Hypotheses
Based on Zajonc’s drive theory \cite{Zajonc1965}, our hypotheses were the following:
-
In the ‘shape matching’ task, the presence of a human would lead to
better performance: fewer mistakes, faster reaction times.
-
In the presence of a human, the ‘Give up’ button would be used less
frequently (or later in the game) due to the social pressure
(self-presentation theory).
-
In the presence of a human, participants would perceive that they
complete fewer rounds of shape matching than they actually do, due to
social facilitation.
-
In the ‘story’ task, the human presence would lead to the impairment in performance such that the participants will recall less facts.
Protocol & Data Collection
\label{sec:meth-data}
We recruited 45 participants after exclusion (25 for the alone condition and 20 for the human condition, 16 males, 29
females, balanced across conditions) on campus. The participants age was
M=20.36 (SD=2.53). We ensured that all participants who enrolled
were not colour-blind (due to the necessity of seeing colour accurately for the
shape matching task) and that they were native English speakers (to prevent
comprehension issues due to language in the story task).
Participants were first given information sheets describing the experiment
(simply entitled “Learning with a touchscreen”, so as not to disclose the role
of the mere presence of the observers). They then gave consent to participate,
compliant with the university ethics committee rules. Participants were told in
writing and verbally that whether or not they decided to withdraw early from the
study, they would receive compensation equivalent to EUR6 (as an Amazon
voucher). We made this point explicit to make sure the participants knew that,
even if they quit the shape matching game early (i.e., between rounds 75 and 200),
they would still receive the full compensation amount.
Results
\label{sec:study1-results}
We did not observe any difference between the two conditions concerning the number of time required to reach the limit of the 75 shapes, average reaction time, number of shape completed, ratio of correct matching or recall performance (cf. Table \ref{tab:results1}).
This means that we do not observe any social facilitation effect in this study. H1 is not supported: the presence of a human by-stander does not impact the performance for the simple task (no significance difference between number of mistakes and reaction time). Neither is H2: the ’Give-up’ button is similarly not used in both cases. Similarly, the presence or absence of a human did not impact the estimation ratio (ratio of perceived shape matching done by the real number) nor the recall of facts, invalidating both H3 and H4.
\label{tab:results1}Results for the shape matching task. No significance has been observed on any of the six metrics reported: time to reach 75 shapes, average reaction time, number of shapes completed, ratio of correct shape, ratio of perceived matching and recall performance.
Metric |
Alone condition \(M(STD)\) |
Human condition \(M(STD)\) |
\(p\)-\(value\) |
Time to 75 shapes (s) |
\(117.7(30.01)\) |
\(110.63(17.25)\) |
\(.349\) |
Average reaction time (s) |
\(1.70(0.47)\) |
\(1.58(0.26)\) |
\(.305\) |
Number of shapes completed |
\(196(11.5)\) |
\(198(7.8)\) |
\(.522\) |
Ratio of correct shapes |
\(0.98(0.02)\) |
\(0.99(0.01)\) |
\(.082\) |
Ratio of perceived matching |
\(0.52(0.34)\) |
\(0.50(0.32)\) |
\(.88\) |
Recall performance |
\(4.81(1.27)\) |
\(5.11(1.49)\) |
\(.473\) |
Explain how our design decision could have impacted the results maybe later
Second Attempt
\label{sec:second}
Reflecting on the lack of effect observed in our first attempt, we designed
a second experiment to address the possible failures of the first one.
Specifically, we chose
to have the human observer closer to the
participant (aiming for greater human influence),
a stronger moral
component (aiming for a greater influence of the human presence),
a more
difficult task (stronger incentive for behavioural differences – i.e., cheating – between conditions),
money reward dependent on performance
(stronger, clearer incentive for behavioural differences between conditions) and
finally,
regarding the methodology, we decided to move away from primarily
using reaction times as metric, so as to avoid any natural performance limit.
Task
Based on these constraints, we designed a new task involving mental arithmetic.
Participants were required to calculate the result of a set of non-trivial
mental additions. The additions each had exactly three 2-digit numbers to sum,
one carry (a digit that is transferred from one column of digits to another),
and their results ranged from 100 to 200. Participants had 5 minutes to perform
as many additions as possible. Each correct answer would earn them a small
financial reward of £0.20 (Figure \ref{fig:sums}).
Critically, following the design of Vohs and Schooler \cite{Vohs2008}, a
supposed ‘glitch’ was showing a pop-up dialogue before each addition. This
dialogue was designed to look like a spurious debug dialogue and contained the
expected answer. The participants were explicitly shown by the experimenter that the
correct answer was erroneously displayed in the dialogue. They were instructed
to ignore the dialogue and to dismiss it. This ‘bug’ was explained to the
participant as being caused by a new operating system on the laptops used for
the test (“Our previous computers did not have this issue”). The bug made it
practically easy for participants to cheat: by briefly glimpsing at the debug
dialogue before dismissing it, they could immediately know the correct answer,
and earn money faster.
The dialogue could be dismissed by pressing ‘enter’ on the keyboard. ‘Enter’ was
also the key used to move to the next question. As such, a double-press would
move to the next question and close the dialogue before it could be seen.
Through this mechanism, it was possible to measure how long it took participants
to close the dialogue, and infer whether they had cheated.