Based on prior knowledge, we can analyze the relationship between the "credit_history" feature and the target task of determining whether a person receives credit or not.

Possible analysis:
- "critical/other existing credit": It is likely that individuals with this credit history may have a higher chance of not receiving credit, as it suggests a negative or risky credit history.
- "existing paid": Individuals with this credit history may have a higher chance of receiving credit, as it indicates a good credit history with existing loans being paid off.
- "delayed previously": Individuals with this credit history may have a higher chance of not receiving credit, as it suggests a history of delayed payments.
- "no credits/all paid": Individuals with this credit history may have a higher chance of receiving credit, as it indicates no past credits or all past credits being paid off.
- "all paid": Individuals with this credit history may have a higher chance of receiving credit, as it indicates a good credit history with all past credits being paid off.

Based on this analysis, we can create the dictionary as follows:

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