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research

Much of our most "successful” science — particularly the natural sciences, including physics, chemistry, and biology — has concerned itself with describing the phenomena of the external world; i.e., the structure of that which is around us, and the physical laws governing that which is around us. But there is also that which is within us humans, particularly our minds, and that which governs our minds in making sense of what is around us. Broadly, I'm interested in Cognitive Science: Using multidisciplinary approaches to study the structure of the human mind, and the mechanisms and computational processes – the "cogs and wheels" – that drive it in reasoning and learning about the world and its underlying order, objects, events, and people. I’m particularly curious about what the initial ‘configuration’ of these cognitive mechanisms – the mind’s “startup software” – looks like in childhood, and how our reasoning processes may change with experience and general developmental progress. To this end, I conduct behavioral experiments with toddlers, children, and adults, and draw on computational methods to build precise theories explaining aspects of human reasoning. (If you're interested in learning more about the 'why' behind all this, see here!)

Currently, I work on the following projects (for past and completed projects, see my CV):

Human Inferences About Task Difficulty

How do we know how hard it is to complete a task even before trying? In our daily lives, we often have to gauge the difficulty of a task before ever having tried the task at hand. We can also think about how hard it would be to achieve virtually any task, even fantastical ones like building a Minecraft house or completing a Mario Kart race. Our current work (manuscript in preparation) suggests that humans face this challenge by drawing inferences about the cost of achieving a goal, guided by an intuitive understanding of real-world physics and action planning. Using block-building tasks—a class of long-horizon object manipulation tasks that is simple in concept but requires complex planning—we show that these inferences are systematic, present early in life, stable across cultures, yet flexible enough to account for variations in the physical environment.

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Prediction Error and Surprise in Early Childhood

How do we respond when our actions have unexpected outcomes? Consider, for instance, the first time you built a snowman. The chances are that the outcome of your attempt was not quite what you expected; perhaps the snowman you built toppled over without manual support, was misshaped, or even outright unrecognizable. Such discrepancies between an expected outcome and the actual outcome are commonplace in everyday life. The notion of a mismatch between expectation and outcome, termed ‘prediction error,’ is a centerpiece of contemporary theories of learning across machine learning, neuroscience, and cognitive science more broadly. Even infants and children hold rich expectations about the world around them and show surprise when observing objects, events, and agents that violate their expectations regarding some physical or social rule. Subsequently, they tend to further explore the surprising thing in question. When children’s very own actions result in outcomes differing from their expectations, do their emotional and behavioral responses also reflect surprise, a sign of prediction error? To investigate this question, we are currently conducting a set of behavioral experiments with young preschoolers. In this line of work, I'm interested in better understanding what enables and drives our youngest learners in building an understanding of themselves as causal agents in the world.

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Error Monitoring and Domain-Specific Learning

Theories help bind together our systems of beliefs and provide broad explanatory accounts of the data we observe. However, our theories about the world are fallible: they are sometimes wrong, sometimes internally inconsistent, and often incomplete. This fallibility is particularly pronounced in children’s and adults’ naïve theories. Indeed, one of the biggest obstacles that science teachers face in the classroom are the naive and inaccurate beliefs constructed in early childhood, as these beliefs bear on—and conflict with—the material educators cover with students. For example, consider the common belief that “air is nothing” or that “air does not take up any space” held by young children. Contrast that with the belief – also held by many children – that “there is no air in outer space” or that “we use air to fill up balloons.” The belief that “air does not occupy space” is inconsistent with the belief that “air can fill up a balloon." Importantly, children do not easily notice this inconsistency at an explicit level, and they do not easily change their belief that “air does not take up any space” upon seeing anomalous data, namely balloons being filled up with air. The existence of such inconsistent beliefs raises the question of how important the general ability to explicitly notice inconsistencies is for the process of theory revision and theory construction. In a study with elementary school-aged children (manuscript in prep), we investigated the relationship between children’s domain-general ability to explicitly notice inconsistencies among statements and their domain-specific progress towards generating more accurate models of reality.

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Belief Hesitancy During Theory Construction in Early Childhood

Does air weigh anything at all? As an adult, you may pause for a moment before responding, but you’ll likely end up settling on “yes.” Children, however, hold and express naive beliefs (e.g., “Air is nothing”) that are often incongruent with our scientific understanding of the physical world (e.g., “Air is matter”). Learners eventually revise these naive beliefs throughout development and formal schooling. In this ongoing study, we ask how children who have not yet started (or are at the very beginning) of acquiring a scientific theory respond to questions that are either congruent or incongruent with their naive theories. In particular, we use a series of online tasks to examine preschoolers' and elementary schoolers' response patterns in answering various questions in the domains of physical and biological reasoning. With this line of work, I seek to shed light on how the minds of young learners operate in reasoning about counterintuitive aspects of the world around them, and by extension, which cognitive challenges they might face in their early science learning when their intuitions lead them astray.

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