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Fluid Biosemiotic Mechanisms Underlie Subconscious Habits

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Abstract

Although research into the biosemiotic mechanisms underlying the purposeful behavior of brainless living systems is extensive, researchers have not adequately described biosemiosis among neurons. As the conscious use of signs is well-covered by the various fields of semiotics, we focus on subconscious sign action. Subconscious semiotic habits, both functional and dysfunctional, may be created and reinforced in the brain not necessarily in a logical manner and not necessarily through repeated reinforcement. We review literature that suggests hypnosis may be effective in changing subconscious dysfunctional habits, and we offer a biosemiotic framework for understanding these results. If it has been difficult to evaluate any psychological approach, including hypnosis, this may be because contemporary neuroscience lacks a theory of the sign. We argue that understanding the fluid nature of representation in biological organisms is prerequisite to understanding the nature of the subconscious and may lead to more effective of treatments for dysfunctional habits developed through personal experience or culture.

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Notes

  1. In addition to the term “semiotic habit,” which can only be applied to the actions and responses of living or purposeful entities, we also use various less restricted and less precise synonyms: procedure, program, routine, algorithm and machine—in order to engage disciplines that use these more familiar terms.

  2. Elsewhere (Alexander 2009: 97; Alexander 2011: 30, 114) makes a distinction between different kinds of selection: statistical selection for most numerous, which gives us average regularity and homogeneity as well as, ultimately, the Second Law of Thermodynamics; formal selection for similarity and proximity, which give us self-organized patterns; and functional selection for reproductive fitness, which gives us evolution. Here we refer to formal selection as “self-organization” to avoid confusion with the other types of selection.

  3. As Prigogine and Nicolis (1977), Salthe (1993), Schneider and Sagan (2005), Deacon (2011) and others have noted, complex adaptive systems (CAS) reduce gradients more efficiently than strictly mechanical systems.

  4. Computer scientists are in the experimental stages of developing reaction-diffusion computing systems that self-organize virtual circuits. These may prove to be more life-like and develop true intelligence at some point. Currently however, such systems aren’t quite as smart as slime mold. These scientists have actually started using slime mold for some of their experiments. See Adamatzky et al. (2005).

  5. The illustration is not meant to accurately represent starling behavior; the purpose is to give the reader a generic idea of a computational machine.

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Authors and Affiliations

  1. Dactyl Foundation, 64 Grand Street, New York, NY, 10013, USA

    V. N. Alexander

  2. New York Council for the Humanities, 150 Broadway, New York, NY, 10038, USA

    V. N. Alexander

  3. The Flow Center, 5310 Harvest Hill Rd #266, Dallas, TX, 75230, USA

    Valerie Grimes

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  1. V. N. Alexander
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Alexander, V.N., Grimes, V. Fluid Biosemiotic Mechanisms Underlie Subconscious Habits. Biosemiotics 10, 337–353 (2017). https://doi.org/10.1007/s12304-017-9298-3

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