Based on prior knowledge and experience, the relationship between the "credit_amount" feature and the task of determining whether a person receives credit (yes or no) can be analyzed as follows:

We can assume that individuals who have higher credit amounts are more likely to receive credit. This is because higher credit amounts usually indicate a higher borrowing capacity and a more established credit history, which makes the applicant more reliable and creditworthy.

Based on this analysis, we can create a dictionary as requested:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [500.0, 800.0, 1200.0, 1800.0, 2200.0]
}
```

The values provided in the dictionary are example credit amounts for each target class ('yes' and 'no'). These values are assumed to be characteristic of typical cases where a person receives credit ("yes") or does not receive credit ("no").