Based on prior knowledge, we can analyze the relationship between the "other_payment_plans" feature and the target variable "Does this person receive a credit?".

It is difficult to make any specific predictions about the relationship between the "other_payment_plans" feature and the target variable without further information or data analysis. The "other_payment_plans" feature represents the presence of other installment plans, such as those offered by banks or stores. It is possible that having other payment plans could be indicative of financial stability and responsibility, making it more likely for a person to receive credit. On the other hand, having multiple payment plans might indicate a higher level of debt or financial instability, which could potentially decrease the likelihood of receiving credit.

To get a better understanding of how this feature relates to the target variable, we need to perform statistical analysis or explore the dataset further. It would be important to examine the distribution of the target variable among different categories of "other_payment_plans" and perform hypothesis testing or statistical modeling if necessary.

Given the lack of specific predictions or insights about the relationship between the feature and target variable, the dictionary will include all possible values of "other_payment_plans" for both the "yes" and "no" classes. 

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

In this case, the possible values of "other_payment_plans" for both classes are "none", "bank", and "stores".