Do Social Rewards from Robots Enhance Offline
Improvements in Motor Skills?
Soto Okumura1,2, Mitsuhiko Kimoto1,2, Masahiro Shiomi1[0000-0003-4338-801X], Takamasa
Iio1,3, Katsunori Shimohara2, and Norihiro Hagita1
1

ATR-IRC, Kyoto, Japan

2 Doshisha Univ., Kyoto, Japan
3 Osaka Univ., Osaka, Japanokumura2016@sil.doshisha.ac.jp

Abstract: In human-human interaction, praise is a fundamental social reward.
Praise provides many benefits to people who receive it, such as positive feelings
and improved motivation as well as on specific skills and performances. In thispaper, we investigate whether praise from two robots influences offline improvements on the motor skills in a manner that resembles praise from humans.
We conducted an experiment with 27 participants to investigate the relationships between the effects of social rewards and the number of robots, by using asequential finger-tapping task which is used in a similar past research work. Our
experiment results showed that people who received praise from two robots
showed significantly better offline improvements on the motor skill improvement than people who did not receive such praise, but there is no significant differences between two robots and one robot. Moreover, questionnaire results
showed higher degree of satisfaction and confidence towards robots’ speechsentences when the robots provided praises.
Keywords: Praise, Social rewards, Human-Robot Interaction, Multiple robots

1

Introduction

Praise is one fundamental social reward for human beings [1]. It provides various
positive effects on people’s mental states by boosting self-efficacy [2], improving
motivation [3, 4], raising academic self-concept [5], and promoting pleasurable emotions [6]. Such positive effects on mental states improve people’s skills and performances: simple go/no-go task performances [4], on-task behavior (e.g., students areengaged in activity which is being facilitated by teacher) [5], academic performancessuch as math skills, [7, 8] and motor skills such as mirror-tracing task [9, 10].
Since people perceive agent systems like computer-graphics agents and robots associal beings [11-13], social rewards from such agents also positively affect people’s
mental states and performance improvements: creating friendly feelings [14, 15],
motivation improvement [16], and increasing of task performances such as gamebased tasks, such as readability testing or puzzles [17, 18] .

2However, even if these works manifested the effectiveness of social rewards fromrobot systems for improvements in just a short-term (e.g. within a day) performance,
the effectiveness for a long-term improvements retention (e.g., more than one day)remain uninvestigated. Such long-term skill consolidation process is essential forhuman skill formation [19, 20], therefore if praise from robots influences such skill
consolidation process and offline improvement (which is a form of consolidation, oneof learning processes related to the amount of time spent [21, 22]) on the motor skill
as directly as human praise [10], that knowledge would contribute to the design ofhuman-robot interaction by focusing on long-term performance improvements.
In this study, we investigate the effects of praise from a robot system toward offline improvements on the motor skills. We conducted an experiment where our system either gave or didn’t give praise to participants during tasks. Our experiments had
two stages: on the first day the participants were praised after they completed tasks,
and on the second day they re-performed tasks without praise.
We are also interested in the possibilities of using two robots for praises (Fig. 1).
Because in human science literature, the number of others increases such social influences as social facilitation/loafing [23, 24], and performance such as creativity [2527]. Using two robots changes the impressions and the behaviors of the interacting
partners [28-30]. Thus, we hypothesize that the number of robots will influence thepraise effects. In this study, we address the following research questions:- Does praise from a robot influence offline improvements on the motor skills?
- Does the number of robots increase the effects of social rewards?

2

System overview

Figure 2 shows an overview of our system that consists of the following components:a robot (Fig. 3) and a GUI (Fig. 4). Participants perform training tasks through GUI,
which sends start/end signals to the robots to control the timing of their speeches.

Fig. 1 Two robots praise a participant
Start/end
signals

Praise rules

Speechsynthesis

Task
performance

Behavior
selector

Gesturecontrol

Robot(s)GUI

Fig. 2 System overview

3Fig. 3 Sota

2.1

(a)

(b)

(c)

(d)

Fig. 4 GUI

Training task design to investigate offline improvement of motor skill

In this study, we employed a task that resembles past research work [10], i.e., a sequential finger-tapping task, where participants repeatedly press four numeric keys
(1,2,3,4) on a standard computer keyboard as quickly and as accurately as possible for
30-seconds periods. This task is commonly used to investigate the offline improvements on the motor task in past studies therefore we also used the task for our study[10] [20] [31] [32]. Similar to thesm, the tapping sequence was common to all participants and trials; we used the following five number sequence in all tasks: 4-1-3-2-4.
To investigate the consolidation processes about the offline improvement on the
motor skill, the participants joined two days experiments. They executed the task 12
time at the first day, and they executed the task five time at the second day. In the past
study, the participants did not know whether they execute the same task at the secondday, therefore we followed the same procedure and task design for our study.
2.2

GUI

Based on consideration of the training task, we developed a GUI (Fig. 4) thatshows numeric numbers based on the pre-defined sequence by using Java (Fig. 4-a).
During the tasks, if the participant correctly pressed the displayed number, it disappeared from the computer screen’s upper part and moves to its bottom part (Fig. 4-b,
“4” was pressed). If the participant made a typing mistake, a cross appeared at thebottom part (Fig. 4-c, where the participant made two typing mistakes). If the participant correctly pressed the displayed numbers, they moved to the bottom part again(Fig. 4-d, “1” and “3” were pressed after two mistakes). At the end of each trial, boththe numbers of correctly and incorrectly pressed keys are displayed and these data arerecorded for analysis. The start/end times are sent to the robot by a network.
2.3

Robot

Figure 3 shows Sota, an interactive humanoid robot characterized by its humanlike
physical expressions. Sota has eight DOFs: three in its head, one for its shoulders, one
for its elbows, and one for its base. It is 28 cm tall, 140 mm wide, 160 mm deep, and4weights 763 g. It is equipped with voice synthesis and network functions. While
speaking, the robot gazes at the participant and slightly moves its arms.
2.4

Details of Praise Comments

Similar to the past study [10], we designed the robot to speak to the participant after
each trial. To avoid skeptical feelings fueled by continuous praise and keep relatively
consistent praises, we mixed pre-defined praise comments, neutral comments, andpraise comments based on their performance in a specific rule as shown in Table 1.
For the pre-defined praise comments, we prepared seven comments that consist of
two sentences, both of which include praise: “your performance is very good, especially if this is your first trial,” “your typing speed seems faster than in the previous
trial,” and so on. We did not use the same sentence twice during the same day.
For neutral comments, we also prepared 12 comments that consist of two sentences. In the neutral comments, neither sentence included praise; 12 sentences consists ofthe current trial number and the rest amount of trials, e.g., “this completes the seventrial, the remaining trial is five”, and two sentences consists of the current trial number and averaged performance, e.g., “this completes the third trial, your performanceresembles that of other participants.”
For praise comments based on performance, we prepared a rule-based selection algorithm. We used three features to decide the praise comments into six categories:
correctly typed numbers, incorrectly typed numbers, and the total typed numbers (Table 2). There are 12 comments in total: two comments for six categories for each.
These comments are used in the middle of tasks to avoid skeptical feelings using continuously pre-defined praise comments, i.e., after 4th trial and 6th trial; if the same
category is selected in both timings, different comments are used in each timing. The
following are the details of the comments in each category:
Increase of correctly typed numbers: the robot praises the number of correctly
typed words if that amount exceeds maximum number at this point: “since you typed
more numbers correctly, your typing speed is becoming faster.”
No errors: the robot praises when the participant did not incorrectly typed: “since
you made no mistakes, your typing is really accurate.”
Certain correct typing: the robot praises the number of correctly typed numbers if it
exceeds a threshold (in this study it was 150): “your typing record is good since thenumber of correctly typed numbers is above average.”
Certain error typing: the robot praises fewer errors if they are less than minimum
number at this point: “since you’ve decreased your typing errors, your typing is becoming more correct.”
Maintaining performance: the robot praises a performance that maintained the training task if the amount of correctly typed words did not exceed maximum number atthis point, the amount of errors did not lower from minimum number at this point, andthe total amount of typed numbers exceed the last task. In this case, since the performance are neither better nor bad, the robot praises the person’s concentration: “youare maintaining your performance and concentrating.”

5

Attitude for training: the robot praises the attitude to the training task when the participant’s performance was not better and when the total amount of typing did notexceeded the last task value. In this case, since the performance did not actually improve, the robot praised the attitude: “since your typing performance seems to havestabilized, your performance will probably increase.”
Table 1. Sequence of praise comments

Table 2. Pseudocode of praise selection

Order// Praise behavior selection algorithm

Behavior categories

1Pre-defined praise behavior

2

Pre-defined praise behavior

if (correct_typing > before_correct_typing)3

Neutral comment
Praise behavior basedon performancePre-defined praise behavior

praise(increasing_correct_typing)

4
5
6

Praise behavior basedon performance

7

Neutral comment

8

Pre-defined praise behavior

9Pre-defined praise behavior

10Pre-defined praise behavior

11

Pre-defined praise behavior

12Pre-defined praise behavior

3

Experiment

3.1

Hypothesis and predictions

else if (miss_typing==0)
praise(no_error)
else if (correct_typing > threshold_correct_typing)praise(certain_correct_typing)else if (miss_typing < before_miss_typing)praise(certain_error_typing)else if (total_typing >= before_total_typing)praise(maintaining_performance)
else
praise(attitude_for_training)

We assume that people’s offline improvements on the motor skills will increase if
they get praise from a robot. Past research in human science literature showed thatsocial rewards enhance the offline improvement on the motor skills of people [10] andproduce positive effects (without offline improvement on the motor skill improvement) of social rewards not only from humans but also non-humans, i.e., agent systems [16-18].
Moreover, we assume that the number of robots will increase the effect of praise,i.e., more enhancing offline improvement on the motor skills of people. Past researchworks showed that many people [23, 24] and agents [28-30] have stronger social influences on people and change their behaviors and/or impressions. Based on these
considerations, we made the following predictions:
Prediction 1: Offline improvement on the motor skills will be enhanced more whenpeople are praised by one robot than people who are not praised.
Prediction 2: Offline improvement on the motor skills will be enhanced more whenpeople are praised by two robots than people who are not praised.

6

Prediction 3: Offline improvement on the motor skills will be enhanced more whenpeople are praised by two robots than people who are praised by one robot.
3.2

Participants

Twenty seven university students (12 women and 15 men who averaged 20.9 years ofage, S.D: 0.87) participated.
3.3

Environment

We conducted it in a laboratory room. We installed one or two robots and a PC on adesk. We recorded video and audio data for analysis.
3.4

Condition

The study had a between-participants design with the following three conditions: one
robot, two robots, and no-praise. We randomly assigned each participant to one ofthree conditions, by keeping gender ratios between them.
One robot: In this condition, we used one robot (Fig. 5). The comment of the robot
after each task is decided based on the rules of table 1 and 2.
Two robots: In this condition, we used two robots (Fig.6). The comment of the robot
after each task is decided based on the rules of table 1 and 2, too. All comments which
include two sentences were separately made by two robots. The speaking sequence
was the same; in this study, the robot to the left of the computer spoke first.
No-praise: In this condition, we used one robot. Unlike the one-robot condition, therobot used 12 neutral comments (Fig. 7). Thus, it only used neutral comments after
each task, instead of pre-defined praise comments and those based on performances.

Fig. 5 One-robot condition

Fig. 6 Two-robot condition
Day 1

Training

Experimentof praise
12 Trials

Fig. 7 No-praise condition

Day 2

RetestFive trials

Fig. 8 Experiment procedure

Evaluation

7

3.5

Procedure

We followed a similar procedure from past research [10], i.e., an experiment that was
conducted on two subsequent days. Before the experiment on the first day, the participants were given a brief description of our experiment’s procedure.
On day 1, participants were trained with sequential finger-tapping tasks on 12 trials. After starting the experiment, the robot greets the participant and explains its role
as a MC. Next it asks the participants to start the training task and explains that theexperiment will end after 12 trials. After each training task, the robot(s) make predefined sentences and rules, as described above (Fig. 8, left). After making its comments, the robot suggests taking a 20-second break before starting the next trial. At
the end of day 1 experiment, we told the participants to do different kind of task attomorrow, to minimize the possibility that they might physically or mentally practicethe training sequence prior to the retest, based on the conclusion of the past study[10].
On day 2, the participants again participated to the sequential finger-tapping task
on five trials (Fig. 8, right), different from the explanations of the last day. On day 2,
the robot(s) did not praise the participants in all conditions, only making neutralcomments. We used the same tapping sequence for all tasks in the both days.
3.6

Measurement

We measured the offline improvement on the motor skills by comparing the performance of the finger-tapping training for two continuous days. Because the offline
performance improvement was defined as the percent increase in average performance from the last three trials during training on day 1 compared with the first threeretest trials on day 2 in the sequential finger-tapping task [10][32], we measured them.

4

Results

4.1

Manipulation check

Figure 9 shows the mean performances during the last three training trials on day 1
and the first three retest trials on day 2. For a manipulation check, we investigatedwhether the performances of each group are significantly different or not. We conducted an ANOVA for the mean performance during the last three training trials on
day 1, and the result did not show significant differences between them (F(2,24)=0.64, p=0.938, partial η2 =0.05). Therefore, the mean performances during thelast three training trials on day 1 were not significantly different between conditions.
4.2

Verification of predictions

Figure 10 shows the offline improvement of each condition, which is the percent increase from the mean performance on day 1 to day 2. We conducted an ANOVA and
its results showed significant differences among the three conditions (F(2, 24)=5.376,
p=0.012, partial η2 =0.309). Multiple comparisons with the Bonferroni method re-

8

250
225
200
175

150
125
100
1

2
Days

No-praise

One

Two

Fig. 9 Mean performance

5

Performance improvement (%)

Number of tapped keys per 30 seconds

vealed a significant difference: two > no-praise (p=.010). There was no significancebetween two and one (p=.289) or one and no-praise (p=.406) Thus, predication 2 was
supported, but predictions 1 and 3 were not.

20
18
16
14
12
10
8
6
4
2
0
No-praise** : p < .05

One

Two

Fig. 10 Offline improvements

Discussion

Our experiment results revealed that praise from two robots positively influenced
their offline improvements on the motor skill improvement, although praise from one
robot did not show significant improvement. The results might suggest that the number of robots increases the effects of social rewards, because the effects of social rewards from one robot did not significantly improve the performance compared to
non-praise condition. One interesting future work is to investigate whether praises
from two persons or agents increase performances more than one person or agent.
Since our experiment was conducted with our robot system and its specific settings, knowledge generality is limited. We cannot confirm whether our finding about
using two robots for praising can be applied to other contexts or different tasks. Another consideration is the naturalness of praise in more complex situations. In thisresearch work, since the robots praised the participants regardless of their actual performance in some parts of the trials, directly applying such other situations as training
many people together is difficult. Also, the praise selection algorithms are implemented heuristically. However, we believe that our setting is adequate to offer knowledge
for readers who are interested in social reward effects from two robots.

6

Conclusion

We investigated the effects of social rewards from two robots for offline improvements on the motor skill improvement. We prepared praise comments for two social
robots and finger-tapping tasks by following past research, which also investigatedthe effects of social rewards. Our experimental results showed that people who werepraised by two robots showed higher motor performance than people who did not9receive such praise. On the other hand, the performances were not different between
people who received praise from two robots or one robot, and between people who
received praise from one robot and who did not receive such praise.

Acknowledgements
This research work was supported by JSPS KAKENHI Grant Numbers JP15H05322
and JP16K12505.

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