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Reaction beats intention A. E. Welchman et al. 5
(b)
phase 1 execution time total execution time
move
movement condition
movement condition
Figure 3. (a) Aerial view illustrating the set-up used in experiment 2. The row of buttons could be placed horizontally (illus-
trated for player 1 who would make a side-to-side movement, white bars) or vertically (illustrated for player 2 who would make
a front-back movement, grey bars). The two players could make the same type of movement (as in experiment 1) or a different
type of movement (as illustrated in the cartoon). (b) The reactive advantage (expressed as a percentage) for phase 1 and total
execution times in experiment 2. Data illustrate the between-subjects mean response with error bars showing s.e.m. Data from
the four different movement conditions are shown.
activated similarly when a participant performs an action
or observes the same movement performed by another
actor (Gallese et al. 1996; Iacoboni et al. 2005), poten-
tially priming movement production circuits and
facilitating the participant’s own actions. Behaviourally,
movement production can be influenced by viewing an
action that is either incongruent with one’s own (Brass
et al. 2000) or a transformed version of what one has to
perform (Craighero et al. 2002).
To investigate the possibility that the opponent’s move-
ment facilitates movement production, we tested whether
the direction in which participants moved influenced the
advantage for reactive movements. In particular, partici-
pants performed the three-button press sequence when
the buttons were configured for side-to-side or front -
back movements (figure 3a). Thus, a player could see
their opponent making a comparable movement that
could act as a model for their own movement (e.g. both
players make a front - back movement), or movement
orthogonal to their own action that would be of less use
in priming action preparation (e.g. one player moves
front - back while the other moves side-to-side). Consist-
ent with our previous experiments, we observed a
significant decrease in execution times for reactive move-
ments (F1,8 ¼ 11.484, p ¼ 0.01). However, there was no
significant effect of viewing a different movement from
one’s own (F1,8 ¼ 3.273, p ¼ 0.108) or any significant
interactions (figure 3b). Thus, the reactive advantage
does not appear to be modulated by viewing the opponent
making similar or dissimilar movements.
(c) Experiment 3
In our final experiment, we tested whether the social con-
text within which the participants found themselves might
be responsible for their facilitated reactive movements.
Previous work suggests differential performance when
humans believe they are interacting with another human
compared with a non-human agent such as a computer,
based on the notion that the mirror neuron system acts to
determine the intentions of others (Kilner et al. 2003;
Stanley et al. 2007; Gowen et al. 2008). To examine the
role that might be played by cortical systems responsible
for encoding the intentions of others, we contrasted
performance when participants competed against another
human (‘Person’ condition) or a computer on whose
display movements were presented symbolically. In
computer opponent conditions, participants were informed
either (i) that they were competing against a computer
(‘computer’ condition), or (ii) that they were competing
against another human located in a different testing room,
interfaced through the computer (‘virtual’ condition). In
actuality, the distribution of movement onset and move-
ment execution times produced by the computer was
determined from data previously recorded from the partici-
pant, meaning that they were playing against a historical
version of themselves, and thus involved in a demanding
competition. Debriefing participants at the end of the
session revealed this manipulation to have been successful,
with only one participant expressing doubts about the
authenticity of their computer-interfaced human opponent.
Consistent with the previous experiments, faster move-
ments were observed under reactive conditions (F1,9 ¼
26.689, p ¼ 0.001). However, the type of opponent
faced by participants (human, computer, virtual
human) neither had significant influence on execution
times (F2,18 ¼ 2.967, p ¼ 0.077) nor was the interaction
between the reactive advantage and the type of opponent
significant (F2,18 ¼ 1.650, p ¼ 0.220). The statistical
analysis on the type of opponent might suggest a marginal
effect. Nevertheless, inspecting the data (figure 4) does
Proc. R. Soc. B