Action Coordination in Groups and Individuals: Learning Anticipatory Control

Abstract

When individuals act alone, they can internally coordinate the actions at hand. Such coordination is not feasible when individuals act together in a group. The present research examines to what extent groups encounter specific challenges when acting jointly and whether these challenges impede extending planning into the future. Individuals and groups carried out a tracking task that required learning a new anticipatory control strategy. The results show that groups face additional demands that are harder to overcome when planning needs to be extended into the future. Information about others' actions is a necessary condition for groups to effectively learn to extend their plans. Possible mechanisms for exerting and learning anticipatory control are discussed.

Full text

Action Coordination in Groups and Individuals:
Learning Anticipatory Control
Gu¨nther Knoblich
Max Planck Institute for Psychological Research Jerome Scott Jordan
Illinois State University
When individuals act alone, they can internally coordinate the actions at hand. Such coordination is not
feasible when individuals act together in a group. The present research examines to what extent groups
encounter specific challenges when acting jointly and whether these challenges impede extending
planning into the future. Individuals and groups carried out a tracking task that required learning a new
anticipatory control strategy. The results show that groups face additional demands that are harder to
overcome when planning needs to be extended into the future. Information about others’ actions is a
necessary condition for groups to effectively learn to extend their plans. Possible mechanisms for
exerting and learning anticipatory control are discussed.
Researchers in the area of action planning and action control use
a host of diverse tasks to investigate the cognitive functions that
enable one to coordinate action alternatives. Examples include
selecting and programming arbitrary actions in response to arbi-
trary stimuli (Hommel & Prinz, 1997), switching between arbitrary
tasks (Allport, 1993; Mayr & Keele, 2000; Rogers & Monsell,
1995), and carrying out two tasks at the same time (Meyer &
Kieras, 1997). Despite the differences between these tasks re-
searchers tend to agree that action control involves a plan of some
sort, be it an action effect (Hommel, Mu¨sseler, Aschersleben, &
Prinz, 2001; Prinz, 1997), a task set (Allport, 1993), or a goal
(Bekkering, Wohlschlaeger, & Gattis, 2000). In addition, they tend
to agree that the mechanisms underlying action coordination are
internal to the actor.
Given this focus on internal mechanisms, it is interesting to note
that groups of individuals can work toward a common goal without
them (Steiner, 1972). Such a deficit is, of course, unimportant if
planning and acting can be clearly separated and there are no
coordination demands during the action phase. Under these con-
ditions groups can overcome their lack of shared internal processes
via the use of language (Clark, 1996). But if individuals have to
time their actions in relation to those of another, language might be
too slow, and the lack of shared internal processes may prove
difficult to overcome. Under these conditions, group members
might simply plan their actions in response to immediate error (i.e.,
current deviations from the goal state). While playing soccer for
example, teammates might coordinate passes by focusing on the
consequences of the other members’ actions and then react. Such
a reactionary form of coordination will prove ineffective, however,
if the timing demands are greater, as is the case in well-played
soccer. Under these conditions effective coordination requires
players to plan and execute their actions in relation to what they
anticipate the other team members will do, not in response to what
they have already done.
Given that groups are able to learn to engage in such anticipa-
tory coordinations, despite their lack of shared internal processes,
the purpose of the present article is to investigate the mechanisms
underlying their ability to do so. We first examine what prior
theories and research imply for joint action coordination. We then
introduce a simple tracking task that allowed us to study individ-
uals and groups under identical conditions and to derive several
strategy and performance measures. The comparison of individuals
and groups allowed us to determine the conditions under which
groups can exert and acquire anticipatory coordination, despite the
lack of shared internal processes.
Prior Research
In one study of group action coordination, R. C. Schmidt,
Carello, and Turvey (1990) instructed 2 participants to swing their
legs either in parallel (Persons 1 and 2 move one of their legs so
that both legs arrive synchronously at the leftmost position, then at
the rightmost position, etc.) or in symmetry (Persons 1 and 2 move
one of their legs so that the legs meet in the middle, move
outwards, etc.). These researchers found that at higher speeds
groups fell into symmetry even when instructed to move in par-
allel. Similar results have regularly been reported for individuals in
bimanual coordination tasks (Heuer, 1996; Kelso, 1997; Mechsner,
Kerzel, Knoblich, & Prinz, 2001). R. C. Schmidt et al. (1990)
concluded from their results that the principles that govern coor-
dination of multiple actions within individuals also hold for coor-
dination across individuals.
Although R. C. Schmidt et al.’s (1990) account seems to hold
for the tasks used in their experiment, we do not believe the task
Gu¨nther Knoblich, Cognition and Action, Max Planck Institute for
Psychological Research, Munich, Germany; Jerome Scott Jordan, Depar-
ment of Psychology, Illinois State University.
We thank Harold Bekkering, Ru¨diger Flach, Chris Frith, Vittorio Gall-
ese, Bernhard Hommel, Iring Koch, and Natalie Sebanz for helpful com-
ments, and Irmgard Hagen, Patrick Back, and Lucia Kypcke for their help
in collecting the data.
Correspondence concerning this article should be addressed to Gu¨nther
Knoblich, Max-Planck-Institut fu¨r psychologische Forschung, Amalien-
strasse 33, Munich 80799, Germany. E-mail: knoblich@psy.mpg.de
Journal of Experimental Psychology: Copyright 2003 by the American Psychological Association, Inc.
Learning, Memory, and Cognition
2003, Vol. 29, No. 5, 1006–1016 0278-7393/03/$12.00 DOI: 10.1037/0278-7393.29.5.1006
1006

captures the unique difficulties groups must deal with when en-
gaging in anticipatory coordination. To be sure, participants in the
study needed to plan their actions in relation to the anticipated
starting and stopping point of the partner’s limbs. But the nature of
the action to be produced was known. Thus, the only thing that had
to be anticipated was the moment of a known action. In the
anticipatory coordinations we proposed to investigate, group mem-
bers have to plan their actions in relation to both the moment of the
other’s pending action as well as the type of action they anticipate
the other will produce. For instance, well-played soccer requires
that one pass the ball in anticipation of another player producing a
particular action alternative (e.g., breaking left) at a particular
time. Under these conditions one does not have access to the action
alternatives of the other, and it is under these conditions that group
coordination is most difficult.
A possible means of assessing how the external versus internal
aspect of joint action might influence anticipatory coordination is
to examine what different theories of individual action control
have to say. One theory (R. A. Schmidt & Lee, 1999) is based on
the notion that individuals choose among action alternatives by
waiting on action feedback from the environment and adjusting
action selection and action timing accordingly. To be sure, this
type of coordination is compensatory in nature (i.e., successive
actions are selected on the basis of immediate feedback) and
therefore may not seem to provide a means of modeling anticipa-
tory coordination in groups. The classical literature on action
control suggests, however, that compensation is the default in
action control (R. A. Schmidt & Lee, 1999), and research in the
area of systems control indicates that (a) people prefer compensa-
tory over anticipatory coordinations, and (b) it is very difficult to
control dynamical systems in which certain actions have delayed
effects on technical systems (Do¨rner, 1990; Reason, 1990). Thus,
even though it is not theoretically clear how the use of a compen-
satory coordination might lead to the emergence of an anticipatory
coordination, it may be the case that the former constitutes a
necessary stage in the development of the latter. If this is the case,
then learning an anticipatory coordination in a group might be
much the same as learning one as an individual. That is, if
immediate action feedback constitutes the essential factor under-
lying the learning of an anticipatory coordination and groups have
just as much access to such feedback as does an individual, both
should be able to learn an anticipatory coordination at roughly the
same rate. In fact, groups might actually have an advantage be-
cause members of a group would be responsible for fewer action
alternatives than would individuals.
Another approach to action coordination is the forward model
approach, and it proposes that an individual coordinates action
alternatives by predicting the sensory consequences of the alter-
natives and selecting among them on the basis of such predicted
feedback (Blakemore & Decety, 2001; Wolpert & Ghahramani,
2000; Wolpert & Kawato, 1998). Although this approach seems
promising as an account of anticipatory group coordination, the
extant literature is not conclusive as to how well even individuals
are able to acquire such models. The postulated mechanisms are
thought to bridge only the 200-ms delay that passes between action
initiation and the arrival of the actual reafferent signal. It is not
clear, therefore, how the temporal reference of a forward model
could be extended beyond this interval to include anticipations
regarding more temporally distal events. One possibility is sug-
gested by Rosenbaum’s (1980) research on action precuing. Spe-
cifically, he demonstrated that individuals are able to preprogram
aspects of actions that are prespecified by external cues. This
suggests that the temporal horizon of a forward model might be
expandable if one is able to integrate anticipated and perceived
events in such a way that action alternatives can be selected on the
basis of anticipated as opposed to perceived events. For an indi-
vidual, the states of all action alternatives would be internal. Thus,
learning an anticipatory coordination would consist in appropri-
ately relating currently perceived events, anticipated events, and
action alternatives. For members of a group, who would not have
access to all action alternatives, learning an anticipatory coordina-
tion would prove more difficult. They might be able to overcome
this difficulty, however, if they had access to external cues that
provided reliable information about the other members’action
alternatives. Such cues could replace the internal information
about action alternatives that are not at one’s own disposal. In
addition, this information could be (a) used to generate anticipa-
tions about what the other will do in a given situation and (b) taken
into account when planning one’s own actions. The following
experimental paradigm was designed to address these issues.
Experimental Paradigm
We developed a task that allowed us to (a) assess individual and
group performance under identical conditions and (b) create a
conflict between compensatory and anticipatory coordination strat-
egies (see Figure 1). This task consisted of keeping a circular ring
stimulus (i.e., the tracker) on top of a smaller dot stimulus (i.e., the
target). The target moved back and forth across a computer screen
at a constant velocity and immediately reversed its course upon
reaching either edge of the screen. The tracker was located in the
same horizontal plane as the target.
Participants controlled its relationship to the target by pressing
a right and a left key that incremented the tracker’s velocity (to the
right or left, respectively). Thus, if the tracker was moving to the
right, a right keypress increased its velocity to the right, whereas a
left keypress decreased its velocity to the right (i.e., increased its
velocity to the left). All keypresses produced equal increments
(decrements) in velocity. If, for instance, the tracker was moving
to the right and a participant wanted to make it move to the left, he
or she first had to press the left key repeatedly to reduce the
tracker’s rightward velocity to zero. Only if he or she continued to
Figure 1. Illustration of experimental paradigm.
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JOINT ANTICIPATORY CONTROL

press the left key would the tracker’s velocity be incremented to
the left.
Given that each keypress directly influenced the tracker’s ve-
locity, different regions of the tracking area required different
patterns of keypresses. Within the middle region, a compensatory
coordination strategy (CCS) was most effective. For instance, if
the target was moving to the right and the tracker was left of the
target, accelerating the tracker by a right keypress was the most
effective action. In the border regions, a conflict arose between a
CCS and an anticipatory coordination strategy (ACS). While using
the CCS, one attempts to stay on target as long as possible. This
strategy serves to minimize immediate error, and it does so until
the target abruptly changes its direction of travel upon reaching the
border. At this point, error begins to increase dramatically because
the tracker can be decelerated only gradually. Several keypresses
are then required to stop it and several more are needed to gain
velocity in the opposite direction. During this interval, the target
continues moving in the opposite direction, constantly increasing
the distance between itself and the tracker. Thus, trying to mini-
mize immediate error in the border regions creates a large future
error. To overcome this error, one might use an ACS. Using this
strategy, one decelerates the tracker before the target makes its
turn. In this case, one is actually producing immediate error for the
sake of minimizing future error. This is the case because the target
continues to move toward the border as the tracker eventually
comes to a halt. The key difference between the CCS and the ACS,
therefore, is that in the former, selection among action alternatives
is being determined by immediate feedback, whereas in the latter
it is being determined by anticipated events.
Research Questions and Predictions
To determine (a) whether groups have additional difficulties
with exerting and learning anticipatory coordination and (b) how
they overcome these difficulties, we compared a condition in
which one individual controlled both keys with a condition in
which two individuals controlled one key each. Participants in the
group condition wore headphones and were separated via a parti-
tion. This prevented them from using either spoken language or
body language as a means of overcoming their lack of shared
internal processes. This, in turn, allowed us to control each par-
ticipant’s access to external cues about the other’s actions. We then
manipulated the availability of such cues by providing half of the
groups a tone that accompanied each keypress. This resulted in a
2⫻2 design in which individual and group conditions entailing
such tones are referred to as the individual (⫹) and the group (⫹)
conditions, whereas those not involving tones are referred to as the
individual (⫺) and group (⫺) conditions. Each of the two keys was
assigned its own unique tone. The tone therefore provided partic-
ipants reliable information regarding the exact moment at which
either of the two keys had been pressed. In the individual condi-
tion, the presence of the tones served to enhance the salience of the
external feedback resulting from each keypress. In the group
condition, however, the tones also had the potential of serving as
a cue regarding the state of the other partner’s action alternative
(whether and when a key was pressed).
These manipulations afforded us the following hypotheses for
general performance. If learning to affect an ACS is based more on
salient immediate feedback than on the ability to generate appro-
priate internal predictions, participants receiving more salient feed-
back (i.e., individual [⫹] and group [⫹] participants) should
perform better than participants not receiving such feedback (i.e.,
individual [⫺] and group [⫺] participants). That is, they should
give rise to less overall error in the border region. Or, if exerting
and learning an ACS relies on internal predictions, participants in
the individual conditions should perform better than those in the
group conditions, at least initially. This is because participants in
the individual conditions would have access to both action alter-
natives at all times, whereas those in the group conditions would
have access to only one alternative. However, if participants in the
group (⫹) condition are able to use the tone as an external cue
regarding the other participant’s action alternative, they may be
able to use such information to also generate predictions regarding
the other’s actions and ultimately perform better than group (⫺)
participants.
Finally, to manipulate the need for an ACS, we manipulated the
impact of the keypresses in such a way that on half the trials they
produced a relatively large increment or decrement in tracker
velocity. On the other half of the trials their impact was relatively
lower. In addition, we crossed this manipulation with a two-level
manipulation of target velocity. The crossing of these factors
produced some trials in which the task was very difficult (i.e.,
high-impact keypresses coupled with fast target velocity) and
others in which it was relatively simple (i.e., high-impact key-
presses coupled with slow tracker velocity).
To be sure, general performance as expressed by tracker–target
distance is only a rough measure of the extent to which participants
successfully acquire and use an ACS. Several more specific mea-
sures of performance and strategy are available within the present
task. Another performance-related index of the degree to which an
ACS is successfully implemented is the position and the velocity
of the tracker at the moment the target reaches the border and
changes direction. The optimal strategy to minimize overall error
is to bring the tracker to a stop (decelerate it to a velocity of zero)
at a certain distance from the target in the moment in which the
target turns. Therefore, deviations from the optimal position and
zero velocity indicate how successfully an ACS was used.
The most direct strategy measure is the relative number of
keypresses in the border region that lead to an increase in distance
between tracker and target—what we refer to as the anticipatory
brake rate. This constitutes a direct measure of the use of an ACS
because it reveals the degree to which the participants’keypresses
actually increase immediate error, as opposed to decreasing it.
Because there is no reason to increase immediate error other than
to avoid future error, the emergence and maintenance of anticipa-
tory braking clearly indicates that participants are selecting suc-
cessive actions on the basis of anticipated versus actual error.
Stopping the tracker requires that anticipatory brakes be pro-
duced in succession. This means that the nonbraking action alter-
native (i.e., the keypress that would decrease immediate error yet
increase long-term error) has to be withheld. We hypothesized the
following: For a participant in the individual condition who is
learning or has already learned the ACS (i.e., he or she engages in
anticipatory braking) withholding the nonbraking action alterna-
tive would not be difficult, for he or she has internal access to both
alternatives at all times. For participants in the group conditions
however, the situation is different. The action alternatives sug-
gested by the ACS (i.e., anticipatory braking) and the CCS (i.e.,
1008 KNOBLICH AND JORDAN

offsetting immediate error) are not available in the same agent.
They are distributed across two agents. Thus, the group member
responsible for minimizing immediate error (CCS) might continue
to do so as the member responsible for minimizing future error
(ACS) attempts to brake. The conflict between these two alterna-
tives can be directly measured by examining the degree to which
anticipatory brakes are followed by interfering compensatory
presses. We refer to this measure as the interference rate.
A further measure of the extent to which an ACS has been
acquired and implemented is the time lag between consecutive
anticipatory braking presses. We hypothesized the following: If the
group member responsible for implementing the ACS (i.e., pro-
ducing anticipatory braking keypresses) anticipates interference
from the other member, he or she may produce successive braking
keypresses as quickly as possible to overcome the partner’s inter-
ference. If the braking member anticipates cooperation, however
(i.e., he or she anticipates the other member will not produce
interfering compensatory presses), he or she will not have to
produce successive anticipatory braking presses as quickly as
possible. Thus, the degree to which partners learn to produce
anticipatory braking presses in anticipation of cooperation from
their partner can be directly measured via the time lag between
anticipatory brakes. Of course, participants in the individual con-
dition have internal access to both action alternatives and thus
should be able to produce anticipatory braking keypresses in
anticipation of no interference. If, however, learning the ACS and
how to implement it is a gradual process, individuals may actually
oscillate between the CCS and the ACS as they attempt to learn the
best moment at which to start producing anticipatory brakes. If this
is the case, then one should see decreases in the number of
interfering keypresses and increases in the time lag between an-
ticipatory brakes as individuals come to acquire a more effective
ACS.
Method
Participants
A total of 111 participants (41 male; 70 female), recruited by advertising
at the University of Munich, Munich, Germany, and in local papers, took
part in the study. Participants ranged in age from 19 to 35 years. All had
normal or corrected-to-normal vision and received payment for their par-
ticipation. They were randomly assigned to the four experimental condi-
tions. Fifteen were assigned to the individual (⫹) condition and 18 to the
individual (⫺) condition. Thirty were assigned to the group (⫹) condition
(forming 15 pairs) and 48 to the group (⫺) condition (forming 24 pairs).
We collected more data for the group without tone condition because pilot
experiments had shown that the variability was higher in this condition.
Material and Procedure
Upon entering the lab, participants were informed about the task. They
were instructed individually in both the group and individual conditions.
Afterward, they were seated in front of a computer monitor at a distance
of 80 cm. They wore a set of headphones. Participants in the group
condition were divided by a partition. They could neither see nor talk to
one another. However, each participant was provided with a separate
computer monitor, and all events taking place during the experiment (e.g.,
the movements of the tracker and the movements of the target) were
presented simultaneously on both monitors. Thus, the only information
shared in both group conditions was the task display. All participants
underwent 12 training trials.
Participants in the individual condition were given a control panel
consisting of a left and a right key. They pushed them with their left and
right hand, respectively. Left keypresses resulted in tracker acceleration to
the left, and right keypresses resulted in tracker acceleration to the right. In
the group condition, each member was given an individual control panel
consisting of one key. Group members pushed the key with their dominant
hand. Keypresses of the individual on the left side of the partition resulted
in tracker acceleration to the left, whereas those of the other individual
produced tracker acceleration to the right. In the individual (⫹) and group
(⫹) conditions each right keypress triggered a 600-Hz tone, and each left
keypress triggered a 200-Hz tone of 100-ms duration. Tones were pre-
sented over the headphones.
The course of each trial was as follows. At the beginning, a solid,
circular target (size 0.3°of visual angle) and a transparent, circular tracker
(size 1.0°of visual angle) were displayed in the middle of the screen for
500 ms, the tracker being superimposed on the target. Then, the target
started moving with constant velocity. Initial target direction was counter-
balanced. After reaching the border, the target abruptly traveled back in the
opposite direction. There were three such target turns during each trial.
After the third turn, the target moved back to the middle of the screen and
vanished. Initial tracker velocity was zero.
The experiment consisted of three blocks of 40 trials each. Target
velocity and impact of keypress varied across the trials within each block.
Target velocity was either slow (3.3°per second) or fast (4.3°per second),
and the impact of keypress was either low (velocity change 0.7°per second
squared) or high (1.0°per second squared). The order of trials was
randomized within each block.
Data Analysis
We restricted the evaluation of all dependent variables to the border
regions (the left or right third of the screen depending on the direction in
which the target traveled) because the action conflict of interest arises only
in these regions. Tracking performance before the first turn was not
included in the analysis because in this phase of each trial the task
requirements were different (e.g., accelerating the tracker from zero ve-
locity). By restricting the analysis to Turns 2 and 3 we also ensured that
each member in the group condition was responsible for braking at one turn
and responsible for accelerating at the other.
We derived six dependent variables from the data, three to assess
performance and three to assess acquisition and implementation of coor-
dination strategies. As a measure of general performance, we computed the
absolute distance between tracker and target in the border regions before
the second and the third turn. Unlike most tracking paradigms, ours did not
use the mean root square error (RSE) as the performance measure. The
reason is that the RSE gives a larger weight to higher deviations, and thus
if control over the tracker is lost, the error becomes enormous. As a
consequence, the RSE would be largely biased by trials in which control
over the tracker was lost. Therefore, the use of the absolute error is more
appropriate. In addition, we analyzed tracker–target distance and tracker
velocity at the moment the target reached the border and changed its
direction of travel. Optimal performance was computed under the assump-
tion of a maximal keypress rate of 5 keypresses per second separately for
each combination of target velocity and impact of keypress.
To assess the extent to which an ACS was used, we derived three further
dependent variables. The first was the percentage of anticipatory brakes in
the border region. Our definition of an anticipatory keypress was as
follows: (a) It had to occur while the tracker was on or behind the target,
(b) it reduced tracker velocity, and (c) it did not occur right after a keypress
that increased tracker velocity. The rationale for the third point (c) is that
switches between two keys can also reflect a CCS that is directed toward
keeping the tracker on target as close as possible. Hence, we used a
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JOINT ANTICIPATORY CONTROL

conservative measure to avoid overestimating the number of keypresses
reflecting an ACS. In the second step, we computed the rate of anticipatory
brakes that were followed by an accelerating keypress relative to the sum
of anticipatory keypresses in each trial. This variable we refer to as
interference rate, because it provides a direct measure of the extent to
which coordination problems were encountered. The third variable was the
temporal distance between consecutive anticipatory brakes.
Results
Initial analyses revealed that there were no differences between
the individual (⫹) and (⫺) conditions in any of the dependent
variables. This was true for each single level of velocity and
impact and throughout consecutive blocks. None of the differences
approached significance (all ps⬎.10). This means that the addi-
tional auditory feedback did not systematically influence individ-
uals’performance or strategy. Therefore, to simplify the analyses,
we collapsed the data across these two conditions and compared
individuals, groups with tones (group [⫹]), and groups without
tones (group [⫺]).
Regarding task difficulty, there were only small differences
between the low velocity–high impact, low velocity–low impact,
and high velocity–high impact conditions. All three turned out to
be relatively easy and qualitatively similar compared with the most
difficult high velocity–low impact condition. Therefore, to further
simplify the analyses, we collapsed the data across these three
conditions. Thus, we analyzed two levels of task difficulty: diffi-
cult (high velocity–low impact) and easy (the remaining three
conditions). Accordingly, each dependent variable was entered in
a mixed 3 (condition: individual, group [⫹], group [⫺]) ⫻3
(block: first, second, third) ⫻2 (task difficulty: easy, difficult)
analysis of variance (ANOVA). Condition was a between-subjects
factor, and block and task difficulty were within-subject factors.
General Performance
Figure 2 shows the results for general performance. Panel A is
for the easy condition and Panel B is for the difficult condition.
Different points on the x-axis refer to different blocks. Accord-
ingly, the slope of each line represents learning effects. The
different lines represent the different experimental conditions.
When the task was easy, substantial performance differences be-
tween the experimental conditions were present only during the
first block. Performance was worst in the group (⫺) condition,
intermediate in the group (⫹) condition, and best in the individual
condition. Later, participants in both group conditions approached
the performance of individuals who performed well from the start.
When the task was difficult, performance in both group conditions
was initially equally worse than in the individual condition. How-
ever, during the second and third blocks, performance in the group
(⫹) condition approached that of the individual condition, whereas
performance in the group (⫺) condition remained worse. In other
words, groups receiving the tone feedback learned to perform as
well as individuals, whereas groups sharing only the display did
not.
The ANOVA revealed significant main effects for condition,
F(2, 69) ⫽10.1, p⬍.001; block, F(2, 138) ⫽83.9, p⬍.001; and
task difficulty, F(2, 138) ⫽154.0, p⬍.001. There was a signif-
icant Condition ⫻Block interaction, F(4, 138) ⫽3.9, p⬍.01; a
significant Condition ⫻Task Difficulty interaction, F(2,
69) ⫽5.8, p⬍.01; and a significant Block ⫻Task Difficulty
interaction, F(2, 138) ⫽43.1, p⬍.001. The three-way interaction
was not significant. We conducted Newman–Keuls tests to further
assess the statistical significance of the differences observed in the
difficult task condition. They showed that during the first block
there was a significant performance difference between the indi-
vidual and group (⫹) conditions (p⬍.05) and the individual and
group (⫺) conditions (p⬍.05). However, there was no significant
difference between the group conditions (p⫽.64). During the last
block, there was a significant difference between the group (⫺)
and the individual conditions (p⬍.01) and the individual and
group (⫹) conditions (p⬍.01) but no significant difference
between the individual and the group (⫹) conditions (p⫽.38).
Specific aspects of performance at target turn. Optimal per-
formance dictates that the tracker should be stopped at a certain
distance from the border at the moment the target turns. The next
Figure 2. General performance: Tracker–target distance in border region across consecutive blocks in easy (A)
and difficult (B) task conditions. In the group⫹condition a tone accompanied each keypress; in the group⫺
condition a tone did not accompany each keypress.
1010 KNOBLICH AND JORDAN

two analyses assess how the participants in the different conditions
differed in achieving the correct distance and the correct velocity.
Tracker–target distance. Figure 3 displays the result of our
analysis of the tracker–target distance. When the task was easy
(Figure 3A) the correct tracker–target distance could generally be
achieved from the start. Only the participants in the group (⫺)
condition had some problems in early trials. When the task was
difficult (Figure 3B) participants in all conditions improved across
consecutive blocks. During the initial block, the distance was less
optimal in the group conditions than in the individual condition. In
Blocks 2 and 3 there were no significant differences between
experimental conditions. This implies that groups improved more
across consecutive blocks.
The ANOVA showed significant main effects of block, F(2,
138) ⫽33.6, p⬍.001, and task difficulty, F(1, 69) ⫽103.1, p⬍
.001. The main effect of condition failed to reach significance, F(2,
69) ⫽2.9, p⫽.06. There was also a significant Condition ⫻
Difficulty interaction, F(1, 69) ⫽3.9, p⬍.05, and a significant
Condition ⫻Block interaction, F(4, 138) ⫽5.4, p⬍.001. The
three-way interaction was not significant. We conducted
Newman–Keuls tests to further assess the statistical significance of
the differences observed in the difficult task condition. During the
initial block the tracker–target distance was significantly higher in
groups than in individuals (p⬍.05). During the last block, there
were no significant differences between conditions (all ps⬎.10).
Tracker velocity. In the next step, we analyzed tracker velocity
at target turn. Optimal performance dictates zero velocity at this
point. Figure 4 illustrates the results.
The results demonstrate that controlling tracker velocity gener-
ally posed more of a challenge for groups than for individuals and
that all participants improved across consecutive blocks. When the
task was easy, participants in the individual condition were able to
fully stop the tracker whereas participants in both group conditions
were not. Similarly, when the task was difficult, participants in
both group conditions continued to have more problems stopping
the tracker all the way through the end of the experiment. How-
ever, whereas participants in the group (⫺) and individual condi-
tions improved by a comparable amount, participants in the group
(⫹) condition improved more. In short, they were better at stop-
ping the tracker in the right moment than were participants in the
group (⫺) condition. Nevertheless, they performed somewhat
worse than individuals. Note that overall, individuals also did not
manage to reduce tracker velocity to zero in the difficult condition.
The results of the ANOVA were as follows. There were signif-
icant main effects of condition, F(2, 69) ⫽17.1, p⬍.001; block,
F(2, 138) ⫽153.9, p⬍.001; and task difficulty, F(1, 69) ⫽476.5,
p⬍.001. There was also a significant Condition ⫻Block inter-
action, F(4, 138) ⫽4.4, p⬍.01; a significant Condition ⫻Task
Difficulty interaction, F(2, 69) ⫽5.6, p⬍.01; and a significant
Block ⫻Task Difficulty interaction, F(2, 138) ⫽25.8, p⬍.001.
The three-way interaction was also significant, F(4, 138) ⫽2.5,
p⬍.05. Again, we conducted Newman–Keuls tests to further
assess the statistical significance of the differences observed in the
difficult task condition. During the initial block, velocity was
faster in both group conditions than in the individual condition
(both ps⬍.001), and there was no significant difference between
the two group conditions. During the last block velocity in the
group (⫹) condition was significantly lower than in the group (⫺)
condition (p⬍.05) but still significantly higher than in the
individual condition (p⬍.01).
Assessment of Control Strategies
The preceding analyses addressed differences in performance.
The remaining analyses address strategy measures that allowed us
to directly assess to what extent an ACS was used and acquired in
the different conditions.
Anticipatory brakes. Figure 5 shows the results of the analysis
of the anticipatory brake rate. When the task was easy the antici-
patory brake rate was highest in the individual condition, lower in
the group (⫹) condition, and lowest in the group (⫺) condition.
This pattern did not change across blocks. The results were dif-
ferent when the task was difficult. Initially, the anticipatory brake
rate in both group conditions was much lower than that in the
individual condition. The anticipatory brake rate increased by the
same amount in the individual and group (⫺) conditions across
Figure 3. Tracker–target distance at target turn across consecutive blocks in easy (A) and difficult (B) task
conditions. In the group⫹condition a tone accompanied each keypress; in the group⫺condition a tone did not
accompany each keypress.
1011
JOINT ANTICIPATORY CONTROL

consecutive blocks. However, the increase in anticipatory brakes
was much larger in the group (⫹) condition. During the last
block, it became almost as high as that in the individual condition.
Thus, groups receiving the auditory feedback learned to use an
ACS to the same extent as did participants in the individual
condition.
The ANOVA revealed significant main effects for condition,
F(2, 69) ⫽21.1, p⬍.001; block, F(2, 138) ⫽90.5, p⬍.001; and
task difficulty, F(1, 69) ⫽5.9, p⬍.05. In addition, there was a
significant Condition ⫻Block interaction, F(4, 138) ⫽3.4, p⬍
.05; a significant Condition ⫻Difficulty interaction, F(2,
69) ⫽4.2, p⬍.05; and a significant Block ⫻Difficulty interac-
tion, F(2, 138) ⫽66.2, p⬍.001. The three-way interaction was
also significant, F(4, 138) ⫽4.4, p⬍.01. Newman–Keuls tests
were conducted to more closely assess the differences between
experimental conditions in the difficult task condition. During the
first block there was a significant difference between both group
conditions and the individual condition (both ps⬍.01). During the
last block, the group (⫹) anticipatory brake rate was significantly
higher than the group (⫺) rate (p⬍.001) but not significantly
lower than the individual brake rate (p⫽.13).
Interference rate. This variable indicates how often a compen-
satory keypress interrupted a sequence of anticipatory brakes and
therefore provides a direct measure of the extent to which coordi-
nation problems were encountered. Figure 6 illustrates the results.
When the task was easy the interference rate was lowest in the
individual condition, higher in the group (⫹) condition, and high-
est in the group (⫺) condition. When the task was difficult the
interference rate was comparable in the group (⫹) and individual
conditions but much higher in the group (⫺) condition. Regardless
of task difficulty and experimental condition the interference rate
slightly decreased across consecutive blocks.
Figure 4. Tracker velocity at target turn across consecutive blocks in easy (A) and difficult (B) task conditions.
In the group⫹condition a tone accompanied each keypress; in the group⫺condition a tone did not accompany
each keypress.
Figure 5. Anticipatory brake rate across consecutive blocks in easy (A) and difficult (B) task conditions. In the
group⫹condition a tone accompanied each keypress; in the group⫺condition a tone did not accompany each
keypress.
1012 KNOBLICH AND JORDAN

The ANOVA revealed significant main effects for condition,
F(2, 69) ⫽27.5, p⬍.001; block, F(2, 138) ⫽22.7, p⬍.001; and
task difficulty, F(1, 69) ⫽68.1, p⬍.001. There were no signif-
icant two-way or three-way interactions. The two-way Condi-
tion ⫻Difficulty interaction fell short of reaching significance
(p⫽.06) despite the fact that the results for the three experimental
conditions look quite different for the easy and difficult tasks.
Therefore we conducted further Newman–Keuls tests to assess
these differences. When the task was difficult the interference rate
was higher in the group (⫺) condition than in the group (⫹) and
the individual conditions (both ps⬍.001). There was no signifi-
cant difference between the group (⫹) and individual conditions
(p⫽.43). When the task was easy, the interference rate in the
group (⫹) condition was significantly higher than in the individual
condition (p⬍.001) and significantly lower than in the group (⫺)
condition (p⬍.05).
Lags between anticipatory brakes. As a last measure of the use
of an ACS we analyzed the mean temporal interval between
anticipatory brakes. Figure 7 illustrates the results of this analysis.
Lags were generally much shorter in the easy than in the
difficult condition. When the task was easy, lags were consider-
ably shorter in the group (⫺) than in the group (⫹) and individual
conditions. This indicates that groups receiving no tone feedback
experienced more time pressure, even when the task was easy. The
lags remained relatively constant across consecutive blocks. The
pattern was different when the task was difficult. During the
initial trials, lags were short in all experimental conditions and
there were no significant differences between conditions. Later,
the lags became somewhat longer but only in the group (⫹) and
individual conditions. Participants braking in the group (⫺) con-
dition pushed the keys as fast as they could until the end of the
experiment.
Figure 6. Percentage of anticipatory brakes followed by a compensatory keypress (interference rate) across
consecutive blocks in easy (A) and difficult (B) experimental conditions. In the group⫹condition a tone
accompanied each keypress; in the group⫺condition a tone did not accompany each keypress.
Figure 7. Lags between anticipatory keypresses across consecutive blocks in easy (A) and difficult (B) task
conditions. In the group⫹condition a tone accompanied each keypress; in the group⫺condition a tone did not
accompany each keypress.
1013
JOINT ANTICIPATORY CONTROL

The ANOVA revealed significant main effects for condition,
F(2, 69) ⫽5.4, p⬍.01; and task difficulty , F(1, 69) ⫽83.8, p⬍
.001. The main effect for block failed to reach significance, F(2,
138) ⫽3.0, p⫽.054. Only the Block ⫻Condition interaction was
significant, F(4, 138) ⫽3.1, p⬍.05. Newman–Keuls tests con-
firmed that when the task was difficult the lags were significantly
higher in the group (⫹) and individual conditions than in the group
(⫺) condition during the last block (both ps⬍.01).
Discussion
The results provide evidence for the following three claims.
First, the saliency of action feedback did not affect the ability to
use and learn an ACS. Second, groups encountered more coordi-
nation problems than did individuals; most of these problems
derived from attempting to exert an ACS in the midst of increasing
task demands. And third, groups were able to overcome their
problems with learning and implementing an ACS if they were
provided an external cue regarding the state of the partner’s action
alternative.
The first claim is supported by the lack of differences between
participants receiving tones (individual [⫹] and group [⫹]) and
participants not receiving tones (individual [⫺] and Group [⫺]
condition) in a variety of performance and strategy measures. If the
saliency of action feedback had been the decisive factor for using
and learning an ACS, one would expect the main differences to
occur not between groups and individuals but between participants
receiving and not receiving salient action feedback. The result is in
accordance with the widely acknowledged fact that individuals use
internal predictions to control the selection and timing of their
actions (cf. Wolpert & Ghahramani, 2000) whereas groups with
distributed action alternatives can obviously not directly derive
predictions for action alternatives not at their disposal.
The second claim, that groups encounter more coordination
problems than do individuals, is also supported by several results.
Initial group performance was generally poorer than individual
performance, and groups did not benefit from the external cues
(tones). The closer analysis of two specific aspects of performance,
tracker position and tracker velocity at target turn, revealed that
groups found it easier to optimize tracker position than tracker
velocity. These results illustrate that the challenge for groups
consisted specifically in the control of the dynamic aspects of the
tracker movement. The differences in the easy task condition were
small compared with the huge differences in the difficult task
condition, in which anticipatory control was required to a large
extent. Accordingly, initially groups had specific problems to
control tracker velocity in the difficult task condition. Further-
more, the initial differences in the anticipatory brake rates between
groups and individuals were immense.
The third claim, that external cues about the other group mem-
ber’s actions allowed groups to effectively implement an ACS, is
also supported by several results. Although the availability of such
information did not affect performance initially, it had a large
impact on performance during later trials. In fact, groups receiving
external cues became more similar to individuals than did groups
that did not. First, their overall performance became almost indis-
tinguishable from that of individuals. Second, they produced al-
most the same anticipatory brake rate as did individuals in later
trials, although they had started out as low as groups receiving no
external cues during the initial trials. Third, the analysis of the
interference rate illustrated that keypresses interfering with antic-
ipatory braking could be avoided. All of these results demonstrate
that groups receiving external cues learned to implement the ACS
to almost the same extent as did individuals. The only aspect of the
task that groups seemed to find more challenging was stopping the
tracker at the moment of target turn.
This pattern of results cannot be explained by the assumption
that external cues about the other’s actions simply made the task
easier. The interference rate for groups receiving external cues was
as low as the one for individuals from the start. Nevertheless,
initial performance was as poor as in groups receiving no external
cues. If external cues had simply made the task easier, participants
in the group (⫹) condition should have been comparable to indi-
viduals from the start. The observed pattern of results can be
explained only by the assumption that an ACS was acquired.
The pattern of results for participants in the group (⫺) condition
remained quite different from all other conditions until the end of
the experiment. Participants in the group (⫺) condition could
increase their anticipatory brake rate only to a small extent, be-
cause they kept interfering with the other during the braking phase.
Because interference could not be avoided, group members in this
condition worked hard to optimize the only parameter fully under
their individual control, that is, the velocity of subsequent key-
presses required for stopping the target. The lag between consec-
utive brakes was much shorter than for the other experimental
conditions. It is possible that applying this individual-centered
strategy actually kept performance lower, because participants
were so busy trying to achieve high keypress rates that they did not
attend to other aspects of the task. Alternatively, high keypress
rates might have been the only way to indicate to the other to not
act. In short, participants in this condition worked harder and
achieved less, because they could implement the ACS to a lesser
extent.
A further important aspect of the results is that in the difficult
task condition the interference rate was generally lower for groups
receiving external cues as compared with groups who did not. At
the same time, the anticipatory brake rates were the same in both
group conditions, during the initial trials. Later, only the group
with additional information achieved the high rates necessary for
successful performance. This means that the external cues were
immediately used to avoid action conflicts. But using the external
cues as a stop signal was not sufficient to more successfully
implement the ACS. Group members who had the signals started
braking too late and continued to accelerate too long. Hence, it is
likely that the external cues also acted as a means to generate
anticipations about the other’s actions.
To summarize, the results of the present study seem to support
the assumption that anticipations about the other’s actions underlie
the ability to jointly learn and implement an ACS. If immediate
feedback had been the critical factor, participants receiving tones
(i.e., individual [⫹] and group [⫹] participants) should have been
better able to exert anticipatory control than those not receiving
tones. The fact that the tones did not influence strategy and
performance of those in the individual condition yet served to
enhance the performance of those in the group (⫹) condition
indicates that the external cue allowed group (⫹) participants to
extend the temporal horizon of planning, presumably by generat-
ing anticipations about the other person’s actions. Participants in
1014 KNOBLICH AND JORDAN

the individual condition did not need such a cue, for they had
internal access to the state of all action alternatives at all times and
were thus able to relate perceived events, action alternatives, and
anticipated events from the start.
Possible Mechanisms for Learning Anticipatory Control
These results raise two important theoretical issues. First, what
are the mechanisms by which anticipated events come to be more
important than current events in action control, especially when the
current events suggest an action alternative that is different from
the one required to achieve successful overall performance? Sec-
ond, how can information about another’s actions become inte-
grated with information about one’s own? In the following, we
address these questions in turn.
One possible means of accounting for the shift from selecting
actions on the basis of immediate events to selecting them on the
basis of more temporally distal consequences is to assume that
predictions are generated for all action alternatives that can be
applied to a certain task. A selection mechanism would then be
responsible for selecting the action whose predicted consequences
most closely match the goal. The most prominent general theory
postulating such processes is the forward model account (Wolpert
& Kawato, 1998). Extending the temporal horizon of action plan-
ning, according to this account, would consist of an extension of
the temporal distality of the prediction for each of the action
alternatives.
Another possibility is that the extension of the temporal horizon
derives from a temporal expansion of the goal itself. This would
imply that there is an additional mechanism that produces such
expansion by integrating information across larger time scales to
enable the prediction of critical intended events (e.g., the position
and velocity of the tracker at target turn in the present task). Hence,
the predictions for each single action alternative would remain
unchanged. Rather, different action alternatives would be selected
because the goal would refer to more temporally removed events.
The modular nature of recent forward architectures (Haruno, Wol-
pert, & Kawato, 2001) seems to suggest this second alternative is
more likely.
Given these two accounts of temporal extension, what do they
imply about integrating information about another’s actions with
information about one’s own? If one assumes that temporal ex-
pansion takes place in the predicted consequences of each action
alternative, one must also assume that such predictions can be
derived for action alternatives that are not currently at one’s own
disposal, to explain the group case. This implies that one would
use one’s own action system to simulate the outcomes of other’s
actions. For the present task this would mean that one uses one’s
own experience in the braking situation to simulate the other’s
braking performance. Several studies support this idea (Blakemore
& Decety, 2001; Gallese & Goldman, 1998; Jeannerod, 1999;
Knoblich, Seigerschmidt, Flach, & Prinz, 2002).
If one assumes that temporal expansion takes place in the goal,
simulation of the other’s action would take another form. It would
be sufficient to generate predictions that take into account the
different types of events perceived in the environment, without
changing the existing forward models. These predictions could be
based on contingencies between one’s own actions, other’s ac-
tions, and the jointly controlled event. As the temporal horizon of
these integrated predictions increased, the reference point for for-
ward models simulating the consequences of one’s own actions
would change. The actions of others would become integrated with
one’s own on a level of distal events (Jordan & Knoblich, in press;
Knoblich & Flach, 2001; Knoblich & Jordan, 2002; Knoblich &
Prinz, 2001).
One advantage of the event integration hypothesis is that it
provides a parsimonious assumption about how self- and other-
generated actions can be integrated to achieve a jointly intended
outcome. Recent accounts (Hommel et al., 2001; Jordan, 1998,
1999; Jordan, Stork, Knuf, Kerzel, & Mu¨sseler, 2002; Prinz, 1997)
suggest that event codes as postulated by this explanation might
play a central role in individual action control. The results of the
present experiments indicate that event codes might also play an
important role in joint action planning (see also Sebanz, Knoblich,
& Prinz, 2003). It is important to note that they might provide a
level at which predictions about one’s own and other’s actions can
be flexibly integrated and temporally extended. In addition, such
predictions could be used as a reference or control parameter for
forward or inverse models.
Generalizability
A further point to discuss is the degree to which our tracking
task is similar to control demands in real-world situations. Regard-
less of the task’s simplicity we are confident it effectively captures
two aspects that characterize a wide variety of real-world situa-
tions. First, there is the need to acquire anticipatory control.
Successful implementation of an ACS requires that future events
become more important to action control than current events. Such
a need characterizes many situations that individuals and groups
encounter (Do¨rner, 1990; Reason, 1990). Under many circum-
stances such predictions become critical for performance. For
instance, it is impossible to steer a canoe without taking the other’s
paddle strokes into account. Otherwise one has a good chance of
going around in circles, as novices often do. A second character-
istic that our tracking task shares with many real-world situations
is the need to perform actions in real time (Heuer, 1996). Without
adjusting the timing of one’s own actions with regard to the other’s
actions and the common goal, one cannot steer the canoe.
One difference between our tracking task and many real-world
situations is that only a small amount of information was shared
between group members. To be sure, the more information one has
about the other’s action the easier it is to acquire anticipatory
control in a group. Regardless of the amount of information
available, however, there remains the task of integrating informa-
tion about events that one can produce and events that others can
produce to make jointly intended events happen. Thus, as technol-
ogies continue to increase the types of tasks that remotely located
individuals can jointly control, as playing an interactive game on
the Internet or participating in joint surgery via remote, maximal
development of an ACS will probably require access to both the
jointly controlled event as well as information regarding the state
of the action alternatives owned by the other.
In conclusion, the present results address questions that tend to
be overlooked in theories of action control. By focusing on dy-
namic control in both individual and group cases, we have ad-
dressed the expansion of the temporal horizon in action plan-
ning—a phenomenon that is essential to the development of an
1015
JOINT ANTICIPATORY CONTROL

ACS in almost any task. Our method of comparing groups and
individuals as they perform the exact same task could be used to
investigate a whole variety of existing experimental paradigms
devoted to the study of action control in the individual, as for
instance, task switching (cf. Allport, 1993; Mayr & Keele, 2000;
Meiran, Chorev, & Sapir, 2000; Rogers & Monsell, 1995) or
dual-tasks paradigms (Meyer & Kieras, 1997). Such a comparison
might allow one to investigate (a) the extent to which individuals
represent action alternatives, goals, or task sets they do not have at
their disposal and (b) whether one’s own and other’s actions and
goals are coded in a similar or qualitatively distinct manner.
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Received July 29, 2002
Revision received January 31, 2003
Accepted March 16, 2003 䡲
1016 KNOBLICH AND JORDAN