Based on prior knowledge, we can analyze the relationship between the feature "other_payment_plans" and the task "Does this person receive a credit?". 

Since the feature "other_payment_plans" represents the presence of other installment plans from banks or stores, it is plausible to assume that individuals with no other payment plans are more likely to receive credit. On the other hand, individuals with other payment plans from banks or stores may have a lower chance of receiving credit.

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

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

In this case, for the target class "yes" (individuals who receive credit), the only possible value for the feature "other_payment_plans" is "none". For the target class "no" (individuals who do not receive credit), the possible values for the feature "other_payment_plans" are "bank" and "stores".

Note that we do not include values that are hard to predict or those not mentioned in the data.