Based on prior knowledge, the feature "age" can be a relevant factor in determining whether a person receives credit or not. Here is an analysis of the relationship between the feature and the task:

1. Generally, younger individuals may have less credit history, which can make it harder for them to receive credit. However, as they grow older and establish a credit history, their chances of receiving credit may increase. So, there might be a positive correlation between age and receiving credit.

2. However, at a certain point, older individuals might face difficulties in obtaining credit due to factors like retirement, limited income, or health conditions. Hence, the relationship between age and receiving credit may not be linear and could turn negative after a certain age.

3. The exact age ranges that determine the probability of receiving credit are highly dependent on the specific data and the context of the problem. It would be best to analyze the data and examine any available patterns or trends to identify the age ranges most relevant to the task.

Based on the analysis, the dictionary representation with typical age values for each target class would be as follows:

```json
{
	"yes": [25.0, 30.0, 35.0, 40.0, 45.0],
	"no": [60.0, 65.0, 70.0, 75.0, 80.0]
}
```

Please note that the provided typical age values are just examples, and the actual values will vary based on the specific data and analysis conducted.