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Open AccessJournal ArticleDOI

有意味学習の実験的研究 i:有意味学習の優位

Giyoo Hatano, +1 more
- 01 Jan 1969 - 
- Vol. 40, Iss: 4, pp 192-200
TLDR
This paper investigated the difference in retroactive inhibition between meaningful and rote learning and found that meaningful learning can achieve more stable knowledge which is less vulnerable to interference learning compared with rote.
Abstract
The present study aimed at investigating the difference in retroactive inhibition between meaningful and rote learning.Ss, who were university students, were required to learn a series of 3 lists of paired-associates. S-term of each pair was a word and R-term was a NS or a combination of 2 or 3 NSs with English connectives (see Table 1). According to the kind of list, Ss were classified into 3 groups: M-M gr., Ss of which could learn both the second and third lists meaningfully, M-R gr., who could learn the second meaningfully but not the third, and R-R gr., who could learn neither of them meaningfully (Table 4).Ss learned Lists 1, 2 and 3 successively. After that, they were required to relearn List 2 and List 3. In each test session, which was given after the learning of one list, Ss were given a test of the list learned immediately before and tests of the other lists learned previously (Table 3). Performance of Ss at the test immediately after the original learning (List 2 or 3) and that of after the interference learning (List 3 or 2, respectively) were compared, in order to clarify the effect of retroactive inhibition.The results were as follows:1) When the original learning had been meaningful, the effect of retroactive inhibition was very small (Table 6 & 7). The mean reduction ratio in number of complete correct response was only 5.2%. In case of rote learning, however, the effect was much larger, i.e., mean reduction ratio was 53.6% (Table 9).2) Meaningfulness in interference learning was irrelevant to the size of the effect of retroactive inhibition (also Table 6-9).These results suggested that meaningful learning, compared with rote learning, can achieve more stable knowledge which is less vulnerable to interference learning.

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References
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Journal ArticleDOI

Some-or-none characteristics of coding behavior

TL;DR: In this article, a variety of experiments dealing with the free recall of categorized word lists is presented in support of a some-or-none rationale. But the results show that when Ss do recall the words of a category, the mean word recall per category appears to be invariant with respect to list length, rate of presentation, serial position effects, sex of S, and size of category.
Journal ArticleDOI

Some evidence for coding processes derived from clustering in free recall

TL;DR: In this paper, it is concluded that category names do not play roles in clustering in the category-clustering situation or in the difference in the clustering found for sets of words which comprise all the items of a category as compared with word sets which do not exhaust a category.
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