Prospective memory (PM) enables people to remember to complete important tasks in the future. Failing to do so can result in consequences of varying severity. Here, we investigated how PM error-consequence severity impacts the neural processing of relevant cues for triggering PM and the ramification of that processing on the associated prospective task performance. Participants role-played a cafeteria worker serving lunches to fictitious students and had to remember to deliver an alternative lunch to students (as PM cues) who would otherwise (...) experience a moderate or severe aversive reaction. Scalp-recorded, event-related potential (ERP) measures showed that the early-latency frontal positivity, reflecting the perception-based neural responses to previously learned stimuli, did not differ between the severe versus moderate PM cues. In contrast, the longer-latency parietal positivity, thought to reflect full PM cue recognition and post retrieval processes, was elicited earlier by the severe than the moderate PM cues. This faster instantiation of the parietal positivity to the severe-consequence PM cues was then followed by faster and more accurate behavioral responses. These findings indicate how the relative importance of a PM can be neurally instantiated in the form of enhanced and faster PM cue recognition and processing and culminate into better PM. (shrink)

The Novelty-Seeking Model can explain incubation's effect on creativity by assuming an adaptive decision threshold. During an impasse, the threshold for novelty becomes too high and biased to previous neural activity, hindering progress. Incubation “resets” this threshold through attentional decoupling, allowing for spontaneous ideas to emerge from subsequent mind wandering or other activities that attract attention, facilitating progress.

Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel, much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.