Based on prior knowledge, we can analyze the relationship between the `personal_status` feature and the task of whether a person receives credit.

By looking at the different categories of the `personal_status` feature, we can make some assumptions about their potential relationship with the target variable:

1. For the category "male single", we can assume there is a possibility of receiving credit as being single may indicate fewer financial responsibilities.

2. For the category "female div/dep/mar", it is difficult to make a definite assumption as there could be variations depending on the reasons for divorce, dependents, or marriage status. Hence, we cannot provide any specific values for this category.

3. For the category "male div/sep", there might be a higher chance of not receiving credit as divorce or separation may lead to financial instability.

4. For the category "male mar/wid", it is difficult to make a definite assumption as it depends on the reasons for marriage or widowhood. Hence, we cannot provide any specific values for this category.

Based on this analysis, we can generate the following dictionary:

```json
{
	"yes": ["male single"],
	"no": ["male div/sep"]
}
```
Note: The lists only include the categories for which a relationship with the target variable can be inferred.