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The spacing effect demonstrates that learning is more effective when study sessions are spaced out. This effect shows that more information is encoded into long-term memory by spaced study sessions, also known as spaced repetition or spaced presentation, than by massed presentation ("cramming").
The analysis highlights Applications, Causes and Overview as prominent areas in the source structure around Spacing effect.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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The extracted context around Spacing effect shows recurring relationship patterns in the source. For example, Spacing effect → Avons, Challis, Cornoldi, Hence, King, Kirsner, Lockhart, Longoni, Mammaralla, Mammarella, McKone, Parkin, Research, Russo, Semantic, Short-term, Smith, Taylor, Thus, Wilks Another extracted example is Spacing effect → Appleton-Knapp, Bjork, Despite, Layout, Spaced, Wickens. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
spacing effect memory learning spaced doi massed recall 10 study effects psychology tasks retrieval encoding variability items processing experimental stimuli
TTTA extracted 53 structured relationships around Spacing effect. Examples in this analysis include free recall → instance of → This robust finding has been supported by studies of many explicit memory tasks and unfamiliar faces → instance of → A number of studies have demonstrated that the semantically based repetition priming approach cannot explain spacing effects in recognition memory for stimuli. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| free recall | instance of | This robust finding has been supported by studies of many explicit memory tasks | 0.80 | text |
| recognition | instance of | This robust finding has been supported by studies of many explicit memory tasks | 0.80 | text |
| cued-recall | instance of | This robust finding has been supported by studies of many explicit memory tasks | 0.80 | text |
| and frequency estimation | instance of | This robust finding has been supported by studies of many explicit memory tasks | 0.80 | text |
| unfamiliar faces | instance of | A number of studies have demonstrated that the semantically based repetition priming approach cannot explain spacing effects in recognition memory for stimuli | 0.80 | text |
| and non-words that are not amenable to semantic analysis | instance of | A number of studies have demonstrated that the semantically based repetition priming approach cannot explain spacing effects in recognition memory for stimuli | 0.80 | text |
| Spacing effect | has cause | Decades | 0.60 | section |
| Spacing effect | has cause | Cepeda | 0.60 | section |
| Spacing effect | related to Advertising | Spaced | 0.60 | section |
| Spacing effect | related to Advertising | Layout | 0.60 | section |
| Spacing effect | related to Advertising | Appleton-Knapp | 0.60 | section |
| Spacing effect | related to Advertising | Bjork | 0.60 | section |
The concept neighborhoods around Spacing effect bring nearby vocabulary together. In this analysis, examples include Spacing, Effects and Memory. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Spacing effect, one of the stronger structural bridges in this analysis connects Spacing effect with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Spacing effect to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Causes & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Spacing effect · EN edition · Analysis: TopicsToTalkAbout
