Based on the given feature "personal_status", there are three different categories: married, single, and divorced/separated/widowed. Each category has different values for the "sex" variable.

To analyze the relationship between the feature and the task of whether a person receives credit or not, we need to examine how each category of "personal_status" relates to the target variable.

Possible values for "personal_status" and their corresponding target classes are as follows:

1. Category: Married
   - Values: "male mar/wid"
   - Target class (yes/no): Both cases are possible as married individuals may or may not receive credit.

2. Category: Single
   - Values: "male single"
   - Target class (yes/no): Both cases are possible as single individuals may or may not receive credit.

3. Category: Divorced/Separated/Widowed
   - Values: "female div/dep/mar", "male div/sep"
   - Target class (yes/no): Both cases are possible as individuals who are divorced, separated, or widowed may or may not receive credit.

Based on this analysis, the dictionary representation of the relationship between the "personal_status" feature and the target variable can be as follows:

```json
{
	"yes": ["male mar/wid", "male single", "female div/dep/mar", "male div/sep"],
	"no": ["male mar/wid", "male single", "female div/dep/mar", "male div/sep"]
}
```

Note: Since all values of "personal_status" are possible for both target classes, the lists of values for "yes" and "no" in the dictionary are identical.