For discussion: We’re all quality professionals, right? We understand root cause analysis, right? But do we understand it and act on it as well as when we were babies?
--Quality Digest editors
If you flip a light switch and nothing happens, there are a couple of possible explanations. One is that something has gone wrong in the external world—maybe the bulb has burned out. Alternatively, you may have made a mistake, perhaps flipping the wrong switch. Learning to make the distinction between our own influence and the impact of the outside world is a critical part of cognitive development. Infants can integrate prior knowledge with statistical data to make these distinctions at a very young age, according to a new study from MIT.
Cognitive scientists Laura Schulz and Hyowon Gweon showed that 16-month-old infants can, based on very little information, make accurate judgments of whether a failed action is due their own mistake or to circumstances beyond their control.
The study, which was published in the June 24 issue of Science, is consistent with probabilistic inference models of cognition. According to these models, very young infants can quickly learn basic principles about how the world works, then use those rules to interpret the statistical evidence they see.
“That’s the amazing thing about what the babies are doing,” says Schulz, the Class of 1943 Career Development Associate Professor of Cognitive Science at MIT. “They can use very, very sparse evidence because they have these rich prior beliefs, and they can use that to make quite sophisticated, quite accurate inferences about the world.”
Schulz, who does her infant cognition experiments at the PlayLab at Boston Children’s Museum, studies how children learn about the world by problem solving—in particular, how they interpret evidence from the world and fit it into what they already know.
In this study, Schulz and Gweon, a graduate student in MIT’s Department of Brain and Cognitive Sciences, looked at how babies react to situations in which their actions fail to produce the expected outcome. In one condition, babies saw a toy that played music when one experimenter pushed a button on the toy but failed when a second experimenter tried, suggesting that the failure was due to the agent. In another condition, the button sometimes activated the toy and sometimes failed for each of the two experimenters, suggesting that something was wrong with the toy. When infants were given either toy, they were unable to activate it.
The researchers hypothesized that infants would use the statistical evidence to consider the plausibility of two explanations—that they were doing something wrong, or that the toy was broken—and determine which was most likely.
Depending on the circumstances of the experiment, the babies did respond differently, indicating that they were able to weigh evidence for each explanation and react accordingly. Infants who saw evidence suggesting the agent had failed tried to hand the toy to their parents for help, suggesting the babies assumed the failure was their own fault. Conversely, babies who saw evidence suggesting that the toy was broken were more likely to reach for a new toy (a red one that was always within reach).
Schulz says she was at first “blown away” that 16-month-olds could use very limited evidence (the distribution of outcomes across the experimenters’ actions) to infer the source of failure and decide whether to ask for help or seek another toy. That finding lends strong support to the probabilistic inferential learning model.
While this is not the first study to show that babies are “impressively smart,” it frames that intelligence in a new way, says Nora Newcombe, professor of psychology at Temple University. “What the finding here seems to be is kids are smart, but the way in which they’re smart is really tracking statistics. They’re really good learners,” she says.
In related work, Schulz is now studying babies’ sensitivity to statistical sampling—that is, how their inferences are affected by whether they believe a sample is representative of a population.
Article by Anne Trafton. Reprinted with permission of MIT News.