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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. At a high level, I'm interested in questions such as: What is knowledge, what is thinking, and where do those come from? What are the components and processes of a mind? How do minds develop and learn as we grow up? And how does all that relate to what happens in brains? I'm interested in taking a Cognitive Science approach to these questions: 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 our cognitive mechanisms — the mind’s “startup software” — looks like in childhood, and how our reasoning processes may change with experience and general developmental progress. This is not an original proposal of mine, but dates back at least as far as the 1950s:

"Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain." (Turing, 1950, Computing Machinery and Intelligence)

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 my 'why' behind all this, see here!)

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


Children's Learning From Unexpected Action Outcomes

As people, we perform lots of actions and make many choices on a daily basis. However, our actions and choices do not always have the outcomes we expect. Maybe your attempt at cooking the other night turned out worse than anticipated, whereas your friend’s painting turned out better than she expected. In short, reality can (and often does) diverge from our expectations about what we can do. Such instances, particularly cases of unexpected success, can be a powerful impetus for learning: They can shift our understanding of what we are capable of ("Wow, I can do this!"), spur us on to search for explanations ("how did I do this?"), and even encourage us to explore our capabilities and improve ("what else can I do?"). In this project, we study how young children respond to situations where they achieve things they thought they could not, and how they relate to subsequent exploration, challenge-seeking, and learning.

Relevant work/references:


Human Inferences About Task Difficulty

When deciding whether to pursue a goal, we think about how difficult it would be to accomplish. These thoughts—representations of task difficulty—are fundamental to our lives; they influence even the most mundane everyday decisions (e.g., should I prepare an instant ramen or a 3-course meal?), help us learn and improve by guiding how we navigate various challenges (e.g., should I try this steep ski slope?), and support effective planning both for ourselves and for others (e.g., which task requires more time, and whom should I help?). Remarkably, as adults, we are able to generate reasonable intuitions about how easy or hard a task might be, even for novel tasks we have never tried before. This ability to reason about task difficulty is particularly important in early childhood— a period of intense learning where learners frequently face novel tasks without much prior experience. Where do these intuitions come from, and how do they develop? Although an intuitive sense of difficulty is critical to many of our everyday decisions, its underlying cognitive processes are not well understood. Using block-building as a case of long-horizon object manipulation tasks, this project (manuscript in prep) brings together developmental, cross-cultural, computational, and virtual reality methods to investigate how humans judge novel task difficulty. We find that the ability to judge task difficulty reflects an understanding of action costs (Exp. 1), emerges early in life (Exp. 2), shows similar developmental trajectory despite significant differences in cultural experiences (Exp. 3), and flexibly adapts to changes in the latent properties of one's physical environment (e.g. friction between blocks; Exp. 4). These findings suggest that human intuitions about difficulty involve more than simple extrapolations from past experiences; rather, they are grounded in rich, sophisticated inferences about how agents act on the physical world to support powerful and flexible reasoning about the difficulty of novel tasks.

Relevant work/references:


Consistency Monitoring and Domain-Specific Learning in Early Childhood

Domain-specific 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 is “not what children lack, but what they have,” namely children’s naïve theories constructed in early childhood (Carey, 2000). As an example of internal inconsistency in children’s understanding of the physical world, consider the common naïve belief that “air is nothing” or that “air does not take up any space” held by 6- and 7-year-olds (Carey, 2009). Contrast that with the belief – also held by many 6- and 7-year-olds – 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 (by explicit, we mean available to verbal report) (Limón & Carretero, 1997), 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 (c.f., Posner, Strike, Hewson, Gertzog, 1982). 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. Indeed, explicitly noticing inconsistencies in one’s own understanding – as contrasted with implicit measures of feelings of uncertainty or slowing down – might facilitate the process of generating and assimilating more accurate models of reality (Limón & Carretero, 1997). In this project (manuscript in press), 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.

Relevant work/references:


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.

Relevant work/references: