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Division of Informatics, University of Edinburgh
Institute of Perception, Action and Behaviour
An Imitation Mechanism Inspired from Neurophysiology
by
George Maistros, Gillian Hayes
Informatics Research Report EDI-INF-RR-0031
Division of Informatics
http://www.informatics.ed.ac.uk/
August 2000
An imitation mechanism inspired from neurophysiology
George Maistros
Gillian Hayes
Institute of Perception, Action, and Behaviour
Division of Informatics
University of Edinburgh,
5 Forrest Hill, Edinburgh, EH1 2QL, UK
{georgem, gmh}@dai.ed.ac.uk
Abstract
found to have both visual and motor properties (i.e.
they respond to both visual and motor stimuli).
Imitation is employed with ease by all primates, but
is rarely found in artificial agents. Recent monkey
brain activation data illustrate interesting neural
characteristics related to imitation. Inspired from
these data and based on Arbib’s Schema Theory,
we are implementing an architecture for imitative
behaviours in artificial agents.
1
Motivation
Imitation plays a very important role in biological
agents. Primates can easily acquire useful information by imitating the behaviour of conspecifics. Imitation could also be very important to artificial
agents. However, integrating sensory information
with motor systems can be both very challenging
and very hard.
Neurophysiology projects new insights on imitation mechanisms. Experiments on non-human
primates demonstrate neural characteristics like a
‘vocabulary’ of motor actions and action recognition (Rizzolatti and Fadiga, 1998). Jeannerod et al.
(1995) and Demiris and Hayes (1998) have demonstrated how such insights may be introduced in the
framework of imitation systems. In the light of the
following neurophysiological results, we hope to develop an architecture with which we can demonstrate imitative behaviour in artificial agents.
2
Figure 1: Lateral view of macaque monkey cerebral cortex. Area F5 receives its main cortical input
from AIP and projects its output onto the motor
area F1. Abbreviations; Ps: principal sulcus; AIs:
inferior arcuate sulcus; IPs: intraparietal sulcus;
Cs: central sulcus. Figure adapted from Jeannerod
et al. (1995), Figure 3.
2.1
Pre-motor area F5
di Pellegrino et al. (1992), Rizzolatti et al. (1996),
and Gallese et al. (1996) experimented on the visual
and motor properties of F5 neurons. They performed single neuron studies recording the activity
of individual neurons at the presentation of various
visual and motor stimuli. These recordings classified the F5 neurons into canonical neurons and
mirror neurons. Most canonical neurons lie near
the bank of the arcuate sulcus, while most mirror
neurons near the cortical convexity.
Both the canonical and the mirror neurons discharge when the monkey actively performs hand or
mouth movements — motor stimuli. However, ca-
Neurophysiological findings
Neurophysiological research focusing on the activation of neurons in macaque monkeys’ brains found a
very important class of neurons in the rostral part
of inferior area 6 (pre-motor area F5, see Figure 1
and Rizzolatti et al. (1988)). These neurons were
1
tical input (see Figure 1), has similar visual and motor properties to F5 (Jeannerod et al., 1995). AIP
is also involved in much of the feature extraction of
the visual input (i.e. colour, lines, edges, faces, etc.)
In fact, AIP forms strong reciprocal connections to
F5 and it is proposed that AIP triggers appropriate
F5 populations of neurons depending on the visual
input (Rizzolatti et al., 2000). This is an interesting
consideration which is discussed further in the next
section.
nonical neurons respond to different types of visual
stimuli than mirror neurons. While canonical neurons discharge at the mere sight of an object, mirror
neurons are triggered at the sight of hand or mouth
interactions with objects. This uncovers the presence of an interesting visuomotor coupling mechanism.
In particular, the discharge of F5 mirror neurons has been thoroughly explored and found highly
consistent. The only goal-directed actions that trigger them are grasping, manipulating, and placing.
Similarly, the only effective agents of those interactions are the hand and the mouth of the monkey
or the experimenter. However, repeated observation of tool usage (e.g. pliers) has been informally
reported to increase the corresponding activation
(Rizzolatti and Arbib, 1998). The interacting objects themselves were found to be insignificant: the
monkeys respond equally strongly to food as they
do to solids, albeit they seem to fast lose interest in
the latter.
Rizzolatti et al. (1999) have also studied the temporal relation between the neural discharge and the
interaction that triggered it. This showed behaviours like neurons discharging for the whole of the
interaction, during early or late preshaping, and discharges starting shortly after contact with the object.
Further studies classified the mirror neurons with
respect to their visuomotor congruence. About 30%
of them are strictly congruent, about 60% of them
are broadly congruent, and nearly 8% of them are
non-congruent.
In strict congruence there is a correspondence
between the observed and executed action both in
terms of general action (e.g. grasp) and the type
of action (e.g. precision grip). In broad congruence there is a loose correspondence between the
observed and executed actions. For example, the
neurons discharge when observing different kinds of
grips and when executing a single kind of grip. In
non-congruent neurons the visual stimulus bears no
relation to the motor stimulus.
Most of our interest lies in the understanding of
the functional role of area F5 and especially in its
implications for the development and demonstration of imitative mechanisms on robots.
3
Possible functional roles of
area F5
The above neurophysiological findings suggest a
strong coupling between observed actions (or objects) and the motor representation of these actions
(or potential actions on these objects). A number
of functional roles have been proposed mostly involving action understanding (di Pellegrino et al.,
1992), action recognition (Rizzolatti et al., 1996),
or a ‘vocabulary’ of motor actions (Rizzolatti and
Fadiga, 1998).
Most of these roles, however, are overlapping;
they suggest a pre-motor system which possesses
a vocabulary of motor actions. It is believed that
this vocabulary serves a dual role. First, observed
actions are mapped onto it (recognition and understanding) and second, actions are generated from
it (behaviour or imitation). It is also proposed
that mirror neurons offer the most obvious mapping between recognition and imitation — mirror
neurons recognising an action can also generate that
action.
Another less obvious system that plays an equally
important role lies outside the pre-motor cortex and
is in AIP. Rizzolatti et al. (2000) suggest that AIP
processes the visual input and extracts features like
edges, faces, finger configurations, etc — features
that are then used to selectively activate the F5
neurons that are directly relevant to the observed
scene.
4
Possible architecture
Our scope is to develop an architecture that both
reflects the properties of F5 and serves its function.
2.2 Other areas
The Schema Theory, originally proposed by Arbib
The investigation of other areas neighbouring F5 of- (Arbib and Cobas, 1990), is specifically designed to
fer even deeper comprehension of the function of the bridge the gap between brain theory and cognitive
pre-motor system. The anterior intraparietal area science. Jeannerod et al. (1995) used the Schema
(AIP), an area from which F5 receives its main cor- Theory for the simulation of brain functions similar
2
to the one of F5. The aim here is to apply the
Schema Theory onto the above properties and roles.
Arbib’s Schema Theory analyses brain functions assuming no localisation of neurons. This is
achieved with an assembly of schemas. A schema
is an active entity involved with either perception (perceptual schema) or motor control (motor schema). A perceptual schema deals with perceptual structures and their encoding. A motor
schema deals with motor commands and their control. Schemas are combined to form a network
which controls the passing of parameters from perceptual to motor schemas. Finally, the notion of a
schema is recursive (i.e. a schema may be a network
of other schemas).
The concept of a schema adequately accommodates the main properties of F5 neurons: the visual
properties correspond to perceptual schemas and
the motor ones to motor schemas. Neurophysiology
can be further reflected by constraining the way
schemas are created and combined.
Temporal studies suggest that neural behaviours
are temporally segmented. Hence, each perceptual
schema should only represent parts of observed actions. Similarly, each motor schema should only
govern parts of actions.
The visuomotor classification suggests different
degrees of congruence. Strict congruence is expressed with each perceptual schema connected to
exactly one motor schema. Broad congruence is
expressed with few perceptual schemas connected
to exactly one motor schema. Non-congruence, although less interesting, is expressed with perceptual
schemas connected to a motor schema, such that the
scene represented bears no relation to the action the
motor schema controls.
The selectivity of F5 neurons with respect to
the agent of the interaction (i.e. hand or mouth)
and the interaction itself (i.e. grasp, manipulation,
etc.) is reflected by limiting the structures that perceptual schemas recognise. The non-selectivity towards the object is similar: the structures recognised should be independent of the nature of the
object.
The function of the F5 area is expressed when a
vocabulary of schemas is formed. Such a vocabulary
may use motor schemas to generate actions, perceptual schemas to recognise them, or both for imitation. Learning may take place by adjusting the actions that perceptual schemas recognise and motor
schemas control. Finally, the function of the AIP
area is expressed in the preprocessing of the visual
input, such that only relevant perceptual schemas
are active each time.
We are currently implementing a schema network
reflecting the aspects described above. This network would then be used by an artificial agent (imitator) aiming to recognise and imitate the actions
of another (demonstrator). The demonstrator’s actions will primarily involve hand grasping only. The
imitator should recognise and differentiate the observed grip among only a few others, and then reproduce it. This scenario, however minimal, requires most, if not all, of the features of this schema
network.
5
Conclusion
From a biological point of view, the F5 system facilitates a subject to recognise and understand the
actions of others. From a computational point of
view, this architecture facilitates efficient action recognition and imitation. The motor vocabulary by
itself decreases the amount of variables one needs
to control (for action generation) or instantiate (for
action recognition). Furthermore, the function of
the AIP area guides imitation and possibly learning, hence reducing the amount of complexity even
further.
The Schema Theory permits us to apply the
neurophysiological constraints on the implementation of this architecture and finally test it on appropriate demonstrator-imitator scenarios.
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An Imitation Mechanism Inspired from Neurophysiology
George Maistros, Gillian Hayes
Informatics Research Report EDI-INF-RR-0031
DIVISION of INFORMATICS
Institute of Perception, Action and Behaviour
August 2000
Abstract :
Imitation is employed with ease by all primates, but is rarely found in artificial agents. Recent monkey brain
activation data illustrate interesting neural characteristics related to imitation. Inspired from these data and based on
Arbib’s Schema Theory, we are implementing an architecture for imitative behaviours in artificial agents.
Keywords : imitation, robotic learning, mirror neurons, visuomotor coupling, macaque monkeys, area F5, inferior area
6, schema theory, affordances
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