CogSci 2025

•

August 02, 2025

•

San Francisco, United States

keywords:

development

big data

learning

psychology

From infancy to adulthood, habituation and dishabituation enable learners to filter out repetitive information and orient to novel information. Because variability in these processes has been linked to differences in later cognitive outcomes, studying individual differences in habituation and dishabituation is crucial for building a more comprehensive model of early learning. Here, we leveraged large-scale datasets spanning infants, preschoolers, and adults to examine how individual differences in habituation predict dishabituation magnitude. We found that faster habituation and higher volatility predicted stronger dishabituation. Moreover, we showed that different measures of dishabituation sometimes yielded divergent patterns, suggesting that measurement choices can influence observed effects and should be carefully considered in developmental research. These findings reveal how endogenous factors are meaningful drivers of looking behaviors. Overall, our results underscore the need for large-scale data approaches to studying visual attention across the lifespan.

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