认知与发展心理学研究室知识库

每个类别的样例都有颜色、形状、纹理等不同维度的特征，这些特征通常在类别学习研究中被认为同等重要。然而，有关物体识别和分类的研究己发现，轮廓比表面特征更重要。为了探讨轮廓、表面特征在内隐类别学习中的作用，本研究采用原型变异任务，通过分别以轮廓特征和表面特征来界定类别，并且类别结构可以分别由规则或相似性界定，考察了不同特征在内隐类别学习中的作用及其机制。

研究一采用3个行为实验考察原型变异任务中类别由规则界定时不同定义特征对内隐类别学习的影响。其中，实验1和实验2探讨被试分别学习以轮廓特征或表面特征定义的类别时，不同特征对内隐类别学习的影响。结果发现，与表面特征界定类别相比，被试对轮廓特征界定的类别表现出更好的学习成绩，而且随着定义特征的数目的增加，轮廓特征界定类别的正确率显著提高，而表面特征界定类别的正确率没有显著变化。实验3结果发现，当学习阶段同时呈现以轮廓特征或表面特征定义的类别时，被试可以同时学到这两个类别，并且其对轮廓特征界定的类别表现出更好的学习成绩。

研究二采用2个实验，通过使用可以通过规则也可以通过相似性分类的两可类别结构，考察在内隐和外显学习中不同特征对个体策略倾向性的影响。实验4结果发现，在内隐类别学习条件下，当定义特征为轮廓特征时，被试表现出对相似性策略的倾向性。而在外显类别学习条件下，高学习成绩被试表现出对规则策略的倾向性。实验_5的结果发现，规则相关特征的突显性会对分类策略的选择产生影响。

研究三采用2个脑电实验考察不同特征在内隐类别学习不同阶段的作用。实验6采用由规则界定的类别，发现在类别判断的早期阶段，表面特征界定的类别引起了更大的前部N1效应的同时却引起了更小的前部P2效应;但在类别判断的晚期阶段，轮廓特征界定类别引起了更大的后部P2效应。实验7采用由相似性界定的类别，发现对于轮廓特征界定类别，不同相似性的刺激会引起不同的前部P2和前部N2效应;而对于表面特征界定类别，不同相似性的刺激主要引起不同的后部P3b和前部P3a效应。这些结果说明，尽管表面特征在早期知觉阶段可以吸引更多的注意，但是轮廓特征在晚期判断阶段会起更重要的作用。

研究四采用近红外技术，考察不同定义特征的类别在内隐和外显类别学习中激活脑区上的差异。结果发现，在内隐学习阶段，轮廓类的刺激比表面类的刺激在初级视觉皮层和颗中回表现出更低的激活水平，在随后的测试阶段，轮廓类比表面类在背外侧前额叶上表现出更高水平的激活。在外显学习过程中，轮廓类的刺激比表面类刺激在初级视觉皮层和梭状回表现出更强的激活，而在之后的测试阶段下，两种类别之间均没有差异。

本研究系统考察轮廓和表面特征在内隐类别学习中作用机制，发现轮廓特征比表面特征在内隐类别学习中发挥更重要的作用，轮廓特征的作用主要体现在对目标特征的探测分析，将当前刺激与记忆中的表征进行匹配的过程;而表面特征即使在分类的早期阶段会吸引更多的注意，在分类的晚期阶段投入了更多的努力和认知资源，但正确率仍然比轮廓特征界定的类别差。在内隐类别学习中，轮廓类刺激的相似模式可以被更好的知觉为同一个类别，表现为枕叶和颗中回激活减弱;在外显类别学习中，轮廓类的刺激可以得到更深的加工以提取类别的概念，表现为枕叶和梭状回激活增强。相关研究发现丰富了内隐类别学习的理论研究和神经机制研究，有助于我们进一步了解内隐类别学习中表征形成及分类的过程。

Although more and more researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures).

The Study 1 used behavioral experiments to investigate whether edge-based features matters more than surface-based features when the category was defined by a rule. The results of Experiments 1 and 2 showed that the performance rating was significantly higher in the edge-based condition than in the surface-based condition in the testing phase, and increasing defined dimensions enhanced rather than reduced classification performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that people could simultaneously learn both edge-based and surface-based categories and importantly, there was a larger learning effect for the edge-based category than for the surface-based category.

The Study 2 used behavioral experiments to investigate the effect of different features on the rule-based or similarity-based category learning by adopting the categories including a rule feature and the similarity features to the prototypes. The results of Experiment 4 showed that in the implicit learning task, subjects made significantly more similarity-based classification only when the defined feature was edge-based feature. In the explicit learning task, the well-learned subjects made significantly more rule-based classification than similarity-based classification only when the defined feature was edge-based feature. The results of Experiment 5 further showed that the contribution of different strategies was mainly determined by the silience of features.

The Study 3 adopted EEG technique to explore the role of different features in different time course of implicit category learning. The results of Experiment 6 revealed that when the category was defined by a rule, the stimuli from the edge-based category elicited larger anterior and posterior P2 effects than the stimuli from the surface-based category, while the stimuli from the surface-based category elicited larger anterior N1 and P3 effects than the stimuli from the surface-based category. The results of Experiment 7 showed that when the category was defined by similarity, stimuli with different similarity to prototype elicited different anterior P2 and N2 effect for the edge-based category, while stimuli with different similarity elicited different anterior P3a and posterior P3b for the surface-based category. The results indicated that although surface-based information might attract more attention during the feature detection, edge-based information plays more important roles than surface-based information in the evaluation of the relevance of the information in making a decision in categorization.

The Study 4 adopted fNIRS technique to explore the neural basis of category learning when the category was defined by edge-based features or surface-based features. The fNIRS results revealed that higher activation in the dorsolateral prefrontal cortexfor the edge-based category than the surface-based category was found only in the testing phase of implicit learning task but not in explicit learning task. Moreover, compared with the surface-based category, the edge-based category elicited lower activations around the primary visual cortex and middle temporal gyrus in the implicit training phase while it elicited higher activations around the primary visual cortex and the fusiform gyrus in the explicit training phase.

The current dissertation explored the cognitive and neural mechanism of the role of edge-based features and surface-based features in implicit category learning, and revealed that edge-based information plays a more crucial role than surface-based information in implicit category learning. Edge-based information matters more in the feature analysis and the evaluation of the relevance of the information in making a decision in categorization. The stimuli from the edge-based category can be better perceived as from the same category and lead to better representation.