Based on prior knowledge, the relationship between the "credit_amount" feature and the target task of whether a person receives a credit can be analyzed as follows:

It is commonly observed that the credit amount requested by a person plays a significant role in determining whether they will receive a credit or not. In general, individuals with lower credit amounts are more likely to receive credit, while individuals with higher credit amounts might be at a higher risk of rejection.

To provide a dictionary with the required format, including typical values for each target class, here's the analysis:

```json
{
	"yes": [1000.0, 2000.0, 1500.0, 2500.0, 1800.0],
	"no": [5000.0, 4000.0, 4500.0, 3500.0, 6000.0]
}
```

The above dictionary suggests that for the target class "yes" (person receives a credit), some typical credit_amount values could be 1000.0, 2000.0, 1500.0, 2500.0, and 1800.0.

Similarly, for the target class "no" (person does not receive a credit), some typical credit_amount values could be 5000.0, 4000.0, 4500.0, 3500.0, and 6000.0.

Please note that these values are just examples and might vary based on the specific dataset and problem context.