Distinct representations of numerical and non-numerical order in the human intraparietal sulcus revealed by multivariate pattern recognition

Neuroimaging studies of numerical cognition have pointed to the horizontal segment of the intraparietal sulcus (hIPS) as the neural correlate of numerical representations in humans. However, the specificity of hIPS for numbers remains controversial. For example, its activation during numerical comparison cannot be distinguished from activation during ordinal judgments on non-numerical sequences such as letters (Fias et al., 2007, J. Neuroscience). Based on the hypothesis that the fine-grained distinction between representations of numerical vs. letter order in hIPS might simply be invisible to conventional fMRI data analysis, we used support vector machines (SVM) to reanalyse the data of Fias et al. (2007). We show that classifiers trained on hIPS voxels can discriminate between number comparison and letter comparison, even though the two tasks produce the same metric of behaviour. Voxels discriminating between the two conditions were consistent across subjects and contribution analysis revealed maps of distinct sets of voxels implicated in the processing of numerical vs. alphabetical order in bilateral hIPS. These results reconcile the neuroimaging data with the neuropsychological evidence suggesting dissociations between numbers and other non-numerical ordered sequences, and demonstrate that multivariate analyses are fundamental to address fine-grained theoretical issues with fMRI studies.

Research Highlights► Conventional fMRI data analyses showed that activation of the intraparietal sulcus (IPS) during ordinal judgements is identical for numerical and non-numerical stimuli. ► IPS activations for number comparison vs. letter comparison can be reliably separated by multivariate classifiers (support vector machines) and discriminating voxels are consistent across participants. ► The results reconcile neuroimaging data with neuropsychological findings and show that multivariate classifier analyses are mandatory for investigating fine-grained theoretical issues with fMRI.