To analyze the relationship between the feature `other_payment_plans` and the task of whether a person receives credit or not, we need to examine how different values of `other_payment_plans` are associated with the target variable.

Based on the feature description, `other_payment_plans` is a categorical variable with three possible categories: 'none', 'bank', and 'stores'. We need to determine the distribution of these categories within each class of the target variable.

Here's the analysis:

- When the target class is 'yes' (person receives credit), the `other_payment_plans` can potentially take any of the three possible categories. Therefore, the possible values can be 'none', 'bank', and 'stores'.

- When the target class is 'no' (person does not receive credit), the `other_payment_plans` can potentially take any of the three possible categories as well. So, again, the possible values can be 'none', 'bank', and 'stores'.

Based on this analysis, the dictionary can be generated as follows:

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