive or negative KR, we believe the double
function of SE to be acceptable in this case. Nevertheless, future studies might operationalize
participants’ a priori motivational state not only by SE but also by using other cognitive-
motivational variables such as expectancies, goal-orientation and self-concept. Such an
approach would provide additional information about other learner traits. Second, we used only
accuracy scores to measure motor performance and learning, while future research might be
improved by other variables such as movement form or kinematic and kinetic analysis.
In conclusion, this study replicated prior findings that KR from good trials facilitated
motor learning and further showed this to be true for both individuals with high and low a priori
SE. We further showed that KR from good trials was associated with particularly enhanced
motivational states of individuals with low self-reported SE at the beginning of the study, while
individuals with high SE maintained these high levels from positive performance KR but
reported lower SE after exposure to poor performance KR. These results support the
OPTIMAL theory of motor learning, positing that enhanced expectancies can facilitate motor
learning (Wulf & Lewthwaite, 2016). Practical implications of these results are the particular
benefits of positive performance KR for novices with low self-expectancies and the beneficial
maintenance effects of high SE from positive performance KR for professional athletes. A
specific focus of future research might be to investigate the relationship between SE and
positive performance KR on the motor performance of professional athletes. Additionally,
future research might investigate the effects of various other types of feedback (e.g., absolute
vs. comparative feedback, KR vs. KP) on different types of motivational states (e.g.,
intrinsic/extrinsic motivation, goal orientation) in order to gain further insights into the
complex relationships between feedback and individual motivation characteristics on motor
performance and learning.
Acknowledgment
We thank all the students who participated in this study.
Conflict of interest
No conflict.
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