Special issue on machine consciousness: Self, integration and explanation | selected papers from the 2011 aisb workshop: Guest editors' introduction

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International Journal of Machine Consciousness
© World Scientific Publishing Company
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SPECIAL ISSUE ON MACHINE CONSCIOUSNESS:
SELF, INTEGRATION AND EXPLANATION –
SELECTED PAPERS FROM THE 2011 AISB WORKSHOP
INTRODUCTION
Ron Chrisley
Sackler Centre for Conscousness Science, Centre for Research In Cognitive Science, and
Department of Informatics, University of Sussex, Brighton BN1 9QJ, United Kingdom
ronc@sussex.ac.uk
Robert W. Clowes
Instituto de Filosofia da Linguagem , Universidade Nova de Lisboa, Av. de Berna, 26 - 4º piso ,
Lisbon, 1069-061, Portugal
robertc@sussex.ac.uk
1. Background
In April of 2011, a symposium, entitled “Machine Consciousness 2011: Self, Integration
and Explanation” (MC2011) was held in conjunction with the conference Artificial
Intelligence and the Simulation of Behaviour at the University of York, UK. The
meeting, organized by Steve Torrance and ourselves, built upon previous AISB
conferences in 2005 and 2006 (and an issue of the Journal of Consciousness Studies
collating work originally presented in these conferences), sought to look at how the field
of machine consciousness is moving forward, what distinctive contributions it has made
so far, and – by focusing on more intensive scrutiny of work so far presented – provide a
forum for proposing and considering future developments in the area.
Although the field of machine consciousness is still in the process of maturation, there is
evidence of its increasing importance with respect to the broader fields of consciousness
studies, artificial intelligence and philosophy of mind. There is, of course, a new journal
dedicated to the field: The International Journal of Machine Consciousness itself. But
there are also now several well-developed research programmes in the area, and work in
the area is increasingly cited by the wider community suggesting widening interest. For
example, a recent textbook introduction to consciousness (Susan Blackmore's
Consciousness: An Introduction, 2nd Edition) gives the topic an extended, three-chapter
treatment. Yet we still do not have anything approaching a general agreement on the best
way forward, or much work assessing the current strengths and weaknesses of the various
programmes. all of which have different methodologies and success criteria.
Nevertheless, there are reasons to think that the field is at the point where it could offer

2 R. Chrisley and R. Clowes
important contributions to both the understanding of mind in general and consciousness
in particular.
For these reasons we organized MC2011, asking for researchers to submit work that
suggests how the various approaches to machine consciousness might be deepened, how
their contributions might be assessed and extended; and how they might better develop
through stronger relationships with related fields. This issue collects together
devlopments of selected work presented at this meeting.
2. Focus questions
The meeting interrogated some of the themes central to recent research in machine
consciousness that admit of a more sustained consideration. In our call for papers, we
encouraged submissions falling under one of more of these topics, addressing one or
more of the following questions:
• Self modelling: What is the relevance of self-modelling to machine consciousness?
What is a self-model and how might it be implemented? The notion seems to inspire
many in the field but is the idea clear? What types of self-modelling might there be?
How does self-modelling relate to previous problems of representationalism? Do we
need to build embodied self-models? Might research into machine consciousness
along these lines help deal with some problems faced by behaviour-based
approaches?
• Information integration: What implications do information integration theories have
machine consciousness? What specific aspects of consciousness might be best
explained by these approaches? Can implemented models shed light on the ways
information might be integrated?
• Explanation: What new explanatory resources does the field of machine
consciousness offer? Has the field made good on helping us explain consciousness? If
so, which aspects, models or approaches seem to offer the most value? What aspects
of consciousness might admit of modelling approaches? Do computational models of
consciousness deserve to be described as “machine consciousness” any more than
computational models of weather deserve to be described as “machine weather”?
• Neuroscience: What should the relation be between research in machine
consciousness and research in neuroscience or in consciousness science in general?
Can machine consciousness models have any scientific validity unless they are
closely allied to detailed findings and models in current neuroscience?
• Functional versus phenomenal consciousness: Is machine consciousness research a
sub-field of artificial intelligence? Is the phenomenology of consciousness fully

Introduction 3
analysable in terms of cognitive functionalities? Would an AI agent whose
intelligence ranked higher than most humans – or indeed whose intelligence was far
higher than any human’s (cf the recent notions of AI+ and AI++ [Chalmers 2010]) –
necessarily also be an artificially conscious agent?
• Ethics: Does machine consciousness research specifically raise any distinctive
ethical problems? What bearing might MC research have on the development of
artificial agents with ethical responsibilities or duties towards us? Or on the
development of artificial agents towards whom we might have ethical
responsibilities?
Of course, not all of these questions were addressed (nor could they have been), but we
were (and are) very pleased with not only the quality, but especially the targeted
relevance of the submissions we received.
3. The articles in this issue
3.1. Self System in a Model of Cognition (Ramamurthy, Franklin, & Agrawal)
Franklin and colleages’ LIDA (Learning Intelligent Distribution Agent) - based on the
Global Workspace Theory, and an extension of the original IDA model [Franklin, 2003] -
is one of the most developed implemented models currently being developed to
understand and explain consciousness [B. J. Baars & Franklin, 2009]. The article in this
volume Ramamurthy, Franklin, & Agrawal [2012] adds a self-model system to the LIDA
architecture. The paper revises the previous work presented at the AISB symposium
[Ramamurthy & Franklin, 2011] primarily to accommodate some rethinking of the LIDA
model itself [McCall, Franklin, & Friedlander, 2010].
The actual theoretical inspiration of the model of self here presented is based around
Damasio’s [2000] interlinked notions of Proto Self, Core Self and Extended Self. The
explanatory project, although not made completely explicit, appears to be to show that
within the LIDA system (and thus Global Workspace Theory) it is possible to integrate a
theoretically motivated model of self. The broad aim of the paper, like its predecessor,
remains to produce a model of self which fits into the larger LIDA project to build a
complete implementation of the cognitive system underlying functional consciousness. It
is shown that a Damasio inspired self-system can be articulated within the LIDA
architecture
But whether this computational compatability demonstrates the compatibility of these
theories at a deeper level is open to question. It is controversial since Damasio defined
proto-consciousness as “a coherent collection of neural patterns which map, moment by
moment, the state of the physical structure of the organism in its many
dimensions”[Damasio, 2000, p.154]. Although the self part of the model is theoretically
based around Damasio’s work, this implementation of the LIDA system has no body; or
rather its embodiment is restricted to a computational model. (See Ziemke [2007] and

4 R. Chrisley and R. Clowes
Torrance [2007] for more on this controversy within the machine consciousness
community.) Arguably a model that had a more convincing or interesting embodiment
would be required to really develop the implications of theoretical compatibility or
further demonstrate the constraints of these theories.
3.2. A Cognitive Neuroscience-inspired Codelet-based Cognitive Architecture for
the Control of Artificial Creatures with Incremental Levels of Machine
Consciousness.( Raizer, Paraense & Gudwin)
The Raizer, Paraense, & Gudwin paper [this issue] outlines an ambitious programme of
research to cumulatively build an architecture for a conscious robot. The way in which
the cognitive model itself is being constructed (and conceptually organised) closely
follows the idea of the triune brain [MacLean, 1990] which posits that the (human) brain
can be understood as a three levelled organ that can be conceptually separated into
reptilian, paleomammalian and neomammalian components. The idea also implies that
much of the brains functional architecture can similarly be conceptualised in a tripartite
way. The paper closely follows the Baars / Franklin approach in particular by by
following the global workspace architecture implemented with codelets [Hofstadter &
Mitchell, 1994; Negatu & Franklin, 2002]. The project however uses this architecture to
control a humanoid robot with a complex embodiment: the iCub [Metta, Sandini, Vernon,
Natale, & Nori, 2008] platform. The paper then assesses the various levels of the
cognitive architecture with reference to the current state of their project in terms of the
Arrabales pragmatic consciousness scale [Arrabales, Ledezma, & ConsScale, 2010].
3.3. Synthetic phenomenology and the high-dimensional buffer hypothesis
(Chella and Gaglio)
In “Synthetic phenomenology and the high-dimensional buffer hypothesis” [this issue],
Antonio Chella and Salvatore Gaglio detail a part of their robot architecture which they
call the "high-dimensional buffer". Synthetic phenomenology is concerned with both the
“characterization of phenomenal states possessed or modelled by a robot” and “the use of
a robot to help specifying phenomenal states.” Chella and Gaglio point out that the work
done in this field to date has typically involved perceptual signals of low dimensionality.
It is their contention that this has limited the field’s explanatory scope. Instead, they
propose the use of a high-dimensional perceptual signal, and show how it can be used to
capture a multi-faceted phenomenal profile, including subconceptual, iconic and
conceptual experience. Rather than creating computational intractability, they contend
that such a high-dimensional space could make it easier for the system to find
representational configurations which best make sense of the world.

Introduction 5
3.4. Inner Speech Generation In a Video Game Non Player Character: From
Explanation to Self? (Arrabales)
What – if any – is the functional role of inner speech in consciousness? Recent theoretical
research in cognitive science has suggested that there might be some important cognitive
benefits to agents capable of using language for communication, to integrating that
language in other part of their cognition architecture [Carruthers, 2002; Clark, 1998,
2005]*. A small body of work in simulation has attempted to put this to the test [Clowes
& Morse, 2005; Mirolli & Parisi, 2011]. There has been much less work on the role of
language in consciousness†, although even here researchers have already suggested
approaches to this that may indicate interesting scope for modelling based approaches
[Clowes, 2007; Haikonen, 2006]. Arrabales [this issue] attempts a much more scaled-up
approach to this territory by embedding an architecture for inner speech in an existing
architecture for non-player characters AIs in a video-game system.
The game scenario employed is a ‘deathmatch’ style first person shoot-en-up, which
as the author’s admit may not be the most obvious scenario for exploring inner speech
but at least makes sure that any AI needs to deal with some issues of embodiment /
embedding in a non-trivial world. The AI architecture is Arrabales own CERA-
CRANIUM [Arrabales, Ledezma, & Sanchis, 2009] software framework, principally
intended to enable the construction of machine consciousness inspired controllers for
video games. CERA-CRANIUM was developed for building controllers for these non-
player characters and is modelled on some of the more important theoretical approaches
to consciousness: Baars Global Workspace Theory [B. Baars, 1997] and Dennett’s
Multiple Draft’s theory [Dennett, 1991].
This work really extends the CERA-CRANIUM architecture with a component that
translates the mission-level description of the system into a simplified natural language.
One of the great advantages of such an approach is that it makes it possible to explore the
trade-offs and interactions between different representation systems (say vision and
speech) in a single system with a potential sophistication not so far attempted. In more
technical detail, the system produces summaries of a given scene and then use a parse
tree mechanism to turn the mission level descriptions into “inner speech”. At the moment
these mechanisms neither play a role in the organisation of the system nor are used
communicatively - they deliver a narrative like ongoing description of the scene – and so
strictly there are questions about whether this should be seen as inner speech rather than
progress toward implementing such. Nevertheless the inclusion of this summarising
function into the CERA-CRANIUM architecture is a step toward the testing of various
hypotheses about inner speech in a video games setting and will hopefully allow the
possibility of exploring a number of particularly hypotheses about the role of inner
speech in consciousness. This work presents an interesting step along that path.
* NB – Such work can be seen as a departure from a traditional view in classical cognitive science that saw all
thought as already being couched in a linguaform Language of Thought [Fodor, 1975]
† But see [Martinez-Manrique & Vicente, 2010; Morin, 2005] for some relevant recent treatments.
† But see [Martinez-Manrique & Vicente, 2010; Morin, 2005] for some relevant recent treatments.

6 R. Chrisley and R. Clowes
3.5. Consciousness, action selection, meaning and phenomenic anticipation
(Sanz, Hernandez & Sanchez-Escribano)
Sanz, Hernandez, & Sanchez-Escribano [this issue] use the notion of agent
phenomenality to guide the specification of a general archictecture for consciousness.
The authors hold that phenomenal states are the ultimate sources of motivation in humans
and possibly other animals and seek to develop robot architectures with the same
properties.
The article reports on the ASys research program which attempts to develop a
“universal technology for autonomy” the object of which is to specify and build robot
architectures which can be used to robustly control autonomous systems on platforms as
diverse as cars and pacemakers. The primary motivation is that control architectures
which reflect conscious minds are thought to allow the construction of more robust and
adequate control systems.
On the more theoretical side rather than trying to build machines which have one or
other aspects of consciousness, Sanz et al believe that we need to design a general
architecture including all of the major sub-systems of consciousness in a single system. In
this their work has similar motivation to some of the other major machine consciousness
programmes [Aleksander & Morton, 2007; Franklin, 2003]) that seek to understand
whole conscious systems. Apart from attempting to build more robust control
architectures, Sanz’s programme seeks to use consciousness research to develop design
patterns for building better robot minds in the future.
3.6. The computational stance is unfit for consciousness (Manzotti)
Central to much work in machine consciousness is the assumption that computation, in its
traditional formal or functional sense, is central to consciousness in that sufficient, or at
least necessary, conditions for consciousness can be stated in computational terms.
Riccardo Manzotti and David Gamez each cast doubt on this assumption. In “The
computational stance is unfit for consciousness” [this issue], Manzotti argues that
computation is not the right kind of interpretation-independent entity that is required for
something to serve as a metaphysical foundation for conscious mentality. Similarly, the
related notion of information is observer-relative in a way that makes it unsuitable for
explaining the place of consciousness in the physical world.
3.7. Empirically grounded claims about consciousness in computers (Gamez)
Gamez, in “Empirically grounded claims about consciousness in computers” [this issue],
takes a similarly anti-computational stance. His point is similar to John Searle’s in that
he does not claim that computers cannot be conscious; rather, the contention is that if a
computer were conscious, it would not be because of implementing this or that formal,
functional characterization. Rather, it would be because it produces “spatiotemporal
patterns in the physical world that match those that are potentially linked with
consciousness in the human brain.” He then goes on to describe how we, armed with the

Introduction 7
uncontestably conscious adult human case as a starting point, might go about
determining the best way to identify and express those correlates. He closes his
discussion by warning that while his method might be used to demonstrate the presence
of consciousness in a system, it could not be used to prove that a given system is not
conscious.
3.8. World-related integrated information: Enactivist and phenomenal
perspectives (Beaton and Aleksander)
One of the possible correlates Gamez considers is the mathematical notion of information
integration; Mike Beaton and Igor Aleksander, in “World-related integrated information:
Enactivist and phenomenal perspectives” [this issue] evaluate this notion’s ability to
explain consciousness. Information integration is a measure that, it has been proposed,
can capture two important aspects of conscious states: that they are “highly
discriminative and yet fundamentally integrated”. In the first part of their paper, Beaton
and Alexander offer what they take to be an enactivist critique of extant proposals for
explaining consciousness in terms of informational integration. The relevant notion of
information, they contend, is information for a whole, embodied, situated, rational
subject, not information attributed by a third party to sub-personal neural states. Thus
information integration theory, as it stands, is fundamentally on the wrong track. In the
second part of the paper, they contend that even if the enactivist critique is set aside,
problems remain for the information integration approach in its current, non-world-
involving form. They analyse at two simulated perceptual systems that, although
equivalent in terms of information integration, differ in that one successfully represents
the sensed external world, while the other does not. They argue that this difference in
representational success manifests a striking difference in phenomenality that therefore
cannot be captured in standard information integration terms.
3.9. Can functional and phenomenal consciousness be divided? (Taylor)
John Taylor's paper addresses the question in his title, "Can functional and phenomenal
consciousness be divided?" [this issue] in a way that simultaneously attempts to
understand consciousness in a causal (and thus evolvable) context, and do justice to a
phenomenally robust notion of subjectivity. He argues that the primary function of
consciousness is to improve attentional control (in terms of both accuracy and speed), and
that one can therefore locate the crucial mechanisms underlying consciousness in the last
of a five-stage evolvable model of attention. Central to this last stage is corollary
discharge (what many readers may know better as "efference copying"), and it is this
which Taylor argues underlies the sense of self, particularly the ownership of
experiences. He cites experimental evidence for the existence of this corollary discharge
in conditions which support the explanatory role he proposes for it.

8 R. Chrisley and R. Clowes
3.10. A role for consciousness in action selection (Bryson)
Although Joanna Bryson agrees with Taylor on several general points, in "A role for
consciousness in action selection" [this issue] she makes a different connection between
attention and consciousness. Relying on ethological results as much as her own expertise
in computing and artificial intelligence, she argues that in many cases, action selection
does not require consciousness. It is only in cases where, e.g., a system must be capable
of attending to subtle distinctions in order to be able to learn what action to take, that
consciousness is worth its cost, and therefore is present. She proposes that machines will
find useful the same heuristics for allocating attention in these cases as animals do:
"allocate attention on the actions you actually perform, and for a time in proportion to
your uncertainty about your next action." Bryson does not stop here, however, instead
making some remarks concerning the ethical dimension of consciousness. Taking a
position strikingly different from, e.g., that of Torrance (see below), she contends that
"only the conscious can be moral agents, but that does not necessarily imply that all
conscious entities must be treated as moral agents." In fact, "we are obliged when we
make intelligent machines to make ones we are not obliged to."
3.11. Super-intelligence and (Super-)Consciousness (Torrance)
Does high intelligence necessarily imply consciousness? Steve Torrance’s paper [this
issue] discusses the possibilities of hyperintelligence (AI++) - the idea that there might be
a being whose intelligence exceeds human intelligence in the way that human’s exceed
mice – and its implications for the study of consciousness. Part of the argument for AI++
stems from Chalmers [2010] consideration of ‘The Singularity’ where he argues that
were we to build an AI that approached or exceeded human intelligence, that machine
could design more intelligent sucessors whose intelligence, leading to a sequence of ever-
more intelligent machines whose intellectual prowess could soon far exceed our own.
This potentially runaway intelligence leads Torrance to what he calls the “Drop-Out
Question” (DOC), namely, would machines that were as or more intelligent than us
necessarily be conscious.
Several MC researchers (especially perhaps Arrabales [2010]) imply as much. The
main part of Torrance’s paper explores a series of different positions on the DOC and
how they have played out in the literature. They range from various sorts of scepticism
(hard, soft and agnosticism) to positions that AI++ would be sufficient for both functional
and even phenomenal consciousness.
As the paper argues, consideration of the DOC has interesting implications for
machine consciousness research more generally. If AI++ turns out to be sufficient for
Consciousness then MC research turns out to be central to artificial intelligence rather
than some odd or radical wing of it).
Torrance further argues that because phenomenal consciousness at least important has
ethical implications, that some answers to the DOP imply that AI itself has important

Introduction 9
ethical implications. Torrance makes the case for further exploration of the linkages and
implied relations between artificial intelligence and machine consciousness.
3.12. Virtualist representation (Clowes and Chrisley)
It must be admitted that we took some liberties in submitting our own paper, "Virtualist
representation" for inclusion in this special issue. Unlike the other papers, it was not
actually presented at the 2012 meeting, but is rather based on developments of work that
we have presented at similar AISB machine consciousness symposia in 2005 and 2006.
We start by noting the prevalence, in recent machine consciousness research and beyond,
of the virtual reality metaphor for consciousness. We then attempt to steer a middle
course between two versions of this metaphor - one ("presentational") which, at its worst,
suffers from the same problems as indirect and snapshot theories of perception, and
another ("enactive") which eschews representation entirely, to its own detriment. We
sketch an expectational theory of consciousness that involves representations (giving it an
edge over enactive virtualism) that are truly virtual, in that they need not be occurrently
tokened in order to contribute to the content of experience (thus avoiding the problems
confronting typical presentationalist accounts). Expectations are, on this account,
essentially predictive, and are therefore related to the kind of architecture proposed by
Bryson, as well as the recent sub-field of predictive coding.
4. Acknowledgements
Robert Clowes would like to gratefully acknowledge a Portuguese Science
Foundation Grant /BPD/70440/2010 that supported the writing of this introduction. We
would also like to thank Steve Torrance for helping organize the meeting on which this
issue is based, John Taylor’s widow Pamela, for her kind permission to include his paper,
Jenny Prince Chrisley for assisting with the editing and preparation of this issue, and the
editor-in-chief of this journal for his support and endless patience.
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