The Cognitive And Neural Mechanism Of Category-based Feature Inference

Categorization and inference are two important functions of category knowledge, besides categorization, category-based feature inference is an important application of category information. In categorization task, the feature values of the test item is known and the subjects need to infer the label of the category which the item belongs to, while in category-based feature inference task, the category label of the test item and parts of its feature values are known, and the subjects need to infer the unknown feature value(s) of the test item. Previous studies on category mainly limited to the categorization tasks, while studies on cognitive mechanism of the category-based feature inference tasks were very small in number, and the studies on the latter suggested that factors such as matching type of label, typical level and causal relations between category features affected category-based feature inference tasks. And so far, there is only one study to explore the neural mechanism of category-based feature inference tasks with fMRI, which found that the PFC (prefrontal cortex) was activated in category-based feature inference task, and the temporal lobe, especially fusiform gyrus, was activated in the categorization task. Though previous studies on the cognitive mechanism found that some factors had effect on category-based feature inference task, but they did not explore the interactions between these factors. And the purpose of this study is to probe further the cognitive and neural mechanism of category-based feature inference task on the basis of previous studies. On probing the cognitive basis of category-based feature inference task, we explore not only the factors which affect the task but also the interactions between these factors. On probing the neural basis of the task, we use ERP for the first time to explore the time course and the ERP components relative to category-based feature inference task with task-segmentation manipulation.From a macroscopical view, category-based feature inference task included two stages, category learning and feature inference, and the factors which affect category-based feature inference task come mainly from two sides, relative category information in learning phase and attributes information of test items in feature inference phase, and accordingly, all experiments in this study utilize learning-testing two phase paradigm, which more fits the feature inference task in natural life, so this study have better ecological validity. Experiment one to four probe the cognitive basis of category-based feature inference task, and experiment five to seven explore the neural basis of category-based feature inference task. The purpose of study one is to discover the relation between category label and category-based feature inference task. In experiment one, we explore how label type, matching type of label and basal probability of typical features affect category-based feature inference task, and this study probe the effect of basal probability of typical features on category-based feature inference task for the first time, and the results of experiment one suggest that all the three factors mentioned above affect category-based feature inference task, and the inference scores for the test items with high basal probability of typical features are higher than the one with low basal probability of typical features, and the interactions between label type and matching type of label are significant, because when label type is category label, the inference scores for the test items with matching label were significant higher than the one with non-matching label, while when label type is feature label, the inference scores for the test items with matching label were not different from the one with non-matching label, further more, the non-matching score (difference score that the mean inference score for the test items with matching label minus the one with non-matching label) in the category label condition is significant higher than the one in the feature label condition, so these results suggest that being different from feature label, category label exert very important role in category-based feature inference tasks. In experiment two, we explore the common effects of matching type of category label and typical level on category-based feature inference tasks, the results suggest that, in accordance with the results from experiment one, matching type of category label affect category-based feature inference tasks, and typical level affect this task too, further more, the interaction between these two factors is significant, that is, the effect of matching type of category label on category-based feature inference task in high typical level condition is stronger than the one in low typical level condition, additionally, the effect of typical level on category-based feature inference task in matching category label condition is stronger than the on in non-matching category label condition. The purpose of study two is to probe how causal relation between category features and typical level affect category-based feature inference task. In experiment three, controlling the typical level of test items, we only probe the effect of causal relation between category features on category-based feature inference task, there are complex casual relations between the typical features of the category learned by subjects in casual relation group, the results suggest that casual relations between typical features affect inference score in casual relation group, that is, the inference score for test items with direct cause-result inference is significant higher than the one with indirect cause-result inference and the one with result-result inference, and there is no difference between the latter two, however there are no casual relations between the typical features of the category learned by subjects in control group and there are no difference in inference scores of the three types of items mentioned above. In experiment four, we study the common effect of the orientation of the relation between key premise and result and typical level on category-based feature inference task when there are casual relations between typical features of the learned category, there are casual relations (called common-cause casual relation) between the typical features of the category learned by subjects in casual relation group, the results from this group suggest that both the orientation of the relation between key premise and result and typical level affect the category-based feature inference task, further more, there are significant interactions between these two factors, because, the effect of typical level on category-based feature inference task in the condition of positive relation between key premise and result is stronger than the one in the condition of negative relation between key premise and result, what's more, the effect of the orientation of the relation between key premise and result on category-based feature inference task in high typical level condition is stronger than the one in low typical level condition. Study three explore the neural mechanism of category-based feature inference task with ERP for the first time. In experiment five, we probe the neural mechanism of category induction process. Category induction is an important process for category learning which is an important part of category-based feature inference task, the results from experiment five discovery that the ERP divergence between category induction and non-induction occurred at both early stage (posterior N1 component) and late stage (LPC component), while previous studies on category induction have not discovery the ERP divergence between the two conditions occurred at early stage, LPC is the key component of category induction and relates to memory updating process. In experiment six, we discovery the ERP component relative to category-based feature inference process, the results suggest that the ERP waves elicit by category-based feature inference condition and non-inference condition differ on P2, P3 and LPC, additionally, the order for category learning also exerts effects on P3b amplitudes in category-based feature inference condition, we conclude that P3 component relates to working memory load level and LPC relates to prohibiting process for task-irrelevant category information in category-based feature inference task. In experiment seven, we study the effect of the level of category learning information amounts on ERP components relative to category-based feature inference task, and the results show that P3 amplitudes elicited by high level category learning information amounts condition is significantly smaller than the one elicited by middle and low level category learning information amounts condition, while there is no difference between the latter two conditions, further more, LPC amplitudes elicited by high level category learning information amounts condition is significantly smaller than the one elicited by middle level category learning information amounts condition, and LPC amplitudes elicited by middle level category learning information amounts condition is significantly smaller than the one elicited by low level category learning information amounts condition, in accordance with the result from experiment six, P3 amplitudes manifest the working memory load level in category-based feature inference task, while LPC amplitudes reflect the prohibition level for task irrelevant category information in the task.This study explores the cognitive and neural mechanism of category-based feature inference task, and the results are as follows:Firstly, basal probability of typical features, matching type of label, typical level of test items and causal relation between category typical features have effect on category-based future inference task.Secondly, there are interactions between matching type of category label and typical level of test items, as while as between the orientation of the relation between key premise and result and typical level when there are casual relations between typical features of category.Thirdly, in category leaning phase, the ERP divergence between category induction and non-induction occurred at both early stage (posterior N1 component) and late stage (LPC component).Fourthly, category-based feature inference condition elicits smaller anterior P2, P3b and LPC amplitudes than non-inference condition, what's more, the order for category learning also exerts effects on P3b amplitudes in category-based feature inference condition, and the items belonging to the category learned first elicit smaller P3b amplitudes than the one belonging to the category learned after.Lastly, the level of category learning information amounts affect the amplitudes of P3 and LPC in feature inference stage, in that, P3 amplitudes elicited by high level category learning information amounts condition is significantly smaller than the one elicited by middle and low level category learning information amounts conditions, additionally, LPC amplitudes elicited by high level category learning information amounts condition is significantly smaller than the one elicited by middle level category learning information amounts condition, and LPC amplitudes elicited by middle level category learning information amounts condition is significantly smaller than the one elicited by low level category learning information amounts condition.The important discovery of this study lies in that we find that basal probability of typical features influences the category-based feature inference task, and there are interactions between matching type of category label and typical level of test items, as while as between the orientation of the relation between key premise and result and typical level when there are casual relations between typical features of category, and we discover that the early effect of category induction occurring at posterior Nl component, and this study for the first time explore the ERP component relative to feature inference, that is, P3 and LPC, and we find the ERP effect of the level of category learning information amounts on category-based feature inference task.This research has important theoretic and practical significance. For the theoretic significance, this study deepen the comprehension for category-based feature inference task, and improve further the theory relative to category induction and category-based feature inference task, and there are innovations in research method, especially in the study on the neural mechanism of category-based feature inference task, we use task segmentation manipulation to simplify the complex cognitive process, and accordingly, we have the probability to explore the ERP component relative to category-based feature inference task. For the practical significance, because category-based feature inference is an important cognitive function for human being, this research has significance to a certain degree for better analyzing and understanding behaviors of human beings and offer neural-psychological diagnostic project for the cognitive function abnormality.