Based on the given feature "other_payment_plans" and the task of determining whether a person receives a credit or not, we can analyze the relationship between the feature and the target variable.

By considering our prior knowledge, we can hypothesize that the presence or absence of other payment plans may affect the likelihood of a person receiving credit. It is possible that if a person has other payment plans in place, such as with a bank or stores, they may be more likely to be eligible for credit. Conversely, if a person has no other payment plans, they may have a lower chance of receiving credit.

To create the dictionary, we need to examine the values of the "other_payment_plans" feature for both the "yes" and "no" classes of the target variable:

```json
{
	"yes": ["bank", "stores"], 
	"no": ["none"]
}
```
In this case, the target class "yes" includes the values "bank" and "stores" for the feature "other_payment_plans", indicating that people who have payment plans with banks or stores are more likely to receive credit. The target class "no" includes the value "none", indicating that people with no other payment plans have a lower chance of receiving credit.

Please note that these conclusions are based on an initial analysis and may require further investigation or validation with additional data.