Based on my prior knowledge, I can analyze the relationship between the feature "other_payment_plans" and the task of whether a person receives a credit or not.

Based on common sense, it is likely that having other installment plans (either with banks or stores) may indicate a higher financial obligation or debt for a person. This increased financial obligation might influence whether a person is deemed eligible for receiving a credit or not.

To create the dictionary with the relationship between the feature and the target, we need to analyze the values of "other_payment_plans" for the target classes "yes" and "no".

Considering this, here is the dictionary:

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

Explanation:
- For the target class "yes" (person receives a credit), possible values for "other_payment_plans" are "bank" and "stores". This implies that if someone has installment plans with banks or stores, they are more likely to receive a credit.
- For the target class "no" (person does not receive a credit), the possible value for "other_payment_plans" is "none". This indicates that if someone does not have any other installment plans, they are less likely to receive a credit.

Note: To provide a comprehensive analysis, it would be ideal to have access to the dataset and examine the distribution and frequency of different values of "other_payment_plans" for each target class.