## 4 Discussion
In addition, too complex tasks are also one of the reasons for the small distinction between the two task states. The difficulty of the MOT task is increased in the task, which makes the subjects pay more attention to the target tracking situation of the MOT rather than the recall of the scene when completing the task. The design of the task adopts the block design method, and there is no long resting state between different tasks. This makes the brain activation state of the subject between tasks affect each other, resulting in a smaller degree of discrimination and an increasing of the difficulty in classification. However, this does not mean that the brain state of the subjects is the same when doing two types of task. In fact, in the process of cross-validation, we have also observed several ideal classification effects. The 10-fold cross-validation used to evaluate the performance of the classifiers in this study also reflects the influence of data partitioning on the performance of the classifiers. In order to study this effect, this study further conducted 500 cross-validations on the convolution-GRU network, and obtained the classifier performance that changed with different data division conditions, as shown in the Figure 7. It can be seen that as the data division continues to change, the performance of the classifier also fluctuates. That means, there are some cases where some samples from the minority class data that are more clearly distinguished from the majority class samples are divided into the training set, making the classifier learns more unique features of the minority class and improves the classification performance. The purpose of this study is to classify the fMRI data of subjects in different task states. The biggest difference between the two categories of tasks is whether the words shown to the subjects are the words in the word-scene pairs they memorized in the previous stages. Subjects associated the given words with the scene in the pre-stage of the task and memorized this association. Obviously, the memory of the word-scene pairs constitutes a simple episodic memory. In a successful retrieval process of episodic memory, when the test word is shown, the subjects will soon be familiar with the word and remember the scene associated with the word and even the other detail of the word-scene learning session. Therefore, the classification of the fMRI data of the two tasks in this study is actually to  
Figure 7: The result of 500 cross-validations on the convolution-GRU network. It should be noted that we are still performing 10-fold cross-validation, so 500 cross-validation is actually 50 rounds of 10-fold cross-validation. The changes in classification performance are almost entirely derived from data division and classifier parameter initialization. distinguish whether the tasks performed by the subjects caused the retrieval process of episodic memory. Some past studies have shown that the recall process of episodic memory is related to the ventral parietal cortex [37; 38]. There are also some studies show that the prefrontal lobe is also involved in the recall behavior of memory [39]. Compared to unsuccessful recall process, the neural activities of the \"core recollection network\" [40], which includes left angular gyrus, posterior cingulate cortex, medial prefrontal cortex, hippocampus, and parahippocampal cortex, are reported to be greater in successful recall process [39]. Similar results are observed in the ICA results of fMRI in this study, which shows that the task design of the word-scene pair successfully caused the recall process of the subject's episodic memory. The ICA is proven to be a good tool to study the patterns of brain activities [15]. From past studies, we can learn the patterns indirectly by the weight of feature in classifiers, or some statistical methods, while the ICA gives us a chance to study the independent components in brain activities directly and can be combined with classification methods.