Based on prior knowledge, we can analyze the relationship between the age feature and the task of whether a person receives credit or not.

Typically, younger individuals may have a harder time obtaining credit due to a lack of credit history and lower income. As individuals get older, they tend to have more stable income and a longer credit history, making it easier for them to qualify for credit. However, there may be an upper limit where older individuals may face difficulties in obtaining credit due to retirement or other factors.

Based on this analysis, we can generate a dictionary with the specific details as requested:

```json
{
	"yes": [25.0, 30.0, 35.0, 40.0, 45.0],
	"no": [18.0, 20.0, 23.0, 28.0, 32.0]
}
```

In this dictionary, we have included 5 typical age values for each target class ('yes' and 'no'). The values are presented as floats in the lists.