Patternism

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The Patternist Philosophy of Mind

The patternist philosophy of mind is a general approach to thinking about intelligent systems which I (Ben Goertzel) have developed during the last two decades (in a series of publications beginning in 1991, most recently The Hidden Pattern). It is based on the very simple premise that mind is made of pattern.

Now, pattern as the basis of mind is not in itself is a very novel idea — this concept is present, for instance, in the 19th-century philosophy of Charles Peirce (1935), in the writings of contemporary philosopher Daniel Dennett (1991), in Benjamin Whorf's (1964) linguistic philosophy and Gregory Bateson's (1979) systems theory of mind and nature. Bateson spoke of the "Metapattern": that it is pattern which connects. In a series of prior writings (Goertzel, 1993, 1993a, 1994, 1997, 2001) and most recently in the philosophical treatise The Hidden Pattern (2006), I have sought to pursue this theme more thoroughly than has been done before, and to articulate in detail how various aspects of human mind and mind in general can be well-understood by explicitly adopting a patternist perspective. This work includes attempts to formally ground the notion of pattern in mathematics such as algorithmic information theory (Chaitin, 1986; Solomonoff, 1964, 1978) and probability theory, beginning from the conceptual notion that a pattern is a representation as something simpler and then utilizing appropriate mathematical concepts of representation and simplicity. In prior writings I have used the term psynet model of mind to refer to the application of patternist philosophy to cognitive theory, but I have now deprecated that term as it seemed to introduce more confusion than clarification.

In the patternist perspective, the mind of an intelligent system is conceived as the set of patterns in that system, and the set of patterns emergent between that system and other systems with which it interacts. The latter clause means that the patternist perspective is inclusive of notions of distributed intelligence (Hutchins, 1996). Intelligence is conceived, similarly to in Marcus Hutter's (2005) recent work, as the ability to achieve complex goals in complex environments; where complexity itself may be defined as the possession of a rich variety of patterns. A mind is thus a collection of patterns that is associated with a persistent dynamical process that achieves highly-patterned goals in highly-patterned environments.

An additional hypothesis made within the patternist philosophy of mind is that reflection is critical to intelligence. This lets us conceive of an intelligent system as a dynamical system that recognizes patterns in its environment and itself, as part of its quest to achieve complex goals.

While this approach is quite general, it is not vacuous; it gives a particular structure to the tasks of analyzing and synthesizing intelligent systems. About any would-be intelligent system, we are led to ask questions such as:

  • How are patterns represented in the system? That is, how does the underlying infrastructure of the system give rise to the displaying of a particular pattern in the system's behavior?
  • What kinds of patterns are most compactly represented within the system?
  • What kinds of patterns are most simply learned?
  • What learning processes are utilized for recognizing patterns?
  • What mechanisms are used to give the system the ability to introspect (so that it can recognize patterns in itself?

Now, these same sorts of questions could be asked if one substituted the word pattern with other words like knowledge or information. However, I have found that asking these questions in the context of pattern leads to more productive answers, because the concept of pattern ties in very nicely with the details of various existing formalisms and algorithms for knowledge representation and learning.

Pursuing the patternist philosophy in detail leads to a variety of particular hypotheses and conclusions. Following from the view of intelligence in terms of achieving complex goals in complex environments, comes a view in which the dynamics of a cognitive system are understood to be governed by two main forces: self-organization and goal-oriented behavior. And more specifically, based on these concepts several primary dynamical principles may be posited, including:

  • Association. Patterns, when given attention, spread some of this attention to other patterns that they have previously been associated with in some way. Furthermore, there is Peirce's law of mind (Peirce, 1935), which could be paraphrased in modern terms as stating that the mind is an associative memory network, whose dynamics dictate that every idea in the memory is an active agent, continually acting on those ideas with which the memory associates it.
  • Differential attention allocation. Patterns that have been valuable for goal-achievement are given more attention, and are encouraged to participate in giving rise to new patterns.
  • Pattern creation. Patterns that have been valuable for goal-achievement are mutated and combined with each other to yield new patterns.
  • Credit Assignment. Habitual patterns in the system that are found valuable for goal-achievement are explicitly reinforced and made more habitual.

Next, for a variety of reasons it becomes appealing to hypothesize that the network of patterns in an intelligent system must give rise to the following large-scale emergent structures

  • Hierarchical network. Patterns are habitually in relations of control over other patterns that represent more specialized aspects of themselves.
  • Heterarchical network. The system retains a memory of which patterns have previously been associated with each other in any way.
  • Dual network. Hierarchical and heterarchical structures are combined, with the dynamics of the two structures working together harmoniously.
  • Self structure. A portion of the network of patterns forms into an approximate image of the overall network of patterns.

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