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8/7/2019 Autonomous Systems and Their Relevance to the Theoretical Reduction of Lolar-Level Psychology to Dynamic Proc…
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Sam Peterson
2/25/2010
Autonomous Systems and Their Relevance to the Theoretical Reduction of Molar-Level
Psychology to Dynamic Processes
The prevalent perspective characterizing biological research has been one of reductive
mechanism. However, it would seem that examining biological processes in the relatively static
and isolated manner which is typical of this perspective is starting to become a thing of the past.
In a forthcoming paper entitled “Complex Biological Mechanisms: Cyclic, Oscillatory, and
Autonomous”, William Bechtel and Adele Abrahamsen articulate the features of a new way of
considering and studying biological phenomena, one which builds off mechanism but adds
crucial temporal and interactive aspects to the mix. This resulting augmentation of the existing
mechanistic paradigm is referred to as “dynamic mechanism”, and is thought to be a more
precise representation of the true manner that biological processes operate by and, as a direct
consequence, more accurate a means of explaining these processes (Bechtel & Abrahamsen, pg
4). The conceptual framework offered by dynamic mechanism, since appropriate for any level of
biological operation, can be used to consider and speculate the manner in which natural cognitive
phenomena operate. This point is iterated by a chapter of Paul Churchland’s 2007 book
Neurophilosophy at Work , entitled “Functionalism at Forty.” While critiquing the framework of
classical functionalist thought, Churchland discusses the possible options for the neurobiological
reduction of the molar-level view of psychology espoused by folk psychology, the manifestation
of functionalist thought in general cognitive studies (Churchland, 2007, pg 28). A very attractive
means of reduction, Churchland posits, is that offered by the dynamic profile outlined by
theoretical biologists like Bechtel and Abrahamsen, but applied to cognitive processing in
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addition to the processes typically studied by most biologists (pg 30). This application allows
Churchland to support his claims regarding the nature of cognitive activity, and what presence it
has in cognitively-active creatures. With the necessary framework laid out by one text and the
implementation of that framework for the purpose of explaining cognitive phenomena in the
other, a unified and cogent account of the dynamic and interactive properties of the cognitive
mechanism emerges.
Bechtel and Abrahamsen’s account of dynamic mechanism is informed by several
properties of actual biological systems which are not addressed adequately in previous
experimental and theoretical explanations. The basic mechanistic explanatory program consists
of identifying the components of a mechanism, ascertaining the operations performed by each
component, and an account of how the components and their purposes fulfill what the concept so
examined and dissected actually does in a biological context (pg 2). This is perfectly legitimate
in and of itself, the authors state, except that this way of thinking tends to produce explanations
that represent inadequately any non-linear and interactive characteristics present within the
complex mechanism examined (pg 2). Acknowledgement and inclusion of these complex,
temporally dynamic, and interactively orchestrated factors into preexisting mechanistic
approaches will do much to extend the explanatory accuracy and power they already possesses,
offering a more genuine representation of the reality of these biological system by including, in
real time, the full extent of their relations with other processes and the continuous, cyclical
organization common to many of these mechanisms, particularly oscillatory processes like
circadian rhythms (pg 3).
Globally, systems that include the crucial presence of dynamic circular and oscillatory
processes are referred to by Bechtel and Abrahamsen as active “autonomous systems”, in which
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these processes provide a means of internal regulation of the organism in question (pg 3).
Autonomous systems are able to operate in a manner which repair and maintain the integrity of
their own form (pg 3, 4). Quoting from Ruiz-Mirazo & Moreno (2004), the authors define the
autonomy of a system as consisting of a means of handling the flux of matter and free energy in
a manner which enables the system to control and adjust its own self-constructive and repair
processes as well as its exchange relations with its environment (pg 19). This is in the interest of
retaining the system in a non-equilibrium state, avoiding death. Living organism systems also
possess metabolic mechanisms, which provide a means of harnessing energy and modifying its
own physical makeup (pg 20).
Cyclic and oscillatory processes are key mechanisms for avoiding death and pursuing the
continuation of their upkeep and development, orchestrating and synchronizing the various
systems in the necessary sequences and dynamics for the most effective means of avoiding the
state of biological equilibrium. Circular, oscillatory regulation can occur through coupling and
the resultant synchronicity of processes, a result of the energetic effect of one process on
surrounding ones and the alignment that might eventually occur, as well as the integration of
feedback loops into operative sequences (pg 21). These aspects of regulatory control have been
overlooked by many researchers, either obscured by the use of summary statistics (means and
standard deviations, for example, which cannot represent cyclic or oscillatory phenomena due to
their equalization of data into convenient, limited numeric values) or misclassified as “data
noise” (pg 10). However, new mathematical modeling procedures provide a means for
oscillations and cyclic processes and their abilities to be examined indirectly, and their purposes
divined. This, combined a more generally pattern-observant program of investigation, is integral
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to understanding dynamic, interactive processes and how they constitute the mechanisms with
which autonomous systems maintain their non-equilibrated status.
The relation of this perspective on biological dynamic mechanism to cognitive activity is
explicated by Churchland, tying these concepts together in a reductive fashion to explain how
cognition operates according to these same principles. Churchland’s intent with the text, in a
general sense, is to refute the positions held by traditional functionalism-fueled folk psychology,
including and insistence on a molar-level view of cognition. This molar-level depiction of
cognition involves rules and classifications abstracted at far more general a level than that
approached by neuroscience and biology; the reduction of this view of cognition to the
phenomena observed by the latter disciplines is distinctly denied and decried by functionalism
(pg 24). To deflate this functionalist point, Churchland presents cognition, in reduced form, as a
dynamic biological process, realized here in but one form (pg 28). Laying out the now familiar
framework which delineates living organisms as non-equilibrium systems capable of and
directed at the acquisition and exploitation of free energy and extraneous matter for the purposes
of growth, repair, reproduction and the general avoidance of reaching thermodynamic
equilibrium, Churchland additionally makes note of the ways in the development of organisms is
affected by a “complexity-inducing ambient energetic waterfall” (pg 29), which spurs particular
structural and operational changes based on how energy is most available to an organism (pg 28,
29).
Cognition, on a level far below the generalities and abstractions of a molar-level
treatment of the subject, is but another case in which the non-equilibrium, energetically-dynamic
framework of biological processes is present (pg 30). Churchland refers to cognitive creatures, in
light of this framework, as “extrasomatic information multipliers”, or, more simply, “epistemic
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engines” (pg 30). Energy-flow takes the form of information, wherein an organism takes in and
utilizes information regarding the environment and integrating it into the process of self-
maintenance and alteration, allowing it to more effectively exploit that environment for its own
regulatory and metabolic purposes. Sensory input is the initial low-entropy form of energy,
which, after undergoing the complex processes of cognition and storing the information
contained therein, is simply dispelled in the form of raw heat (pg 30). This information is
processed as neural activation patterns, vectors, and transformations, with the end form of gained
knowledge as a change in the connectivity and sensitivity of synaptic linkages (pg 32). This view
of neural population coding as a means of semantic representation, along with the massively-
parallel action of the brain’s overall processing, is consistent with modern neuroscience, and has
been, according to Churchland, accepted as the most salient theory regarding the nature of
thought and meaning-laden mental representation. This incorporation of the activation-vector
model of brain structure and operation into the biological framework of non-equilibrium, energy-
obtaining dynamic processing provides a coherent version of the biological reduction of
cognitive activity from a molar-level in a wide variety of realizations, which was in fact used by
Churchland to falsify one of the functionalist claims (pg 32).
This notion of realizability is addressed by Churchland in the contexts of individual
differences and artificial intelligence research, and is pointed to as a unifying characteristic of
cognitive creatures (along with the universal framework of thermodynamic and information-
processing/generating activity). Different organisms, while similar in their brain and neural
activation patterns, are nonetheless unique in that regard; however, it is the present similarities
and adherence to a massively-parallel cognitive engine that allow cognitive computing to occur
in the same fashion between different realizations of cognitive creatures (pg 32). Research in
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References
Bechtel, W., & Abrahamsen, A. (as yet unpublished). Complex biological mechanisms: Cyclic,
oscillatory, and autonomous. To appear in Handbook of the Philosophy of Science, 10.
Churchland, P. (2007). Neurophilosophy at work . New York, NY: Cambridge University Press.