Abstract
Mirror neurons within a monkey's premotor area F5 fire not only when
the monkey performs a certain class of actions but also when the
monkey observes another monkey (or the experimenter) perform a similar
action. It has thus been argued that these neurons are crucial for
understanding of actions by others. We offer the hand-state hypothesis
as a new explanation of the evolution of this capability: the basic
functionality of the F5 mirror system is to elaborate the appropriate
feedback - what we call the hand state - for opposition-space based
control of manual grasping of an object. Given this functionality,
the social role of the F5 mirror system in understanding the actions
of others may be seen as an exaptation gained by generalizing from
one's own hand to an other's hand. In other words, mirror neurons
first evolved to augment the "canonical" F5 neurons (active during
self-movement based on observation of an object) by providing visual
feedback on "hand state," relating the shape of the hand to the shape
of the object. We then introduce the MNS1 (mirror neuron system 1)
model of F5 and related brain regions. The existing Fagg-Arbib-Rizzolatti-Sakata
model represents circuitry for visually guided grasping of objects,
linking the anterior intraparietal area (AIP) with F5 canonical neurons.
The MNS1 model extends the AIP visual pathway by also modeling pathways,
directed toward F5 mirror neurons, which match arm-hand trajectories
to the affordances and location of a potential target object. We
present the basic schemas for the MNS1 model, then aggregate them
into three "grand schemas" - visual analysis of hand state, reach
and grasp, and the core mirror circuit - for each of which we present
a useful implementation (a non-neural visual processing system, a
multijoint 3-D kinematics simulator, and a learning neural network,
respectively). With this implementation we show how the mirror system
may learn to recognize actions already in the repertoire of the F5
canonical neurons. We show that the connectivity pattern of mirror
neuron circuitry can be established through training, and that the
resultant network can exhibit a range of novel, physiologically interesting
behaviors during the process of action recognition. We train the
system on the basis of final grasp but then observe the whole time
course of mirror neuron activity, yielding predictions for neurophysiological
experiments under conditions of spatial perturbation, altered kinematics,
and ambiguous grasp execution which highlight the importance of the
timing of mirror neuron activity.
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