Memory for temporal order in novel sequential action

ABSTRACT

Order is critical for many daily activities. Developmental research has shown that memory for order in action is the least prioritised in a processing hierarchy, and is sensitive to deviant input. The current research investigated these aspects of sequence learning are also present in adults. Participants learned a novel sequence across several exemplars with either easy- or difficult-to-categorize items, which either did or did not involve a deviant order on one exemplar, and were later asked to recall the sequence. Memory for individual sub-actions and order was stronger in the easy conditions, and the deviant order significantly deteriorated ordered recall in the difficult condition only. These findings support the theorised processing hierarchy, with the presence of a deviant order having a larger effect on memory when the load at the earlier item stage is increased. These results have implications for theories of working memory and learning in real-world contexts.     

Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.