Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults

Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited insufficient older particularly memory-impaired individuals. A parametric modulation encoding-related with confidence could overcome this limitation. Here, we applied cross-validated Bayesian model selection (cvBMS) for first-level fMRI models visual subsequent young (18–35 years) (51–80 adults. Nested cvBMS revealed that models, especially non-linear transformations ratings, outperformed explaining signal variance encoding. We thereby provide framework improving modeling applying