Memetics and Neural Models of Conspiracy Theories

TL;DR

This paper explores how rapid changes in neural plasticity can lead to the formation of conspiracy theories as stable attractor states in brain models, providing a neurodynamic explanation for distorted beliefs.

Contribution

It introduces a neurodynamic model demonstrating how high plasticity and rapid plasticity decrease can produce memes with large attractor basins, explaining conspiracy meme formation.

Findings

High neuroplasticity leads to large attractor basins in neural networks.

Memes with distorted associations emerge from rapid plasticity changes.

System-level models can simulate the creation of conspiracy beliefs.

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.