Multidimensional scaling allows visualizing high-dimensional data as 2D maps with the premise that insights in 2D reveal valid information in high-dimensions, but the resulting projections always suffer from artifacts such as false neighborhoods and tears. These artifacts can be revealed by interactively coloring the projection according to the original dissimilarities relative to a reference item. However, it is not clear if conveying these dissimilarities using color and displaying only local information really helps to overcome the projections artifacts. We conducted a controlled experiment to investigate the relevance of this interactive technique using several datasets. We compared the bare projection with the interactive coloring of the original dissimilarities on different visual analysis tasks involving outliers and clusters. Results indicate that the interactive coloring is effective for local tasks as it is robust to projection artifacts whereas using the bare projection alone is error prone.