Based on my prior knowledge, I can analyze the relationship between the "credit_amount" feature and the task of determining whether the person receives credit or not.

An analysis of the relationship between the "credit_amount" feature and the task reveals the following insights:

- For the "yes" class (person receives credit), typical credit amounts may range from small to high values, depending on the creditworthiness of the individual. However, to give some examples, I will include five typical credit amount values for the "yes" class as follows:

    1. 1000.0
    2. 2500.0
    3. 5000.0
    4. 10000.0
    5. 20000.0
    
- For the "no" class (person does not receive credit), typical credit amounts may also vary, but they are usually lower compared to the "yes" class. To provide some illustrative examples, five typical credit amount values for the "no" class are:

    1. 500.0
    2. 1000.0
    3. 1500.0
    4. 2000.0
    5. 3000.0

Based on the above analysis, the dictionary representing the relationship between the "credit_amount" feature and the task can be constructed as follows:

```json
{
    "yes": [1000.0, 2500.0, 5000.0, 10000.0, 20000.0],
    "no": [500.0, 1000.0, 1500.0, 2000.0, 3000.0]
}
```

Please note that the provided credit amount values are just illustrative examples, and actual ranges and values may vary depending on domain knowledge or dataset specifics.