Predictions Drive Deeper Learning—True for Pre-verbal Infants as well as for You and Me

Good testing demands explicit predictions to sharpen our minds and prepare us to learn. I summarized the benefits of predictions in this recent post. A prediction is an expression of what we expect to happen when we run a relevant test.

It appears that the contrast between expectation and what we see is fundamental to human learning. Aimee Stahl and Lisa Feigenson in the 3 April 2015 issue of Science describe a series of experiments with 11-month old infants that provide support to this proposition.

Here’s the abstract of their article:

“Given the overwhelming quantity of information available from the environment, how do young learners know what to learn about and what to ignore? We found that 11-month-old infants (N = 110) used violations of prior expectations as special opportunities for learning. The infants were shown events that violated expectations about object behavior or events that were nearly identical but did not violate expectations. The sight of an object that violated expectations enhanced learning and promoted information-seeking behaviors; specifically, infants learned more effectively about objects that committed violations, explored those objects more, and engaged in hypothesis-testing behaviors that reflected the particular kind of violation seen. Thus, early in life, expectancy violations offer a wedge into the problem of what to learn.”

For those of us older than 11 months, the same phenomenon seems to hold—we’re more likely to learn when we contrast expectation (our mental model of the world) with what our senses tell us about the real world. When running tests, making a prediction forces our conscious attention to prediction—to articulate our expectation. If we don’t make expectations explicit, we lose an opportunity to really learn about the systems we’re seeking to improve.

Stahl and Ferguson conclude their article crisply:

“… our experiments reveal that when infants see an object defy their expectations, they learn about that object better, explore that object more, and test relevant hypotheses for that object’s behavior. Seen through this lens, the decades of findings that infants look longer at surprising events suggest not only that infants are equipped with core knowledge about fundamental aspects of the world but also that this knowledge is harnessed to empower new learning even in infancy. Thus, core knowledge is not an alternative to learning but is instead a key ingredient in driving learning forward.”

Effective tests use our existing knowledge to form our predictions; the contrast between prediction and actual results catalyzes further learning, in a series of cycles. All this should sound familiar, we’re just restating the essence of the Model for Improvement:

 

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From Analysis to Action by Way of Experiments