The Neural Basis Of Domain Specificity In Rapid Object Categorization

With the help of the semantic system,human beings can categorize the external objects efficiently,so as to obtain certainty in cognition and make more effective behaviors.Rosch et al.(1976)believed that categorization could occur at three levels: superordinate level,basic level and subordinate level.However,in the past research on rapid object categorization,the priority or immediacy of the three levels has been controversial.Zhang Yiqing(2017)believes that the categorization of fast-presented objects is restricted by the domain,that is,when the target object and the interfering object are animate and inanimate respectively,the categorization efficiency is higher(the reaction time is shorter,the correct rate is higher).Neuroimaging evidence also shows that animate objects are separated from inanimate objects in the location of brain representation,for example,animate objects are represented in the ventrolateral fusiform gyrus,posterior dorsolateral superior temporal sulcus,and inanimate objects are represented in the ventrolateral fusiform gyrus and dorsolateral middle temporal gyrus.However,whether the representation of animate objects and inanimate objects exhibits network differences,that is,the neural basis of rapid object classification which is constrained by domain,remains to be studied.Previous researchers have explored the network representation of different types of objects using resting state functional connection method,but it is impossible to compare the differences between different networks.Based on the topological properties of graph theory,this study attempts to compare the differences between animate networks and inanimate networks by using graph theory analysis method.Firstly,in the task state,we use functional magnetic resonance(fmr)to obtain the activation regions of different kinds of objects in the subjects' brains,and obtain the seed points of different kinds of objects;then,in the resting state,we extract the time series of resting state according to the seed points;then,Pearson correlation is used to get the correlation matrix of the time series of different seed points.Based on the correlation matrix between seed points of different kinds of objects,the functional connectivity strength in graph theory is selected as the core index to compare the difference of the connectivity strength between animate objects and inanimate objects.The results showed that there were differences in the functional connectivity between animate and inanimate object networks,and that the functional connectivity of animate object networks was higher than that of inanimate objects.Graph theory method is priori-driven,so we used data-driven hierarchical clustering analysis to find that there is no obvious separation between animate and inanimate objects.Finally,we selected the typical processing brain regions of the object as the regions of interest,and found the separation between animate and inanimate objects.All in all,the results of this study found that the connection strength of animate object network and inanimate object network is different,and the connection efficiency of animate object network was higher.Cluster analysis further found that this difference was mainly reflected in the higher brain regions.