Connectionists: Neural Models of Conspiracy Theories.

[Wlodzislaw Duch]

Dear Connectionists, 
 
research on conspiracy theories has been vigorously pursued by historians, philosophers, psychologist, sociologists or political scientists (Routledge has a whole series of books on conspiracy theories), but our understanding of the brain mechanisms is completely lacking. European Union sponsors cooperation within “Comparative Analysis of Conspiracy Theories” (COMPACT) COST action program. Unfortunately there are no members of this action concerned with brain mechanisms behind conspiracy theories. Even discussion panel “Are We Hard-Wired to Believe in Conspiracy Theories?” during COMPACT meeting did not included neuroscience experts. This is perhaps not surprising, as not much has been written on this subject in neuroscience literature.  
I thought that it is time to change it, investigate  deeper reasons related to brain processes that are behind believes in conspiracy theories.
A few years ago I have made a model of learning and associative memory distortions, and have now updated it. This is now a very important subject so perhaps time is ripe to work on it also from the neural modeling perspective. 
 
Memetics and Neural Models of Conspiracy Theories. 
 
Abstract: 
Conspiracy theories, or in general seriously distorted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than science. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal characteristics of the incoming information and the plasticity of the network, memory may self-organize creating memes with large attractor basins, linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins, distorting input pattern representations in associative memory. Such system-level models may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics.
 
 <https://arxiv.org/abs/1508.04561> https://arxiv.org/abs/1508.04561    see the updated version v1
 
With best regards,  
Włodzisław Duch

Fellow,  <https://www.inns.org/> International Neural Network Society,  Past President,  <https://e-nns.org/> European Neural Network Society
Head of  <https://damsi.umk.pl/en/centre/neuroinformatics-and-artificial-intelligence/> Neuroinformatics and Artificial Intelligence Group, Center of Excellence, and  <https://icnt.umk.pl/en/research/zifi-team/neurocognitive-laboratory/> Neurocognitive Laboratory,  <https://icnt.umk.pl/en/research/zifi-team/neurocognitive-laboratory/> CMIT 
 <https://www.umk.pl/en/> Nicolaus Copernicus University, Poland
 <http://www.google.com/search?q=Wlodzislaw+Duch> Google: Wlodzislaw Duch
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