Based on prior knowledge, it is reasonable to assume that the price of maintenance would have an impact on the decision to buy a car. Typically, higher maintenance costs would be associated with a lower rating for the decision to buy the car. 

To analyze the relationship between the "maint" feature and the target variable (decision to buy the car), we can examine the distribution of each maint category for different target classes. Based on this analysis, we can create a dictionary with the required format.

Here is the dictionary for the "maint" feature:

```json
{
	"unacceptable": ["very high"],
	"acceptable": ["very high", "high"],
	"very good": ["low", "medium"],
	"good": ["low", "medium", "high"]
}
```

In this case, the possible values of the "maint" feature for each target class are as follows:
- For the "unacceptable" target class: the maint category is limited to "very high".
- For the "acceptable" target class: the maint categories include "very high" and "high".
- For the "very good" target class: the maint categories include "low" and "medium".
- For the "good" target class: the maint categories include "low", "medium", and "high".

The values are presented as strings in lists as required.