=Paper=
{{Paper
|id=Vol-2287/paper18
|storemode=property
|title=Conscious Enactive Computation
|pdfUrl=https://ceur-ws.org/Vol-2287/paper18.pdf
|volume=Vol-2287
|authors=Daniel Estrada
|dblpUrl=https://dblp.org/rec/conf/aaaiss/Estrada19
}}
==Conscious Enactive Computation==
https://ceur-ws.org/Vol-2287/paper18.pdf
Conscious Enactive Computation
Daniel Estrada
New Jersey Institute of Technology, Newark NJ 07102
djestrada@gmail.com
Abstract. This paper looks at recent debates in the enactivist liter-
ature on computation and consciousness in order to assess conceptual
obstacles to building artificial conscious agents. We consider a proposal
from Villalobos and Dewhurst (2018) for enactive computation on the
basis of organizational closure. We attempt to improve the argument by
reflecting on the closed paths through state space taken by finite state
automata. This motivates a defense against Clark’s recent criticisms of
“extended consciousness”, and perhaps a new perspective on living with
machines.
Keywords: enactivism, artificial intelligence, computation, Turing ma-
chine, state space, finite state automata, predictive coding, consciousness
1 Introduction
Enactivism challenges the dominant cognitive paradigm in psychology with an
account of intentional (purposive) agency that is grounded in the emergent dy-
namics of biological complexity [15, 43, 46]. Specifically, enactivism holds that
biological life is characterized by adaptive self-constitution: living systems con-
struct and maintain their own organized structure through their active engage-
ment with a changing world [4, 35]. This approach motivates a systematic ac-
count of autonomy [3, 33, 41, 48], intentional agency [17, 31], subjective conscious-
ness [19, 28], and identity in complex dynamical systems [5, 6], with the promise
of a consistent and unified explanatory framework across the full range of bio-
logical processes, from the biomechanics of single-celled organisms to ecologies
and societies [18, 26, 44].
Despite the emphasis on biological complexity, enactivism has from its in-
ception maintained a robust research program investigating artificial intelligence,
artificial life, and robotics (hereafter AI) [1, 2, 13, 16, 20, 42]. This research aims
to develop models, simulations, and robots that assist in the scientific investiga-
tion of biological complexity and adaptive systems. For instance, AI that exhibits
some dynamically self-organizing behavior might serve as a useful “proof of con-
cept” demonstrating key enactivist principles (see [20] for examples). However,
while robotics research has already felt a significant impact from the embodied
approach [37, 38], enactivist AI is often advanced against a backdrop of criticism
directed at “merely” computational or representational explanations [22, 23]. As
a founder of enactivism Francisco Varela put it, “This fundamental paradigm of
the digital computer program will not do for biology, nor for AI.” [46]
2 Estrada
A recent set of papers from Villalobos, Dewhurst, Ward and colleagues (here-
after Villalobos) [14, 49–51] address these historical tensions between enactivism
and computation. Villalobos argues that the enactivists are mistaken to treat
computers as mere symbolic processors of abstract representations. Drawing on
a mechanist account of computation, Villalobos suggests an interpretation of
the classical Turing machine which they claim would meet enactivist condi-
tions for self-determination. If so, it would suggest that embodied agency could
be given a computational rather than biological basis without sacrificing enac-
tivism’s theoretical commitments to the dynamical interactions between agent
and world. This argument strikes at the foundations of the enactivist program,
and threatens to overturn more than twenty years of enactivist thought on AI
and computation.
The central concern of this paper is to assess the proposal for enactive com-
putation put forward by Villalobos. Their argument turns on the enactivist in-
terpretation of self-determination in terms of organizational closure. While we
think Villalobos’ examples fail to meet strong enactivist conditions on closure,
we suggest they can be improved through explicit consideration of the structure
of the finite state automata (FSM) that controls a classic Turing machine. This
highlights an important form of closure that is, we argue, more fundamental than
organizational closure: namely, the closed path through state space taken by the
FSM. We claim that computation is fundamentally concerned with the structure
of paths through state space, and that all living organisms can be characterized
by such paths. This result suggests computation as the fundamental basis from
which the enactivist program must emerge. We then consider the implications of
this argument for a particular strand of criticism raised by Clark [10, 12] against
enactivist proposals for “extended consciousness” [36]. We conclude with general
thoughts on the implications these arguments have for living with machines.
2 Organizational closure and Turing’s machine
Organizational closure serves as the basis for the enactivist approach to au-
tonomous intentional (purposive) behavior, and names the sense in which biolog-
ical organisms are self-determined [3, 4, 47]. A system is organized when its con-
stitutive components are arranged into a network of functionally interdependent
processes and constraints [27]. An organization is closed when the operation of its
constitutive components are themselves sufficient for the adaptive construction
and generation of its organized state [35]. Enactivists argue that organizational
closure provides an intrinsic basis for identifying organisms and their boundaries
as unified wholes. Furthermore, enactivists emphasize that organisms are pre-
cariously situated within a dynamic world to which they must continually adapt
in order to maintain their constitutive organization. This precariousness creates
conditions that demand coordinated action from the organism as a unified sys-
tem [7]. This gives rise to what enactivists call adaptive sense-making, which
serves as the basis for investigations into consciousness and phenomenology [19,
28, 43].
Conscious Enactive Computation 3
Beyond its central role in the enactivist theory of autonomous agency, orga-
nizational closure also figures in enactivist criticisms of classical computation1 .
Enactivists contrast the closed structure of biological organisms with the open or
linear structure of traditional computing machines [20]. On this view, computers
operate through a sequence of formal operations that transforms symbolic “in-
put” into symbolic “output”. Enactvists claim at least two important differences
between computation and the adaptive self-constitution of biological organisms.
First, computers perform stepwise formal operations on symbolic input, rather
than performing dynamic mechanical operations within a changing world. Sec-
ond, computers don’t “build themselves” in the sense relevant for adaptive self-
constitution, which requires organizational closure. Put simply, computers aren’t
self-determined wholes with a world of their own, and so cannot serve as the in-
trinsic subject of an experience. Instead, computers are artifacts created through
external processes of human design and manufacturing. Such considerations lead
Froese and Ziemke [20] to distinguish the behavioral autonomy characteristic of
certain kinds of self-controlled machines (say, a dishwasher on a timer), from the
constitutive autonomy characteristic of living biological systems.
Villalobos’ argument for enactive computation in [51] is designed to show that
a Turing machine can meet the conditions for self-determination as described by
Maturana (1988) [30]. Here, self-determination is identified with functional clo-
sure. A system has functional closure when its organizational structure contains
closed feedback loops. As an example, Villalobos offers a thermostat regulating
the temperature of a house. The behavior of the thermostat-house system is char-
acterized by a feedback loop between these two components which satisfies func-
tional closure on Manturana’s definition. Of course, while the thermostat-house
system “controls itself” with respect to temperature, it is not adaptively self-
constituting in any deeper sense; thermostats and houses don’t build themselves
with their parts alone. Thus, functional closure is not sufficient for organizational
closure of the sort required for constitutive autonomy. Nevertheless, Villalobos
argues this control structure does not connect inputs to outputs through a linear
sequence of symbolic processes, and so is not “open”. It is, they argue, closed
and minimally self-determining in a sense relevant for enactivist theory.
Villalobos then applies this feedback loop model to the classic Turing ma-
chine. Turing [45] proposed a computing machine with three components: a tape
with discrete cells; a read-write head that operates on the tape; and a program
which controls the operation of the head. On the enactivist interpretation, the
tape serves input to the machine and records output from the machine, and
the machine (the head and program) performs formal operations that convert
the former to the latter as a linear process. Against this view Villalobos offer
an alternative, inspired by Wells [54] and Piccinini [39, 40], that interprets the
1
Enactivists are not universally hostile to computation. Importantly, Mossio et al
[34] render an organizationally closed system in the λ-calculus, and argue that
“there are no conceptual or principled problems in realizing a computer simula-
tion or model of closure.” Such arguments have resulted in a split between radical
anti-computationalists [22] and more traditional versions of enactivism. See [8, 53].
4 Estrada
Turing machine in terms of looping interactions between the machine and the
tape. This forms a functionally closed loop, much like the thermostat-house sys-
tem, which implies self-determination in the sense that the computer’s state is
determined by the interactions between the machine and the tape. In an analog
computer these constraints might appear as features of the physical mechanisms
of the device, thereby eliminating any symbolic aspect of the computation. Thus,
Villalobos argues, even a classical Turing machine can be understood as purely
mechanical and functionally closed, and so evades both enactivist criticisms of
computation. While this argument doesn’t entail that computers are conscious
living creatures of equivalent complexity to biological organisms, it does confront
a major hurdle within the enactivist literature to treating computing machines
as genuinely purposive agents with a world of their own.
Does Villalobos’ argument succeed? Briefly, no: functional closure alone is
not sufficient for adaptive self-constitution of the sort relevant for intentional
agency or adaptive sense-making. Villalobos’ ‘enactive’ Turing machine is merely
behaviorally and not constitutively autonomous. While Maturana’s account is
influential, recent work has developed more rigorous constraints on organiza-
tional closure. For instance, Mossio et al. [32, 35] present a model of closure
which requires that constitutive constraints operate across multiple scales or
levels of organization to achieve closure. While the thermostat-house system is
functionally closed, we might say that closure occurs at a single scale, namely
the feedback loop that controls temperature. At other scales, for instance the
internal structure of the thermostat mechanism, the system is not closed or self-
determining but depends directly on external processes. Similarly, Turing’s ma-
chine appears to be functionally closed only at the level of operations of the head
on the tape and nowhere else. Biological systems, on the other hand, are in some
sense self-determining all the way through—or at least they are self-organized
across a range of scales from inter-cellular biochemistry through geopolitics that
covers the breadth of our experiences of a meaningful world as human agents. A
Turing machine might be functionally closed, but it covers nothing close to the
same range of interactivity.
How many levels of organizational constraints are required to distinguish be-
tween behavioral and constitutive autonomy? Mossio’s model suggests at least
two. If so, Villalobos’ argument might be improved by describing a Turing ma-
chine with two layers of self-determining organizational constraints rather than
one. In the next section, I will discuss how the classic Turing machine already
captures organizational closure across two layers of constraint.
3 Closed paths through state space
If we suspend the anti-representational commitments of enactivism for a mo-
ment, there’s an important feature of Turing’s machine which is not explicitly
addressed in these arguments: the structure of the program which controls the
read-write head. In Turing’s model, the program takes the form of a finite state
machine (FSM). FSMs are abstract automata that are characterized by a finite
Conscious Enactive Computation 5
number of discrete states, a set of rules that describe the conditions for transi-
tioning between states depending on what is read from the tape. These rules can
be represented as a state transition table, which can be realized2 in a physical
machine in a number of ways. The physical Turing machine is ‘programmed’
insofar as it realizes the abstract state transition structure of the FSM.
The abstract nature of the FSM should not worry enactivists [27]. An FSM
can in principle be realized by simple physical mechanisms; there’s nothing in-
herently “symbolic” about the FSM. The FSM is not necessarily used by a
computer to “represent the world”. The FSM is just an abstract model of the
states a machine can be in, and the conditions for transitioning between these
states. Enactivist literature is often directly preoccupied with systems being in
certain states, like the equilibrium state (homeostasis), and with the activities
organisms must perform to maintain these states [25, 41]. To this extent, enac-
tivist theory depends on state abstractions of the same sort used to describe
the FSM. Describing the autonomy of an organism in terms of “organizational
closure” is already to appeal to control structure that achieve an abstract state,
so there should be no principled objections from enactivists to discussing the
equally abstract structure of the FSM.
While the FSM can be represented as a transition table, it is also customary
to represent an FSM with a state space diagram with states represented as circles,
and arrows between circles representing the transitions between states. A state
space diagram has a closed path (or loop) if some sequence of operations will
return the system to a previous state. Such closed paths are typical in discrete,
finite computing automata, but are also familiar from continuous cases in the
physical world. Suppose I take water at room temperature, freeze it to ice, then
let it thaw back to room temperature. The water crossed a state transition,
then crossed back; we can represent this as a path through the state space of
water that loops back to where it began and in this sense is closed across the
relevant state transition. Homeostasis is an interesting state for living biological
organisms precisely because they maintain the state as a fixed point attractor,
returning to equilibrium after minor disturbances. This is another way of saying
that homeostasis is characterized by a closed path in state space (CPSS).
With these considerations in mind, we propose that CPSSs, and paths in
state space generally, are of fundamental relevance to enactivist models of self-
determination. Moreover, CPSSs put computers and organisms on equal onto-
logical footing. Recall the theoretical motivation for appealing to organizational
closure to explain autonomy: it provides an intrinsic basis for individuating a
system as a unified whole, and so serves as a basis for adaptive sense-making.
2
For historical reasons originating with Putnam [21], it is often taken for granted
that a definition of computation in terms of finite state automata cannot distinguish
between different realizations of a computer, and so cannot in principle provide an
explanation for cognitive behavior. Piccinini [39] cites this as an explicit motivation
for developing his mechanistic account of computation. There are good reasons for
thinking that Putnam’s concerns are overstated [9, 24], but this issue is beyond the
scope of this paper. Thanks to Jon Lawhead for pointing this out.
6 Estrada
We claim that a CPSS accomplishes the same theoretical task: organisms can
be identified intrinsically as the collection of processes and constraints that walk
a CPSS. This definition is intrinsic in the same sense as organizational closure:
whether a path counts as “closed” is set by the constitution of the system it-
self and the state space it traverses. The abstraction of state space traversals
is general enough to apply consistently across physics, biology, and computer
science. More strongly, we claim that any organizationally closed system can
be characterized by a collections of CPSSs with a fixed attractor at the con-
stitutive organized state. This suggests that CPSSs are theoretically a more
fundamental form of closure than organizational closure. Indeed, the important
sense of ‘closure’ captured by the enactivists has less to do with daisy-chained
functions looping on themselves, and more to do with the structure of the state
space traversals those functional relationships enable. Strictly speaking, neither
functional nor organizational closure is necessary for walking a CPSS.
Not every Turing machine will walk a CPSS, but it is exceedingly common
for them to do so3 . We can think of the CPSSs which characterize a Turing
machine’s program as another scale of closure, one which directly controls the
looping interactions between head and tape. With two scales of closed loops,
this would appear to meet Mossio’s stronger constraints on closure, and thus we
have shown the classical Turing machine might already constitute an adaptively
self-constituting system on enactivist grounds. Or, perhaps more realistically,
the depth of closure matters less than what states those functional relationships
(closed otherwise) make available for the organism as it walks paths in state
space.
4 Extended consciousness
To appreciate how CPSSs can be useful to enactivism, consider a recent debate
on the bounds of consciousness. Despite his strong influence on enactivism, Clark
has pushed back against attempts to locate processes constitutive of conscious
experience in the world [10]. Clark argues there is no good reason to do so;
the activity constitutive of a conscious experience occurs immediately within
patterns of neural firings. Clark advocates for an explanatory approach called
”predictive coding” which uses “a hierarchical generative model that aims to
minimize prediction error within a bidirectional cascade of cortical processing”
[12]. Clark argues that the model works by rapidly updating on the basis of new
information. This leaves little bandwidth for external changes to impact the
updating model beyond sensory input; the dominant influence on most neurons
is the activity of other neurons. Thus, Clark argues, it is unlikely that external
processes play a constitutive role in conscious experience.
Ward [52] offers a response to Clark on behalf of enactivists that appeals to
multiple layers of interactions between the agent and world. Clark’s mistake, on
3
The question of deciding in general whether a path in state space will close is formally
equivalent to the halting problem, and so is not computable. See [29].
Conscious Enactive Computation 7
this view, is to localize consciousness to any single process in the organized hier-
archy. The appeal to multiple layers should by now be a familiar enactivist move,
one Clark rejects as superfluous in this case [11]. Whatever world-involving pro-
cesses enactivists believe are important, Clark claims he can account for them
with predictive coding that occurs strictly within neural activity. So conscious-
ness appears stuck in the head.
Clark’s alternative doesn’t appeal to enactivists because the world-involving
aspects of predictive coding appear linear and open, like a computer, rather than
closed like an organism. This isn’t an accurate perception; the cascade of neu-
ral activity develops with looping feedback until the neurons reach stability, so
there are functionally closed processes; those processes just aren’t extended and
world-involving beyond the brain. Enactivists are attracted to externalism be-
cause they view consciousness as inherently world-involving and organizationally
closed. Just as with Villalobos’ computer, enactivists are hoping to find a closed
organizational structure associated with the embodied conscious state. Since
closure is an indicator of unification and wholeness, enactivists expect neural
activity and world-involving processes to demonstrate dynamic functional in-
terdependencies. Clark’s argument that the neural activity is not functionally
dependent on external processes is therefore fatal to extended consciousness.
Perhaps CPSSs can help resolve this conflict amicably? If we think about
closure in terms of CPSSs we can recover the looping interactions that are in-
herently world-involving and closed in state space, while conceding to Clark that
the neural activity is sufficiently explanatory of the neurophysiological interac-
tions that give rise to the conscious state. By considering consciousness from
the perspective of traversals in state space, we are no longer confined to a single
closed loop of spanning organizational levels of physical or biological interac-
tions. Instead, dynamical activity across different scales will form many different
kinds of closed paths in different state spaces. Some of these CPSSs will be char-
acterized by inherently world-involving states, and in this sense will recover an
enactivist sense of extended consciousness compatible with predictive coding.
Consider, for instance, that it is easier to maintain your balance with your
eyes open than closed. Here we have two cortical cascades that traverse through
a conscious state: one producing visual experiences, and one producing motor ac-
tivity to maintain balance. These two systems work in concert and reinforce each
other. On the enactivist framing, maintaining balance is a precarious state that
inherently involves the configuration of the body as a massive physical object
with specific dimensions. Thus, the configuration of my body is a fundamental
factor in whether I am in a balanced state4 . From the perspective of state space,
the balanced state is a fixed attractor for certain CPSSs; the neural cascades
that produce my balancing behavior are associated with attempts to close a
traversal in state space and return to the balanced state. This brings in looping,
inherently world-involving processes into an explanation of my behavior as an
4
This is the case whether or not I am explicitly conscious of my body’s configuration
as a physical object. I might be aware that I am balanced, but lack proprioceptive
awareness of the bodily configuration that produces the balanced state.
8 Estrada
agent without committing to implausible functional interdependencies between
neurons and world. The important dependencies for closure, and ultimately for
autonomy, identity, and consciousness, are found in state space.
5 Conclusion
We don’t view CPSSs as a threat to enactivism’s positive theory of autonomy or
adaptive sense-making. Instead, we see it correcting the over-emphasized anti-
computationalism that has historically motivated the view. We think enough
speaks in favor of the enactive approach that it needn’t appeal to a problematic
ontological distinction between computing machines and biological life. Insofar
as Villalobos’ argument also serves these goals, this paper is meant to push
harder in the same direction.
We believe the significance of this correction extends beyond enactivist the-
ory, and is relevant to general debates concerning the relationship between com-
puting machines and biological organisms. State space descriptions provide a
convenient abstraction within which these distinctions cannot be motivated on
apriori or ontological grounds, but must be defended by appeal to specific pat-
terns of dynamical behavior. State space descriptions may also help us model
the elaborate functional interdependencies between ourselves and the technoso-
cial superstructures we inhabit, and thereby help us to better appreciate the
capacities and perspectives of the machines we live alongside.
References
1. Agmon, E., Egbert, M., Virgo, N.: The biological foundations of enactivism: A
report on a workshop held at Artificial Life XV. Artificial life 24(1), 49–55 (2018)
2. Aguilar, W., Santamara-Bonfil, G., Froese, T., Gershenson, C.: The Past, Present,
and Future of Artificial Life. Frontiers in Robotics and AI 1 (Oct 2014)
3. Barandiaran, X.E.: Autonomy and enactivism: Towards a theory of sensorimotor
autonomous agency. Topoi 36(3), 409–430 (2017)
4. Bechtel, W.: Biological mechanisms: Organized to maintain autonomy. In: Systems
biology, pp. 269–302. Elsevier (2007)
5. Bechtel, W.: Identity, reduction, and conserved mechanisms: Perspectives from
circadian rhythm research (2012)
6. Bechtel, W.: Systems biology: Negotiating between holism and reductionism. In:
Philosophy of Systems Biology, pp. 25–36. Springer (2017)
7. Burge, T.: Primitive agency and natural norms. Philosophy and Phenomenological
Research 79(2), 251–278 (2009)
8. Cárdenas, M.L., Letelier, J.C., Gutierrez, C., Cornish-Bowden, A., Soto-Andrade,
J.: Closure to efficient causation, computability and artificial life. Journal of The-
oretical Biology 263(1), 79–92 (2010)
9. Chalmers, D.J.: Does a rock implement every finite-state automaton? Synthese
108(3), 309–333 (1996)
10. Clark, A.: Spreading the Joy? Why the Machinery of Consciousness is (Probably)
Still in the Head. Mind 118(472), 963–993 (Oct 2009)
Conscious Enactive Computation 9
11. Clark, A.: Dreaming the whole cat: Generative models, predictive processing, and
the enactivist conception of perceptual experience. Mind 121(483), 753–771 (2012)
12. Clark, A.: Whatever next? Predictive brains, situated agents, and the future of
cognitive science. Behavioral and brain sciences 36(3), 181–204 (2013)
13. De Loor, P., Manach, K., Tisseau, J.: Enaction-based artificial intelligence: Toward
co-evolution with humans in the loop. Minds and Machines 19(3), 319–343 (2009)
14. Dewhurst, J., Villalobos, M.: The Enactive Automaton as a Computing Mecha-
nism: Enactive Automaton as a Computing Mechanism. Thought: A Journal of
Philosophy 6(3), 185–192 (Sep 2017)
15. Di Paolo, E., Thompson, E.: The enactive approach. The Routledge handbook of
embodied cognition pp. 68–78 (2014)
16. Di Paolo, E., Buhrmann, T., Barandiaran, X.: Sensorimotor life: An enactive pro-
posal. Oxford University Press (2017)
17. Di Paolo, E.A.: Autopoiesis, adaptivity, teleology, agency. Phenomenology and the
cognitive sciences 4(4), 429–452 (2005)
18. Froese, T., Di Paolo, E.A.: The enactive approach: Theoretical sketches from cell
to society. Pragmatics & Cognition 19(1), 1–36 (2011)
19. Froese, T., Gallagher, S.: Phenomenology and artificial life: toward a technological
supplementation of phenomenological methodology. Husserl Studies 26(2), 83–106
(2010)
20. Froese, T., Ziemke, T.: Enactive artificial intelligence: Investigating the systemic
organization of life and mind. Artificial Intelligence 173(3-4), 466–500 (2009)
21. Hilary, P.: Representation and reality. Cambridge, Mass.: A Bradford Book (1988)
22. Hutto, D.D., Myin, E.: Radicalizing enactivism: Basic minds without content. Mit
Press (2012)
23. Hutto, D.D., Myin, E.: Evolving enactivism: Basic minds meet content. MIT Press
(2017)
24. Joslin, D.: Real realization: Dennetts real patterns versus putnams ubiquitous au-
tomata. Minds and Machines 16(1), 29–41 (2006)
25. Kauffman, S.A.: Investigations. Oxford University Press (2000)
26. Kirchhoff, M.D., Froese, T.: Where there is life there is mind: In support of a strong
life-mind continuity thesis. Entropy 19(4), 169 (2017)
27. Levy, A., Bechtel, W.: Abstraction and the organization of mechanisms. Philosophy
of science 80(2), 241–261 (2013)
28. Lutz, A., Thompson, E.: Neurophenomenology integrating subjective experience
and brain dynamics in the neuroscience of consciousness. Journal of consciousness
studies 10(9-10), 31–52 (2003)
29. Luz Crdenas, M., Letelier, J.C., Gutierrez, C., Cornish-Bowden, A., Soto-Andrade,
J.: Closure to efficient causation, computability and artificial life. Journal of The-
oretical Biology 263(1), 79–92 (Mar 2010)
30. Maturana, H.: Ontology of observing: The biological foundations of self conscious-
ness and the physical domain of existence. The Irish Journal of Psychology 9(1),
25–82 (1988)
31. Merritt, M.: Thinking-is-moving: dance, agency, and a radically enactive mind.
Phenomenology and the Cognitive Sciences 14(1), 95–110 (2015)
32. Montvil, M., Mossio, M.: Biological organisation as closure of constraints. Journal
of Theoretical Biology 372, 179–191 (2015)
33. Moreno, A., Mossio, M.: Biological autonomy. A Philo (2015)
34. Mossio, M., Longo, G., Stewart, J.: A computable expression of closure to efficient
causation. Journal of Theoretical Biology 257(3), 489–498 (2009)
10 Estrada
35. Mossio, M., Moreno, A.: Organisational closure in biological organisms. History
and philosophy of the life sciences pp. 269–288 (2010)
36. Noë, A., Thompson, E.: Are there neural correlates of consciousness? Journal of
Consciousness studies 11(1), 3–28 (2004)
37. Pfeifer, R., Gomez, G.: Interacting with the real world: design principles for intel-
ligent systems. Artificial life and Robotics 9(1), 1–6 (2005)
38. Pfeifer, R., Iida, F., Bongard, J.: New robotics: Design principles for intelligent
systems. Artificial life 11(1-2), 99–120 (2005)
39. Piccinini, G.: Computing mechanisms. Philosophy of Science 74(4), 501–526 (2007)
40. Piccinini, G.: Physical computation: A mechanistic account. OUP Oxford (2015)
41. Ruiz-Mirazo, K., Moreno, A.: Basic autonomy as a fundamental step in the syn-
thesis of life. Artificial life 10(3), 235–259 (2004)
42. Suzuki, M., Floreano, D.: Enactive robot vision. Adaptive Behavior 16(2-3), 122–
128 (2008)
43. Thompson, E.: Life and mind: From autopoiesis to neurophenomenology. A trib-
ute to Francisco Varela. Phenomenology and the cognitive Sciences 3(4), 381–398
(2004)
44. Thompson, E.: Mind in life: Biology, phenomenology, and the sciences of mind.
Harvard University Press (2010)
45. Turing, A.M.: On computable numbers, with an application to the entschei-
dungsproblem. Proceedings of the London mathematical society 2(1), 230–265
(1937)
46. Varela, F., Thompson, E., Rosch, E.: The Embodied Mind: Cognitive Science and
Human Experience MIT Press. Cambridge, Massachusetts (1991)
47. Varela, F.G., Maturana, H.R., Uribe, R.: Autopoiesis: the organization of living
systems, its characterization and a model. Biosystems 5(4), 187–196 (1974)
48. Vernon, D.: Enaction as a conceptual framework for developmental cognitive
robotics. Paladyn, Journal of Behavioral Robotics 1(2), 89–98 (2010)
49. Villalobos, M., Dewhurst, J.: Computationalism, Enactivism, and Cognition: Tur-
ing Machines as Functionally Closed Systems. In: AIC. pp. 138–147 (2016)
50. Villalobos, M., Dewhurst, J.: Why post-cognitivism does not (necessarily) entail
anti-computationalism. Adaptive Behavior 25(3), 117–128 (2017)
51. Villalobos, M., Dewhurst, J.: Enactive autonomy in computational systems. Syn-
these 195(5), 1891–1908 (May 2018)
52. Ward, D.: Enjoying the Spread: Conscious Externalism Reconsidered. Mind
121(483), 731–751 (Jul 2012)
53. Ward, D., Silverman, D., Villalobos, M.: Introduction: The varieties of enactivism.
Topoi 36(3), 365–375 (2017)
54. Wells, A.: Turing’s analysis of computation and theories of cognitive architecture.
Cognitive Science 22(3), 269–294 (1998)