Observational Learning

Tell Them What They Are about to Watch and They Will Learn Better

Luc Proteau and Mathieu Andrieux

Département de kinésiologie, Université de Montréal, 2100 Édouard-Montpetit, Montréal, Canada

1 OBJECTIVES

Observing a model performing a motor skill

improves the learning of that skill by naïve

observers (see Ste-Marie et al. 2012 for a recent

review on observation learning). Research indicates

that observation enables one to identify the key

spatial and/or temporal features of the task, thereby

obviating the need to create a cognitive

representation of the action pattern through trial and

error (Blandin et al. 1994; Buchanan and Dean 2010;

Carroll and Bandura 1982). This finding is

supported by neurophysiological studies showing

that the observation and production of an action

share a common neural network known as the

“action observation network”, which is activated

both when individuals perform a given motor task

and when they observe others performing that same

motor task (Buccino et al. 2001; Cisek and Kalaska

2004; Cross et al. 2009). Recent research has shown

that optimal observational learning occurs with the

observation of both novice and expert models rather

than either a novice or an expert model alone

(Andrieux and Proteau, 2013; Rohbanfard and

Proteau, 2011).

Considering the advances in video capture

technology, it is very easy to film both expert and

novice athletes and use these films to teach novel

motor skills to children and adults. In the present

study, we assessed whether learning is optimized

when the learner knows beforehand whether he or

she would be observing an expert, an intermediate,

or a novice performance. Advance knowledge of this

information might guide one’s observation (observe

for something to reproduce or for something to

correct/avoid) and improve learning. However,

being uncertain of whether the next demonstration

would be that of a novice or of an expert might

activate more elaborate cognitive processes, thereby

leading to improved learning.

The task that we chose required that the

participants changed the relative timing pattern that

naturally emerged from the task constraints to a new

imposed pattern of relative timing. This is much like

changing one’s tempo when executing a serve in

tennis or a drive in golf (see Rohbanfard and

Proteau, 2011).

2 METHODS

Sixty right-handed university undergraduate students

(30 males and 30 females; mean age = 20.8 years;

SD = 1.7 years) participated in the experiment.

Participants were naive as the purpose of the study

and had no prior experience with the task. The

participants completed and signed an individual

consent form before participation.

The apparatus was similar to that used by

Rohbanfard and Proteau (2011). The task consisted

in hitting successively four targets of equal size in a

clockwise motion. The distances between each

barrier were 15 cm, 32, 18, and 29 cm, respectively.

The participants were required to complete each of

the four segments of the task in an intermediate time

(IT) of exactly 300 ms for a total movement time

(TMT) of 1200 ms.

The participants were randomly assigned to one

of the three groups of 20 participants (10 females per

group): control (C), variable observation +

feedforward (VO+FW) and variable observation +

feedback (VO+FB). All groups performed four

experimental phases.

All participants received verbal instructions

regarding the goal TMT and IT before the first

experimental phase. This first experimental phase

was a pre-test in which all participants performed 20

trials without knowledge of the results (KR) on their

TMT and the ITs. It was immediately followed by

an acquisition phase of 60 trials. In this phase,

participants in the two observation groups (VO+FW,

VO+FB) individually watched a video presentation

of two models performing the experimental task and

for which they were informed of the models

performance (both TMT and ITs) in ms, either

before the demonstration for the VO+FW group or

Proteau L. and Andrieux M..

Observational Learning - Tell Them What They Are about to Watch and They Will Learn Better.

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

after the demonstration for the VO+FB. The model

was alternated every 5 trials (i.e., model 1: trials 1–5

and model 2: trials 6–10, and so on). For each model

we showed video clips that illustrated performances

in each one five subcategories going from that of an

expert to that of someone who had never practiced

the task before. The resulting 60 trials (2 models x 5

levels of performance x 6 repetitions) were

randomized so that the five levels of performance

were presented once into each consecutive set of

five trials. Participants in the control group did not

take part in the observation protocol and rather read

a provided magazine for the same duration as the

observation phase for the other groups. All

participants completed the third and fourth

experimental phases: 10-min and 24-hour retention

phases, similar in all points to the pre-test.

The data from the pre-test and the two retention

phases were regrouped into blocks of five trials. For

each block, we computed the absolute value of each

participant’s constant error for TMT (|CE|, the

constant error indicates whether a participant

undershot [negative value] or overshot [positive

value] TMT) and the variable error of TMT (VE, or

within-participant variability) to determine the

accuracy and consistency of TMT, respectively. For

IT, we computed a root mean square error (RMSE),

which indicates in a single score how much each

participant deviated from the prescribed relative

timing pattern. For each trial,

RMSE =



∑

Segment 4

Segment1





ITi-target



 (1)

where ITi represents the intermediate time for

segment “i”, and target represents the goal

movement time for each segment of the task (i.e.,

300 ms). The data for each dependent variable were

submitted independently to an ANOVA contrasting

three groups (C, VO+FW, VO+FB) x three phases

(pretest, 10-min retention, 24-hour retention) x four

blocks of trials (1-5, 6-10, 11-15, and 16-20) with

repeated measures on the last two factors.

3 RESULTS

The ANOVA computed for the |CE| (Figure 1, upper

panel) and VE of TMT (not illustrated) revealed that

the three groups did not differ significantly in the

pre-test. Both observation groups significantly

outperformed the control group, but only in the 10-

min retention test: |CE|, F (4, 114) = 3.4, p = .01,

VE, F (4, 114) = 2.48, p = .05). Concerning the

relative timing data, the ANOVA computed for the

RMSE revealed that the three groups did not differ

significantly from one another in the pre-test. In

addition, in both the 10-min and 24-hour retention

tests, although the VO+FB group significantly

outperformed the control group, it was, in turn,

significantly outperformed by the VO+FW group, F

(4, 114) = 7.48, p < .001.

Figure 1: Absolute constant error of TMT and root mean

square error of relative timing as a function of the

experimental phases and experimental groups.

4 DISCUSSION

Live or video observation (Rohbanfard and Proteau,

2012) of a model practicing a motor skill favours the

learning of that skill by the observers. The advance

of video capture technology enables coaches and

educators to film the performance of a variety of

actors/models to help children and adults learn a

new skill. One goal of our laboratory is to determine

the conditions of observation that would optimize

learning.

The results of the present study confirms

previous findings indicating that one can learn a new

relative timing pattern through observation

(Andrieux and Proteau, 2013; Rohbanfard and

Proteau, 2011). In that previous work, it was showed

that the positive effects of observation for motor

learning are significantly larger when one does not

only observe either near perfect performance or the

usually large errors committed by novice

participants. Rather, the results indicated that

learning was optimized when one can observe a

variety of performances. The most important finding

of the present study is that we have showed that the

positive effects of varying the quality of the

observed performance is optimized when one knows

beforehand whether he or she will be watching a

very good, an intermediate or rather a poor

performance. Decety et al. (1997) have shown that

different areas of the brain become more active

when one observes to recognize (for example, when

observing a novice model/poor or intermediate

performance) than when one observes to imitate

(fore example, when observing on expert model).

We suggest that the benefits of informing the

observer of the quality of the performance that will

be presented enables her or him to pre-activate the

recognition/imitation of the brain as a function of

what will be observed, which results in better

learning.

In conclusion, observation is a powerful learning

tool that is now available to anyone with minimal

equipment requirement. The benefits of observation

for learning a new motor skill become larger when

one has access to a variety of models ranging from

novices to experts. These benefits are optimized if

the observer knows beforehand the quality of the

performance she or he is about to observe.

ACKNOWLEDGEMENTS

This research was supported by a Discovery Grant

awarded by the provided by the Natural Sciences

and Engineering Research Council of Canada.

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