Identification aims to identify a specific stimulus from a finite stimulus dataset. Two main applications can be distinguished, namely identification and reconstruction. These brain reading or decoding approaches attempt to make inverse inferences from brain activity to the stimulus space. A decoding stage may be used in combination with encoding, adding practical applications such as the so-called “brain reading” approach 6. For example, using the same visual stimulus set and brain data, the anterior occipital cortex was better modelled by a semantic model as compared to a visual model that in contrast showed highest accuracies in early visual cortices 5. Encoding models enable the explicit assessment of different theoretic models or representations in the brain. These previous studies established both the theoretical and practical advantages of using rich stimulus descriptions for analysing functional magnetic resonance imaging (fMRI) data. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.Įncoding and decoding models were first introduced in the visual and semantic domains 1, 2, 3, 4. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. Encoding models can reveal and decode neural representations in the visual and semantic domains.
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