Based on prior knowledge, we can analyze the relationship between the feature RIAGENDR (Respondent's Gender) and the task of predicting the age group (Senior or non-senior). 

Typically, the age group for seniors is considered to be 65 and above. Therefore, we can assume that the target class 'Senior' includes respondents who are 65 years old or older, while the target class 'Adult' includes respondents who are younger than 65.

Based on this analysis, we can create the following dictionary:

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

In this dictionary, the `"Adult"` key represents the target class for non-senior individuals, and the associated values [1.0, 1.0, 2.0, 2.0, 2.0] represent typical values of feature RIAGENDR for this target class. Similarly, the `"Senior"` key represents the target class for senior individuals, and the associated values [1.0, 1.0, 2.0, 2.0, 2.0] represent typical values of feature RIAGENDR for this target class.