We describe how an arousal system that controls the levels of awareness can be implemented in a robot. The di erent levels of awareness correspond to di erent states of consciousness and we argue that an arti cial arousal system modeled after its biological counterpart has a useful function in controlling the cognitive processing of a brain-like cognitive architecture. The level of awareness depends on arousal that in turn is controlled by novel or emotionally charged stimuli as well as by a circadian clock. Arousal is also modulated during cognitive tasks to control the randomness of decision processes and to select between exploration and exploitation.
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Why is the study of consciousness relevant for robotics? In our view, there are processes that re ect the level of awareness in humans that can play an important role in the control of a robot. These processes are commonly associated with di erent conscious states. However, our interest in these processes is not primarily that they are correlated with di erent conscious states, but rather that they are useful as control mechanisms in their own right. We believe that this gives a solid basis for studying consciousness in AI systems since the di erent mechanisms are motivated by their functional role rather than by subjective notions of awareness or conscious experience.
The present paper presents an overview of the arousal system for a humanoid
robot. This arousal mechanism, which is is roughly modeled after its biological
counterpart, determines the level of awareness and controls processing in a
memory system that supports many cognitive operations. The arousal system decides
how information is processed in memory, how thoughts are focused or allowed to
wander, and sets the balance between exploration and exploitation. The level of
arousal can be considered one of several meta-parameters that control processing
in a natural or arti cial cognitive system [
A central componenent of the robot control architecture is a biologically
motivated memory system. We have argued elsewhere that a conscious robot
needs to maintain an inner world to support various cognitive functions. This
inner world is based on a combination of episodic, semantic and working memory
structures [
In the memory system, memories correspond to attractor states of the
network and perception and recall is the process of nding the attractor state most
consistent with the input. The latching dynamics make attractors semi-stable.
This causes the memory state to jump from attractor to attractor in an episodic
manner unless the state is explicitly locked in the current attractor. As a
consequence, learned episodes can later be recalled as episodic memory transitions.
In addition, the memory system supports semantic transitions where a stimulus
associates to other similar stimuli [
A critical parameter of the memory system is its gain that controls whether
the system stays in an attractor or is allowed to transition to other memory
states. With a very high gain, the memory system will be locked in an attractor.
With lower gain, it will start to transition through learned episodic sequences.
If the gain is lowered even more, the randomness of the state transitions will
increase as a results of noise which can produce novel transitions from
combination of old episodes [
In the brain, the level of awareness is controller by the ascending reticular
activating system. It consists of a number of nuclei that can control processing
in the whole of the brain, including wakefulness and sleep. Here we will focus
on the locus coeruleus (LC), which is the primary nucleus for the control of
wakefulness though its noradrenargic projections to cortical, subcortical,
cerebellar and brainstem circuits of the brain. A high LC activity is associated with
heightened attention. During sleep, the LC is almost completely turned o . We
have earlier developed a computational model that is able to explain how the LC
is activated by novel or emotionally charged stimuli [
It has been suggested that the level of noradrenaline functions as gain
modulation in cortical processing by controlling the randomness of decision processes
[
Di erent levels of awareness all have roles to play in cognitive processing, ranging from intense focus to drowsiness and sleep. This is a continuous scale, but we here focus on some qualitatively di erent levels of awareness. All these states have useful functions in the control of a robot.
Focus At the highest level of arousal, the attentional and memory systems are a
focused on a single stimulus or memory state. During manual actions, this is the
relevant level of arousal as it allows the visual system to lock on to a stimulus
without being distracted by irrelevant stimuli [
Although intense focus could sound like an ideal state, this is not always the
case. The problem is that it locks the current memory state and if it is not the
correct one, it will be hard to move away from it. This can be seen, for example,
during the tip-of-the-tongue phenomenon [
Planning and Problem Solving To nd novel combinations of episodic
sequences, as is necessary during problem solving, it is required that arousal is kept
at a high, but not too high level. Focused exploration of alternatives is combined
with unfocused mind wandering to generate new alternatives. When the robot
fails to move forward in a task, the arousal is decreased to allow novel solutions
to emerge. These solutions are either tested in the environment or evaluated in
memory. The process is similar to simulated annealing where noise is introduced
in an arti cial neural network to allow its memory state to jump to a novel
solution [
Planning is similar to problem solving in many ways, but need not be
concerned with an immediate problem. When planning, the system can be viewed
as stringing together behaviours and episode-fragments in memory to compose
a plausible path from the present context to some desired goal state. The
exploration is hence goal-directed and more focused than the mind wandering,
daydreaming state described below. However, depending on the general arousal
level of the system, planning may degenerate into daydreaming, or oscillate
between the two. During planning, the memory system will produce the kind of
forward sweeps seen during vicarious trial and error in animals [
Daydreaming During daydreaming or mind wandering the arousal is lower
compared to planning. The memory transitions of the robot mirror the state in
which the default network dominates access to working memory [
Drowsiness Drowsiness is associated with a very low level of arousal. In terms
of gain control, it allows the memory state to drift freely without any goal
direction or focus. This is a transitional state that can be linked to the
sleepwake cycle, at the transition stage to sleep. However, it can also be used to
simulate the propensity to seek novelty or change task if the current task does not
yield su cient reward. The same can happen if the task is too energy-draining
compared to the level of reward, or the level of perceived progress towards a goal.
Drowsiness might also be entered into if the robot is understimulated. That is,
the activation of its perceptual system is below some threshold. For example,
the robot may be presented with a static or blank scene or it may be required
to not move for a too long period of time. In humans and animals, a lack of
stimulation from proprioceptors and other signals from the peripheral system
associated with sitting or laying for extended periods of time tend to induce
drowsiness and lethargy [
Drowsiness can end up in a sleep state, but because it also motivates action,
it can also cause the robot to do something novel to increase arousal. Which
direction the arousal will take depends on contextual factors such as the
availability of objects in the environment that can stimulate actions. We have earlier
modeled how novel or emotional stimuli can increase arousal through the
activation of a simulated LC [
Sleep At the lowest level of arousal we nd sleep. The obvious use for a sleeplike state in a robot is that it serves energy conservation. However, sleep is not equivalent to being turned o . The sleeping robot can quickly become aroused and move to an active attentive state if something unexpected happens. This implies that rudimentary sensory processing must also occur during sleep, but perhaps at a lower resolution or at a lower rate to save energy. As in the brain, the sleep-cycle is also controlled by a clock, which determines at what time it is appropriate to sleep, and allows the robot to quickly go back to sleep again after being woken up by something unexpected.
In general, monitoring of the environment during sleep depends on passive
attention to the environment. Such attention is controlled by processes of
habituation that form a model of the environment that can detect unexpected
disturbances [
Having a system that dynamically transitions between arousal states similar to those found in animals allows the study of aspects of consciousness that may be hard or time consuming to study in biological systems. It may also allow insights into the phenomenon of consciousness as such, by accessing it indirectly through arousal, rather than attempting to tackle it directly. Thus, the infamous \hard" problem of consciousness may perhaps be chipped away at without becoming stuck in the traditional debates surrounding this issue.
Closer to everyday concerns, perhaps, are the requirements of arti cial systems to conserve and optimize energy use when not connected to a continuous energy supply. We argue that modelling arousal levels in general, and the LC noradrenergic system in particular a ords a biologically plausible way of managing energy expenditure, as well as a highly interesting avenue for studying dynamic cognitive behaviour with a high level of experimental control. Moreover, a robot with a biologically plausible arousal system may allow insight into human maladaptive conditions associated with the noradrenergic system. This includes depression and anxiety disorders, attention de cit disorders, schizophreniaand narcolepsy.
To summarize, we have outlined the function of a biologically motivated arousal system for a humanoid robot. This central system controls how the memory system transitions from one state to the next to recall episodic memories or to produce novel states and transitions based on earlier experiences. The level of arousal is closely connected to level of awareness and acts as a speed control for thought.