The spacing effect (learning sessions which are distributed across time tend to generate superior learning compared to when sessions are massed) is a robust finding which has been replicated across many domains (Benjamin & Tullis, 2010). Despite the assertion that spacing should be considered an educational standard, extensive application is not a reality (Kim et al., 2019). This may be due to a lack of understanding combined with discrepancies between laboratory-based experiments and real-world applications. Some researchers maintain that spacing research is not ecologically valid nor generalisable to the more complex learning that occurs in real-world settings (Kapler et al., 2015). This research aims to contribute to current spacing literature by exploring a number of factors related to the spacing effect, such as interval, age, sleep, and medium of presentation. Furthermore, this thesis aims to address gaps in the literature by exploring the existence of a spacing effect when learning face-name associations, and by investigating the electrophysiological correlates of spacing through ERP analysis. Behavioural and electrophysiological measures were used to examine the spacing effect while learning face-name associations at recent and remote intervals (twenty-four hours, one week, and one month) in younger and older adults across different mediums of learning. Results suggest that spacing is beneficial when learning face-name associations, particularly at longer intervals of one month, and that older adults may also benefit from spacing at longer intervals, though to a lesser extent than younger adults. This effect is also evident in online learning, though to a lesser extent than in-person learning. Sleep quality does not affect objective measures of cognition, but does impact psychological measures and may contribute to our understanding of spacing. Furthermore, there is evidence of separate neural networks for spaced- versus massed-trained participants when retrieving correct face-name associations. These findings support a hybrid model of spacing, in particular one that encompasses theories of deficient processing and study-phase retrieval.