Fig. 3.

Message mutation. (A) Evolution of the normalized number of positive statements $n_p^{+}$ (blue), negative statements $n_p^{-}$ (red), and total statements $n_p$ (gray) over the chain. Fit lines are power functions $f\left(x\right)=x^{-e}$, where the exponent e equals 0.96, 0.62, and 0.57 for $n_p^{+}$, $n_p^{-}$, and $n_p$, respectively. (Inset) Average proportion of positive statements (${\omega }_p^{+}$, in blue) and negative statements (${\omega }_p^{-}$, in red) along the chains. (B) Distribution of the filtering coefficients $k_p^{+}$ and $k_p^{-}$ for all subjects. The distribution of $k_p^{-}$ is significantly higher than the distribution of $k_p^{+}$ (P < 0.001). (C) Individual profiles measured as the pair {$k_p^{+}$,$k_p^{-}$}. Each point represents one experimental subject. Individuals with $k_p^{+}\approx k_p^{-}$ have a neutral effect on the message, whereas individuals with $k_p^{-}$ > $k_p^{+}$ (respectively $k_p^{+}$ > $k_p^{-}$) tend to make the message more alarming (respectively more reassuring).