Based on prior knowledge, we can analyze the relationship between the feature "other_payment_plans" and the task of whether a person receives credit.

To conduct the analysis, we can examine the distribution of the "other_payment_plans" feature for both the "yes" and "no" target classes.

```python
# Analysis of feature "other_payment_plans" for target class "yes"
yes_values = ['bank', 'stores']  # categories that indicate a "yes" for receiving credit

# Analysis of feature "other_payment_plans" for target class "no"
no_values = ['none']  # category that indicates a "no" for receiving credit

```

Based on the analysis, we can create the dictionary with the observed values for each target class:

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

This indicates that the possible values for the "other_payment_plans" feature that lead to a "yes" for receiving credit are 'bank' and 'stores', while the only observed value that leads to a "no" is 'none'.