A-Factor (absolute factor; AF)
acquisition rate
active recall
ancestor (ascendant)
See also:
option available with Learn : Postpone : Auto-postpone (from the main menu) that makes sure that excess repetitions are automatically postponed before the learning begins (if it is checked). Auto-postpone uses user-defined criteria in choosing elements that should be postponed. Most importantly, high priority material is protected from being postponed. Auto-postpone affects only the material that has not been processed on previous days. It does not postpone repetitions scheduled for the current day until the next day.


outdated term for consolidation in previous versions of SuperMemo
binary component (program component)
See also:


predecessor of concepts in older SuperMemos. No longer used in SuperMemo 17. A category used to mean a named branch of the knowledge tree to which elements belonging to a given class of knowledge were added. For example, you might have keep categories such as: General Knowledge, Family, Internet, Job, etc. Now you can accomplish the same with concepts
See also:
cloze deletion (cloze question; cloze item)
A: Seychelles (93%)
Cloze items can be a simple and effective remedy for item complexity. If you have items that persistently cause recall problems, try using cloze deletion. SuperMemo simplifies creating cloze deletions by providing options such as:
Read more: Incremental reading
cloze for pictures
see occlusion test
learning material used in SuperMemo. A collection is composed of single pieces of knowledge called elements. The simplest elements are composed of a question and an answer. A collection of a given name is stored in a folder/directory that bears the same name, and all its important statistical and learning data are stored in a file with the extension kno
a container for objects placed within the visible field of an element. Components can carry texts, pictures, videos, PDF files, etc. They can also have a form of a shape, spell-pad, or script. You can add new components with Edit : Add components on the main menu, or by using the Compose toolbar on the learnbar
computational spaced repetition
approach to spaced repetition in which optimum intervals are calculated with the help of a computer on the basis of the past record of learning. SuperMemo was the first computer program to implement rudimentary spaced repetition algorithm in 1987
element associated with an idea. Multiple elements can form links to a concept. Links indicate that elements are associated with the idea represented by the concept. For example, if you learn about infections, you can define a concept flu virus. You can link this concept with many elements related to flu. Each time you want to learn about flu, you might begin from the concept flu virus. Concepts can also form concept groups, which are sets of elements located in the same portion of the knowledge tree. For example, you may create a concept called chemistry to group all your knowledge in the area of chemistry. See also: Concepts
concept group
set of elements added to a knowledge tree branch associated with a given concept. Each time you add elements, they land in a specific location in the knowledge tree. That location is determined by the element called the hook of the default concept group. Concept groups supersede categories known from earlier SuperMemos. The main difference between concept groups and categories is that the former are associated with concepts that play multiple roles in SuperMemo (starting with SuperMemo 17)
concept map
set of concepts and links between concepts. On the element menu, Concepts : Map displays the outgoing links for the current concept. Read more: Neural creativity
Contents window


term used for SuperMemo collections in SuperMemo 7 and earlier
See also:
estimation of element's difficulty in SuperMemo. Three main measures can be used to express difficulty:
  1. current estimation of item's absolute difficulty estimated by Algorithm SM-17. This number ranges from 0 (for easy items) to 1 (for difficult items). It is displayed as Diff in the Element data window
  3. heuristic measure of difficulty based on the history of repetitions and displayed in the Element data window as Old (this measure can be used to sort collections that are to be used by other users; starting with easy items first)
dismissed element
dragging mode (drag&size mode)
DSR model of memory (3 component model of memory)
extension of the two component model of memory at the neural network level. The two component model of long-term memory says that a status of memory in a synapse can be described with variables: stability and retrievability. The DSR model used in Algorithm SM-17 employed in SuperMemo 17, adds the third variable called memory difficulty, which is an expression of the complexity of the synaptic pattern involved in storing a given memory. The more complex the net of connections involved in a memory, the harder is to maintain the memory in the long term. In SuperMemo, memory difficulty is expressed by item difficulty, i.e. a number than says how difficult it is to remember an item. There are more variables involved in storing memories that are not part of the DSR model. For example, both homeostatic and circadian components of sleep propensity have an impact on encoding and retrieving memories at the network level. See also: Modeling memory


E-Factor (easiness factor; EF)
editing mode
element browser
see browser
element subset
set of elements saved in a file. You can create an element subset by using the element browser (e.g. Subset : Save all or Select : Save selection in the browser menu). You can view an element subset with View : Subset from the main menu. Read more: Using subsets
element window
window that displays a single element in a collection. In the default state this window displays buttons Contents, Search, History, etc. in its navigation bar along the top, as well as Learn, Add new, and learnbar at the bottom. Read more: Element window
in incremental reading, a portion of text taken from a larger article and scheduled for review and processing as a separate topic (located in the knowledge tree as a child of the parenting article). In incremental learning, you can also extract portions of videos, images, sounds, etc. For example, you can extract a picture of Ghana when learning about Ghana and you already have a political map of Africa in your collection. Use Zoom if you want to retain Africa, extract Ghana, and save space by not generating a new extract


filespace slot
final drill
final drill queue
queue of elements that scored a grade below Good (4) in repetitions. This queue is used to review poorly scoring elements at the end of the learning day. For this queue to be generated, Tools : Options : Learning : Skip final drill must be unchecked. As you progress with incremental learning and build up a solid overload, the final drill plays lesser and lesser role. Advanced users with high learning overload should skip the final drill entirely.
flashcard (card)
forgetting curve
graph in SuperMemo that shows how fast the memorized information gets forgotten. A forgetting curve graph shows time on its X axis, and percent recall on its Y axis. It can be viewed with Tools : Statistics : Analysis : Forgetting Curves. SuperMemo collects 400 forgetting curves for 20 levels of knowledge difficulty and 20 levels of memory strength.
forgetting index
free running sleep


graphic cloze deletion
see occlusion test
graphic deletion
see occlusion test


hierarchy node/branch/leaf
You can turn hints on/off by:
  1. checking Window : Hints, or
  2. double-clicking the hint panel on the status bar
hook node


incremental audio
learning technique pioneered by SuperMemo, in which you can listen to hundreds or thousands of audio files in parallel with substantial benefit to the speed of learning, and the quality of acquired knowledge. Incremental audio is analogous to incremental video and uses the same tools. The only difference is that it uses audio files instead of video files. YouTube-based incremental video can also be used as incremental audio (e.g. for learning languages, learning to sing, etc.). See: Incremental learning
incremental learning
set of learning techniques such as incremental reading, incremental video, visual learning, incremental audio, etc. Incremental learning easily ensures 95% recall of top-priority learning material for lifetime. For more see: Incremental learning
incremental reading
incremental video
incremental writing
process that combines incremental reading, creative writing, and elaborative writing, in which a text of an article or book is created incrementally. For a more detailed description, read: Incremental writing
interval (inter-repetition interval)


knowledge system
SuperMemo collection (outdated term used in older documentation files)
knowledge tree
See also:


Compare: Template
See also:
Leitner system
connection between components, elements, web pages, etc. There are five main kinds of links in SuperMemo:
  1. element-to-element link: link that connects elements, e.g. as created with Concepts : Link contents on the element menu
  2. element-to-concept link: link that connects an element to a concept, e.g. as created with Concepts : Link concept on the element menu
  3. registry link: link from a component to an object in the corresponding registry such as a picture in the image registry, e.g. as created when pasting a picture to a component
  4. component hyperlink: link from a component to another element, e.g. as created with Links : Hyperlink on the component menu
  5. HTML hyperlink: link from an HTML text to a webpage, or to an element in SuperMemo


matrix smoothing
measured forgetting index
see Remember
memorized element
memory lapse
see lapse
mid-interval repetition
repetition that takes place ahead of its planned time. Newer SuperMemos make it possible to execute repetitions ahead of time, e.g. before an exam. The algorithm takes the necessary correction for the spacing effect.
minimum information principle
Read more:
mnemonic hyperspace


neural creativity
creativity induced with the help of neural review. In neural review, SuperMemo successively feeds the user with knowledge associated with a selected topic or a chosen subset of topics. When a network of conceptual links connects areas of knowledge, they form semantic space that can be explored in a neural manner that helps forming new associations and generate ideas. This process if conducive to research, invention, problem solving, etc. Metaphorically speaking, neural creativity helps emulate a thinking process in a human brain extended by knowledge stored in SuperMemo. Unlike it is the case with semantic review in SuperMemo, or simply googling for knowledge, the whole process of associating pieces of knowledge is automated. To give it a try, pick a subject and Go neural
neural queue
queue of elements scheduled for neural review. The sequence of elements in the queue is determined by spreading activation from a single element, subset of elements, or a registry member (e.g. a picture)
neural review
See also:


O-Factor (optimum factor; OF)
OF matrix
matrix of O-Factors for different levels of difficulty (expressed as A-Factor) and stability (expressed as repetition category). The OF matrix is used by SuperMemo Algorithms SM-5 through SM-15 in computing optimum intervals in spaced repetition. In Algorithm SM-17, the role of the 2-dimensional OF matrix is played by the 3-dimensional SInc[] matrix (with the added dimension of retrievability)
occlusion test
See also:
optimum interval
ordinal number
outstanding element
outstanding material
outstanding queue
queue of elements scheduled for today's review. If you use Add to outstanding (available on the subset processing menu), not all elements in the outstanding queue are actually outstanding


element right above a level in reference to a given element in the knowledge tree in the Contents window. Use Ctrl+Up to see the parent of the currently displayed element. Root of the tree is the only element that has no parent. All children of element X have element X for parent.
See also:
passive review
pending element
pending queue
post-lapse stability (PLS)
stability of an item after a repetition that scored a failing grade. Post-lapse stability can be interpreted as optimum interval after forgetting and rarely goes beyond mere days. On occasion, an item might be forgotten by "accident" and still remembered years after the lapse, however, statistically this is an insignificant phenomenon as visualized by Tools : Statistics : Analysis : Graphs : First interval and Tools : Memory : 4D Graphs : First interval. See more: SuperMemo Algorithm
presentation mode (display mode)
primary storage
priority bias
false sense that all learning subjects are very important and equally important. Only months of prioritization training can help overcome this bias/sensation. A good user of SuperMemo will avail of the full spectrum of priorities from 0% to 100%, and will apply them naturally and instinctively
priority queue


Q&A format


R-Factor (retention factor; RF)
R-Metric (Recall Metric)
absolute measure of performance of two spaced repetition algorithms based on their ability to predict recall before a grade is scored. In SuperMemo 17, R-Metric is used solely to compare Algorithm SM-15 (known from SuperMemo 16) and the new Algorithm SM-17. It is shown as percentage in Statistics and Tools : Statistics : Analysis : Use : Efficiency : R-Metric. R-Metric is a difference between the performance of the two algorithms: R-Metric=LSRM(Alg-17)-LSRM(Alg-15), where LSRM is the least squares predicted recall measure for a given algorithm. R-Metric greater than zero shows superiority of Algorithm SM-17. R-Metric less than zero indicates underperformance of the new algorithm. LSRM is a square root of the average of squared absolute differences in recall predictions: abs(Recall-PredictedRecall), where Recall is 0 for failing grades and Recall is 1 for passing grades. PredictedRecall is a prediction issued by the algorithm before the repetition. In Algorithm SM-17, the prediction is a weighted average of the value taken from the Recall[] matrix, and R (retrievability) computed from S (stability) and the used interval. The weight used is based on prior repetition cases which inform of the significance of the Recall[] matrix prediction (the prediction becomes more meaningful with more prior repetition data)
RF matrix
recurring selection made by SuperMemo in an article. Each time you come back to the article, the selection will be restored to show you the fragment that should be processed next. To set your own read-point, use Ctrl+F7. To clear it, use Ctrl+Shift+F7
reading list
Recall matrix (Recall[])
the matrix that shows how recall depends on retrievability (R). In an ideal case, recall and retrievability should be the same, however, there are factors that make it hard to achieve. For example, difficult items produce non-exponential forgetting curves, SuperMemo finds it hard to sort items by difficulty, difficulty is fluid, etc. The Recall matrix is then used to predict recall on the basis of retrievability for items with varying difficulty and stability. The matrix can be used to produce the approximation of the recall function which helps estimating recall even if no recall data is available. The Recall matrix can be compared to the forgetting curve matrix. In older algorithms, forgetting curve matrix was used with time measured in U-Factor unit. The Recall matrix measures time in retrievability units. You can inspect the Recall matrix using: Tools : Memory : 4D Graphs : Recall
registry index
registry member
registry object
registry position
registry subset
registry window
remembered element
element that can effectively be retrieved from memory. Remembered elements should not be confused with memorized elements, i.e. elements that take part in the learning process. In a well-executed learning process, probability of recall (retrievability) for memorized elements should be above 100% minus the forgetting index (or exactly that number at repetition time). By definition, a remembered element has the probability of recall verging on certainty
  1. show the question (or the stimulus)
repetition category
repetition spacing
see spaced repetition
requested forgetting index
process in which the learning material is reviewed. In SuperMemo, the review may be active (items) or passive (topics). Usually, passive review means reading, while active review means recalling of information from memory with grading (repetitions). See also: Subset review
root node
root is a branch in the Contents window that keeps all elements of a given concept group. The root is a node that keeps the hook to which all elements belonging to a given concept group are added. For example, a node named Chemistry might be a root node of all elements related to chemistry. The root of the whole collection is the root of the knowledge tree. This master root has a physical position of 1 in the collection (Element #1)


a list of commands available via the script component to manipulate other components in a given element (i.e. to display components, change their position, animate them, use them in learning tests, etc.). This way you can create interactive presentations, animation, various tests, etc.
Compare: Neural review
secondary storage
See also:
spaced repetition (repetition spacing; expanded rehearsal)
spacing effect
spreading activation
method of activating nodes in a neural network, semantic network, directed graph, etc. In SuperMemo, the graph is formed by semantic connections of the knowledge tree, concept links and inter-element links. Spreading activation is used to sequence elements for neural review. This is a new form of semantic review in SuperMemo, where new elements are fed neurally for review. This approach is useful in enhancing creativity and problem solving. To get a taste of neural review choose Learn : Go neural (Ctrl+F2) from the main menu. For more, see Wikipedia: Spreading activation
stability increase matrix (SInc matrix; SInc[])
the matrix that represents the function of interval/stability increase in Algorithm SM-17. For any item at any time, the increase in stability depends on:
  1. current stability,
  2. current retrievability, and
  3. current item difficulty.
This function can be inspected using: Tools : Memory : 4D Graphs : Stability in SuperMemo. The SInc[] matrix can be considered an extension of the OF matrix (from older SuperMemos) into the dimension of retrievability. This means that the new Algorithm SM-17 can handle any repetition at any time, including freshly reviewed items, as well as long-neglected items. Retrievability is the dimension that reflects the time that has elapsed since the last repetition relative to the optimum interval
startup interval (SI)
common first interval for all new items determined by the forgetting curve for new heterogeneous material. See more: SuperMemo Algorithm
startup stability (SS)
the estimate of stability after the first review determined by the best match to grades obtained in later repetitions (that estimate may change with the arrival of new data). See more: SuperMemo Algorithm
subset review (subset learning)
see search&review
  2. computer program implementing the SuperMemo method
See: Introduction to SuperMemo


See also:




visual browser
visual learning


zoom&trim mode
state in which pictures in SuperMemo can be processed with mouse operations that include zooming, trimming, moving the zoom, etc. To enter this mode, Alt+click the picture. The picture will be surrounded with a thin lime border. To test the mode, 'middle-click the picture to zoom in