We report non-hierarchical precision (PPV) on the results against both gold standards.
We recall various adaptation problems and we introduce modelling techniques and some solutions within hierarchical context considering precision of the interfaces, their layering and flexibility.
This scenario is consistent with attentional exacerbation of a hierarchical imbalance in precision and the dissociation between the representation of a symptom (intermediate level) and the intentional context in which that symptom is perceived (higher level).
We have relied on the NCBI hierarchical taxonomy, but increased precision might eventually be obtained by using a system containing many additional levels.
For hierarchical groups, we calculated precision and sensitivity for the most specific groups and for trees that were reconstructed according to the hierarchy.
The Bayesian perspective provides a unifying account of these failures to represent uncertainty and the key role of attention this is because both rest on optimizing the precision in hierarchical models.
One limitation of the hierarchical modeling is loss in precision in these estimates, because the last regression (Equation 5) has only 90 observations.
Crucially, the delicate balance of precision at different hierarchical levels has a profound effect on veridical inference – and may also offer a formal understanding of false inference in psychopathology (Adams, Stephan, Brown, Frith, & Friston, 2013; Fletcher & Frith, 2009; Friston, 2013).
These vary according to the site (hierarchical level) of changes in precision, and include: The emergence of quasi-periodic attractors, manifested as tremor or other repetitive stereotyped movements (Friston et al. 2010 ).
