How Percieved Effort Affects Resistance to Learning New Tools
These three smart ovens can produce cakes of varying quality. Which one would you spend your time on mastering? Try it out here.
Many of us are familiar with asking someone to use a new software or technology and being met with resistance to the change. Perhaps you’ve even felt it yourself, feeling that the tool you currently use works well enough and that the new tool doesn’t warrant the effort needed to learn it. To investigate how this resistance to new tools relates to the effort required to learn the tool and the age of the user, I’ve designed a set of studies which ask people to learn to use kitchen appliances of varying difficulty. You can try one of the experiments
here.
The Effect of Practice in Overcoming Age-Related Performance Gaps
Shown here are the probabilities of an older adult catching up to a younger adult on Lumosity games testing different cognitive abilities. Values of at least 0.50 mean that an older adult who practiced 80 more times than a younger adult was able to beat the younger adult’s score. The axis numbers are decade markers, such that “50” represents those users 50-59 years old and “60” means 60-69 years old, etc.
It’s been fairly well studied that younger adults learn faster and perform better on cognitive tasks testing memory and multitasking ability compared to adults even just 10 years older. This performance gap only increases as the age gap gets larger. However, could the older person ever match or even surpass the younger person’s performance given unlimited time to practice the task?
I investigated this question using data obtained from users of the popular brain training website
Lumosity. I analyzed the performance data of nearly 10,000 users aged 18-90 years old and found that if older adults practice more than younger adults,the probability of older adults catching up to younger adults increases. Additionally, the extra practice allows older adults to match the performance of adults up to 20 years younger in many games (see graphic above). This work was peer-reviewed and you can read the published paper
here.
How Autonomy Affects Task Engagement
Participants are given the option to choose the types of images to classify. You can try this experiment for yourself here.
When scientists usually run human behavioral experiments, especially online, the researcher has total control over the experiment flow and the participant simply reacts to the stimuli presented. This works well enough, but we wondered whether giving participants more agency during the experiment would have an effect on their engagement and performance on the task. To investigate this we devised a series of classification tasks where, depending on the task, participants are allowed to choose the category of images that they label, the number of images in each category to label, or even when to quit the experiment. The accuracy and number of images classified of participants who are given agency is compared to those of participants who aren’t given any agency and go through a “traditional” version of the task.
