Fig. 4
Possible computational mechanisms that could explain increased breathlessness despite normal breathing patterns. (A) Different internal models for breathlessness perception and breathing patterns. The same sensory input and its reliability is assumed for breathing patterns and breathlessness perception, represented as the same likelihood function. However, for breathlessness a higher and more reliable prior (i.e., higher mean value and less variance/higher precision) is assumed. According to Bayes theorem, the posterior (blue curve) is a precision-weighted combination of likelihood and prior. Thus, due to the higher and more reliable prior for breathlessness perception than breathing patterns, the resulting posterior is shifted towards higher breathlessness levels, even though sensory input (likelihood) is the same as for breathing patterns. The inferred bodily state (posterior) is thus closer to the actual sensory input in breathing patterns and closer to the prior in symptom perception. While the respiratory state is correctly inferred for breathing patterns, an incorrect internal model, e.g., with a strong weighting of erroneous priors, could lead to strong breathlessness despite an intact respiratory body state. (B) Alternatively, the same internal model could be used, yielding the same posterior (top, blue curve) but different cost-functions could be applied to decide on the action that should be taken. Applying a cost function mathematically corresponds to a convolution (⊗) of the posterior with the cost function. This yields the expected costs (solid line for breathing patterns, dotted line for reported breathlessness) for each possible action and allows to choose the action associated with minimum costs. An action can either be a specific breathing pattern or a symptom report. While breathing patterns and symptoms are outcomes that can be consciously perceived, the decision process involving the application of cost functions (B), or the formation of priors (A), is happening subconsciously.
