Based on your task and the given feature of foreign_worker, we need to analyze the relationship between this feature and the target variable of whether the person receives credit.

To conduct this analysis, we can look at the distribution of the foreign_worker feature for each target class (yes or no). Let's assume we have a dataset where the foreign_worker feature is available for each instance. 

Here is the dictionary that represents the relationship between the foreign_worker feature and the target variable:

```json
{
    "yes": ["yes"],  
    "no": ["no"]  
}
```

Since the foreign_worker feature is a categorical variable with categories "yes" and "no", in this particular case, we observe that the value of "yes" for the foreign_worker feature is associated with the target class of "yes". Similarly, the value of "no" for the foreign_worker feature is associated with the target class of "no".

In the context of this analysis, it is important to note that this dictionary assumes that all instances where the foreign_worker feature is equal to "yes" will be classified as receiving credit, and all instances where the foreign_worker feature is equal to "no" will not receive credit. It is important to further validate this assumption using appropriate modeling techniques and significance tests.