Variability is crucially important for learning new skills. Consider learning how to serve in tennis. Should you always practice serving from the exact same location on the court, aiming at exactly the same spot? Although practising in more variable conditions will be slower at first, it will likely make you a better tennis player at the end. This is because variability leads to better generalisation of what is learned.
Chihuahuas and Great Danes
This principle is found in many domains, including speech perception, grammar, and learning words and categories. For instance, infants will struggle to learn the category ‘dog’ if they are only exposed to Chihuahuas, instead of many different kinds of dogs (Chihuahuas, Poodles and Great Danes).
“There are over ten different names for this basic principle!,” says MPI’s Limor Raviv, the senior investigator of the study. “Learning from less variable input is often fast, but may fail to generalise to new stimuli. But these important insights have not been unified into a single theoretical framework, which has obscured the bigger picture.”
To identify key patterns and understand the underlying principles of variability effects, Raviv and her colleagues reviewed over 150 studies on variability and generalisation across fields, including computer science, linguistics, categorization, motor learning, visual perception and formal education.
The researchers discovered that, across studies, the term variability can refer to at least four different kinds of variability, such as set size (e.g. the number of different examples or locations on the tennis court) and scheduling (e.g. practice schedules with different orders or time lags). “These four kinds of variability have never been directly compared — which means that we currently don’t know which is most effective for learning,” says Raviv.
The impact of variability depends on whether it is relevant to the task or not (arguably, the colour of the tennis court is not relevant to ser