Based on prior knowledge, we can analyze the relationship between the feature RIAGENDR (Respondent's Gender) and the target task of predicting the person's age group.

Since the feature RIAGENDR represents gender, we expect that the distribution of genders may vary between adults and seniors. Let's assume that the age groups are defined as follows:
- Adult age group: 18 to 64 years old 
- Senior age group: 65 years old and above 

To analyze the relationship between RIAGENDR and the age group, we can consider the following information:
- If the values of RIAGENDR are predominantly 1 (Male) for the adult age group, then it might indicate that males are more likely to be classified as adults.
- If the values of RIAGENDR are predominantly 2 (Female) for the adult age group, then it might indicate that females are more likely to be classified as adults.
- Similarly, we can analyze the distribution of RIAGENDR values for the senior age group.

Based on the above analysis, we can create a dictionary with the typical RIAGENDR values for each target class, as requested:

```json
{
	"Adult": [1.0, 1.0, 2.0, 2.0, 1.0],
	"Senior": [2.0, 2.0, 1.0, 2.0, 1.0]
}
```

Please note that the actual values in the lists might vary depending on the specific dataset and its distribution. The values provided above are just for example purposes.