Measuring Cognitive Abilities in the Wild: Validating a Population-Scale Game-Based Cognitive Assessment.

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide-ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab-based experiments, new efforts have been underway toward greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game-based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging narrative. Skill Lab consists of six mini-games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10,725), we conducted a comprehensive validation in the wild of a game-based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users' in-game behavior. Follow-up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game-based measures are five times faster to complete than the equivalent task-based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross-sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in-the-wild systematic assessment of cognitive abilities as a promising first step toward population-scale benchmarking and individualized mental health diagnostics.

Does the size of rewards influence performance in cognitively demanding tasks?

Classic micro-economic and psychology theories propose different implications of monetary incentives on performance. Empirical studies in sports settings show that athletes generally perform worse when the stakes are higher, while a range of lab studies involving cognitively demanding tasks have led to diverging results, supporting positive, negative and null-effects of higher (vs. lower) stakes. In order to further investigate this issue, we present a pre-registered, randomized, controlled trial of 149 participants solving both anagrams and math addition tasks. We do not find a statistically significant effect of the size of the reward on neither performance, self-reported effort nor intrinsic motivation. We propose that future studies should contrast the potential impact of rewards on different kinds of task, e.g. compare tasks that solely require cognitive effort vs. tasks that require motor skills, as in sports.

Remote optimization of an ultracold atoms experiment by experts and citizen scientists.

We introduce a remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.