Coding serial order of information is a fundamental ability of our cognitive system, and still, little is known about its neural substrate. This study examined the neural substrates involved in the retrieval of information that is serially stored in verbal working memory task using a sensitive multivariate analysis approach. We compared neural activity for memorized items stemming from the beginning versus the end of a memory list assessing the degree of neural pattern discordance between order positions (beginning vs. end). The present results confirmed and refined the role of the intraparietal sulcus in the processing of serial order information in working memory. An important finding is that the hippocampus showed sensitivity to serial order information. Our results indicate that the representation of serial order information relies on a broader set of neural areas and highlight the role of the intraparietal sulcus and the hippocampus, in addition to the supramarginal gyrus and the SMA. The contribution of different neural regions might reflect the involvement of distinct levels of serial order coding (i.e., spatial, attentional, temporal) that support the representation of serial order information.

This fMRI study aimed at unraveling the neural basis of learning alphabet arithmetic facts, as a proxy of the transition from slow and effortful procedural counting-based processing to fast and effortless processing as it occurs in learning addition arithmetic facts. Neural changes were tracked while participants solved alphabet arithmetic problems in a verification task (e.g., F + 4 = J). Problems were repeated across four learning blocks. Two neural networks with opposed learning-related changes were identified. Activity in a network consisting of basal ganglia and parieto-frontal areas decreased with learning, which is in line with a reduction of the involvement of procedure-based processing. Conversely, activity in a network involving the left angular gyrus and, to a lesser extent, the hippocampus gradually increases with learning, evidencing the gradual involvement of retrieval-based processing. Connectivity analyses gave insight in the functional relationship between the two networks. Despite the opposing learning-related trajectories, it was found that both networks become more integrated. Taking alphabet arithmetic as a proxy for learning arithmetic, the present results have implications for current theories of learning arithmetic facts and can give direction to future developments.