Based on the description, the feature "FastingBS" represents the fasting blood sugar levels of a patient. The feature is binary, with a value of 1 if the fasting blood sugar is greater than 120 mg/dl and 0 otherwise. 

To analyze the relationship between the feature and the task (detecting heart disease), we need to consider prior knowledge. Generally, high fasting blood sugar levels may indicate the presence of heart disease. Therefore, we can hypothesize that higher values of "FastingBS" (i.e., 1) are more likely to be associated with a "yes" response indicating heart disease. Conversely, lower values of "FastingBS" (i.e., 0) may be more likely to be associated with a "no" response indicating no heart disease.

To create the dictionary, we can choose typical values for each target class ("yes" and "no") based on the hypothesis mentioned above. Here's the dictionary:

```json
{
	"no": [76.0, 88.0, 95.0, 100.0, 115.0],
	"yes": [130.0, 140.0, 150.0, 160.0, 180.0]
}
```

In this example, we selected 5 typical fasting blood sugar values for each target class. For the "no" class (no heart disease), we chose values that are relatively lower. For the "yes" class (heart disease), we chose values that are relatively higher. These values are just illustrative and may vary based on the actual dataset and domain knowledge.