Influence of the perception of biological or non-biological motion on

movement execution

C. A. BOUQUET1, V. GAURIER1, T. SHIPLEY2, L. TOUSSAINT1, & Y. BLANDIN1

1Laboratoire Performance, Motricite et Cognition, MSHS, Poitiers, France and

2Department of Psychology, Temple

University, Philadelphia, PA, USA

(Accepted 27 July 2006)

Abstract

The perception of a human actor performing movements may involve processes related to action execution. This resonanceof the motor system may support observational learning and imitation, and could also explain the fact that observers’/actors’movements are disturbed by the observation of a human model making different movements (Kilner et al., 2003). In thisstudy, we tried to specify what information available in the model’s behaviour triggers this influence on an observer’sbehaviour. In two experiments, we had participants make horizontal or vertical arm movements while observing similarmovements. In the first experiment, the observers’ pattern of behaviour was affected by the observation of a human modelmaking incongruent movements. In the second experiment, similar results were obtained with participants observing amoving dot depicting either biological or non-biological motion. Movement execution was affected differentially bybiological and non-biological motion observation. These results show that an observer’s behaviour is sensitive to informationavailable in biological motion.

Keywords: Observation of action, action generation, perception of biological motion

Introduction

Perceiving and understanding the activities of our

conspecifics is essential for human social interac-

tions. In addition, by watching a model’s behaviour,

an observer can learn new skills. Perception of

human movement (biological motion) is thus one

of the essential ingredients of human development

that allows humans to integrate themselves into a

social structure and is a prerequisite to observational

learning. Observational learning (where execution of

the observed movement is deferred and made in the

absence of a model), as well as imitation (when

actors match their own movement to those of

others), requires the observer/actor to translate visual

information gained by observing others into motor

commands to produce the observed behaviour.

Thus, research on parameters that affect perception

of biological motion can contribute to the under-

standing of processes underlying observational learn-

ing and imitation (see Hodges, Hayes, Breslin, &

Williams, 2005).

The human perceptual system exhibits exquisite

sensitivity to biological motion. As far as the

perception of dynamic events is concerned, one of

the most compelling examples of the visual system’s

sensitivity is adults’ and children’s ability to extract

information from point-light biological motion dis-

plays, in which human movement is depicted only by

a set of lights on the joints of a human figure moving

in a dimly illuminated room (Johansson, 1973;

Pavlova, Krageloh-Mann, Sokolov, & Birbaumer,

2001). Although no explicit contours or textures

indicate the presence of a human form, observers of

these simplified point-light displays can extract the

structure of a human body within a fraction of a

second and identify numerous activities, as well as

discriminate the gender of the actor from the motion

of these elements (Dittrich, 1993; Hill & Johnston,

2001; Johansson, 1976; Kozlowski & Cutting, 1977;

Mather & Murdoch, 1994).

Increasing evidence suggests that processes under-

lying the perception of human movement differ from

those involved in perceptual analyses of other types

of motion. First, converging evidence indicating the

existence of specialized brain systems that analyse

biological motion comes from neuropsychological

research. Extrastriate lesions can affect the ability to

perceive non-biological motion displays – that is, for

example, to detect the movement of single moving

Correspondence: C. A. Bouquet, Laboratoire Performance, Motricite et Cognition, MSHS, 99 Avenue du Recteur Pineau, 86000 Poitiers, France.

E-mail: cedric.bouquet@univ-poitiers.fr

Journal of Sports Sciences, March 2007; 25(5): 519 – 530

ISSN 0264-0414 print/ISSN 1466-447X online � 2007 Taylor & Francis

DOI: 10.1080/02640410600946803

dots or objects, while sparing the perception of point-

light walker displays (McLeod, Dittrich, Driver,

Perret, & Zihl, 1996; Vaina, Lemay, Bienfang,

Choi, & Nakayama, 1990). Further evidence comes

from neurophysiological research in primates which

has shown that the perception of biological motion

relies on specific brain areas, such as the superior

temporal sulcus (STS) of the human cortex or the

superior temporal polysensory area (STP) of maca-

que monkeys (Grossman et al., 2000; Grossman,

Blake, & Kim, 2004; Oram & Perrett, 1994; Pelphrey

et al., 2003). In a recent study, Grossman and Blake

(2002) contrasted (1) the perception of point-light

animations of biological motion with (2) perception

of scrambled animations containing the same

motion vectors as the biological ones and (3)

perception of dots moving sinusoidally and depicting

a three-dimensional object. Area STS was especially

sensitive to point-light animations of biological

motion.

Furthermore, biological motion represents a spe-

cial type of motion in the sense that it can be

generated by the observers themselves. Therefore,

perception of biological motion differs from percep-

tion of other movements (non-biological) because it

depends upon the activity of the motor system (e.g.

Grezes et al., 2001; Jeannerod, 2001). Visual

sensitivity to human movement may result from

such a functional linkage between the visual and

motor systems (for a more extensive review of this

linkage between perception and action, see Vogt &

Thomaschke, in this special issue).

On the basis of a large body of psychological and

neurophysiological experiments, investigators have

postulated a functional equivalence between action

generation and perception of action (for a review, see

Jeannerod, 2001). According to this view, the

observed action would activate, in the observer’s

brain, the same motor processes or motor schemas

that would be activated were that action intended or

performed by the observer (e.g. Jeannerod, 2001;

Wohlschlager, Gattis, & Bekkering, 2003). The

implication of this view is that the motor system

not only executes actions, but also resonates with

observed actions.

Neurophysiological findings have demonstrated

this perception – action coupling with the so-called

mirror neurons in the monkey’s premotor area that

fire both during the observation and execution of

particular actions (e.g. Gallese, Fadiga, Fogassi, &

Rizzolatti, 1996; di Pellegrino, Fadiga, Fogassi,

Gallese, & Rizzolatti, 1992). However, neuroimaging

research with humans has failed to demonstrate a

complete overlap between activation patterns asso-

ciated with action perception and those detected

during action execution (for a discussion of this

point, see Decety & Grezes, 1999).

Further evidence for participation of motor pro-

cesses in action perception comes from research

using single point-lights. Viviani and Stucchi (1992,

Experiment 3) found that when participants ob-

served a movement described by a single spotlight

tracing ellipses, movements complying with the two-

thirds power law, which links the kinematics of

handwriting with a movement trajectory (Lacquanti,

Terzuolo, & Viviani, 1983), were perceived as

uniform. Thus, motion perception appears to be

influenced by motor constraints that are character-

istics for human movements (for similar results with

apparent motion of the human body, see Shiffrar &

Freyd, 1990).

Thus, whatever the neural mechanisms underlying

the visual perception of human movement, one can

conclude that (1) the perception of a human actor

executing some movement may involve a specialized

process that analyses biological motion, and (2) that

the observed action has a resonance with the motor

system of the observer.

In line with this idea, investigators have tested

whether executing some movement influences action

perception. Reed and Farah (1995) had participants

make judgements of a model’s limb position while

they either moved or remained still. They found that

observers’ ability to detect changes in the model’s leg

position was improved when they moved their own

legs. In the same way, recent investigations have

demonstrated enhanced perceptual discrimination of

a given action following motor practice of this action

without vision (Casile & Giese, 2006; Hecht, Vogt, &

Prinz, 2001).

Conversely, Kilner, Paulignan, and Blakemore

(2003) examined whether action observation influ-

ences execution of action. They had participants

make sinusoidal movements with their arm while

observing either another human or a robotic arm

making the same (congruent) or different (incon-

gruent) arm movements. The observers’/actors’

movements were only disturbed by the observation

of an incongruent movement executed by the human

model. In contrast, this interference effect did not

occur when participants observed the robotic arm

making incongruent movements. The authors sug-

gested that biological motion contains information

about motor activity, and perception of motor infor-

mation induced activity in the motor system that

interfered with action generation. Perception of the

robotic arm did not tap such motor information

(Blakemore & Frith, 2005; Kilner et al., 2003).

Nevertheless, the perception of human movement

involves the integration of both form and motion

information (Johansson, 1973; Pinto & Shiffrar,

1999; Shipley, 2003); therefore, as pointed out by

Kilner et al. (2003), compared with the robotic arm,

there are many aspects of human movement that

520 C. A. Bouquet et al.

could have caused the interference effect, including

the velocity profile of the movement, as well as the

presence of bodily, head or facial features of the

human model. Human movement or biological

motion can provide two sources of information.

One is the structural information about the geometry

of the human body (perception of the global

structure of a human form). The second is dynamic

information such as velocity and direction of the

movements of body segments (Shipley, 2003; Troje,

2002).

Thus, the fact that motor interference in the study

of Kilner et al. (2003) was produced by the

perception of a human model, but not by a robotic

arm, may have been related to the structural match

between model and observer, irrespective of the

movement produced by the model. Shiffrar and

Freyd (1990) have indeed argued that the human

form is sufficient to alter the perception of apparent

motion in such a way that it appears biological. In

other words, an interference effect, as observed by

Kilner et al. (2003), could be in part due to

the perception of structural information about a

human form, instead of perception of dynamic

information.

In the present study, we removed all structural

information and, using a paradigm similar to that

used by Kilner et al. (2003), tried to determine

whether the perception of a single moving dot

depicting biological or non-biological motions could

affect observers’ movements. In the biological con-

dition, a dot depicted the biological motion of a

moving human hand. In the non-biological condi-

tion, the dot depicted a motion similar to the

biological motion, but with constant velocity over a

linear trajectory. If motor interference relies on the

perception of dynamic information, it could be

triggered by the observation of a single moving dot

depicting biological motion. In addition, if this

motor interference is due to the involvement of the

motor system in the perception of biological motion,

the observers’ behaviour should not show an inter-

ference effect when they perceive a non-biological

motion – that is, a motion whose spatiotemporal

structure differs from that of biological motion.

However, as the use of point-light displays

involved the use of video, we first had to make sure

that an interference effect could be obtained in a

condition where participants observed a video of a

human model. A video of a movement could be

processed differently from a real movement itself,

and three-dimensional perception might be a pre-

requisite for the production of interference. There-

fore, in the first experiment, the interference

produced by the perception of a real human model

was compared to that induced by the perception of a

video of the human model.

Experiment 1

Experiment 1 served two purposes: (1) to replicate

the previous finding that movement observation may

influence an observer’s behaviour, and (2) to

ascertain whether the observation of a video se-

quence induces an interference effect. The general

procedure employed was similar to that used by

Kilner et al. (2003) and consisted of asking partici-

pants to perform whole arm movements while

watching a model making similar or different move-

ments. Furthermore, a between-participants com-

parison was made: the first group observed

movements made by a live human model and the

second group observed video sequences of similar

movements made by the same model.

We anticipated that, in both groups, observers’

movements would be disturbed by the observation of

a model making different movements. In contrast, in

the congruent condition, where observers and the

model made similar movements, we expected the

match between observers’ and the model’s beha-

viours to facilitate movement execution, although

Kilner et al. (2003) did not observe such a facilitation

effect.

Methods

Participants. Sixteen undergraduate and postgraduate

students (mean age 27.8 years, s¼ 3.3) from the

University of Poitiers participated. They were ran-

domly assigned to one of two experimental groups: a

video group and a live group. All participants had

normal or corrected-to-normal vision, reported

themselves to be right-handed, and were unaware

of the experimental goals. Each participant read and

signed an informed consent form before taking part

in the experiment.

Apparatus. To record arm movement trajectories,

participants and the human model wore infrared

light-emitting diodes (IREDs) as markers. One

marker was attached to the wrist (near the tip of

the radius) of the participant and another to the edge

of the hand at the level of the fifth metacarpus. An

Optotrak1 CertusTM (Northern Digital, Waterloo,

Ontario, Canada) recording system recorded three-

dimensional coordinates (x, y, z) of these IREDs.

These coordinates of the IREDs were recorded at a

rate of 150 Hz, with the y-dimension corresponding

to vertical movements of the participants and the

x-dimension corresponding to movements in the

horizontal direction. Unfiltered, raw data were used

in the analyses.

For the video group, the video sequences were

projected onto a 1806140 cm screen using a

projector that had a spatial resolution of

Motion observation and action 521

10246768 pixels and the temporal resolution was

60 Hz.

Human model and construction of video stimuli. An

experimenter was highly trained to make vertical and

horizontal sinusoidal movements of the right arm

from the shoulder and served as the model. The live

group observed movements made by this human

model and the video group observed video sequences

of this same human model making similar horizontal

or vertical movements (these video sequences of the

model were recorded at the end of the training

period before the start of the experiment).

The model stood upright with the right arm

outstretched. For vertical movements, starting posi-

tion was the right arm at approximately 908 with

respect to the medio-lateral axis of the trunk and 458

with respect to horizontal plane (Figure 1). Vertical

movements consisted in moving the arm from top to

bottom at a rate of 0.5 Hz, such that the resulting

amplitude of hand displacement was approximately

80 cm (see Table I for specific details). For

horizontal movements, starting position was the right

arm held in the horizontal plane with an external

angle between the arm and the medio-lateral axis of

the trunk of approximately 758. Horizontal move-

ment involved moving the arm from left to right at a

rate of 0.5 Hz such that the resulting amplitude of

the hand displacement was approximately 70 cm.

Ten sinusoidal horizontal movements and ten

sinusoidal vertical movements were filmed using a

Sony (CCD-TR515E) video camera with the model

facing the camera. The frame rate of the camera was

25 Hz. The video capture did not include the

model’s legs. Each sequence began with the model

immobile for 5 s in the starting position before

beginning to move. During video recording and

when watched by the participants, the model was

blindfolded and wore headphones delivering pulses

at a rate of 1 Hz.

Task and procedure. In each condition, the partici-

pants were required to make sinusoidal movements

of the right arm, as described above for the model.

The experimental phase consisted of two sessions.

Each session consisted of six different conditions,

each being defined according to what was observed

by the participant. There were two neutral conditions

in which participants moved their arm horizontally or

vertically while not observing anything (neutral

horizontal and neutral vertical condition, respec-

tively). In the other conditions, participants per-

formed horizontal or vertical movements while facing

a human model (the live group) or a video of this

model (the video group) making similar (congruent)

or different (incongruent) movements. In the incon-

gruent horizontal condition, participants made hor-

izontal movements while the model made vertical

movements, and vice versa for the incongruent

vertical condition. In the congruent horizontal

condition, both participants and model made hor-

izontal movements. In the congruent vertical condi-

tion, both participants and model made vertical

movements. There were thus six conditions: neutral

vertical or horizontal, congruent vertical or horizon-

tal, and incongruent vertical or horizontal. Partici-

pants performed one trial per condition. One trial

consisted of ten sinusoidal movements.

Within a session, participants alternated between

horizontal and vertical movements and each session

began with the horizontal and vertical neutral

conditions (half of the participants began with

horizontal movements). The order of the subsequent

conditions was counterbalanced across participants.

The second session was performed immediately after

the first session.

Participants faced the screen or the model at a

viewing distance of approximately 230 cm. For both

the congruent and incongruent conditions, partici-

pants were instructed to watch the hand of the

experimenter. Participants were also asked to start

their movement when the model started to move his

arm and then to make their movements in time with

those of the model.

Figure 1. Horizontal (left) and vertical (right) sinusoidal move-

ments performed by the model and the participants in Experi-

ments 1 and 2.

Table I. Mean characteristics of model’s movements observed by

the live and video groups in Experiment 1.

Group Movement

Frequency

(Hz)

Amplitude

(mm)

Variance

(mm2)

Speed

(m � s71)

Video Horizontal 0.49 664 149 0.67

Vertical 0.5 794 101 0.80

Live Horizontal 0.49 631 128 0.64

Vertical 0.5 693 125 0.70

522 C. A. Bouquet et al.

Before the experimental phase, there was a training

phase in which participants were familiarized with

the tasks and learned the movement patterns. During

this training phase, each participant learned the

movement patterns first with a metronome providing

pulses at a rate of 1 Hz. Then, when the participant

performed sinusoidal movements at a rate of 0.5 Hz,

the metronome was switched off, and the participant

was instructed to maintain the same rate. Finally,

participants performed one trial in each condition of

congruence and incongruence.

It should be noted that beginning with the neutral

condition probably led to higher performance in both

the incongruent and congruent conditions. This

ordering is likely to have led to a facilitation effect

for congruent movements (i.e. the comparison be-

tween neutral and congruent conditions), but such a

facilitation, which was absent in the study by Kilner

et al. (2003), was not central to the present work. We

were interested in replicating the interference effect

produced by the incongruent condition and whether

changes to the medium (i.e. live vs. video model)

reduce the likelihood of detection of such an effect.

Dependent measure and data analysis. The dependent

variable was the variability in movement trajectory

orthogonal to the dominant dimension of movement

(e.g. the x-dimension when participants were in-

structed to move their arm horizontally). Thus,

although three-dimensional data were recorded, only

data from the x- and y-dimensions were used in the

analysis.

To calculate variability in movement trajectory,

the data were split into segments of motion. One trial

consisted of 10 sinusoidal movements. For each trial

we had 20 segmented movements corresponding

to 10 discrete movements from left to right (or top to

bottom), and 10 discrete movements from right to

left (or bottom to top) for horizontal (and vertical)

movements. This resulted, for each participant, in 40

segmented movements per condition (two sessions

with one trial per condition). For each segmented

movement, we quantified variability by calculating

the variance in the movement orthogonal to the

dominant dimension of movement. In other words,

when the participant made horizontal movements,

the x-dimension was the dominant movement

dimension and the variance was calculated for

movements in the y-dimension (i.e. for each seg-

mented horizontal movement, we calculated the sum

of squared deviation from the mean for each y

coordinate). Conversely, when the participant made

vertical movements, the variance was calculated for

movements in the x-dimension. Due to movement

time variability and sample frequency, the variance

was calculated with about 130 – 170 x or y coordi-

nates for each segmented movement. For each trial

the mean of the variances was calculated across all

segmented movements, and for each condition the

mean of the movement variances was calculated

across all trials.

Note that the use of variance as a measure of

movement consistency was motivated by the fact that

participants were asked to move on a straight line

and because we sought to replicate the results of

Kilner et al. (2003). Although we also report mean

absolute orthogonal error, we restrict the analyses

and interpretations to the variance, as the two

measures yielded similar patterns of results.

Statistical analysis. Data were analysed using an

analysis of variance (ANOVA) with Group as the

between-participants factor (video group and live

group), and Direction (horizontal and vertical move-

ments) and Congruency (neutral, congruent, and

incongruent) as within-participants factors. Partial

eta-squared (Z2p) values are reported for all signifi-

cant effects as an estimate of effect size. Statistical

significance was set at P5 0.05. Note that although

the use of variance to quantify variability may lead to

positively skewed distributions, ‘‘if the populations

can be assumed to be symetric, or at least similar in

shape (e.g., all negatively skewed) [ . . . ] the analysis

of variance is most likely to be valid’’ (Howell, 2002,

p. 340).

Results and discussion

The mean variance analysis is depicted in Table II

(the mean absolute error is presented in Table III).

Statistical analysis showed a main effect of Con-

gruency (F2,28¼ 12.70, P5 0.01, Z2p¼ 0.48). There

was no main effect of Direction (F1,14¼ 0.08,

P¼ 0.78) and no main effect of Group (F1,14¼

0.60, P¼ 0.45). Neither the Group6Direction

interaction (F2,28¼ 2.49, P¼ 0.13) nor the Group6

Congruency interaction (F2,28¼ 0.006, P¼ 0.99)

was significant.

Post-hoc analyses for congruency indicated greater

variability in the incongruent condition than in the

neutral and congruent conditions (both P5 0.01),

but no significant difference between the congruent

and the neutral condition. Thus, in line with the

results of Kilner et al. (2003), our data indicated that

the observation of an incongruent movement inter-

fered with the observer’s behaviour, while no

facilitation of movement execution was found in

the congruent condition compared with the neutral

condition.

Of primary interest was the Group6Congruency

interaction, which was not significant, indicating that

the two groups did not differ in the pattern of

variance across conditions. The video projection

of the model influenced the observers’ pattern of

Motion observation and action 523

behaviour in the same manner as the observation of

the real model. Thus, as far as the interference effect

is concerned, this result suggests that the information

essential for an interference effect is extracted during

observation of a live model and during observation of

a video.

The present results were obtained under condi-

tions in which the model made movements whose

endpoint moved mainly in the vertical plane. Thus,

relevant characteristics of the observed movement

were mainly related to the horizontal and vertical

axes and were probably available during observation

of the two-dimensional video projection of this

model.

Kilner et al. (2003) reported a lack of facilitation

effect in the congruent conditions. They suggested

any facilitation effect could have been masked by an

increased attentional demand in the congruent

conditions, where participants had to synchronize

their movements with the model. Here the atten-

tional demand might have interfered with movement

production. In addition, the congruency effect might

have been lower because the participants were

watching mirror images of their movement. Recent

work using transcranial magnetic stimulation have

reported that the enhancement of cortico-spinal

excitability when observing hand actions was sig-

nificantly greater when the orientation of the

observed hand corresponded to that of the observer

(Maeda, Kleiner-Fisman, & Pascual-Leone, 2002).

Experiment 2

The second experiment addressed our primary

research question. We sought to determine whether

the perceptual influences on movement were related

to perception of the dynamic information available

when the model moved. As noted above, the model

provides two potential sources of information: (1)

structural information about the geometry of the

body (defining a human form) and (2) dynamic

information (biological motion per se).

To test for an effect of dynamic information, while

excluding structural information, we asked the

participants to perform movements while watching

a single moving dot depicting biological or non-

biological motions. The dot’s trajectory was based

either on the recording of a human model’s move-

ment (biological motion) or defined artificially (non-

biological motion). The non-biological motion

matched all the basic spatial and temporal para-

meters (mean speed, mean amplitude) without any

of the variability (e.g. acceleration, orthogonal

variations) associated with biological motion. Biolo-

gical and non-biological motions were either con-

gruent or incongruent with the movement executed

by the observers. As in Experiment 1, a neutral

condition, where participants made movements

while not observing any motion, was included.

Several predictions can be made. Because Kilner

et al. (2003) reported that an observer’s behaviour

is sensitive to the nature of the observed model

(a human or a robotic arm) in incongruent condi-

tions but not in congruent conditions, we anticipated

an effect of the type of observed motion in the

incongruent conditions but not in the congruent

conditions (i.e. an interaction between the type

of observed motion and congruency condition).

Our primary focus was the presence or absence

of an interference effect in the biological and

Table II. Mean variance (mm2) for the two groups as a function of Congruency condition and Movement direction in Experiment 1

(mean+ s).

Horizontal movement Vertical movement

Group Neutral Congruent Incongruent Neutral Congruent Incongruent

Live 56+ 32 52+ 34 113+ 50 75+ 53 64+42 119+78

Video 55+ 45 44+ 28 123+ 129 44+ 32 44+21 81+50

Mean 55 48 118 60 54 100

Table III. Mean absolute error (mm) for the two groups as a function of Congruency condition and Movement direction in Experiment 1

(mean+ s).

Horizontal movement Vertical movement

Group Neutral Congruent Incongruent Neutral Congruent Incongruent

Live 5.5+ 1.5 5.4+ 1.8 8.3+ 2.3 6.7+2.2 6.1+ 1.8 8.3+3.4

Video 5.4+ 2.1 4.8+ 1.1 8.0+ 4.3 5.0+1.9 5.2+ 1.4 6.8+2.4

Mean 5.4 5.1 8.1 5.9 5.6 7.5

524 C. A. Bouquet et al.

non-biological incongruent conditions. Note that

this interference effect refers to an increase in

movement variability compared with the neutral

condition and is expected in an incongruent condi-

tion (see Experiment 1 and Kilner et al., 2003). We

thus ran separate analyses to compare results

obtained in each condition of observation to this

neutral condition. Kilner et al. (2003) found no

evidence for an interference effect when participants

observed a robotic arm making incongruent move-

ments and, consequently, no differences were

expected in terms of movement variability between

the neutral condition and the incongruent, non-

biological motion condition. In contrast, Kilner et al.

(2003) reported an interference effect when partici-

pants observed a human model. Hence, (1) if this

interference between executed and observed move-

ments is related to a resonance of the observer’s

motor system and (2) if the perception of the velocity

profile of a model’s movement is crucial to trigger

this resonance of the motor system, then a single

moving dot depicting an incongruent biological

motion should interfere with the observer’s beha-

viour, compared with the neutral condition.

Methods

Participants. Twelve undergraduate and postgraduate

students (mean age 24.8 years, s¼ 3.1) from the

University of Poitiers participated. All participants

had normal or corrected-to-normal vision, reported

themselves to be right-handed, and were unaware of

our goals in the experiment. Each participant read

and signed an informed consent form before taking

part in the experiment.

Procedure and apparatus. The same procedure and

apparatus as in Experiment 1 were used to record

arm movement trajectories and to present the video

stimuli.

Stimulus generation. The same experimenter that took

part in Experiment 1 served as the model. The model

made two sequences of ten vertical sinusoidal

movements (V1 and V2) and two sequences of ten

horizontal sinusoidal movements (H1 and H2), such

as those described in Experiment 1, and movement

trajectories were recorded. For each movement, the

xy coordinates of the marker fixed on the hand of the

model were used to create a ‘‘biological motion

stimulus’’ and a ‘‘non-biological motion stimulus’’.

These stimuli consisted of a moving white dot (0.78

visual angle) projected onto a video screen with a

homogeneous black background. The horizontal

display consisted of a moving dot beginning on the

left side of the screen and moving from left to right at

a rate of 0.5 Hz. The vertical display consisted of a

moving dot beginning at the top of the screen and

moving from top to bottom at a rate of 0.5 Hz. The

movement of the dot was defined by the xy

coordinates of the marker, using a program (Bor-

land) that enabled the reproduction of the dimen-

sions, velocity, and temporal course of the original

hand movements. Amplitude and speed of the non-

biological motion were constant and set to the mean

amplitude and mean speed of the biological motion.

In addition, for the non-biological motion, the

endpoints’ locations were held constant and the

trajectory was a straight line (no variability in the

orthogonal direction).

To avoid effects due to peculiarities of individual

stimulus motions, two records of vertical and

horizontal biological motions were employed. Each

of the four biological motions was used to generate

a matching non-biological stimulus. Thus, four

pairs of stimuli were used: two pairs based on

vertical biological motion matched with a vertical

non-biological motion (V1b/V1n-b and V2b/

V2n-b), and two pairs based on horizontal biologi-

cal motion matched with a horizontal non-biological

motion (H1b/H1n-b and H2b/H2n-b). Each pair

consisted of two stimuli that differed in velocity and

variability in the orthogonal axis, whereas overall

duration, amplitude size, and mean speed were

equivalent (Table IV). The stimuli V1b, V1n-b,

H1b, and H1n-b were used for half of the

participants, and stimuli V2b, V2n-b, H2b, and

H2n-b for the other half.

Task and procedure. In each condition, participants

were required to make only horizontal sinusoidal

movements of the right arm, as described in

Experiment 1. Participants performed only horizon-

tal movements because no significant effects invol-

ving movement direction were found in Experiment

1. As in Experiment 1, there was a training phase, in

which participants were familiarized with the task

and learned the movement pattern. The instructions

were the same as those in Experiment 1, except that

Table IV. Mean characteristics of observed biological motions in

Experiment 2.

Movement

Frequency

(Hz)

Amplitude

(mm)

Variance

(mm2)

Speed

(m � s71)

H1b 0.48 659 212 0.69

H2b 0.48 667 57 0.69

V1b 0.48 789 128 0.82

V2b 0.48 796 79 0.84

Note: For non-biological motions (H1n-b, H2n-b, V1n-b, and

V2n-b), amplitude and speed were constant and equivalent to the

mean characteristics of the biological motions, while variance was

zero.

Motion observation and action 525

participants were instructed to watch the stimuli

described above, instead of a human model.

The experimental phase consisted of a neutral

condition in which the participants moved their arm

horizontally while not observing anything and two

congruent and incongruent conditions in which the

participants performed horizontal movements while

watching the stimuli. Participants observed alter-

nately a biological or a non-biological motion

stimulus. In the biological congruent condition the

participants watched a horizontal biological stimulus,

while in the non-biological congruent condition the

participants watched a horizontal non-biological

stimulus. In the biological incongruent condition

the participants watched a vertical biological stimu-

lus, while in the non-biological incongruent condi-

tion the participants watched a vertical non-biological

stimulus.

The order of conditions was counterbalanced

across participants. Participants performed six se-

quences of ten sinusoidal movements in each condi-

tion of congruency. The biological and non-biological

motion stimuli were clearly dissimilar due to differ-

ences in orthogonal variations and speed variations,

but the participants were not informed about the

biological or non-biological nature of each stimulus.

Dependent measure and statistical analyses. The depen-

dent measure was the same as that used in

Experiment 1. An ANOVA with Congruency (con-

gruent vs. incongruent) and Type of motion (biolo-

gical vs. non-biological) as within-participant factors

was conducted on measures of variability. The pair

of movements observed (ex. V1b vs. V2b) was not

included in the analysis. We made no prediction

regarding a possible effect of the pair observed and

the number of participants was too small to detect

any reliable effect.

To test for interference and facilitation effects in

the congruent and incongruent conditions, data

obtained in these conditions were compared with

those obtained in the neutral condition using a t-test

for paired samples and a Bonferroni adjustment for

multiple tests was applied.

Results and discussion

The statistical analysis revealed a main effect of

Congruency (F1,11¼ 8.62, P5 0.05, Z2p¼ 0.439),

with a larger variance in the incongruent condition

than in the congruent condition (see Table V). There

was also a main effect of Type of motion

(F1,11¼ 5.20, P5 0.05, Z2p¼ 0.32) due to a larger

variability in the biological conditions than in the

non-biological conditions. The Congruency6Type

of motion interaction was not significant

(F1,11¼ 0.04, P¼ 0.84). Similar results were ob-

served for mean absolute error, although the main

effect for Type of motion was only marginally

significant (P5 0.06). In both the congruent and

incongruent conditions, observation of biological

motion was thus associated with an increase in

movement variability relative to observation of non-

biological motion.

Data obtained in these conditions were compared

with those for the neutral condition to determine

whether the observation of motion affected move-

ment execution compared with a condition in which

participants saw no motion. This allowed us to test

our prediction regarding a specific effect (an inter-

ference effect) of the biological motion in the

incongruent condition.

The variance observed when participants watched

biological motion or non-biological motion in the

congruent condition did not differ from that mea-

sured in the neutral condition (t¼70.06, P¼ 0.94

and t¼ 1.39, P¼ 0.19 respectively). This finding

indicates that the significant main effect of Con-

gruency reported above was related to an increase of

variance in the incongruent condition, rather than to

a decrease of variance in the congruent condition.

Both the biological and non-biological incongruent

conditions showed greater variability than the neutral

condition (t¼73.70, P5 0.01 and t¼72.85,

P5 0.025 respectively). In contrast to our predic-

tion, watching an incongruent non-biological motion

induced an interference effect.

These results indicate that an interference effect

can be produced without the presence of a human

form. The specific motion of the tip of the hand is

sufficient to trigger motor interference. This is

consistent with previous research in which it has

been shown that during observation of arm move-

ments, observers show a tendency to focus upon

movement of the hand (Mataric & Pomplun, 1998).

In human movements, the extremities are the parts

of the body that experience maximum velocity,

making them a crucial indicator of motion, and they

are also the parts of the body most likely to interact

with objects and surfaces, making them natural focal

points for observational learning, imitation, and

recognition of intent (see also Hodges et al. in this

special issue).

Table V. Mean variance (mm2) and absolute error (mm) by

Congruency condition and Type of motion in Experiment 2

(mean+ s).

Congruent Incongruent

Measure Neutral Biological

Non-

biological Biological

Non-

biological

Variance 31+11 32+27 26+ 15 54+24 46+23

AE 4.2+ 0.8 4.2+1.6 3.8+ 1.2 5.7+1.3 5.3+1.3

526 C. A. Bouquet et al.

In line with this significant effect of motion

information, we found that the type of observed

motion modulated the effect on movement execu-

tion. This result is consistent with the idea that

processes underlying the perception of human move-

ment differ from those involved in perceptual

analyses of other types of motion (Pelphrey et al.,

2003; Shiffrar & Freyd, 1990; Vaina et al., 1990).

General discussion

The assumption that action perception involves

processes related to action execution (Fadiga,

Fogassi, Pavesi, & Rizzolatti, 1995; Jeannerod,

2001; Wohlschlager et al., 2003) provides a good

framework for interpreting the benefit of observing a

movement before its execution. Conversely, this may

explain the fact that movements are disturbed by the

observation of a human model making different

movements (Kilner et al., 2003). In this study, we

sought to specify what information available in the

model’s behaviour triggers this motor interference

and thus, implicitly, what information resonates with

the motor system.

In Experiment 1 we replicated some of the results

of Kilner et al. (2003) and showed that observers’

pattern of behaviour was affected by the observation

of a conspecific’s behaviour. The observers’ move-

ments were disturbed by the simultaneous observa-

tion of a model making movements that were

different from the observers’ movements. In Experi-

ment 2, similar results were obtained with partici-

pants observing a moving dot, emphasizing the

importance of the perception of dynamic informa-

tion. Although one cannot exclude the possibility

that biological surface properties such as the shape of

the body or human-like faces, contribute – to some

extent – to the motor interference observed in

Experiment 1, they are not necessary as demon-

strated by a motor interference effect in Experiment

2 where these properties were absent. Conversely, as

non-biological motion should not resonate with the

motor system of the observer, we hypothesized, in

line with previous work (Blakemore & Frith, 2005;

Kilner et al., 2003), that the perception of an

incongruent non-biological motion should not inter-

fere with movement execution. However, in Experi-

ment 2, the perception of an incongruent non-

biological motion induced a significant increase in

variance compared with the neutral condition.

Before discussing this latter result, we first con-

sider the significant main effect for Type of

motion (biological vs. non-biological). For the non-

biological motions there was no variation in the

orthogonal direction or endpoint location, whereas

for the biological motions such variability was

present (and therefore there was variation in speed

and amplitude as well). Presumably, the effect of the

type of motion is due to this movement variability. It

is possible that, on the basis of this variability,

participants guessed that in the biological motion

condition the dot’s trajectory depicted a hand’s

trajectory. We tested this hypothesis in a control

experiment where ten participants, after executing

horizontal movements, were presented with the two

types of motion and asked to describe them.

Although all participants reported differences be-

tween the two types of motion, none of them

described the biological or the non-biological motion

in terms of hand or arm movements.

Nevertheless, previous researchers who have pre-

sented different biological-like motions as a single

point-light found that the biological motions were

unconsciously processed differently from the non-

biological motions (Orliaguet, Kandel, & Boe, 1997;

Viviani & Stucchi, 1992). Our results do not enable

us to determine exactly what information contained

in the biological motion produced effects different

from those observed with the non-biological motion.

However, the above-mentioned research suggests

that the perceptual system detects biological motion

on the basis of kinematic information rather than

spatial information (Orliaguet et al., 1997). Thus, the

interference effect obtained in Experiment 2, where

participants watched the trajectory of the arm move-

ment’s endpoint, and the significant effect for Type

of motion suggest that changing the velocity profile

of the biological motion influenced the effects

associated with the observation of such a motion.

Taken together, these data indicate that the inter-

ference effect observed in Experiments 1 and 2 is

due, at least in part, to the perception of a biological

motion velocity profile.

The interference effect demonstrated by the video

group in Experiment 1 (mean difference between

incongruent and neutral conditions for horizontal

movements¼ 68.9 mm) is larger that the interfer-

ence effect obtained in the biological condition in

Experiment 2 (mean difference¼ 22.9 mm). Given

the differences in procedures used in Experiment 1

and Experiment 2, any conclusions are speculative.

The differences in magnitude suggest that the

interference effect is partly related to the perception

of a human form. However, researchers have usually

failed to show an advantage of observing point-light

displays over the observation of a video of the full

model (Breslin, Hodges, Williams, Curran, &

Kremer, 2005; Horn, Williams, & Scott, 2002).

Alternatively, this difference may be ascribed to other

information contained in the full-body display. One

candidate would be the information indicating

activity in the musculature that underlies the control

of observed action. In the present case, this would be

information about the shoulder musculature, which

Motion observation and action 527

is presumably not present in the single point-light

display. In the full-body display, perceiving the

incongruent activity of the shoulder may have led

to interference beyond that due to the incongruent

motion of the tip of the finger.

The main effect of the type of motion in the

absence of interaction with congruency is intriguing.

This implies that in both the congruent condition

and the incongruent condition, the observation of

the non-biological motion was associated with a

variance smaller than that measured during the

observation of the biological motion. Although this

effect was predicted for the incongruent condition, it

is difficult to explain such a reduction of variance

with perception of non-biological motion in the

congruent condition within the framework of the

perception – action coupling hypothesis. However,

this effect could be due to a bias related to our

biological/non-biological motion manipulation. The

biological motion had variance in the orthogonal

axis, but the non-biological motion contained no

orthogonal deviations. Thus, in the congruent

condition, where both observed and executed move-

ments are in the same direction, the absence of

orthogonal variations in the non-biological motion

might have facilitated movement execution com-

pared with the biological motion. Furthermore,

when performing horizontal movements, partici-

pants, supposedly, tried to move in a straight line.

The fact that the endpoint locations of the non-

biological motion were held constant (in contrast

with the biological motion where endpoint location

was variable) might have favoured the control of

movement in a straight line. When executing such

horizontal movements, the endpoint of the move-

ment would be the main variable for motor control

and planning (Smeets & Brenner, 2004). Alterna-

tively, one might speculate that given that the

executed movement is controlled by the proximal

musculature, in the biological condition viewing a

single moving dot depicting only the trajectory of an

endpoint may have interfered with the observer’s

behaviour because the location of the information

source was distant from the effector muscles and

perhaps incompatible with the motor commands.

The major finding in the present study is that, in

Experiment 2, both types of motion induced an

increase of movement variability in the incongruent

condition. It appeared that the perception of an

incongruent non-biological motion induced a sig-

nificant increase in variance compared with the

neutral condition. These results clearly contrast with

those of Kilner et al. (2003), who reported a lack of

an interference effect when participants observed

non-biological movements made by a robotic arm.

One possible ad-hoc explanation for these appar-

ently conflicting results is that in our experiment we

presented a single moving point-light, which may

have led to a greater accessibility of motion informa-

tion than the robotic arm used by Kilner et al.

(2003), whose endpoint was less salient. In a recent

paper by Hodges et al. (2005), participants had to

learn a kicking-type action from different point-light

displays where motion of the toe, the foot or the leg

was available. They found that compared with the

other (multiple) point-light displays, the single point-

light depicting only motion of the toe in particular

facilitated observational learning. Thus, the absence

of structural information may have led participants to

be more attentive to the motion information (see

Hodges et al., 2005; Horn et al., 2002; Runeson,

1994). However, individuals already show a natural

tendency to focus upon movement of the hand

during observation of arm movements (Mataric &

Pomplun, 1998). In addition, in the study of Kilner

et al. (2003), participants were instructed to focus on

the tip of the robotic arm. Moreover, a greater

accessibility of motion information associated with

the single moving point-light does not explain why

the non-biological motion produced an interference

effect. We did not expect an interference effect

during the observation of non-biological motion,

under the assumption that this effect is due to an

activation of the motor system specifically contingent

upon observation of biological motion. The results of

some previous neurophysiological and behavioural

studies indicate, however, that the participation of

motor processes in visual perception is not only

driven by the presence of biological motion in the

observed stimulus.

First, it has been reported that the presentation of

graspable objects to human participants activates the

premotor cortex even when no motor response is

required (Grezes & Decety, 2002). The perception of

graspable objects or potential goals could activate

motor representations appropriate to interact with

them (Fadiga & Craighero, 2003; Wohlschlager

et al., 2003). In line with this, it has been reported

that, for target-directed movements made in envir-

onments in which distractors are present, movement

trajectories deviated towards the distractors (Welsh,

Elliott, & Weeks, 1999). Another relevant result is

provided by Wohlschlager and Bekkering (2002),

who had participants imitate movements of an index

finger. Their participants were presented with

pictures of two hands. In one condition the observed

finger touched dots (ipsi- or contralaterally), and in

the other condition dots were absent. The mere

presence of dots increased the number of errors. The

participants had a tendency to move towards the

dots touched by one of the observed fingers

while ignoring which finger moved. The authors

concluded that the dots seem to ‘‘attract’’ the

movement.

528 C. A. Bouquet et al.

For the present research, it is particularly interest-

ing that even in the absence of explicit intentions to

act, viewing an object can activate possible actions

towards it. We speculate that when making move-

ments while watching a single moving dot depicting

an incongruent trajectory, the participants perceived

a goal and its movement interfered with their own

actions, irrespective of the type of motion described

by the dot (biological or non-biological). The single

dot may have activated possible actions towards it. In

contrast, in the study of Kilner et al. (2003), the

robotic arm would be less likely to be considered a

goal for action.

In conclusion, we extend the results of previous

work on the influence of movement observation on

movement execution (Kilner et al., 2003). Our

results indicate that movement execution is differ-

entially affected by biological and non-biological

motion observation, and therefore show that ob-

servers’ behaviour is sensitive to information avail-

able in biological motion. There are several

properties of the observed motion that may have

triggered the motor interference, including move-

ment variability and trajectory. We have yet to

establish which of these properties resonate with

observers’ behaviour and therefore interfere with

movement execution, as well as facilitate observers’

attempts to reproduce the behaviour of another.

Acknowledgements

We wish to thank Kinda Moussa and Mohamed Tlili

for running the experiment.

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