Figure 2.

Neurocomputational framework. Representation of the current approach deployed at both the cognitive and physiological levels to address the automatic adaptive learning at play during auditory processing, and to disambiguate the mapping of precision weights and prediction errors onto physiological responses. First-level analysis (upper panel): the expected perceptual learning of the oddball rule is first tested at the computational level (left) as well as its physiological implementation within a fronto-temporal hierarchy (right). Second-level analysis (lower panel): adaptation of this learning to the manipulation of predictability is then tested through the examination of model parameters for each condition (UC, PC), both at the computational (left) and physiological (right) levels. Gray boxes highlight the specific differences that were tested. Different learning time constants τ (left) would support hierarchical learning with opposite effects on precision weighting and prediction errors, that are testable (hence separable) using DCM. First-level and second-level rules are described in Figure 1A. Dynamic models: pl (perceptual learning), dcm (dynamic causal model); cortical sources: HG (Heschl's gyrus), PP (planum polare), IFG (inferior frontal gyrus), SF (superior frontal); experimental contexts: PC/pc (predictable context), UC/uc (unpredictable context). D: deviant. Forward/self: DCM forward/self-inhibition connection strength parameters.