Based on prior knowledge, I can analyze the relationship between the feature "other_payment_plans" and the task of determining whether a person receives a credit or not.

To conduct this analysis, I would compare the distribution of "other_payment_plans" values for the two target classes, "yes" and "no". By examining the frequency of each "other_payment_plans" category for both classes, I can determine if certain values are more prevalent in one class compared to the other.

After this analysis, I will create a dictionary containing the possible values of "other_payment_plans" for each target class.

Based on the given feature description, the dictionary would look like this:

```json
{
	"yes": ["none", "bank", "stores"],
	"no": ["none", "bank", "stores"]
}
```

In this case, all three categories (`"none"`, `"bank"`, `"stores"`) for "other_payment_plans" are presented for both target classes (yes and no).