類別學(xué)習(xí)中的典型性效應(yīng)和診斷性效應(yīng)
發(fā)布時間：2018-07-17 08:24
【摘要】：類別學(xué)習(xí)是人類認識世界的一項基本認知活動,類別學(xué)習(xí)能力是人類的基本能力。類別學(xué)習(xí)主要存在兩種學(xué)習(xí)方式:分類學(xué)習(xí)和推理學(xué)習(xí)。分類學(xué)習(xí)是指告知類別所有信息,判斷類別標簽;推理學(xué)習(xí)是告知類別標簽和部分特征值,判斷缺失特征值。當(dāng)前研究所使用的的類別材料多是光柵圖和家族相似性圖片材料,更多的進行的是知覺加工,因此也稱之為知覺類別學(xué)習(xí)。但是在現(xiàn)實生活中,自然界的刺激并不單單是以圖像形式呈現(xiàn)的,還有相當(dāng)一部分是以言語或文字的形式呈現(xiàn),這種呈現(xiàn)方式更多的涉及到語義加工,因此也稱之為語義類別學(xué)習(xí)。一方面,在以往研究中,分類學(xué)習(xí)和推理學(xué)習(xí)對知覺類別學(xué)習(xí)的影響還存在爭議,哪種學(xué)習(xí)方式更有利于知覺類別學(xué)習(xí),并且學(xué)習(xí)的結(jié)果是怎樣的有待更多的證據(jù)支持;另一方面,目前對語義類別學(xué)習(xí)的研究相對比較少,那么在語義類別學(xué)習(xí)中,分類學(xué)習(xí)和推理學(xué)習(xí)的影響又是怎樣的尚不清晰,如果有影響,是怎樣影響的。因此本研究主要通過兩個實驗探討知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)的影響機制。為了是兩者更具有比較性,語義類別學(xué)習(xí)的學(xué)習(xí)材料是直接把知覺類別學(xué)習(xí)的材料用文字表述,這樣可以在一定程度上保證兩種學(xué)習(xí)材料在難度上和傳達的信息等方面盡可能相同。實驗一主要通過兩個小實驗分別探討在知覺學(xué)習(xí)條件下和語義類別學(xué)習(xí)條件下,分類學(xué)習(xí)和推理學(xué)習(xí)的結(jié)果。結(jié)果表明,在知覺類別學(xué)習(xí)條件下,推理學(xué)習(xí)優(yōu)于分類學(xué)習(xí);在語義類別學(xué)習(xí)條件下,分類學(xué)習(xí)優(yōu)于推理學(xué)習(xí),知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)產(chǎn)生了相反的結(jié)果,這說明知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)可能存在不同的影響機制;實驗二則在實驗一的基礎(chǔ)上進一步探討知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)的影響機制。通過典型性評價和單特征測試考察在兩種學(xué)習(xí)條件下的特征典型性效應(yīng)和樣例診斷性效應(yīng)。通過上述兩個研究,主要得出如下結(jié)論:在知覺類別學(xué)習(xí)中,推理學(xué)習(xí)優(yōu)于分類學(xué)習(xí);在語義類別學(xué)習(xí)條件下,分類學(xué)習(xí)優(yōu)于推理學(xué)習(xí),主要體現(xiàn)在學(xué)習(xí)成績和策略轉(zhuǎn)換速度;知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)都會受到樣例典型性的影響。知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)都能進行原型學(xué)習(xí);知覺類別學(xué)習(xí)和語義類別學(xué)習(xí)都會存在診斷性效應(yīng)。在知覺類別學(xué)習(xí)中,診斷性特征對分類學(xué)習(xí)很重要,而在語義類別學(xué)習(xí)中,診斷性特征的學(xué)習(xí)成績更好,但是對非診斷性特征的學(xué)習(xí),分類學(xué)習(xí)好于推理學(xué)習(xí)。說明在語義類別學(xué)習(xí)中,分類學(xué)習(xí)的注意廣度更廣泛。
[Abstract]:Category learning is a basic cognitive activity for human beings to understand the world, and category learning ability is the basic ability of human beings. There are two main types of learning in category learning: classified learning and reasoning learning. Classification learning means to tell all the information of the category and judge the label of the category, while the reasoning learning is to tell the label of the category and some characteristic values, and to judge the missing feature value. At present, most of the materials used in the research are raster images and family similarity images, and most of them are perceptual processing, so they are also called perceptual class learning. But in real life, the stimulation of nature is not only presented in the form of images, but also in the form of words or words, which is more involved in semantic processing. Therefore, it is also called semantic category learning. On the one hand, the influence of classification learning and reasoning learning on perceptual category learning is still controversial, which learning method is more favorable to perceptual category learning, and what kind of learning results need more evidence to support; On the other hand, the current research on semantic category learning is relatively few, so in the semantic category learning, the impact of classification learning and reasoning learning is not clear, if there is an impact, how to influence. Therefore, this study mainly discusses the influence mechanism of perceptual class learning and semantic category learning through two experiments. In order to be more comparative, the learning material of semantic category learning is to express the material of perceptual category learning directly in words. This ensures that the two learning materials are as difficult as possible in terms of difficulty and information. Experiment one mainly discusses the results of classified learning and inference learning under the condition of perceptual learning and semantic category learning through two small experiments. The results show that under the condition of perceptual category learning, inference learning is superior to classification learning, and in semantic category learning condition, classification learning is superior to reasoning learning, and perceptual category learning and semantic category learning have the opposite results. This shows that there may be different influence mechanisms between perceptual category learning and semantic category learning, while experiment two further discusses the influence mechanism of perceptual category learning and semantic category learning on the basis of experiment 1. Through the typical evaluation and the single feature test, the characteristic typicality effect and the sample diagnostic effect under the two learning conditions were investigated. Through the above two studies, the following conclusions are drawn: in perceptual category learning, reasoning learning is superior to classification learning; in semantic category learning, classification learning is superior to reasoning learning, which is mainly reflected in learning achievement and strategy conversion speed. Both perceptual class learning and semantic class learning are affected by the typicality of the sample. Both perceptual category learning and semantic category learning can carry out prototype learning, and both perceptual category learning and semantic category learning have diagnostic effects. In perceptual category learning, diagnostic features are very important for classification learning, while in semantic category learning, diagnostic features have better learning performance, but classification learning is better than reasoning learning for non-diagnostic features. It shows that in semantic category learning, the attention span of classification learning is more extensive.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：B842.2
本文編號：2129790
[Abstract]:Category learning is a basic cognitive activity for human beings to understand the world, and category learning ability is the basic ability of human beings. There are two main types of learning in category learning: classified learning and reasoning learning. Classification learning means to tell all the information of the category and judge the label of the category, while the reasoning learning is to tell the label of the category and some characteristic values, and to judge the missing feature value. At present, most of the materials used in the research are raster images and family similarity images, and most of them are perceptual processing, so they are also called perceptual class learning. But in real life, the stimulation of nature is not only presented in the form of images, but also in the form of words or words, which is more involved in semantic processing. Therefore, it is also called semantic category learning. On the one hand, the influence of classification learning and reasoning learning on perceptual category learning is still controversial, which learning method is more favorable to perceptual category learning, and what kind of learning results need more evidence to support; On the other hand, the current research on semantic category learning is relatively few, so in the semantic category learning, the impact of classification learning and reasoning learning is not clear, if there is an impact, how to influence. Therefore, this study mainly discusses the influence mechanism of perceptual class learning and semantic category learning through two experiments. In order to be more comparative, the learning material of semantic category learning is to express the material of perceptual category learning directly in words. This ensures that the two learning materials are as difficult as possible in terms of difficulty and information. Experiment one mainly discusses the results of classified learning and inference learning under the condition of perceptual learning and semantic category learning through two small experiments. The results show that under the condition of perceptual category learning, inference learning is superior to classification learning, and in semantic category learning condition, classification learning is superior to reasoning learning, and perceptual category learning and semantic category learning have the opposite results. This shows that there may be different influence mechanisms between perceptual category learning and semantic category learning, while experiment two further discusses the influence mechanism of perceptual category learning and semantic category learning on the basis of experiment 1. Through the typical evaluation and the single feature test, the characteristic typicality effect and the sample diagnostic effect under the two learning conditions were investigated. Through the above two studies, the following conclusions are drawn: in perceptual category learning, reasoning learning is superior to classification learning; in semantic category learning, classification learning is superior to reasoning learning, which is mainly reflected in learning achievement and strategy conversion speed. Both perceptual class learning and semantic class learning are affected by the typicality of the sample. Both perceptual category learning and semantic category learning can carry out prototype learning, and both perceptual category learning and semantic category learning have diagnostic effects. In perceptual category learning, diagnostic features are very important for classification learning, while in semantic category learning, diagnostic features have better learning performance, but classification learning is better than reasoning learning for non-diagnostic features. It shows that in semantic category learning, the attention span of classification learning is more extensive.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：B842.2
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