The largest human cognitive performance dataset reveals insights into the effects of lifestyle factors and aging

Abstract

Making new breakthroughs in understanding the processes underlying human cognition may depend on the availability of very large datasets that have not historically existed in psychology and neuroscience. Lumosity is a web-based cognitive training platform that has grown to include over 600 million cognitive training task results from over 35 million individuals, comprising the largest existing dataset of human cognitive performance. As part of the Human Cognition Project, Lumosity's collaborative research program to understand the human mind, Lumos Labs researchers and external research collaborators have begun to explore this dataset in order uncover novel insights about the correlates of cognitive performance. This paper presents two preliminary demonstrations of some of the kinds of questions that can be examined with the dataset. The first example focuses on replicating known findings relating lifestyle factors to baseline cognitive performance in a demographically diverse, healthy population at a much larger scale than has previously been available. The second example examines a question that would likely be very difficult to study in laboratory-based and existing online experimental research approaches at a large scale: specifically, how learning ability for different types of cognitive tasks changes with age. We hope that these examples will provoke the imagination of researchers who are interested in collaborating to answer fundamental questions about human cognitive performance.

Keywords: aging; cognition; cognitive enhancement; fluid intelligence; learning; lifestyle factors.

Figure 1

(A) Lumosity includes exercises designed to improve cognitive performance targeting five areas of cognition, along with assessments based on standard neuropsychological tasks. (B) Demographic information is available from users' profiles and surveys that users can choose to participate in. (C) A map of users' locations based on their IP address at last login. The map was generated from a database of user IP addresses at login. Approximate Latitude and longitude coordinates were obtained for each IP address using MaxMind's GeoLiteCity database (available at http://www.maxmind.com/app/geolitecity). These coordinates were then rounded to the nearest 1/100th of a degree and aggregated to obtain a count of the number of users at each rounded coordinate. The size of each dot was mapped to the floor of the base-10 log of the number of users. As IP addresses were missing for some users, and in some cases IP addresses could not be mapped to geographic coordinates, the data used to generate the map was based on the geographic coordinates for 15,162,193 users.

Figure 2

(A) Exercises used in the analysis of the health and lifestyle survey. (B) The effect of reported sleep on game performance. (C) The effect of reported alcohol intake on game performance (controlling for age, gender, and level of education).

Figure 3

The four exercises used in the aging and learning analysis, and demographic information for each game.

Figure 4

(A) Mean game score by age at baseline. (B) Difference between 25th and 1st game score by age for each game. Error bars represent standard errors of the mean.