Aaron Gutknecht supports our bottom-up approach, specifies possible limits and highlights interesting future aspects. His added perspective is valuable and interesting to us. As we fully agree with his view, we only add some complementary remarks

Cruse, Holk

Schilling, Malte

English

Gutenberg Open

The Bottom-Up Approach: Benefits and
Limits
A Reply to Aaron Gutknecht
Holk Cruse & Malte Schilling
Aaron Gutknecht supports our bottom-up approach, specifies possible limits and
highlights interesting future aspects. His added perspective is valuable and inter-
esting to us. As we fully agree with his view, we only add some complementary
remarks.
Keywords
Bottom-up approach | Concept clarifying machine | Emergence | Emotions in arth-
ropods | We-model
Authors
Holk Cruse
holk.cruse@uni-bielefeld.de   
Universität Bielefeld
Bielefeld, Germany
Malte Schilling
malte.schilling@uni-bielefeld.de   
Universität Bielefeld
Bielefeld, Germany
Commentator
Aaron Gutknecht
aaron-gutknecht@gmx.de   
Johann Wolfgang Goethe-Universität
Frankfurt a. M., Germany
Editors
Thomas Metzinger
metzinger@uni-mainz.de   
Johannes Gutenberg-Universität
Mainz, Germany
Jennifer M. Windt
jennifer.windt@monash.edu   
Monash University
Melbourne, Australia
1 Introduction
We  appreciate  the  comments  given  by  Aaron
Gutknecht very much, in particular his discussion
and clarification of the term “emergence” and its
philosophical  background.  This  discussion  com-
prises a sensible completion of our article going
beyond the scope of our expertise. In this context,
Aaron Gutknecht correctly states that our way of
using the term “emergence” may cover two as-
pects, one called “weak emergence”, the other he
addressed as “implementational emergence”. We
have – possibly forming some kind of common de-
nominator - a third characterization in mind, one
that  covers  different  description  levels:  a  phe-
nomenon is considered emergent if it turns out
Cruse, H. & Schilling, M. (2015). The Bottom-Up Approach: Benefits and Limits - A Reply to Aaron Gutknecht.
In T. Metzinger & J. M. Windt (Eds). Open MIND: 9(R). Frankfurt am Main: MIND Group. doi: 10.15502/9783958570931 1 | 3
www.open-mind.net
that known properties of the network could also
be characterized on a different level of description
than the  one  currently  used.  On this  different
level the phenomenon conceptually constitutes a
term or definition. If we, for example, describe the
structure and function of reaCog on the neuronal
level, we may realize at some point that there are
behavioral aspects which could, by an outside ob-
server,  be  characterized by a term that  is  not
defined at a neuronal level of description, such as,
for example, “intention”. 
2 The bottom-up approach
This way of using the term emergence is directly
related to the bottom-up approach applied here.
This  approach  is  inspired  by  Feynman,  who
stated that we understand a system only when we
are able to construct it (in  Hawking 2001) and
may be even dated back to  Giambattista Vico
(1710).  The  bottom-up  approach  allows  us  to
study the extent to which linguistic concepts pro-
posed in the literature may correspond to proper-
ties realized by our artificial system. If one was
not  prepared to accept  that  a specific  concept
would correspond to selected properties of the ar-
tificial system, either the linguistic concepts might
be adapted accordingly, or the artificial system
might be judged as to show deficits. The latter
case could then give rise to adapt the current sim-
ulation model to better match the verbal proposal
given. This capability of the bottom-up approach
led  Manuela Lenzen (2014)  to  characterize
reaCog as a “concept clarifying machine” (“Be-
griffspräzisierungsmaschine”).
3 Possible limits of the bottom-up 
approach
Aaron Gutknecht further proposes a well-chosen
list of issues that should be taken into account
when  following  a  bottom-up  approach  as  pro-
posed here, namely “adequate matching criteria”,
“biological plausibility” and “transparency”. 
Concerning the first issue, “adequate match-
ing criteria”, Aaron Gutknecht addresses a pos-
sibly critical point. In section 8 (Emotions), we
characterize happiness by the property that risky
decisions are made more probable. We admit that
our example is formulated in a sketchy way, only
addressing one basic aspect for illustration. There
are, however, more deeply founded examples that
have been briefly referred to in the main text and
will be explained in more detail here. Two recent
studies, one in crayfish, the other in the fruitfly,
provide strong hints that emotion-like states can
be found in simple organisms as arthropods or,
more  specifically  in  the  latter-case,  insects.  In
crayfish,  Fossat et  al. (2014) have convincingly
shown that context-independent, anxiety-like be-
havior can be induced by experimentally applied
stress or by application of serotonin. Both meth-
ods lead the animals to avoid illuminated sections
of their environment which they are normally in-
terested to explore. Anxiety is related to fear but
considered a secondary emotion that occurs after
the stressing signal  has disappeared.  Thus,  the
probability of selecting specific behaviors, in this
case  exploration  of  illuminated  places,  is  de-
creased. This avoidance behavior could be abol-
ished after application of drugs that are known to
have anxiolytic effects in mammals.  Applied to
reaCog, these results could be interpreted in the
following way. Emotion-like states would not only
influence the global WTA net, but also thresholds
of local, lower level WTA networks that are re-
sponsible for switching between different proced-
ures.
Another interesting case has been reported
by Yang et al. (2014) in Drosophila. These anim-
als learnt that various behaviours selected in try-
ing to avoid a problem, in this case escape from a
heated  ground,  were  not  successful.  As  a  con-
sequence, they ended up in a state of passivity.
This result has been discussed as an example of
“learned helplessness”, which is considered an an-
imal model of depression. In our framework, this
could simply be realized by freezing activity in
the  Spreading  Activation  Layer  network  that
provides input to the WTA net (section 4).
Concerning  the  second  issue,  “biological
plausibility”, we fully support Gutknecht’s per-
spective and have only a minor aside. Applica-
tion  of  non-spiking  neurons  is  not  necessarily
biologically  implausible.  Rather,  non-spiking
neurons do exist in invertebrate and in verteb-
rate  brains.  They  play  important  functional
roles, but are generally less well-known, mainly
Cruse, H. & Schilling, M. (2015). The Bottom-Up Approach: Benefits and Limits - A Reply to Aaron Gutknecht.
In T. Metzinger & J. M. Windt (Eds). Open MIND: 9(R). Frankfurt am Main: MIND Group. doi: 10.15502/9783958570931 2 | 3
www.open-mind.net
because investigating them involves methodolo-
gical  problems  that  are  more  difficult  than
those  of  spiking  neurons.  The  third  issue,
“transparency”,  addresses  the  view  that  the
bottom-up strategy may eventually exhaust its
potential when the complexity of the system is
further  increased.  Although  we  agree  with
Gutknecht here, we would like to add that the
bottom-up approach still  bears  the  advantage
that, as the details of such a system are known,
its  properties  can  be  thoroughly  analyzed  by
physical  and/or  mathematical  methods.  This
ability, of course, does not guarantee that one
will  find answers in  such a hypothetical  case,
but there are various methods available to ad-
dress  such questions.  Further,  we believe that
the  problem of  lacking  transparency may not
happen to occur too often. This belief is sup-
ported  by  the  observation  that  already  our
simple system, reaCog,  appears  to be able  to
reach integration levels characterized by terms
such as intention, volition and consciousness. 
4 What should be done next?
Aaron Gutknecht closes his comments by con-
sidering future aspects. Again, we agree with his
recommendations and have, partly, indeed star-
ted with two of the aspects addressed. We ap-
plied the internal model in a cooperative scen-
ario in which the visual impression of another
agent  performing an action  was mapped onto
the system’s own internal body model. In this
way the internal model was driven by the visual
input  and the  internal  model  reenacted  what
the other agent was doing. This mapping allows
one to connect the experiences of somebody else
to one’s own action repertoire as one steps into
the shoes of the other (Schilling 2011; see also
Gallese &  Cuccio this collection).  Second,  as
mentioned in the main text, shared circuits are
required for an agent to represent the action of
a partner (Cruse & Schilling this collection, fig-
ure 9). In order to allow for ToM, an additional
separate representation of the partner’s memory
is required (figure 10). To be able to apply a su-
permodel  (or  we-model,  Tomasello 2009),  a
more complex model is required (see  Cruse &
Schilling 2011, figure 6).
5 Conclusion
The bottom-up approach advocated here to un-
derstand  higher-level  phenomena  may  be  con-
sidered a non-Platonic approach that aims to con-
struct artificial, but strongly biologically inspired
systems. These systems should be able to simu-
late complex behavioral tasks, but do so by ap-
plication  of  simple  elements,  artificial  neurons,
and a simple decentralized neuronal architecture.
If successful one could then study whether more
abstract  concepts  introduced  in  psychology  or
philosophy, for example, could sensibly be applied
to such a system. We claim to have shown an ex-
ample supporting this approach.
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Cruse, H. & Schilling, M. (2015). The Bottom-Up Approach: Benefits and Limits - A Reply to Aaron Gutknecht.
In T. Metzinger & J. M. Windt (Eds). Open MIND: 9(R). Frankfurt am Main: MIND Group. doi: 10.15502/9783958570931 3 | 3


The bottom-up approach : benefits and limits ; a reply to Aaron Gutknecht

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The bottom-up approach : benefits and limits ; a reply to Aaron Gutknecht

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