Based on prior knowledge, a higher capital loss might indicate a lower income, as it represents the financial loss incurred by the individual. Hence, we can analyze the relationship between the feature (capital-loss) and the task (income > $50000) as follows:

For individuals who earn more than $50000 (target class: yes), we can expect to see lower capital loss values, as a higher income generally implies better financial stability. Therefore, the feature "capital-loss" is likely to have lower values for the target class "yes".

On the other hand, for individuals who do not earn more than $50000 (target class: no), we can expect to see higher capital loss values. This is because individuals with lower incomes may face financial hardships or unexpected expenses, resulting in a higher capital loss.

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

```json
{
   "no": [1504, 1564, 1700, 1721, 1876],
   "yes": [-280, -578, -625, -693, -880]
}
```

Please note that the values provided are just examples and you may need to further analyze the dataset to identify typical capital-loss values for each target class accurately.