Paintings, Not Noise—The Role of Presentation Sequence in Labeling

Abstract

Abstract Labeling is critical in creating training datasets for supervised machine learning, and is a common form of crowd work heteromation. It typically requires manual labor, is badly compensated and not infrequently bores the workers involved. Although task variety is known to drive human autonomy and intrinsic motivation, there is little research in this regard in the labeling context. Against this backdrop, we manipulate the presentation sequence of a labeling task in an online experiment and use the theoretical lens of self-determination theory to explain psychological work outcomes and work performance. We rely on 176 crowd workers contributing with group comparisons between three presentation sequences (by label, by image, random) and a mediation path analysis along the phenomena studied. Surprising among our key findings is that the task variety when sorting by label is perceived higher than when sorting by image and the random group. Naturally, one would assume that the random group would be perceived as most varied. We choose a visual metaphor to explain this phenomenon, whereas paintings offer a structured presentation of coloured pixels, as opposed to random noise.