Based on prior knowledge, we can assume that the age group for "seniors" typically starts from around 60 years old. Therefore, I would consider anyone 60 years old or older as a senior, and anyone below 60 as a non-senior.

To analyze the relationship between the feature RIAGENDR (Respondent's Gender) and the task of predicting the age group, we can examine the distribution of gender within each age group.

Here is the dictionary with the feature values for each target class:

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

The "Adult" class contains typical values for the feature RIAGENDR: [1.0, 2.0], indicating that both males and females can belong to this age group.

The "Senior" class also contains typical values for the feature RIAGENDR: [2.0, 1.0], indicating that both males and females can belong to this age group as well.

Please note that the above values are just examples and do not reflect the entire dataset. They are meant to demonstrate the format of the dictionary and provide a general understanding of the relationship between the feature and the target.