Based on prior knowledge, we can analyze the relationship between the "buying price" feature and the task of rating the decision to buy the car. 

In general, a higher buying price may indicate a higher level of satisfaction with the purchase, as it suggests that the buyer invested a significant amount of money in the car. However, this is not always the case, as there may be other factors influencing the overall rating of the decision.

To create the dictionary, we need to identify the possible values of the "buying price" feature for each target class of the rating task. Here's the dictionary:

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

Please note that this is a simplified analysis, and the specific relationship between the "buying price" feature and the target variable may vary depending on the dataset and its context.