On Magical Realism, Memes, and Good Addictions

Stories and information tend to stay in the minds of people for as long as their contents are something that could be informally referred to as “sticky”. Memes, which are certain especially infectious bundles of information, spread successfully when their concepts are sticky enough to stay in active memory and bear repeating. The memetic epidemiology of stories is a long-studied practice in several fields, from the expected areas of anthropology and sociology to the more startling realms of religion. There are reasons for stories to stay with us, to be loved, to be carried and shared. Previous posts on this blog have covered such attracting factors such as empathizing with characters and a search for truth; this post will consider a third, stranger reason. 

Magical Realism is extremely satisfying to its audiences because people are addicted to patterns. People are wired to see random information, consider how it relates to itself and larger contexts, and then draw conclusions and information based on the patterns they see in the noise and static. We literally can’t stop attempting to find patterns in everything we see— sometimes, we even see patterns where there are none! This is an experience called apophenia. On balance, though, this useful trait is responsible for creativity and much of human communication. 

This need to pattern-match gives people an incurable metaphor addiction. Magical Realism, a genre that exists entirely as an experience in metaphor to describe reality in ways that are difficult without these patterns, is therefore understandably popular. While High Fantasy is a way to escape to strange worlds, and Science Fiction speculates on what the world may become, Magical Realism uses its genre conventions to show something harsh and true. 

Research undertaken by Su, Gomez, and Bowman in their article “Analysing Neurobiological Models Using Communicating Automata” (2014), suggests that complex biological systems, like human nervous systems, operate on several levels for the problems life throws at them. There are many different ways to approach these multiple-perspective systems, but “probably the longest standing and most extensively investigated question is how to relate descriptions at different levels of abstraction” (2014). There are more abstract levels where the system, whether an artificial intelligence (AI) or human, processes what has to be done, and progressing levels of detail sorting and parsing parts of the issue to understand how to accomplish the task. In order to understand how to make better and faster AIs, the math behind how humans pattern-match has been extensively studied. Metaphors help our brains to do cool, intense things the best computers can only currently attempt; they relate abstractions to detail in our lives. 

It follows, somewhat unsurprisingly, that being able to understand metaphors and match patterns is actually wired to be satisfying on a chemical level . In Ramachandran and Hirstein’s article, “The Science of Art: A Neurological Theory of Aesthetic  Experience”,  the creation of metaphor is described as a “mental tunnel” connecting seemingly disparate subjects in deep and meaningful ways, contributing to a greater understanding of the subjects than the observer would find with each separately. “Although it is uncertain whether the reason for this mechanism is for effective communication or purely cognitive,” Ramachandran and Hirstein report, “the discovery of similarities between superficially dissimilar events leads to activation of the limbic system to create a rewarding process” (1999). They went on to cite studies of individuals suffering from the inability to integrate certain levels of detail mentally. Their pleasure responses to images such as familiar faces were stunted or absent— though the people were recognized, the participants’ inability to parse the details of the faces and make connections from their memories took away from the experience by significant and measurable amounts.

While it may seem that Magical Realism and Science Fiction work at cross-purposes, there is an overlap in genre that results in fantastic literature! One of the most treasured works of Science Fiction cannon, William Gibson’s Neuromancer, has strong elements of Magical Realism. Gibson, who predicted the rise of the world wide web with stunning accuracy, is the epitome of the function of Science Fiction to project possibilities into the future, and the speculation is beautifully complex. Gibson also used surrealistic and complex visual representations of virtual realities and their gestalt with the “real world” in a way that presents an excellent example of Magical Realism. In Karen Tei Yamashita’s essay “Virtual Reality vs. Magic Reality” (as cited in Kamioka, 1998), Gibson is referred, “along with main-stream writers like John Irving and Kurt Vonnegut, as one of the writers who adopt the technique of Magic Realism in order to represent the “virtual” First World.” This experience is vivid, wonderful, and a must-read for any reader who is the least bit interested in Science Fiction.

For readers who enjoy this surprising mixture of clashing genres, Eyeless in Gaza by Aldous Huxley is a sturdy recommendation. Written in 1936 after his magnum opus, Brave New World, Eyeless in Gaza continues the fusion of Magical Realism’s bald statements of the fantastic with Science Fiction’s wistful projections of the same. 

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Link List:

William Gibson:

Neuromancer

Aldous Huxley:

Brave New World

Eyeless in Gaza

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Resources:

Please comment if any links are broken!

Kanioka, Nobu (1998). Cyberpunk Revisited: William Gibson's Neuromancer and      the "Multimedia Revolution". The Japanese Journal of American Studies, 9.        Retrieved from
     http://sv121.wadax.ne.jp/~jaas-gr-jp/jjas/PDF/1998/No.09-053.pdf

Ramachandran, V.S. & Hirstein, W. (1999). The Science of Art: A Neurological        Theory of Aesthetic Experience. Journal of Consciousness Studies, 6. 
     Retrieved from

     http://www.imprint.co.uk/rama/art.pdf

 Su, Li; Gomez, Rodolfo ; and Bowman, Howard (2014). Analysing         
     Neurobiological Models Using Communicating Automata. Formal Aspects of 
     Computing, 26, 1169-1204.

     doi: 10.1007/s00165-014-0294-y