心理学报
心理學報
심이학보
Acta Psychologica Sinica
2014年
8期
1052~1061
,共null页
类别学习 分类学习 推理学习 诊断性信息 典型性信息
類彆學習 分類學習 推理學習 診斷性信息 典型性信息
유별학습 분류학습 추이학습 진단성신식 전형성신식
category learning; classification learning; inference learning; diagnostic information; prototypical information
先前研究者普遍认为,类别推理学习条件下可以同时表征诊断性信息和非诊断性信息,而类别分类学习条件下中只能表征诊断性信息,不能表征非诊断性信息。而最近又有研究者发现部分呈现条件下的类别分类学习可以表征非诊断性信息。本研究通过两个实验系统比较了全部呈现和部分呈现条件下类别分类学习的结果,进一步探讨了分类学习条件下信息的表征情况,并进一步探讨了部分呈现条件下的分类学习能够表征非诊断性信息的原因。实验1发现全部呈现6个特征、缺失1个特征(即部分呈现5个特征)、缺失2个特征(即部分呈现4个特征)3种条件下都能表征诊断性信息,但只有部分呈现条件下能表征非诊断性信息。实验2发现全部呈现7个特征、缺失2个特征(即部分呈现5个特征)、全部呈现5个特征3种条件下都能表征诊断性信息,但只有部分呈现条件下能表征非诊断性信息。总的实验结果表明:全部呈现条件下的分类学习只能表征诊断性信息,而部分呈现条件下的分类学习能够同时表征诊断性信息和非诊断性信息,并且部分呈现条件下表征非诊断性信息的原因是被试进行了推理学习,而非注意广度的变化。
先前研究者普遍認為,類彆推理學習條件下可以同時錶徵診斷性信息和非診斷性信息,而類彆分類學習條件下中隻能錶徵診斷性信息,不能錶徵非診斷性信息。而最近又有研究者髮現部分呈現條件下的類彆分類學習可以錶徵非診斷性信息。本研究通過兩箇實驗繫統比較瞭全部呈現和部分呈現條件下類彆分類學習的結果,進一步探討瞭分類學習條件下信息的錶徵情況,併進一步探討瞭部分呈現條件下的分類學習能夠錶徵非診斷性信息的原因。實驗1髮現全部呈現6箇特徵、缺失1箇特徵(即部分呈現5箇特徵)、缺失2箇特徵(即部分呈現4箇特徵)3種條件下都能錶徵診斷性信息,但隻有部分呈現條件下能錶徵非診斷性信息。實驗2髮現全部呈現7箇特徵、缺失2箇特徵(即部分呈現5箇特徵)、全部呈現5箇特徵3種條件下都能錶徵診斷性信息,但隻有部分呈現條件下能錶徵非診斷性信息。總的實驗結果錶明:全部呈現條件下的分類學習隻能錶徵診斷性信息,而部分呈現條件下的分類學習能夠同時錶徵診斷性信息和非診斷性信息,併且部分呈現條件下錶徵非診斷性信息的原因是被試進行瞭推理學習,而非註意廣度的變化。
선전연구자보편인위,유별추이학습조건하가이동시표정진단성신식화비진단성신식,이유별분류학습조건하중지능표정진단성신식,불능표정비진단성신식。이최근우유연구자발현부분정현조건하적유별분류학습가이표정비진단성신식。본연구통과량개실험계통비교료전부정현화부분정현조건하유별분류학습적결과,진일보탐토료분류학습조건하신식적표정정황,병진일보탐토료부분정현조건하적분류학습능구표정비진단성신식적원인。실험1발현전부정현6개특정、결실1개특정(즉부분정현5개특정)、결실2개특정(즉부분정현4개특정)3충조건하도능표정진단성신식,단지유부분정현조건하능표정비진단성신식。실험2발현전부정현7개특정、결실2개특정(즉부분정현5개특정)、전부정현5개특정3충조건하도능표정진단성신식,단지유부분정현조건하능표정비진단성신식。총적실험결과표명:전부정현조건하적분류학습지능표정진단성신식,이부분정현조건하적분류학습능구동시표정진단성신식화비진단성신식,병차부분정현조건하표정비진단성신식적원인시피시진행료추이학습,이비주의엄도적변화。
Previous researches have showed that category learning by inference way can represent diagnostic information and nondiagnostic information, but learning by classifying way only can represent diagnostic information such as exemplar features information. However, recently studies show that learning partial exemplars by classifying also can represent nondiagnostic information (Taylor & Ross, 2009). Taylor & Ross (2009) offered an explanation of selective attention that there are comparably loose of attention resources in partial condition than entire condition. They left out the possibility that the subject might inference the missing features in the partial condition. In the real world, exemplars often appear with occluded features, but in laboratory research, they are almost always presented in their entirety. Two experiments were conducted to explore how partial classification leads to nondiagnostic features learning. Experiment 1 replicated the Taylor & Ross (2009) finding that learners who classified exemplars with missing features (the partial condition) processed nondiagnostic features. Experiment 2 explored how partial exemplars of classification learning could represent nondiagnostic (prototypical) information. Linearly separable category structures were used in this study. Experiment 1 used the "6 dimensions category" and experiment 2 used the "7 and 5 dimensions category". During learning phase, an individual exemplar was presented, the participant was asked to infer and indicate which category (Deeger or Koozle) the exemplar belonged to, and feedback as to whether the subject was right or wrong was provided. After a number of such trials of inference and feedback, participants reached the learning criterion and were considered to have formed new category knowledge. During the transfer phase, different prototypical and diagnostic exemplars were presented, the participant was asked to estimate their categorical typicality. Experiment 1 replicate the finding of Taylor & Ross (2009) that the entire and the partial conditions both can represent diagnostic information, but only the partial condition can represent prototypical information. In other word, the entire condition only can represent diagnostic information, but partial condition not only can represent diagnostic information but also nondiagnostic information. The results of experiment 2 support the previous prediction that subject inference the missing features automatically but not adjust their attention in the partial learning condition.