The Ebbinghaus forgetting curve: definition and how to retain information
The Ebbinghaus forgetting curve shows that memory drops quickly after first exposure when memorization is not reinforced. The good news is that with well-timed review and spaced repetition, learning becomes more durable and information can be retained far longer with less effort.
The Ebbinghaus forgetting curve describes how quickly information disappears from memory without review.
🕒 6 min read📚 Updated: April 2026🔬 Based on Ebbinghaus (1885) and later research
Key points
About 70% of new information can disappear within 24 hours without reactivation
Forgetting follows a decreasing exponential curve, identified by Ebbinghaus in 1885
Each review “resets” the curve at a higher level—the slope gradually becomes less steep
Spaced repetition is designed to intervene just before the critical drop point
Understanding this curve lets you plan reviews far more efficiently
Topics
Memory
Forgetting curve
Spaced repetition
Active recall
Ebbinghaus and the birth of memory science
Hermann Ebbinghaus: the first researcher to measure forgetting
Hermann Ebbinghaus (1850–1909) was the first psychologist to study memory experimentally and quantitatively. By using lists of meaningless syllables as learning material—to remove familiarity effects—he measured retention rates at different intervals after learning.
His results, published in 1885 in Über das Gedächtnis (On Memory), revealed two foundational findings still valid today: the forgetting curve and the spaced learning curve.
These numbers concern information learned once, with no review at all. They are striking—but they do not mean the situation is hopeless.
After 20 minutes: about 42% is already lost
After 1 hour: about 56% is lost
After 24 hours: about 67% to 70% is lost
After 1 week: about 75% is lost
After 1 month: about 79% is lost
What the curve actually shows
The forgetting curve is a decreasing exponential curve. It describes how fast information fades from memory when no review occurs.
Visualizing the forgetting curve
This chart illustrates how quickly information is forgotten without review, and how spaced repetition improves long-term retention.
Visualization of the Ebbinghaus forgetting curve and the impact of spaced repetition on long-term memory retention.
Key Statistics About the Forgetting Curve
Research on memory retention shows that forgetting happens extremely quickly after initial learning, especially without active review.
≈ 40–50% forgotten after 1 hour
Memory decay starts very quickly after initial learning.
≈ 70% forgotten after 24 hours
Without review or retrieval practice, a large portion of newly learned information disappears within a day.
4 to 6 reviews are often enough
Research on spaced repetition suggests that a small number of well-timed reviews can strongly stabilize long-term memory.
10–30% better retention
Studies on the spacing effect consistently show better retention compared to cramming.
Sources: Ebbinghaus (1885), Cepeda et al. (2006), Murre & Dros (2015).
Comparison: Memory With and Without Spaced Repetition
This simplified table illustrates how memory retention evolves depending on whether information is reviewed or not.
Time elapsed
Without review
With spaced repetition
1 hour
≈ 50–60% retained
≈ 90–100% retained after rapid reinforcement
24 hours
≈ 30% retained
≈ 80–95% retained
7 days
≈ 20–25% retained
≈ 75–90% retained
31 days
≈ 10–15% retained
≈ 70–85% retained
These values are simplified educational estimates based on Ebbinghaus’ work and modern spaced repetition research. Actual retention varies depending on the type of learning and review quality.
⚠️ Important nuance
Ebbinghaus obtained exact percentages with artificial material (nonsense syllables). With meaningful content—courses, vocabulary, concepts—the curve is less steep because meaning supports encoding and associations. The overall shape stays valid, but percentages are usually less severe in real learning contexts.
Forgetting dynamics
Why do we forget so quickly after learning?
The Ebbinghaus forgetting curve explains why information disappears quickly after learning: early memory traces are fragile until they are reactivated.
The drop is especially fast in the first hours after learning. This is linked to short-term memory dynamics: recently encoded information is temporarily stored in the hippocampus. Without consolidation—mainly happening during sleep—it starts degrading quickly.
Without review, newly learned information can disappear within days. As time passes, the curve flattens: memories that survive the first hours are relatively more stable.
So “why we forget” is less about lack of intelligence and more about normal memory dynamics when retrieval is not repeated.
The concept of memory strength
Later research introduced the concept of memory strength: strongly consolidated information resists forgetting better and requires much longer review intervals. Poorly consolidated information decays rapidly.
This concept underlies modern spaced-repetition algorithms: they estimate each memory's strength and schedule the next review accordingly.
Spaced repetition
Why spaced repetition works against the forgetting curve
spaced review timing is considered one of the most effective methods to fight forgetting because it schedules review when memory starts to weaken.
Ebbinghaus's most useful finding is not the forgetting curve itself, but what he observed about review effects. Each time you review, several things happen.
This is the core mechanism of spaced review timing: exploit forgetting-curve dynamics to maximize retention with minimal reviews.
Retention rises again—sometimes near 100% if review happens early enough
The curve slope softens—the information will fade more slowly after this review than after first exposure
The optimal interval before the next review increases—so you can review less often while maintaining the same retention level
💡 The logic of the optimal interval
Reviewing too early (while retention is still very high) is inefficient: the memory is already strong, so the added gain is small. Reviewing too late (after memory collapse) forces near-relearning from scratch. The optimal interval is when retention starts dropping meaningfully—typically around 70% to 90% retention depending on the study.
From theory to practice
The forgetting curve and long-term memory
durable retention requires repeated retrieval and reinforcement. Without recall opportunities, knowledge feels familiar but fades quickly over time.
Ebbinghaus's work directly inspired spaced-repetition algorithms developed from the 1970s onward. Piotr Wozniak, creator of SuperMemo, formalized optimal intervals in the SM-2 algorithm, still the base for many flashcard apps today, including Anki.
More recently, the FSRS algorithm (Free spaced review timing Scheduler) refined this model by integrating newer cognitive findings—especially memory stability and perceived card difficulty. Results show significantly better interval precision than older algorithms.
Modern flashcard applications directly rely on Ebbinghaus’ discoveries to improve memory retention with data-driven review timing.
The first review should happen quickly—ideally within 24 hours, and certainly before the end of the week. This is where the curve drops fastest, so review has the greatest relative impact. A quick evening pass over notes can cut information loss dramatically.
2. Avoid massed review right before an exam
Cramming creates short-term fluency but very weak retention after 48 hours. Content reviewed all at once the night before follows the forgetting curve from the next day—with a rapid, predictable drop.
3. Accept partial forgetting as normal—and useful
A partially forgotten memory reactivated just before disappearing consolidates more strongly than one reviewed while still very fresh. Partial forgetting is a condition for durable memorization, not an obstacle.
🔬 What recent research says
A Cepeda et al. (2009) study analyzed optimal intervals across thousands of question-answer pairs and confirmed that spacing should grow exponentially with successful repetitions. Their model predicts that after one learning event, the ideal delay before first review is one to two days—not a few hours, not a week.
Cepeda et al. (2009), Optimizing Distributed Practice, Experimental Psychology
Practical case
A practical example of the forgetting curve
Take an exam-preparation scenario: learning 30 vocabulary words in a foreign language. On Day 0, you complete initial learning with examples and a quick self-check.
Within 24 hours, rapid forgetting appears: some words already feel uncertain without retrieval practice. You then schedule spaced reviews on Day 1, Day 3, Day 7, and Day 21.
Each review stays short but active: recall first, correct immediately, then retrieve again. This is a practical way to study effectively while reducing total revision time.
The result is better learning retention: instead of cramming and relearning, you progressively consolidate knowledge into long-term memory.
Initial learning: understanding plus active encoding
Rapid forgetting detected: first recall difficulties by Day 1
Spaced reviews: Day 1, Day 3, Day 7, Day 21
Consolidation: easier retrieval and stronger long-term retention
Pitfalls to avoid
Common mistakes that accelerate forgetting
Some study habits feel productive in the moment but significantly reduce long-term retention.
Reviewing too late, only right before an exam
Rereading passively instead of testing yourself
Using long massed sessions instead of short spaced sessions
Ignoring sleep after intensive learning sessions
Scientific perspective
The scientific limitations of the Ebbinghaus forgetting curve
The original curve is based on a very specific setup: Ebbinghaus largely experimented on himself and used nonsense syllables. This gave clean measurements, but it does not represent the full variety of real-world learning.
So the exact percentages should not be applied mechanically to every context. Learning meaningful concepts, solving problems, or studying connected material engages additional mechanisms compared with memorizing arbitrary lists.
Forgetting speed also depends on attention during encoding, prior knowledge, meaning, sleep quality, and usage context. The Ebbinghaus Ebbinghaus forgetting curve remains a powerful learning model, but it should be treated as a practical guide rather than a universal fixed law.
In short, the forgetting curve is a high-value educational model for effective studying—not a strict law that predicts every learner in every context.
Frequently asked questions
Does the forgetting curve apply to all kinds of learning?
It applies to declarative learning—facts, vocabulary, concepts, dates. The slope varies with meaning, attention during encoding, and associations. For procedural learning (motor skills, automatisms), forgetting is usually less steep—skills resist time better.
Can we flatten the forgetting curve long-term?
Yes. That is exactly what spaced repetition does over time. With enough well-spaced reviews, intervals can stretch to months or even years. Some highly consolidated memories seem to resist forgetting indefinitely, though the notion of truly permanent memory remains debated.
How many reviews are needed for long-term stability?
It depends on complexity and spacing quality. In many cases, 4 to 6 well-spaced reviews are enough to stabilize memory for months. With an SRS algorithm, this happens naturally without manually calculating intervals.
How fast do we forget information?
There is no single timeline. Without review, forgetting is often steep in the first 24 hours and then slows down. The exact pace depends on meaning, attention during learning, prior knowledge, and whether the information is actively reused.
What is the best way to remember information long-term?
The most reliable approach combines active recall and spaced repetition. In practice, test yourself, correct mistakes immediately, and increase intervals progressively. This aligns with forgetting-curve dynamics and maximizes long-term retention.
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This article relies on established scientific work in cognitive psychology and memory science to explain forgetting mechanisms and spaced-repetition principles.
