Based on my prior knowledge, the relationship between the "credit_amount" feature and the task of determining whether a person receives credit or not can be analyzed as follows:

Typically, higher credit amounts may indicate a higher likelihood of the person receiving credit. However, there can be variations depending on other factors such as income, credit history, and debt levels. To analyze this relationship effectively, it would be helpful to look at the distribution of credit amounts for both the "yes" and "no" targets.

Based on this analysis, here is the dictionary with the specific details:

```json
{
	"yes": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0],
	"no": [500.0, 1000.0, 1500.0, 2000.0, 2500.0]
}
```

In the above dictionary, the "yes" key represents the target class where the person receives credit, and the "no" key represents the target class where the person does not receive credit. The corresponding values for both keys are lists of typical float values for the "credit_amount" feature. The list for "yes" contains credit amounts ranging from 1000.0 to 5000.0, whereas the list for "no" contains credit amounts ranging from 500.0 to 2500.0.

Note: The specific values presented above are just illustrative examples and should be interpreted as potential ranges rather than definitive thresholds.