Based on prior knowledge, it is expected that the credit history feature would have a strong relationship with the task of determining whether a person receives a credit or not. Different categories of credit history might indicate different levels of risk or credibility of the individual, which would influence the decision of whether to grant credit.

To analyze the relationship between the credit history feature and the target variable, we can examine the distribution of different credit history categories for individuals who received credit and those who did not.

Now, let's create the dictionary:

```json
{
	"yes": ["existing paid", "no credits/all paid", "all paid"], 
	"no": ["critical/other existing credit", "delayed previously"]
}
```

In this case, the credit history categories "existing paid", "no credits/all paid", and "all paid" are associated with individuals who receive credit. On the other hand, the categories "critical/other existing credit" and "delayed previously" are associated with individuals who do not receive credit.

Please note that this analysis is based on general prior knowledge and it is always recommended to perform specific data analysis to confirm the relationship between the feature and target variable in the specific dataset being analyzed.