The paper “Noise, fake news, and tenacious Bayesians“, with sole author Dorje Brody, has been published in Frontiers in Psychology. It is published open access (link here). The paper addresses the question “How can one best model dynamical systems (like human behaviour) driven by the unravelling of noisy information?” The paper shows that the answer is given by the theory of signal detection: by modelling the flow of noisy information directly and applying techniques of optimal signal processing, it is possible to describe and predict dynamical behaviours of such systems. Because the model input of this approach is the specification of the flow of information, it enables the representation of (i) reliable information, (ii) noise, and (iii) disinformation, in a unified framework. It is then possible to quantify the impact of information control, which is not only useful for marketing but also for understanding the impacts of disinformation. It is shown in the paper that if a decision maker assigns an exceptionally high weight to one of the alternative realities, then under the Bayesian logic their perception hardly changes in time, even if evidences presented indicate that this alternative corresponds to a false reality. Thus, in contrast to what is often argued in the psychology literature, confirmation bias need not be entirely incompatible with Bayesian updating. In fact, the approach is sufficiently versatile to allow for the simulation of a false reality, or of the behaviour of a pathological liar. An important feature of Bayesian logic, previously unknown in the literature, is unravelled to show that the behaviour of a pathological liar is necessarily erratic, jumping from one false reality to another. By observing the role played by noise in other areas of natural sciences, where noise is used to excite the system away from false attractors, a new approach to tackle the dark forces of fake news is proposed.