Based on the provided feature description, the feature "personal_status" seems to contain information about the personal and marital status of the person. To determine how this feature relates to the task of whether this person receives a credit or not, we need to analyze how different values of personal_status correspond to the target classes.

Here is the analysis of the relationship between the feature "personal_status" and the target task:

- For the target class "yes" (person receives credit), the possible values of personal_status are likely to be:
    - "male single"
    - "female div/dep/mar"
    
  These values suggest that being single and having a status of "div/dep/mar" (divorced/dependent/married) are associated with a higher likelihood of receiving credit.

- For the target class "no" (person does not receive credit), the possible values of personal_status are likely to be:
    - "male div/sep"
    - "male mar/wid"
    
  These values indicate that being divorced or separated (div/sep) or being married or widowed (mar/wid) might be associated with a lower chance of receiving credit.

Based on this analysis, we can construct the dictionary with the requested format:

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

Please note that the analysis and the resulting dictionary are based on general assumptions and prior knowledge. The actual relationship between the feature and the target can vary depending on the specific dataset and context.