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Labels Effect in Category Learning: Evidence from Eye Movements and Event-Related Potential

Author: TangZhiWen
Tutor: XingQiang
School: Guangzhou University
Course: Developmental and Educational Psychology
Keywords: Category Learning Scenario tab Language Tags Eye movement Event-related potentials
CLC: B842.3
Type: Master's thesis
Year: 2010
Downloads: 142
Quote: 2
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Abstract


Category Learning (category learning) is the classification of the individual through constant practice, learn to type determination process, that stimulate the class to learn how to classify process. Is the most basic human cognitive activity, has its own unique psychological mechanism. Category learning process includes a variety of effects, there are a variety of different psychological effects classification criteria. Stigmatization is an important effect, one effect is the so-called label category name can be used as an effective classification clues to promote and raise awareness of category learning, but the labeling effect is based on the classification based on the similarity of class or category-based knowledge and there is no uniform conclusion, past studies generally considered adult category learning is based on similarity. By analyzing previous studies, they found that in the classification used for well stimulation materials without distinction, and thus difficult to distinguish stigmatization basis for classification. The study is divided into two parts, the first part of the study of language tags, the second part of the label on the scene in the class to learn the role of research. The second chapter artificial language labels, labels as classified material in real language, explore the language tags for category learning. Study used EyeLink II eye tracker, college students were tested in different categories under the stimulation frequency of fixation duration and gaze were analyzed. The results showed that the classification based on artificial language label is based on similarity; authentic language on the label label uses the familiar explanation-based classification, unfamiliar language tag similarity-based classification. The third chapter is based stimulus similarity, respectively, at different times under the present stimulus to explore Category Learning similarity-based classification under electrophysiological evidence. Studies using ERP technology, the students were tested in the processing of stimuli presented under different EEG recording, extracted from EEG wave of early and late negative component analysis. The results showed that under different stimulus EEG there were significant differences, showing stimulation than 200ms, the activation of the corrected P300, indicate the stimulus for a more processed. Late in category learning has N400 wave appears. Latent semantic stimuli to be significantly shorter than the picture stimuli. During the classification process, will activate the semantic components, based on the language label classification is a faster and more efficient way of category learning. Chapter IV of this study, the use of eye movement techniques and event-related potentials, research scenario labels category learning. Experimental tasks were asked to classify the stimuli, Experiment 1 were tested through behavioral experiments allow for non-classified the object scene. Experiment II, the objects placed in certain scenarios, for object classification. Experiment 3, there will be no scenario scenarios and test objects to be classified, analyzed event-related potentials. It was found that a) the classification of scenario-based activities under the label of the object classification accuracy and confidence have improved; 2) were tested on the scene labels gaze frequency and duration were significantly longer than non-scene groups. 3) at 120ms after stimulus amplitude variance, based contexts N200, P300 amplitude higher, the activation of the wider region, the incubation period is shorter than the incubation period without scenarios. The results showed that: Scenario category label can effectively promote learning, improve the speed and accuracy of classification, based on a scenario it possible to label the classification process.

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