Based on prior knowledge, marital status can potentially have an impact on an individual's earnings. Generally, it is observed that married individuals tend to have higher earning potential compared to unmarried individuals. However, it is important to note that this relationship may not hold true for all cases.

To analyze the relationship between the marital-status feature and the task, we can look at the distribution of marital status for individuals earning more than $50,000 per year and those earning less.

Here is the analysis:

For individuals earning over $50,000 per year (target class "yes"):
- Possible values of marital-status can include: 'Married-civ-spouse', 'Widowed', 'Divorced', 'Separated', 'Married-spouse-absent', 'Married-AF-spouse'

For individuals earning less than or equal to $50,000 per year (target class "no"):
- Possible values of marital-status can include: 'Never-married', 'Married-civ-spouse', 'Widowed', 'Divorced', 'Separated', 'Married-spouse-absent'

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

```json
{
	"no": ["Never-married", "Widowed", "Divorced", "Separated", "Married-spouse-absent"],
	"yes": ["Married-civ-spouse", "Widowed", "Divorced", "Separated", "Married-spouse-absent", "Married-AF-spouse"]
}
```

This dictionary captures the possible values of the marital-status feature for individuals who earn more than $50,000 per year and those who earn less. Note that the hard-to-predict values are not included in the dictionary.