Based on prior knowledge, we can analyze the relationship between the age feature and the task of whether a person receives credit or not. Age can be a significant factor in determining creditworthiness, as older individuals may have a more established credit history and potentially higher income stability.

Here's a dictionary representing the relationship between the age feature and the task of receiving credit:

```json
{
	"yes": [27.0, 33.0, 37.0, 45.0, 52.0],
	"no": [19.0, 21.0, 24.0, 30.0, 41.0]
}
```

In the given dictionary, "yes" represents the target class where the person receives credit, and "no" represents the target class where the person does not receive credit. The ages (in years) are listed as float values, and I have included five typical age values for each target class. These values are based on a hypothetical scenario and can vary depending on the specific dataset and context.