Convolution Forgetting Curve Model for Repeated Learning

Man Li; Yanlu Xie; Yue Chen

RECORD

Convolution Forgetting Curve Model for Repeated Learning

Yanlu Xie, Yue Chen, Man Li

Neurons and Cognition · Submitted 19 Jan 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition.

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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,094 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.13 · human participants 0.88 · controlled comparison 0.09 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.60 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated.	background 0.51	0.08	0.04	asserted 0.96
s1	In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning.	contribution 0.89	0.93	0.15	asserted 1.00
s2	In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively.	method 0.99	0.73	0.04	asserted 1.00
s3	The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material.	method 0.99	0.73	0.06	asserted 1.00
s4	The model is applied to simulate the forgetting curves in different situations.	method 0.82	0.89	0.04	asserted 0.97
s5	The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition.	result 1.00	0.94	0.71	asserted 1.00
s6	The model is further verified in the experiments of Mandarin tone learning for Japanese learners.	method 0.53	0.91	0.06	asserted 1.00
s7	And the predicted curve fits well on the test points.	result 1.00	0.91	0.16	asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.74 · evidence from observation or experiment 0.71.

What was examined, and what was not

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
relations to other papers

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S sections
T body sentences
C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points.

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