more than an imitation game top down modulation of the human mirror system

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Title: More than an Imitation Game: Top-down Modulation of
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Authors: Megan E.J. Campbell, Ross Cunnington
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MORE

THAN AN IMITATION

GAME: TOP-DOWN MODULATION

OF THE

HUMAN

MIRROR SYSTEM

Literature Review

Megan E. J. Campbell1 and Ross Cunnington1,2

1. The Queensland Brain Institute, 2 School of Psychology, The University of Queensland,
St Lucia, 4072, Australia

HIGHLIGHTS





Mirroring properties are acquired and malleable
Context, task-relevance and prior sensorimotor experience modulate mirror system
activity
Mirror system regions are involved in non-imitative action responses
Cognitive control networks can modulate learned mirror representations for counterimitation

ABSTRACT
All interpersonal interactions are underpinned by action: perceiving and understanding the
actions of others, and responding by planning and performing self-made actions. Perception
of action, both self-made and observed, informs ongoing motor responses by iterative
feedback within a perception-action loop. This fundamental phenomenon occurs within


single-cells of the macaque brain which demonstrate sensory and motor response
properties. These ‘mirror’ neurons have led to a swathe of research leading to the broadly
accepted idea of a human mirror system. The current review examines the putative human
mirror system literature to highlight several inconsistencies in comparison to the seminal
macaque data, and ongoing controversies within human focused research (including mirror
neuron origin and function). In particular, we will address the often-neglected other side to
the ‘mirror’: complementary and opposing actions. We propose that engagement of the
mirror system in meeting changing task-demands is dynamically modulated via frontal
control networks.

Keywords: mirror neuron system; cognitive control; sensorimotor associations; perceptionaction; imitation; counter-imitation

1 INTRODUCTION
Perception and action are inextricably linked processes, and together form the basis of every
aspect of our experience of and interaction with the world. Of particular importance are the
interactions humans have with each other. These require complex, concurrent processes for
perceiving the actions of the self and other. Such perceptual representations inform the
preparation of corresponding motor responses, through to the execution of the action and
the perception of the outcome of this action (known as the perception-action-loop). A
phenomenon variously termed motor resonance (e.g. Cross and Iacoboni, 2014a), mirroring
(Rizzolatti and Fogassi, 2014) and vicarious activation (Keysers and Gazzola, 2009), has
been identified as a critical part of this perception-action-loop. Of course this began with the

report of ‘mirror neurons’ in the premotor cortex of the rhesus macaque, discovered some 20
years ago by Rizzolatti’s group (di Pellegrino et al., 1992; Gallese et al., 1996). Mirroring
refers to the apparently similar neural processing of observed actions as for self-made
actions, particularly within regions of the brain previously thought of as selectively coding
motor control, i.e. self-made actions. Critically we avoid a definition based on a strict
congruence between observed and executed actions.

Here we review the human ‘mirror system’ literature to highlight a number of inconsistencies
with the original macaque data, and to discuss ongoing controversies within the field. By
contrasting various theories of mirror system origin and function, we point to a convergence
of views and provide a useful framework from which to pose further questions. In particular,
we will address the often-neglected other side to the ‘mirror’, i.e. complementary and
opposing action responses, and how an action “mirroring” system might allow alternative
task-demands to be met. Some level of ‘mirroring’ may always occur (Kilner et al., 2003), but
we argue these representations are propagated depending on prior associations between
stimulus and response actions, and the context of the task at hand. Control processes, such
as response-selection, conflict detection, and ongoing goal-maintenance can be engaged to
gauge the task-relevance of incoming sensory information to optimise the generation of
motor responses. Even in situations where stimulus and response actions are not perfectly
compatible, the mirrored representations of observed actions may still be usefully integrated
to prepare complementary responses. We argue that activation of mirror regions is
dynamically adaptive and integrated with the top-down control systems of frontal networks.
Cognitive control collectively refers to higher-order executive functions which enable one to
coordinate lower-level processing toward meeting internal goals, while remaining flexible to
changing demands. (Dosenbach et al., 2008; Koechlin et al., 2003). These processes and
the networks underlying them have been reviewed in detail elsewhere (for theoretical review
Botvinick et al., 2001; Miller and Cohen, 2003; Ridderinkhof et al., 2004). Here we focus on
the influence of cognitive control on dynamic, adaptive and predictive sensorimotor

associations in the action-perception and motor-response loop. This view aligns with the
associative sequence learning account of mirror neuron development and evolution (Heyes,
2010a), a parsimonious theory for the sensorimotor associations linking the representations
of both observed and executed actions. Hence, we apply a system-level framework to
sensorimotor mirroring, incorporating existing cognitive and computational models of how
the brain optimises behavioural responses to sensory information (Kilner et al., 2007a;
Körding and Wolpert, 2004).

2 MIRROR NEURON TO MIRROR SYSTEM
How we conceive of action perception and action execution has profoundly changed by the
discovery of motor neurons with sensory properties in the ventral premotor region F5 in the
macaque monkey, (di Pellegrino et al., 1992; Gallese et al., 1996). The response properties
of these cells vary but their distinguishing feature is that their firing is modulated both by
action execution and action observation, varying depending on the degree of action
specificity. The coining of the term ‘mirror neuron’ describes this unique feature of being
responsive to both motor and sensory action-related inputs.

The purported function of mirror neurons is not ubiquitously agreed upon (e.g. Casile et al.,
2011; Cook and Bird, 2013; Hickok, 2013). Many researchers refer to mirror neurons as
encoding action-goals and subserving action understanding, without clarifying these
functions or how such functionality arises. Although much of the monkey physiology data
seemed to demonstrate specificity of responses to goal-directed actions (i.e. object-oriented
as in picking up food), Ferrari and colleagues have shown non-goal directed mouth actions
(‘communicative’ gestures) to elicit activity in mirror neurons in the monkey pre-ventral
cortex (Ferrari et al., 2003). Hence the idea of mirror neurons only responding to goaldirected actions is left wanting (Catmur, 2012). This is not to imply that higher-order
cognition about intentions and goals are not influenced by mirror-matching sensorimotor
information; however, there is a tendency in the literature to over-simplify the description of

‘mirroring’ and then ascribe extraordinary consequences to this mechanism (Heyes, 2010b;
Kilner and Lemon, 2013). This is further confused by hypothesised functions of mirror
neurons becoming entangled with explaining the origin of mirror neurons. The genetic
account of mirror neurons assumes their fundamental role is action understanding, for which
the development of mirror neurons is genetically predisposed due to natural selection
pressure favouring this function (Lepage and Théoret, 2007; Rizzolatti and Craighero, 2004).
Therefore, the hypothesised function of mirror neurons is offered as an account of the origin
of mirror neurons (Cook et al., 2014). This view of mirror neurons was apparently affirmed
by neonatal imitation research (e.g. seminal studies Meltzoff & Moore, 1977, 1989; and more
recent review chapter: Meltzoff, 2002). However, this line of evidence has been strongly
refuted by a recent longitudinal study (Oostenbroek, Suddendorf, Nielsen et al., 2016).
Epigenetic accounts improve on the rigid genetic perspective by incorporating the influence
of learning and experience, while arguing for a level of innate properties upon which
experience builds (Bonini and Ferrari, 2011; Ferrari et al., 2013; Giudice et al., 2009). As
such, this epigenetic perspective draws nearer to a view of mirror properties being
experience-based.

2.1 EXPERIENCE-BASED MIRRORING
The Associative Sequence Learning account of mirror neurons offers a parsimonious
explanation for how neurons acquire mirroring properties: sensorimotor associations form
based on the experience of contingent and repeated activation of a sensory and a motor
representation of a particular action (Catmur, 2012; Catmur et al., 2009; Cook et al., 2014;
Heyes, 2013; 2010a; Hickok and Hauser, 2010, Heyes, 2016). Being experience-based,
such connections are adaptable which allows for a wide variety of sensory inputs to mirror
neurons. These then code for particular motoric responses experienced in contingent
relationships with a certain range of effective sensory inputs over the course of an
individual’s learning history (Catmur, 2012). The domain-general process of associative
learning allows for mirror neurons to make contributions to action understanding and social

cognition but does not assume this (Cook et al., 2014). From this perspective mirroring may be
active for imitation without being for imitation (Brass & Heyes, 2005; Hickok, 2013). Thus action
understanding can take advantage of automatic imitation without precluding experience-based
changes in sensorimotor associations and context-dependent inhibition of imitative tendencies.

A complementary account of mirroring is the Hebbian learning model proposed by Keysers
and colleagues (Keysers and Gazzola, 2014; Keysers and Perrett, 2004). Based on
anatomical connectivity of the macaque brain, Keysers summarises the mirror circuitry as a
series of reciprocal connections between area PF of the inferior parietal lobule and both
premotor area F5 and the superior temporal sulcus (STS, Keysers and Perrett, 2004). All
three of these areas respond to the sight of another agent’s action, but only areas PF and F5
also respond to the monkey’s self-generated actions. To explain the mirror properties of F5
and PF, Keysers and Perrett apply the Hebbian learning rule of consistent repeated cellfiring increasing the efficiency of synaptic connections between pre and post-synaptic cells,
and thus leading to spike-timing dependent synaptic plasticity. Importantly in their model of
STS-PF-F5 circuit, the STS functions to cancel out the agent’s own movements based on
temporal correlations between visual, auditory and motor representations occurring during
the action observation and self-made action execution. It is hypothesised that a similar
feedback loop exists in the human neocortex, between homologue regions (Keysers and
Gazzola, 2014).These two perspectives, Hebbian and associative, are not mutually
exclusive and rather offer insight to different levels of abstraction. The Associative Sequence
Learning account (Catmur et al., 2009; Heyes, 2010a), a cognitive model, is focused at the
functional level, and remains agnostic about the precise neural mechanisms underlying the
acquisition of new associations. As such it is compatible with the Hebbian learning
predictions for the neural level, with spike-timing dependent plasticity reflecting contingent
sensory and motor inputs.

Further insight into the functioning of mirror neurons is offered by the computational
perspective of predicative coding via Bayesian inference. Importantly, this view also holds

that experience is a significant factor for mirroring. Kilner (2007b) focuses on a systems-level
model of the predictive and generative feedback between sensory and motor
representations. Predictive coding is based on minimising error via reciprocal interactions
between a hierarchy of cortical areas in a Bayes optimal fashion. Each level generates
predictions based on the representations in the level below and concurrently feeds
backwards to the lower level for comparison with the latest sensory input to produce
prediction errors. This error is then reiterated to the higher level to update predictions,
providing contextual guidance towards the most likely cause of sensory inputs (Kilner et al.,
2007b). Within this framework, mirror neuron firing rates are open to top-down modulation.
Firing during action observation is not merely driven by visual input, rather it constitutes a
part of a generative model actively predicting sensory input. By extension, sensorimotor
connections and predictive coding allow for optimizing motor planning and control in the face
of sensory uncertainty (Körding and Wolpert, 2004; Wolpert et al., 2011; Wolpert and Landy,
2012). This predictive coding framework is also compatible with Associative Sequence and
Hebbian learning perspectives. Together these provide an explanation of how repeated
experience of sensorimotor associations build predictive expectancies, which are reflected in
the one-to-many (motor to sensory) response mapping properties of mirror neurons.

Despite the convergence of theories that view mirroring as a result of experience, human
‘mirror system’ studies often tend towards both over-simplification in the undue emphasis of
congruent mirror matching and an aggrandisement of what the putative human mirror
system is able to achieve. Cook and Bird (2013) highlight a glaring inconsistency between
the seminal mirror neuron studies based on direct unit-recordings (di Pellegrino et al., 1992;
Gallese et al., 1996) and the commonly misconstrued simplification adopted by ‘human
mirror neuron system’ studies. The typical definition of the putative human mirror system
places emphasis on strict sensorimotor congruency of observed and executed actions.
Although this fits intuitively with the term ‘mirror’, it does not reflect the complexity described
by those responsible for first measuring this phenomenon: “a particular set of F5 neurons,

which discharged both during monkey's active movements and when the monkey observed
meaningful hand movements made by the experimenter” (Gallese et al., 1996, p. 594).
Indeed Gallese classified 60.9% (56 of 92 cells recorded) of mirror neurons as merely
‘broadly congruent’; the majority of these displaying activation to the observation of two or
more actions (diPellegrino:1992tg ; see Casile, 2013 for a thorough review of macaque
mirror neuron physiology; Gallese et al., 1996). More recent work in monkeys has described
the existence of pyramidal tract neurons which discharge for the execution of an action, but
are inhibited during passive observation (Kraskov et al., 2009; 2014). This was interpreted
as systematically suppressing self-action representations during observation and actively
preventing mirroring. In the excitement to validate the existence of a human mirror neuron
system much of the nuanced variability in the response properties of mirror neurons has
been glossed over.

2.2 A MIRROR SYSTEM IN THE HUMAN BRAIN
The existence of mirror neurons in humans is broadly accepted and yet only a single report
has provided direct measurement by single-cell recordings of mirror neurons in humans
(Mukamel et al., 2010). All other data is based on non-invasive techniques such as
functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS).
In terms of fMRI-based research, the most elegant experiments have applied fMRI
adaptation paradigms to the question of ‘mirror’ activity. fMRI adaptation refers to the effect
of repeated presentations of a sensory stimulus causing decreased firing rates in neurons
which encode that stimulus feature, and by extension leads to dampening in the bloodoxygen-level dependent (BOLD) signal, relative to that elicited by a novel stimulus
(Krekelberg et al., 2006, yet; for caution see, Larsson and Smith, 2012). The application of
this technique to the question of the human ‘mirror system’ aims to determine the presence
of neural populations selective for particular actions, regardless of whether the action was
observed or executed (e.g. Chong et al., 2008). However the studies published using this
technique (Chong et al., 2008; Dinstein et al., 2007; Kilner et al., 2009; Lingnau et al., 2009;


Press et al., 2012) have produced mixed results, with only three reporting results consistent

with the presence of mirror neurons. Two highlighted the inferior frontal gyrus (Kilner et al.,
2009), and one the inferior parietal lobe (Chong et al., 2008), as areas homologous to the
regions of the macaque fronto-parietal ‘mirror neuron system’ (areas F5 and PFG, Rizzolatti
and Craighero, 2004). Another important note on these adaptation studies is that the results
were not bi-directional. Observation followed by execution elicited repetition suppression, but
not execution followed by observation. This suggests that mirroring is only involved in
priming self-made actions in response to observed actions and not vice-versa, and supports
the notion of sensorimotor associations (Catmur et al., 2007) rather than direct-matching
models which suggests mirroring occurs regardless of modality order. Thus, mirror
responses reflect the facilitation of the motor system due to learned associations between
sensory representations of actions and the motor programs which generate them (Catmur,
2012; Catmur et al., 2007; Hickok, 2009).

Another general limitation of many of the human imaging studies reporting mirror neuron
activity is the failure to include action-execution conditions corresponding to actionobservation. Indeed, a meta-analysis by Molenberghs and colleagues (2012) revealed that
70% of studies reporting visuo-motor mirror effects were based on only action observation
manipulations. This meta-analysis did yield converging evidence of sensorimotor mirroring in
cortical areas including the inferior frontal gyrus, ventral premotor cortex and inferior and
superior parietal lobules (Molenberghs et al., 2012), which confirms the positive results of
the handful of fMRI adaptation studies (Chong et al., 2008; Kilner et al., 2009; Press et al.,
2012). Moreover these meet predictions of human homologues based on monkey single-cell
physiology (Gallese et al., 1996; Kilner and Lemon, 2013). Molenberghs and colleagues
(2012) describe these areas as “a core network of brain areas … which in humans is reliably
activated during tasks examining the classic mirror mechanism, typically involving the visual
observation and execution of actions” (p.348). Moreover, additional regions were shown to
be activated relative to modality (e.g. post-central gyrus for somatosensory simulation and


experience), which fits in with the view heralded by Keysers and Gazzola’s group that
vicarious brain activity, made possible by mirror neurons, encompasses more than actions;

extending to the sensations and emotional states of others (Keysers and Gazzola, 2009).
Furthermore, this perspective is compatible with the associative sequence learning account
of mirror neuron development (Catmur et al., 2009; Heyes, 2013; 2010a), with principle of
contingent inputs becoming associated over repeated experience being domain-general this
theory permits for multi-modal ‘mirroring’ (Keysers and Gazzola, 2014).

Returning to our focus on mirroring for action: the association between observed and
executed actions built though common experience, leads to the sensory input of observing
another’s action feeding forward as motor representations then priming a matching motor
plan (Heyes, 2010a). There is firm evidence for this model of action mirroring based on
multiple studies employing single-pulse transcranial magnetic stimulation (TMS). This
technique can be used in conjunction with electromyography (EMG) to measure the corticospinal excitability of muscle specific representations of actions (varying size of motor-evoked
potentials, MEP), and it has been shown that the passive observation of an action selectively
enhances the excitability of the representations of muscles involved in executing the
observed action (for example, Baldissera et al., 2001; Clark et al., 2004; Fadiga et al., 2005;
1995). Put simply, viewing another’s action triggers sub-threshold activation of the motor
plan to imitate that action (Cooper et al., 2013; Cross et al., 2013). This account is in line
with the description of mirror neuron response properties outlined above. Namely, the
existence of both strictly congruent cells which are sensitive to low-level features of
observed actions (direction of motion, viewing angle, effector used, etc.), and more broadly
congruent cells that are responsive to a variety of related actions irrespective of the
particulars of action performance (Heyes, 2014). A recent review by Cook and colleagues
(2014) provides an exhaustive and persuasive account of the evidence supporting the
associative view of sensorimotor mirror-neurons. Importantly, such congruent action
mirroring is reduced following disruption by repetitive TMS over the ventral premotor cortex


(part of the core regions of the putative mirror system, Molenberghs et al., 2012)
demonstrating the causal role of this region in mirroring for actions (Avenanti et al., 2007).

3 IMITATION AND COUNTER-IMITATION
Automatic imitation refers to a particular kind of stimulus-response compatibility
effect (SRC effect, Prinz, 1997; Zwickel and Prinz, 2012) in which task-irrelevant action
stimuli facilitate the execution of similar actions, and interfere with the execution of dissimilar
actions (Heyes, 2011). It is termed ‘automatic’ in so far as it is not dependent on the actor’s
intentions, but rather results from long-term sensorimotor connections. Automatic imitation
has been explained in terms of associative learning which relies on temporal contiguity and
contingency; the predictive relationship between stimulus and response (Heyes, 2011). In
the same vein, automatic imitation effects are reduced by TMS disruption of the premotor
area, highlighting the link between the motor ‘mirror system’ and automatic imitation (Cross
et al., 2013). This tendency to match motor plan to action representations is suggested to
simply result from the bulk of experiences being of matching gestures, such as
communicative gestures like waving in greeting or nodding in agreement. As such, the
default stimulus-compatible motor plans evoked by action observation are underpinned by
mirror representations being forwarded to the primary motor cortex (Rizzolatti and
Craighero, 2004). This converges with earlier work from Brass et al. (2001) who approached
the tendency to imitate from an alternative angle, seeing the response-time bias as an
imitation-inhibition effect resulting in longer response latency. With participants performing a
simple predefined finger-movement at the onset of congruent or incongruent an action
stimulus, the incidentally mismatching ‘counter-imitation’ trials were found to reliably activate
lateral prefrontal regions important for response inhibition. This reframing of mirroring in
terms of inhibitory control points to the necessity of adaptable stimulus-response mappings
within the putative human mirror system.


The temporoparietal junction (TPJ) has been implicated in the inhibition of automatic
imitation by both neuroimaging (e.g. Brass, Ruby & Spengler 2009; Marsh et al. 2016) and
neuro-stimulation studies. Hogeveen and colleagues (2014) applied transcranial direct
current stimulation (tDCS) to either the IFC (inferior frontal cortex) or TPJ, versus a sham
stimulation control group, and found distinct behavioural effects. Imitation inhibition was

manipulated (compatible/incompatible actions versus effectors) in a behavioural task and a
separate social interaction task coded instances of social mimicry during a participant’s
interaction with a confederate. TPJ stimulation, hence increased neural excitability, was
correlated with increased imitation inhibition, but no change in responses to effector
compatibility or social mimicry. This was distinct from IFC stimulation which was related to
increased social mimicry. Although this study is laudable for introducing a naturalistic social
mimicry task, the attribution of tDCS effects to specific regions is questionable given the
poor spatial resolution of tDCS as compared with TMS, as the authors themselves note.
Sowden and Catmur (2015) disrupted the right TPJ functioning with repetitive TMS and
measured a decrease in the ability to inhibit the tendency to imitate irrelevant actions. This
was taken to implicate the TPJ as having a casual role in the control of imitation. Further
corroborating evidence comes from a recent fMRI study by Marsh, Bird and Catmur (2016)
examining the modulation of imitation by social factors (mere-group membership and eye
gaze). The TPJ was shown to be more engaged during imitatively incompatible than
compatible trails and that this was immune to modulation by social factors. These studies
revolve around predefined action execution and task-irrelevant action observation, rather
than intentionally non-imitative responses.

It is easy to think of many everyday situations where imitating others would be counterproductive, such as catching a ball thrown towards you or having a mug handed to you
(precision grip if offered handle-first versus wide grasp for holding the body of the mug,
(Newman-Norlund et al., 2007; van Schie et al., 2008). In situations of complementary and
joint-actions, the interaction between action-perception and action-preparation is more


complicated than direct matching, or ‘mirroring’, would imply. Indeed, several studies have
reported fMRI evidence of ‘mirror’ activity during non-imitative action. Newman-Norland and
colleagues (2007) reported that complementary (i.e. non-matching) responses provoked
greater activation in putative ‘mirror system’ areas than imitative response. Complementary
object-directed actions (precision grip versus wide grip) were performed across different
task-set contexts: preparing imitative or complementary responses. This finding was clarified

by Ocampo and colleagues (2011) who employed the equivalent task: reaching to grasp a
wine glass at the stem or bowl, hence differing in precision of the grip. Obviously, in a realworld situation of being handed a wine glass, the complementary grip to that of the observed
action would be the most suitable response (e.g. if the glass were passed by holding the
stem, one would reach with a power grip for the bowl of the glass rather than a precision grip
used to hold the stem). The addition of non-action, spatial compatibility manipulations (arrow
cues in in/congruent directions relative to responses), allowed for compatibility effects
specific to action-stimuli to be isolated (Ocampo et al., 2011). This highlighted the role of the
inferior parietal lobe (IPL) and inferior frontal gyrus (IFG). IFG and IPL activations varied
relative to the similarity between observed and executed actions, and were postulated to
mediate non-imitative responses which may underpin joint-actions and inter-actor
cooperation.

These

studies

highlight

the

importance

of

investigating

action

execution/observation beyond strictly congruent sensorimotor pairings, and indeed
emphasise the real-world relevance of non-imitative responses.

The flexibility of sensorimotor mirroring for non-imitative contexts has been elegantly
scrutinised through non-invasive brain stimulation. Cross and Iacoboni (2014a) investigated
involuntary, covert imitation within the motor system, with the strategic changes in the
degree of motor resonance reflecting the usefulness of imitation in achieving the task at
hand. MEPs were measured following single-pulse TMS probes while participants prepared
to imitate or counter-imitate finger movements presented by video stimuli. The imitative
compatibility task manipulated the period of preparation before action observation and


execution, with participants either cued to prepare an imitative or counter-imitative response,
or else given no preparation period until an imperative stimulus was presented. Thus the
‘usefulness’ of motor resonance to the task varied: for counter-imitation and no-preparation
trials motor resonance would be counter-productive. Only in the conditions where
preparation to imitate was cued would motor resonance always prime the correct response.
Cross and Iacoboni (2014a) reported the expected results of showing motor resonance
during action observation period, only when the participant had been preparing an imitative
response. Measured motor resonance was suppressed on trials where participants were
preparing to counter-imitate and when the stimulus-response mapping was unknown (nopreparation cue trials). Hence, the tendency for automatic activation of stimulus-compatible
responses can be strategically suppressed when this would otherwise interfere with task
demands. Cross and Iacoboni surmise that this occurs through modulation of mirror system
activity. Furthermore, by merely emphasising counter-imitation in the task instructions, these
mirror-suppression processes are engaged during passive action observation. Bardi and
colleagues (2015) instructed participants across two sessions to either imitate or counterimitate videos of index or little finger movements, but prior to making any actions, passively
observed these stimuli with MEPs recorded to measure muscle-specific excitability.
Observing actions under the compatible-response instruction showed a typical mirror effect
preferential to the muscle which would be involved in performing the observed action.
Critically, counter-mirror instructions suppressed this pattern, showing that mere instruction
can override tendencies for automatic imitative response (Bardi et al., 2015). The
adaptability of the mirror system is task-dependent, as evidenced by manipulating the

context and task-relevance of counter-mirror responses, and without additional sensorimotor
training. We argue that this accumulation of evidence suggests an integral role for cognitive
control processes integrated with experience-dependent mirroring produces a malleable
mirror system.

4 COGNITIVE CONTROL REGULATES SENSORIMOTOR MIRRORING


Incorporating the associative learning and predictive coding frameworks, sensorimotor
mirroring can be viewed as adaptively utilising the representation of external action stimuli to
efficiently inform the motor planning of optimal responses. Predictions of stimulus-response
pairings are based on the prior experience of the action-response which has most often
been related to a given action-stimulus (Cross et al., 2013; Cross and Iacoboni, 2014a;
2014b). The missing ingredient is then how such sensorimotor associations are up or down
regulated to suit current task demands. A past review (Chong and Mattingley, 2008) has
examined research relating specifically to selective attention and mirroring, and concluded
that such interactions were relatively unexplored, and this largely remains the case.
Nevertheless through converging evidence from patient studies and fMRI data, Chong and
Mattingley (2008) highlighted the importance of prefrontal regions in imitation inhibition,
concluding that the frontoparietal mirror system ought to be viewed as mutually informed by
wider networks rather than mere automatic visual-to-motor mapping. More recently, the
involvement of domain-general cognitive control processes has been proposed both in
preparatory (Cross and Iacoboni, 2014a) and reactive modulation of mirroring (Cross et al.,
2013).

Cross and colleagues have framed modulation of stimulus-response mapping in terms of
stimulus-response compatibility (SRC) effects within the dual-route model proposed by
Braver (2012). Firstly, an automatic fast route links stimulus-compatible responses reflecting
long-term associations. Secondly, a parallel intentional indirect route links stimulus and
response according to temporary rules meeting the current demands.

For action

observation to action execution mapping, the automatic fast route represents compatible,
imitative responses enlisted to copy the observed action. However in order to counter-imitate
an observed action, this automatic route must be suppressed to allow the slower taskrelative, intentional route to prepare the correct stimulus-incompatible response. Using fMRI
to investigate this model, Cross et al. (2014a) had participants perform an counter-imitation
task in response to either biological motion stimuli (a video of a hand making finger lifting


actions), or a ‘non’ biological cue (two moving dots). The areas found to be involved in
stimulus-general preparatory suppression (that is, the intentional indirect route) included left
dorso-lateral pre-frontal cortex, frontal pole, posterior parietal cortex and early visual regions
(Cross and Iacoboni, 2014b). Importantly, the details of whether this reflects a top-down
biasing of visual input to the mirror system or rather a suppression of the motor matchedrepresentation are not discernible from these results. Part of this picture has been
illuminated by Sasaki and colleagues (2012), who applied dynamic causal modelling (DCM)
to ‘mirroring’ activity with a very targeted focus on effective connectivity between the STS
and ventral premotor cortex (vPM). They have proposed an inverse internal model linking
the STS to vPM, converting visual representations into a motor plan. In their view the
reverse connection, vPM to STS, forms a forward internal model, translating motor plans into
the sensory outcomes of executed actions. Thus the putative mirror system is proposed as a
dynamic feedback system during action observation for prior associations to prime likely
responses. These findings align with the description of automatic imitation inhibition in terms
of either input or output modulation (Heyes, 2011). Input modulation refers to mediating the
processing of action stimuli; whether the motor activations associated with this input is
inhibited or permitted to influences overt motor responses is output modulation. Essentially,
attentional effects modulate input while social cognitive factors relate to output modulation
(see Heyes 2011 for a more extensive review of automatic imitation). By incorporating
domain-general executive functions into this view of mirroring, mirror activity can be
modulated by top-down control relative to current task demands. Thereby allow for

adaptable stimulus-response mappings within a dynamic mirror system, rather than purely
passive and automatic processes.

Where effortful task-dependent processing of stimulus-response pairs is necessary, higher
order, domain-general functions may be recruited to modulate the mirror system’s
representation of actions. Cognitive control is subserved by interrelated brain regions,
organised into two dissociable components (Dosenbach et al., 2008; Nomura et al., 2010).


Initiation and rapid moment-to-moment adjustment of control engages a ‘fronto-parietal’
network while longer goal-maintenance for the duration of a task is supported by the
‘cingulo-opercular’ component. These two networks are distinguished both by resting-state
connectivity, and by double-dissociations in lesion related impairments being restricted to
either network (Nomura et al., 2010). Further to being a region in this dual-network of
cognitive control, the anterior cingulate cortex (ACC) has been highlighted as critical for
conflict detection and error-related processes. Of particular relevance to counter-imitation
and dynamic mirroring is a purported role of the ACC in detecting conflicts between
competing simultaneous representations (Carter and van Veen, 2007). Importantly, conflict
detection by the ACC occurs in conjunction with moment-to-moment modulatory control by
the dorsolateral prefrontal cortex (dlPFC), in what is referred to as a conflict-control loop.
Imitation and counter-imitation require such dynamic adjustment of control depending on
conflict between internal and external action representations. We propose that top-down
processes are engaged more by the effortful contradiction of a learnt stimulus-response
association, as in counter-imitation, than in simply imitating observed actions, even
intentionally.

In those situations where the salient stimulus is another agent’s action, the mirror system is
expected to be recruited to represent the perceived action of others in terms of the same
motor codes as used for internally generated motor planning, as a matter of efficiency.
Where there is conflict between the incoming motor representations and internal action plan

(as is the case for incidentally incongruent stimulus-response contexts), conflict detection
will be engaged responsively. Moreover, when tasked with actively opposing an observed
action, conflict detection and response selection processes may be engaged in preparation;
i.e. in advance of viewing the action stimulus rather than in reaction to it (Cross and
Iacoboni, 2014b). This enables the task of encoding of the action stimulus and planning the
countering action to be achieved; concurrently holding two contradictory action
representations at the time of observation and response planning. As compared to


incidentally matched or mismatched stimulus-response action pairs, intentional imitation or
counter-imitation means the action stimulus is salient to response preparation. This context
and task-relevant input is actively encoded through wider recruitment of top-down cognitive
control networks. Repeated contingent experience of a stimulus-response pair (regardless of
whether they are congruent, incongruent or complementary) will lead to the automatic
pairing (as per associative sequence learning account (Catmur et al., 2009). Henceforth this
acquired sensorimotor association (learnt ‘mirror response’) will require task-dependent
modulation should it contradict the current task-set. In the right context, counter-imitation can
become automatic, thereby allowing for less recruitment of executive control functions,
especially in incidental stimulus-response mapping. Via integration of context cues and goalmaintenance processes, the sensorimotor mirror system would theoretically acquire multiple
and opposing associations which are context-dependent. Thus cognitive control processes
can be engaged to work harmoniously with more automatic sensory-to-action associations
(‘mirroring’) to allow for adaptive behaviour.

5 CONCLUSIONS
The mirror system underlying motor resonance is not exclusively involved in stimulus-toresponse matching. Rather the association of related but dissimilar stimulus-response pairs
when they occur repeatedly together, allows for variable sensorimotor mapping. Indeed
viewed as part of a wider action observation network, the mirror system is active during the
preparation of complementary actions as well as stimulus-matched responses (NewmanNorlund et al., 2007; 2008; Ocampo et al., 2011). We consider mirroring to be a property that
is acquired through associating contingent experiences, of internally and externally driven
representations, and that brain areas with neurons exhibiting this property may contribute to

a wide range of higher-order processes (e.g. understanding intention, social interaction)
without necessarily having evolved specifically for these functions (Catmur, 2012). In the
right context a non-imitative response can become the automatic response to an action
stimulus, given the acquisition of mirror system representations (Catmur, Walsh, & Heyes,


2009; Keysers, & Gazzola, 2014) and their malleability via training (Catmur et al 2008;
Heyes et al, 2013). Indeed, should a particular stimulus-response pair be well learned, a
reduction in the reliance on top-down control would indicate its automaticity over other less
frequently experienced responses. Hence, mirror processes must be regarded as
functioning within a dynamic system that is task- and experience-dependent, rather than
fixed. To this end, the integration of mirroring with domain-general and higher-order
processes, including cognitive control, is a necessary direction for mirror neuron research to
take. For example, a novice dancer carefully observing the movement of their partner and
performing a complementary action requires fluid interplay between cognitive control and
mirror system processes. With practice this observed-executed action pair becomes less
effortful and relies less on response-selection or goal-maintenance, and rather the two
actions become tightly associated by repeated pairing and co-activate the mirror system
within that context. It remains unclear at which points within mirror networks modulation by
top-down feedback occurs, nor exactly how this changes with learning. We see the current
trend towards a systems neuroscience approaches, rather than a focus on functional
specialisation, is well suited to examining such modulation of sensorimotor mirroring.
Mirroring should be framed within hierarchical networks including frontal regions associated
with response-selection and goal maintenance. Ultimately, the demands upon the human
mirror system engendered by the complexity of human interactions necessitates that
sensorimotor representations within this system are adjustable, and not simply set to a
mirror-matched default for imitating everyone’s actions.

ACKNOWLEDGEMENTS
The authors research is funded by the Australian Research Council Special Research

Initiative (SR12030015) Science of Learning Research Centre.


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