Based on prior knowledge, we can analyze how the feature "other_payment_plans" relates to the task of whether a person receives credit or not.

Possible relationship:

1. if other_payment_plans is 'none': It is likely that individuals with no other installment plans would have a higher probability of receiving credit as they have fewer current financial obligations.

2. if other_payment_plans is 'bank': Individuals with installment plans through banks may have a lower probability of receiving credit as they already have financial obligations with a bank.

3. if other_payment_plans is 'stores': Individuals with installment plans through stores may also have a lower probability of receiving credit as they already have financial obligations with stores.

Based on this analysis, we can create the dictionary:

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

Note that we only include the values that have a clear relationship with the target variable. Values like "other" or other unknown categories that are hard to predict have not been included in the dictionary.