Based on my prior knowledge, I can analyze the relationship between the feature "Recency" (months since last donation) and the task of determining if the person donated blood.

From a common intuition, we can expect that individuals who have donated blood recently are more likely to donate again, while those who haven't donated in a long time may be less inclined to donate. 

Based on this assumption, I can create a dictionary with typical Recency values for each target class.

```json
{
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0],
	"no": [6.0, 10.0, 12.0, 18.0, 24.0]
}
```

In this case:
- For the target class "yes" (people who donated blood), the typical Recency values could be [1.0, 2.0, 3.0, 4.0, 5.0] indicating recent donations within the past 5 months.
- For the target class "no" (people who did not donate blood), the typical Recency values could be [6.0, 10.0, 12.0, 18.0, 24.0] indicating no recent donations within the past 6 to 24 months.

Please note that these values are assumptions based on prior knowledge and may not represent actual data. It is always important to analyze the specific dataset to make accurate conclusions about the relationship between features and the target variable.