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

Analysis:
Typically, younger individuals may face challenges in obtaining credit due to a lack of credit history or limited income. On the other hand, older individuals may have more established credit histories and stable income, making it easier for them to receive credit. However, this relationship may vary depending on the specific context and dataset.

To create the dictionary, we need to identify five typical age values for each target class ('yes', 'no') based on the relationship described above.

```json
{
   "yes": [20.0, 24.0, 26.0, 30.0, 35.0],
   "no": [45.0, 50.0, 55.0, 60.0, 65.0]
}
```

Please note that the provided age values are just examples and should be considered indicative. The actual age ranges and their association with the target variable may differ depending on the dataset and context.