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 these two.

Analyzing the relationship between "other_payment_plans" and the target variable "Does this person receive a credit?"

- If the person has "none" in the "other_payment_plans", it means they do not have any other installment plans with banks or stores. This may indicate that they are financially stable and do not have other outstanding debts, making it more likely for them to receive credit. Thus, we can expect that the value 'none' in "other_payment_plans" would be associated with the target class 'yes'.

- If the person has "bank" in the "other_payment_plans", it suggests they have installment plans with banks. Having installment plans with banks may indicate that they already have financial obligations, potentially impacting their creditworthiness. Therefore, the value 'bank' in "other_payment_plans" might be associated with the target class 'no'.

- If the person has "stores" in the "other_payment_plans", it indicates they have installment plans with stores. Similar to having installment plans with banks, having installment plans with stores may also suggest existing financial obligations that can affect their creditworthiness. So, the value 'stores' in "other_payment_plans" could also be associated with the target class 'no'.

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

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

Please note that this analysis is based on general assumptions and prior knowledge. The actual relationship between the feature and target variable may vary depending on the specific dataset and context.