Computational modelling of behavioural data
For analysis of the behavioural data from the learning task, we modelled the trial-by-trial changes in participants’ choices with the HGFThe first level of the HGF simply represents the occurrence of the auditory and visual stimuli (that is, perception of the ‘stimulus category’). The second level captures the learning of the conditional probabilities of the visual stimulus given the auditory cue. This learning involves the formation of a sensory prediction error (the ‘low-level choice prediction error’ in the HGF), which is then weighted with an adaptive learning rate (the ‘precision weight’ at the second level in the HGF) to construct an adaptive prediction error (the ‘low-level precision-weighted choice prediction error’ in the HGF) that, in turn, drives behavioural updating. The third level of the HGF tracks the ‘log volatility of the environment’; that is, it reflects the rate of change of the contingencies and therefore the need to decrease or increase the rate of learning in the lower level (for a formal derivation, see Supplemental Experimental Procedures, Section A, by Iglesias et al.In this study, our main hypothesis rests on the assumption that metabolic signals from the periphery control DA neuron function in the mesoaccumbens pathway and therefore will modulate the learning of cue–outcome associations. Thus, based on mounting evidence for adaptive prediction error encoding in the ventral striatum and midbrainFor the analysis, we used the implementation of the HGF as introduced by Iglesias et al.The sensory prediction error about the visual outcome in a given trial, which corresponds to the ‘low-level choice prediction error’ in the HGF (see Supplemental Experimental Procedures, Section B, by Iglesias et al.The adaptive learning rate, which corresponds to the ‘precision weight’ at the second level in the HGFThe adaptive prediction error, that is, the product of the sensory prediction error (i) and the adaptive learning rate (ii). The adaptive prediction error relates to the ‘precision-weighted choice prediction error’ about the visual outcome in the HGF and was used here as parametric modulator for the subsequent fMRI analysis.Note that in the original HGF formulation, the second-level update equation capturing associative learning relies on (‘low-level’) prediction errors, which are encoded in the perceptual reference frame and are unsigned. However, prediction errors can be seamlessly re-encoded in the action space, yielding a signed (‘low-level choice’) prediction error, positive when the participant made a correct choice and negative when the participant made an incorrect choice