text: Finally, there is a third essential element that determines the 
  accuracy of the inference: precision control. In predictive cod- 
  ing, the influence of prediction errors on inference is weighted 
  by their precision, i.e. inverse variance (pink triangles in Fig. 1). 
  This weighting would ensure that very reliable sensations have 
  more impact on inference than unreliable sensations. However, 
  precision (like all other variables) needs to be estimated, but this 
  might be incorrect. An incorrect setting of precisions has been 
  associated with various psychopathological conditions, such as 
  psychosis (Adams et al. 2013), eating disorders (Barca and Pezzulo 
  2020), panic disorders (Maisto et al. 2021), symptom perception 
  (Pezzulo et al. 2019), depression (Barrett et al. 2016), and many 
  others (Khalsa et al. 2018, Paulus et al. 2019). Intuitively, assign- 
  ing excessively high weight to noisy sensations yields an incorrect  These simple examples of Bayesian inference illustrate two 
  things. First, sensory observations that are unpredictable given 