Chefs Know More than Just Recipes: Professional Vision in a Citizen Science Game

Abstract

Some citizen science projects use “games with a purpose” (GWAPs) to integrate what humans and computers, respectively, can do well. One of these projects is Foldit, which invites talented players to predict three-dimensional (3D) models of proteins from their amino acid composition. This study investigated players’ professional vision and interpret their use of recipes, small scripts of computer code that automate some protein folding processes, to carry out their strategies more easily when solving game puzzles. Specifically, this study examined when, how and why the players ran recipes when solving the puzzles, and what actions those recipes performed in the gameplay.Autoethnographic accounts of players at different levels of experience (beginner, intermediate, and expert) with playing the game were analyzed using a grounded theory approach. The analysis of what these players observed and did visualized the professional vision necessary to use recipes sensibly and effectively. The findings highlight three key abilities: (a) seeing beauty; (b) repairing errors made by recipes, and (c) monitoring a large quantity of information to perform actions effectively. This study indicates that players indeed have to develop a professional vision independent of what the game itself can highlight. This is related to the nature of the game where it seems impossible for the game developers to show the affordances, because they are unknown. Players must learn to see the affordances and develop a professional vision, which means that they have to learn these skills through gaming.