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

Since this feature refers to other installment plans from banks or stores, it is reasonable to assume that having other payment plans might indicate a higher financial responsibility or reliability. On the other hand, not having any other payment plans might suggest a lower level of financial commitment.

Let's analyze the feature in relation to the target variable:

- If the person receives a credit (target class: yes), they might have other payment plans such as credit from banks or installment plans from stores. We can expect the possible values of "other_payment_plans" for this class to include 'bank' and 'stores'.

- If the person does not receive a credit (target class: no), they might not have any other payment plans in place. Hence, the possible value for "other_payment_plans" for this class can be 'none'.

Based on this analysis, we can create a dictionary as follows:

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

Please note that this is a general analysis and the actual relationship between the feature and target variable can only be determined by analyzing the specific dataset and conducting statistical tests.