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

To analyze the relationship, we can look at the distribution of the "other_payment_plans" values for each target class (yes or no) and identify any patterns.

Let's start by examining the possible values of "other_payment_plans":
- 'none': Indicates that there are no other installment plans.
- 'bank': Suggests that the person has an installment plan with a bank.
- 'stores': Implies that the person has an installment plan with a store.

Now, let's analyze how these values of "other_payment_plans" relate to the target variable:

1. If a person receives a credit (target = yes):
   - They may have no other installment plans ('none').
   - They might have an installment plan with a bank ('bank').
   - They could also have an installment plan with a store ('stores').

2. If a person does not receive a credit (target = no):
   - They may have no other installment plans ('none').
   - They might have an installment plan with a bank ('bank').
   - They could also have an installment plan with a store ('stores').

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

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

Note that we include all the possible values of "other_payment_plans" for both target classes, ensuring that the lists for each target class are not empty.