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Spacing effect: Applications, Causes & Overview
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").
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Spacing effect topic overview
The analysis highlights Applications, Causes and Overview as prominent areas in the source structure around Spacing effect.
Related topics
23
Source areas
2
Connected nodes
25
Extracted relationships
186
Concept neighborhoods
15
Bridge connections
25
What this topic covers Research coverage
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.
Overview · 12 topics
Causes · 11 topics
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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Spacing effect connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Spacing effect shows recurring relationship patterns in the source. For example, Spacing effect → Acta Neurobiologiae Experimentalis, American Journal, American Psychologist, Appleton-Knapp, Avons, Bellezza, Benefits, Bird, Bjork, Bloom, Bussenius, Cepeda, Cermak, Challis, Chase, Clara, Cognition, Cognitive Psychology, Consumer Research, Contemporary Educational Psychology Another extracted example is Spacing effect → Avons, Challis, Cornoldi, From, Hence, However, King, Kirsner, Lockhart, Longoni, Mammaralla, Mammarella, McKone, Parkin, Research, Russo, Semantic, Short-term, Smith, Taylor. Use these groups to spot repeated connection types before inspecting the individual relationships.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 186 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
In
0.60
section
Spacing effect
has cause
Cepeda
0.60
section
Spacing effect
has cause
As
0.60
section
Spacing effect
related to Advertising
The
0.60
section
Spacing effect
related to Advertising
For
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
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.
Spacing effect
Spacing
Effects
Memory
Repetition
Priming
Recall
Russo
Account
Cued-memory
Stimuli
Semantic
Learning
spacing effect
Spacing
Effects
Study
Memory
Variability
Found
Recall
Repetition
Priming
Russo
Encoding
Account
spaced repetition
Massed
Repetitions
Processing
Presentation
Spacing
Items
Retrieval
Russo
Study
Recall
Stimuli
Repetition
memory
Psychology
Experimental
Stimuli
Journal
Recognition
Spacing
Retrieval
Repetition
Semantic
Priming
Encoding
Tasks
recognition memory
Psychology
Experimental
Found
Stimuli
Journal
Recognition
Spacing
Retrieval
Tasks
Repetition
Semantic
Priming
forced-choice recognition memory
Psychology
Experimental
Found
Stimuli
Journal
Recognition
Spacing
Retrieval
Tasks
Repetition
Semantic
Priming
incidental learning
Spacing
Journal
Experimental
Memory
Psychology
Effects
Spaced
Recall
Research
Items
Retrieval
Also
cued-memory tasks
Tasks
Effects
Russo
Stimuli
Priming
Information
Spacing
Semantic
Processing
Retrieval
Recognition
Research
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
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.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Spacing effect
Nodes26
Edges25
Triples186
Avg. degree1.92
Density0.076923
Components1
Source & methodology
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.
