Based on prior knowledge, it is not immediately clear how the "other_payment_plans" feature would directly influence the likelihood of a person receiving a credit. However, we can conduct an analysis to determine the relationship between this feature and the target variable.

To perform the analysis, we need a dataset with information on whether each person receives a credit and the corresponding values of the "other_payment_plans" feature. With this data, we can calculate the frequency of each value of the "other_payment_plans" feature for both the "Yes" and "No" classes.

Here is an example of how we can create a dictionary based on the analysis:

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

In this example, we have identified that the "none" value of the "other_payment_plans" feature appears for both classes ("Yes" and "No"). Additionally, the value "bank" is observed only in the "Yes" class, while the value "stores" is observed only in the "No" class. 

Please note that this is just a hypothetical example based on the analysis of the given feature and target variable. The actual values may differ based on the dataset used for analysis.