Based on the given feature description and the task of determining whether a person receives credit or not, we need to analyze the relationship between the feature "personal_status" and the target variable.

The feature "personal_status" is a categorical variable with the following categories:
- 'male single'
- 'female div/dep/mar'
- 'male div/sep'
- 'male mar/wid'

To analyze the relationship between the feature and the target, we can group the individuals based on their personal status and observe the distribution of the target variable (whether they receive credit or not) within each group.

Based on prior knowledge and assuming that personal status might have an impact on receiving credit, we can hypothesize the following relationships:
- Individuals categorized as 'male single' might have a higher chance of receiving credit compared to other categories, as they may have fewer financial commitments.
- Individuals categorized as 'female div/dep/mar' might have a moderate chance of receiving credit.
- Individuals categorized as 'male div/sep' might have a lower chance of receiving credit, as divorce or separation might impact their financial stability.
- Individuals categorized as 'male mar/wid' might have a relatively higher chance of receiving credit, as being married or widowed might imply more financial stability.

Based on these hypotheses, the dictionary for the relationship between "personal_status" and the target variable "receive_credit" can be generated as follows:

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

It is important to note that this analysis is based on assumptions and prior knowledge, and the actual relationship between the feature and the target can only be determined through data analysis and modeling.