An EEG investigation of the trial-by-trial updating of complex knowledge structures

Schemas are higher-level knowledge structures that store an abstraction of multiple previous experiences, allowing us to retain a multitude of information, but without the cost of storing every detail. Schemas are believed to be relatively stable, but occasionally have to be updated to remain useful in the face of changing environmental conditions. Once a schema is consolidated, schema updating has been proposed to be the result of a prediction error (PE) based learning mechanism, similar to the updating of less complex knowledge. However, for schema memory this hypothesis has so far been difficult to test since the tools to track small modifications to abstracted memory schemas have been too coarse. Here I am using EEG and continuous memory measures recorded during the encoding of new schema consistent and inconsistent material to test the behavioural and neural correlates of schema updating. I am testing for updating in a memory test 24 hours later, to demonstrate the long-term effect of such PE-based learning. I observed a stronger relationship between behavioural PE and schema updating measures for inconsistent compared to consistent material, in line with the idea that more updating is required when a schema changes. Moreover, the P3 EEG signal tracked both the PE at the time of learning, as well as the updating of the memory schema one day later in the inconsistent condition. These results demonstrate for the first time that schema updating in the face of inconsistent information is indeed driven by PE-based mechanisms, and that similar neural mechanisms underlie the updating of consolidated long-term schemas and short-term belief structures.