Based on prior knowledge and analysis, let's analyze the relationship between the feature "other_payment_plans" and the target variable "Does this person receive a credit?".

Since the feature "other_payment_plans" describes different types of installment plans, it is reasonable to assume that individuals with a specified plan in place might have a higher chance of receiving credit. On the other hand, individuals with no specified plan might have a lower chance of receiving credit.

To further analyze this relationship and create the requested dictionary, we would need a dataset or a sample of data containing the feature and the corresponding target variable. By analyzing the frequencies of different values in the feature for both the "yes" and "no" classes of the target variable, we could determine the possible values of the feature for each class.

Without actual data, we can make an educated guess and provide a potential dictionary format:

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

This assumes that individuals with installment plans from banks or stores might have a higher chance of receiving credit, while those with no other payment plans would have a lower chance.

Please note that without actual data, this analysis is based on assumptions and may not accurately represent the true relationship. Actual data would be required to provide a more accurate dictionary.