Memory Retention Studies                SuperMemo

   FNF:    Memory Retention Studies         http://www.hi.is/~joner/eaps/wh_memr.htm       2001-02-05
.
Memory Retention Studies     
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Memory Retention Studies    http://www.hi.is/~joner/eaps/wh_memr.htm       2001-02-05

"Case studies of personal memories...

Marigold Linton     http://www.sacnas.org/bio/lintohig.html    NL_ALL_Marigold Linton

 Linton (1975, 1982, 1986) recorded two events in her diary every day, writing the details on separate index cards, which she later used to test her retention. 

Over a period of six years she continued with this recording procedure, testing herself every month on two randomly chosed cards.  This impressive diligence in recording and testing is matched by a similarly high level of retention.   Even after three to four years, some 65 per cent of the incidents she recorded where, remembered
, albeit not always in great detail.

An aspect of the testing procedure used provided a serendipitous, but important finding.   Cards were drawn at random, but some were, by chance, drawn more frequently than others.  Analysis of her performance on these repeated incidents was systematically better than for those which had not previously been tested (Fig 10.5)

It is easy to see why this might be so:  since the retrieval of these items was more practised, they were reexperienced more recently than others, and of course, relearning was also possible . 

     Linton also judged the importance and emotionality of events both when the original recording was made, and also at retrieval.  Linton notes her tenency to forget less pleasant incidents more rapidly.  Interestingly, in the light of our discussions above with regard to the desirability of an emotional organisation of personal memories, she found that the rated importance and emotionality at encoding and retrieval showed rather poor agreement.  Another aspect of the findings linton reports is also worthy of comment, namely, that repeated events appeared to lose their particular episodic qualities, leaving the memory of the type of event readily accessible, but only its generic aspects.  Neisser (1986), in describing a phenomenon he valls re-episodic blurring, also notes that frequently encountered events lose episodic information, leading to abstraction and generalisation of what is retrieved.  Nigro and Neisser (1983) suggest that in such cased, when the events are remembered they seem to shift from a person- (i.e. self) centred to an observer-centred viewpoint on the memory.  It is perhaps worth noting that in remembering my earliest memory, it feels as if I am seeingthe scene from a different perspective than if I was merely looking through the eyes of a not quite three foot tall four-year-old.

 

 

Willem Wagenaar   NL_ALL_Willem Wagenaar

 

Wagenaar (1986, 1994) carried out a similar exercise to that of Linton, albeit using a rather more sound procedure. He recorded one or two of the most remarkable incidents per day, over a four-year period, taking care to report who else was involved in the incident, what actually happened, where the incident had occurred, and when it occurred. 

Each event was rated in terms of what he termed its ‘salience’, that is, how often the event might be expected to recur (i.e. once per day, once per week, once per month, once per year, etc.), the degree of emotional involvement evoked by the incident (i.e. none, little, moderate, condiserable, extreme), and the valence of these emotions (extremely unpleasant to extremely pleasant).

In the main part of the study, these detailed records of 1,605 incidents allowed him systematically to investigate the relative cueing power of the ‘who’, ‘what’, ‘where’, ‘when’ cues in isolation and in combination with each other.   Overall, memory for the recorded events declined from about 70 per cent after one year to about 45 per cent after five years (Fig 10.6.

   Wagenaar shows that, ‘who’, ‘what’ and ‘where’ cues were almost equally effective when used in isolation, but cueing himself with when an event occurred was almost always associated with poorer memory. 

Not surprisingly, when cues where combined, retrieval improved substantially, even some five years after the origninal event.  Moderate and high levels of salience and emotional involvement were associated with substantially higher rates of recall than events that were likely to recur on a daily or weekly basis, or that were associated with little or no emotional involvement. 

The shapes of the functions are remarkably similar, remaining more or less parallel across the five-year period (Fig 10.6).  Interestingly, very inpleasant incidents are less likely to be recalled than pleasant or very pleasant incidens for up to two years after the incidents occurred, but by the third year plesant and unplesant events seem to be equally memorable (Fig 10.6)  It is possible that this differential remembering of positive and negative events is actually motivated rather than merely an accident of encoding, vividness, distinctiveness and so forth, that is, perhaps what we remember and forget fulfils a role in our individual psychological make-up. "


See much more in the full original:

Memory and Remembering : Everyday Memory in Context
  (p. 226 - 230)
by John A. Groeger  Our Price: $29.95 Paperback - 302 pages (October 1997)
Addison-Wesley Pub Co; ISBN: 0582292204 
AZ
Amazon.com Sales Rank: 463,322


See also

Forgetting Curves http://www.ntu.ac.uk/soc/bscpsych/memory/forget.htm   
NL_TI_Forgetting Curves

Excellent Student Work   http://www.ntu.ac.uk/soc/bscpsych/memory/student.htm

Mnemonics
Exceptional Memory NL_Exceptional Memory
LJ_Memory
Prodigies

http://www.supermemo.com/

http://www.psy.ohio-state.edu/psy312/permstor.html
http://hamp.hampshire.edu/~AWAKE/CS108/messages/25.html

http://www.supermemo.com/archive/sm7help/theory.htm

P: Collaborative Hyperm. replace lectures  http://www.hi.is/~joner/eaps/wh_collh.htm 2001-02-05 .

2001-02-05

 


"
Theoretical foundations of SuperMemo


The optimization technique used in SuperMemo is based on the work of a Polish biologist, Piotr A.Wozniak, who has
developed a mathematical model of the decline of memory traces.
The model makes it possible to determine the optimum spacing of repetitions for any desired level of knowledge retention.

SuperMemo uses the Wozniak model, and can be programmed to allow knowledge retention in the 90-99% range.


Although the details of the optimization technique are covered by SuperMemo World's trade secret, the general outline of the
used algorithm is presented below (patent pending).

All forms of learning produce molecular changes in neuronal synapses which form connections between nerve cells in the
nervous system. These changes are gradually obliterated in the process of forgetting, which plays an important evolutionary role
in optimizing the organism's responses to the outside environment.

Forgetting affects all synapses that are able to learn, and can be prevented only by means of repetition. Every attentive learner
knows, that forgetting can ruin the delicate fabric of knowledge that may take months or years to build. Latin saying Repetitio
mater studiorum est is as old as the art of learning.

However, not everybody knows that it is not possible to learn something once and to remember it forever without
repetition!


Even a person's own name could be forgotten if not used, or in other words, repeated as often as it is. The problem with
repetitions is that they consume one of the most valuable assets in the modern lifestyle, time. Therefore, the key to effective
learning is to find ways to reduce the number of repetitions needed.


SuperMemo allows learning to approach the maximum natural capacity of the human brain to form memories
.
This is done by the optimization of repetition scheduling, i.e., finding out when and which portions of knowledge should be repeated. Given a
piece of knowledge, two criteria are used to determine the length of optimal intervals which should separate repetitions: (1)
minimization of the number of repetitions, and (2) maximization of overall knowledge retention.


In other words:

* intervals between repetitions should be as long as possible to reduce time spent on repetitions and to produce maximum
memory effect

* intervals should be short enough to ensure that the repeated knowledge is not forgotten

Because forgetting has a stochastic nature, i.e., it cannot easily be predicted when a given piece of knowledge will be forgotten,
a statistical approach to the process of learning has to be applied. By statistical analysis, we can determine when a given
proportion of memorized knowledge will be forgotten; hence, the following definition of optimal intervals: optimal intervals are
intervals that result in a small, previously determined fraction of knowledge being forgotten.

This fraction, called the forgetting index, may be chosen by the learner, and usually falls in the range 5 to 15 percent. By using optimal intervals in the process of learning, SuperMemo produces an incredible increase in the rate of knowledge acquisition without affecting knowledge retention. Optimal intervals will vary for different sorts of knowledge and different learners. The former problem is dealt with in SuperMemo by splitting the learned knowledge into the smallest possible pieces called itemsitem.

The optimization procedure, i.e., computation of optimal intervals, is then applied to each of the pieces separately, producing a
unique repetition spacing in each of the cases. The principle of applying items of maximum simplicity is called the minimum
information principle.

The problem of differences between learners is solved in SuperMemo by application of self-modifying algorithms that adjust
repetition spacing to individual needs.

SuperMemo procedures can detect what sort of learning and what sort of learner are subject to optimization.

The net result is that a determined level of knowledge retention may be maintained in the process of learning to approach the
maximum natural speed at which the learner's brain can form memories.

See also Outline of the SuperMemo optimization algorithm
"             http://www.supermemo.com/archive/sm7help/sm768od.htm

http://www.supermemo.com/archive/sm7help/theory.htm



Optimization algorithm used in SuperMemo

Below is an outline of how SuperMemo makes it possible to learn at your maximum capacity:

* learned knowledge is split into the smallest possible pieces called items

* items are formulated in question-answer form; they can also include graphics, sound, and programmed elements such as
specific repetition procedures, video, and even virtual reality (only in programmed databases)

* items are memorized in a self-paced manner by means of a drop-out technique, i.e., by responding to the asked questions as
long as it takes to provide all correct answers

* after memorizing an item, the first repetition is schedule after an interval that is the same for all items (it is randomly shortened
or lengthened for the sake of speeding up the optimization), and which statistically produces FI lapses of memory (FI stands for
the forgetting index and equals 10% by default)

* the first interval is computed for an average learner, but as soon as the value of FI deviates from the requested level, the
length of the first interval is modified

* after each repetition, the learner produces a grade, which determines the accuracy and easiness of reproducing the correct
answer

* on the basis of response ratings, items are classified into difficulty categories. Difficulty is reestimated in each successive
repetition

* different optimal intervals are applied to items of different difficulty

* different intervals are applied to items that have been repeated a different number of times

* the function of optimal intervals is constantly modified in order to produce the desired level of knowledge retention (i.e. the
level determined by the selected forgetting index)

* the function of optimal intervals is represented as the matrix of optimal factors (OF-matrix), defined as follows:

I(1,EF)=OF(1,EF)

I(n,EF)=I(n-1,EF)*OF(n,EF)

where:

I(n,EF) - n-th interval for item difficulty EF

OF(n,EF) - optimal factor for the n-th repetition and difficulty EF

The matrix of optimal factors may be inspected by means of the option Analysis : Advanced : Optimal factors or graphically
by means of Analysis : Advanced : Optimal factors 3D

* difficulty of items is expressed by means of E-factors (see EF in the formulas from the previous paragraph). Historically,
E-factors were used to determine how many times intervals should increase in successive repetitions of items of given difficulty.
At present, E-factors are only used to index the matrices of optimal factors and retention factors, and may bear little relevance
to the actual interval increase. Intially, all items have their E-factors set to 2.5. In the course of repetitions, E-factors are
increased or decreased depending on the grades given by the learner. Good grades (i.e. grades above four) gradually increase
E-factors, while bad grades decrease E-factors.

* matrix of optimal factors is produced by smoothing the matrix of retention factors (RF-matrix); (matrix of retention factors
may be inspected by means of the option Analysis : Advanced : Retention or graphically by means of Analysis :
Advanced : Retention factors 3D)

* each entry of the RF matrix equals the current estimation of the optimal factor OF that is expected to produce FI lapses of
memory (i.e. RF(n,EF)=OF(n,EF))

* entries of the RF-matrix, the so-called retention factors (R-factors), are computed from forgetting curves whose shape is
sketched on the basis of repetition history

* each difficulty category and repetition number has its own record of repetitions used to sketch a separate forgetting curve
(forgetting curves may be inspected by clicking the mouse over one of the matrix entries displayed with following options on the
Analysis : Advanced menu: Retention factors, Optimal factors, Optimal intervals, Retention or Cases)

* intervals used in learning, including the first interval, are slightly dispersed around their optimal values in order to increase the
accuracy of forgetting curve sketching and make the distribution of repetitions in time more uniform

See also Theoretical foundations of SuperMemo

http://www.supermemo.com/archive/sm7help/sm768od.htm

http://www.supermemo.com/

  XX    FNF:  XX  2001-03-26 LJ
NL

HS
LI

    FNF: XX   http://www.hi.is/~joner/eaps/wh_memsut.htm     2001-03-26  JE VS: 526.4666 (-5)



"

 


"
Theoretical foundations of SuperMemo


The optimization technique used in SuperMemo is based on the work of a Polish biologist, Piotr A.Wozniak, who has
developed a mathematical model of the decline of memory traces.
The model makes it possible to determine the optimum spacing of repetitions for any desired level of knowledge retention.

SuperMemo uses the Wozniak model, and can be programmed to allow knowledge retention in the 90-99% range.


Although the details of the optimization technique are covered by SuperMemo World's trade secret, the general outline of the
used algorithm is presented below (patent pending).

All forms of learning produce molecular changes in neuronal synapses which form connections between nerve cells in the
nervous system. These changes are gradually obliterated in the process of forgetting, which plays an important evolutionary role
in optimizing the organism's responses to the outside environment.

Forgetting affects all synapses that are able to learn, and can be prevented only by means of repetition. Every attentive learner
knows, that forgetting can ruin the delicate fabric of knowledge that may take months or years to build. Latin saying Repetitio
mater studiorum est is as old as the art of learning.

However, not everybody knows that it is not possible to learn something once and to remember it forever without
repetition!


Even a person's own name could be forgotten if not used, or in other words, repeated as often as it is. The problem with
repetitions is that they consume one of the most valuable assets in the modern lifestyle, time. Therefore, the key to effective
learning is to find ways to reduce the number of repetitions needed.


SuperMemo allows learning to approach the maximum natural capacity of the human brain to form memories
.
This is done by the optimization of repetition scheduling, i.e., finding out when and which portions of knowledge should be repeated. Given a
piece of knowledge, two criteria are used to determine the length of optimal intervals which should separate repetitions: (1)
minimization of the number of repetitions, and (2) maximization of overall knowledge retention.


In other words:

* intervals between repetitions should be as long as possible to reduce time spent on repetitions and to produce maximum
memory effect

* intervals should be short enough to ensure that the repeated knowledge is not forgotten

Because forgetting has a stochastic nature, i.e., it cannot easily be predicted when a given piece of knowledge will be forgotten,
a statistical approach to the process of learning has to be applied. By statistical analysis, we can determine when a given
proportion of memorized knowledge will be forgotten; hence, the following definition of optimal intervals: optimal intervals are
intervals that result in a small, previously determined fraction of knowledge being forgotten.

This fraction, called the forgetting index, may be chosen by the learner, and usually falls in the range 5 to 15 percent. By using optimal intervals in the process of learning, SuperMemo produces an incredible increase in the rate of knowledge acquisition without affecting knowledge retention. Optimal intervals will vary for different sorts of knowledge and different learners. The former problem is dealt with in SuperMemo by splitting the learned knowledge into the smallest possible pieces called itemsitem.

The optimization procedure, i.e., computation of optimal intervals, is then applied to each of the pieces separately, producing a
unique repetition spacing in each of the cases. The principle of applying items of maximum simplicity is called the minimum
information principle.

The problem of differences between learners is solved in SuperMemo by application of self-modifying algorithms that adjust
repetition spacing to individual needs.

SuperMemo procedures can detect what sort of learning and what sort of learner are subject to optimization.

The net result is that a determined level of knowledge retention may be maintained in the process of learning to approach the
maximum natural speed at which the learner's brain can form memories.

See also Outline of the SuperMemo optimization algorithm
"             http://www.supermemo.com/archive/sm7help/sm768od.htm

http://www.supermemo.com/archive/sm7help/theory.htm



Optimization algorithm used in SuperMemo

Below is an outline of how SuperMemo makes it possible to learn at your maximum capacity:

* learned knowledge is split into the smallest possible pieces called items

* items are formulated in question-answer form; they can also include graphics, sound, and programmed elements such as
specific repetition procedures, video, and even virtual reality (only in programmed databases)

* items are memorized in a self-paced manner by means of a drop-out technique, i.e., by responding to the asked questions as
long as it takes to provide all correct answers

* after memorizing an item, the first repetition is schedule after an interval that is the same for all items (it is randomly shortened
or lengthened for the sake of speeding up the optimization), and which statistically produces FI lapses of memory (FI stands for
the forgetting index and equals 10% by default)

* the first interval is computed for an average learner, but as soon as the value of FI deviates from the requested level, the
length of the first interval is modified

* after each repetition, the learner produces a grade, which determines the accuracy and easiness of reproducing the correct
answer

* on the basis of response ratings, items are classified into difficulty categories. Difficulty is reestimated in each successive
repetition

* different optimal intervals are applied to items of different difficulty

* different intervals are applied to items that have been repeated a different number of times

* the function of optimal intervals is constantly modified in order to produce the desired level of knowledge retention (i.e. the
level determined by the selected forgetting index)

* the function of optimal intervals is represented as the matrix of optimal factors (OF-matrix), defined as follows:

I(1,EF)=OF(1,EF)

I(n,EF)=I(n-1,EF)*OF(n,EF)

where:

I(n,EF) - n-th interval for item difficulty EF

OF(n,EF) - optimal factor for the n-th repetition and difficulty EF

The matrix of optimal factors may be inspected by means of the option Analysis : Advanced : Optimal factors or graphically
by means of Analysis : Advanced : Optimal factors 3D

* difficulty of items is expressed by means of E-factors (see EF in the formulas from the previous paragraph). Historically,
E-factors were used to determine how many times intervals should increase in successive repetitions of items of given difficulty.
At present, E-factors are only used to index the matrices of optimal factors and retention factors, and may bear little relevance
to the actual interval increase. Intially, all items have their E-factors set to 2.5. In the course of repetitions, E-factors are
increased or decreased depending on the grades given by the learner. Good grades (i.e. grades above four) gradually increase
E-factors, while bad grades decrease E-factors.

* matrix of optimal factors is produced by smoothing the matrix of retention factors (RF-matrix); (matrix of retention factors
may be inspected by means of the option Analysis : Advanced : Retention or graphically by means of Analysis :
Advanced : Retention factors 3D)

* each entry of the RF matrix equals the current estimation of the optimal factor OF that is expected to produce FI lapses of
memory (i.e. RF(n,EF)=OF(n,EF))

* entries of the RF-matrix, the so-called retention factors (R-factors), are computed from forgetting curves whose shape is
sketched on the basis of repetition history

* each difficulty category and repetition number has its own record of repetitions used to sketch a separate forgetting curve
(forgetting curves may be inspected by clicking the mouse over one of the matrix entries displayed with following options on the
Analysis : Advanced menu: Retention factors, Optimal factors, Optimal intervals, Retention or Cases)

* intervals used in learning, including the first interval, are slightly dispersed around their optimal values in order to increase the
accuracy of forgetting curve sketching and make the distribution of repetitions in time more uniform

See also Theoretical foundations of SuperMemo

http://www.supermemo.com/archive/sm7help/sm768od.htm

http://www.supermemo.com/

"


See the full original at:   http://www.supermemo.com/archive/sm7help/sm768od.htm

See also

XX

 
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Jón Erlendsson 2001-03-26  (16.00)                             
TEL:  WORK:   354-526.4666    354-526.4666    HOME:  354-565.2238 
Email: joner@hi.is         Home Page  http://www.hi.is/~joner/eaps/je_cvmy.htm       
P: Memory Retention Studies     http://www.hi.is/~joner/eaps/wh_memr.htm       2001-02-05
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