Hyowon Gweon

The Double-edged Sword of Pedagogy: Instruction limits spontaneous exploration and discovery

Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated childrens exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naïve adult demonstrated the function, and at baseline. Preschoolers in the pedagogical condition focused almost exclusively on the target function; by contrast, children in the other conditions explored broadly. In Experiment 2, we show that children restrict their exploration both after direct instruction to themselves and after overhearing direct instruction given to another child; they do not show this constraint after observing direct instruction given to an adult or after observing a non-pedagogical intentional action. We discuss these findings as the result of rational inductive biases. In pedagogical contexts, a teachers failure to provide evidence for additional functions provides evidence for their absence; such contexts generalize from child to child (because children are likely to have comparable states of knowledge) but not from adult to child. Thus, pedagogy promotes efficient learning but at a cost: children are less likely to perform potentially irrelevant actions but also less likely to discover novel information.

Infants consider both the sample and the sampling process in inductive generalization.

The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the process by which evidence is sampled. Because neither the property extension nor the sampling process is directly observable, the learners ability to make accurate generalizations depends on what is known or can be inferred about both variables. In particular, different inferences are licensed if samples are drawn randomly from the whole population (weak sampling) than if they are drawn only from the propertys extension (strong sampling). Given a few positive examples of a concept, only strong sampling supports flexible inferences about how far to generalize as a function of the size and composition of the sample. Here we present a Bayesian model of the joint dependence between observed evidence, the sampling process, and the property extension and test the model behaviorally with human infants (mean age: 15 months). Across five experiments, we show that in the absence of behavioral cues to the sampling process, infants make inferences consistent with the use of strong sampling; given explicit cues to weak or strong sampling, they constrain their inferences accordingly. Finally, consistent with quantitative predictions of the model, we provide suggestive evidence that infants’ inferences are graded with respect to the strength of the evidence they observe.

Differences in the right inferior longitudinal fasciculus but no general disruption of white matter tracts in children with autism spectrum disorder

Significance One of the most accepted brain “signatures” of autism spectrum disorder (ASD) is a reduction in the integrity of long-range white-matter fiber tracts. Here, we assessed known white matter tracts in children with ASD by using diffusion-weighted imaging. In contrast to most prior studies, we carefully matched for head motion between groups. When data quality was matched, there was no evidence of widespread changes in white-matter tracts in the ASD group. Instead, differences were present in only one tract, the right inferior longitudinal fasciculus. These data challenge the idea that widespread changes in white-matter integrity are a signature of ASD and highlight the importance of matching for data quality in future diffusion studies of ASD and other clinical disorders. One of the most widely cited features of the neural phenotype of autism is reduced “integrity” of long-range white matter tracts, a claim based primarily on diffusion imaging studies. However, many prior studies have small sample sizes and/or fail to address differences in data quality between those with autism spectrum disorder (ASD) and typical participants, and there is little consensus on which tracts are affected. To overcome these problems, we scanned a large sample of children with autism (n = 52) and typically developing children (n = 73). Data quality was variable, and worse in the ASD group, with some scans unusable because of head motion artifacts. When we follow standard data analysis practices (i.e., without matching head motion between groups), we replicate the finding of lower fractional anisotropy (FA) in multiple white matter tracts. However, when we carefully match data quality between groups, all these effects disappear except in one tract, the right inferior longitudinal fasciculus (ILF). Additional analyses showed the expected developmental increases in the FA of fiber tracts within ASD and typical groups individually, demonstrating that we had sufficient statistical power to detect known group differences. Our data challenge the widely claimed general disruption of white matter tracts in autism, instead implicating only one tract, the right ILF, in the ASD phenotype.

16-Month-Olds Rationally Infer Causes of Failed Actions

Infants use statistical inference to decide what went wrong. Sixteen-month-old infants (N = 83) rationally used sparse data about the distribution of outcomes among agents and objects to solve a fundamental inference problem: deciding whether event outcomes are due to themselves or the world. When infants experienced failed outcomes, their causal attributions affected whether they sought help or explored.

Developmental Cognitive Neuroscience of Theory of Mind

This chapter is about what we know, and what we do not know, about how the human brain acquires its amazing capacity for theory of mind (ToM). In the past few decades, ToM has been studied intensively in childhood development (using behavioral measures) and in the adult human brain (using functional neuroimaging). Converging evidence from these two approaches provides insight into the cognitive and neural basis of this key human cognitive capacity. However, as we highlight, we are especially excited about the future of ToM in developmental cognitive neuroscience: studies that combine both methods, using neuroimaging methods to directly study cognitive and neural development in childhood.

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology: (Trends in Cognitive Sciences 20, 589–604; July 19, 2016)

Due to an oversight in the preparation of this Feature Review article, the authors mistakenly labeled Figure 2 Panel E “Forego low-cost and high-cost plans”. The correct label for Figure 2 Panel E is “Forego low-reward and high-reward plans”. Figure 2 has been corrected in the article online. The correct version of the panel is also shown here.View Large Image | Download PowerPoint Slide

Development of children’s sensitivity to overinformativeness in learning and teaching.

Effective communication requires knowing the “right” amount of information to provide; what is necessary for a naïve learner to arrive at a target hypothesis may be superfluous and inefficient for a knowledgeable learner. The current study examines 4- to 7-year-olds’ developing sensitivity to overinformative communication and their ability to decide how much information is appropriate depending on the learner’s prior knowledge. In Experiment 1 (N = 184, age = 4.09–7.98 years), 5- to 7-year-old children preferred teachers who gave costly, exhaustive demonstrations when learners were naïve, but preferred teachers who gave efficient, selective demonstrations when learners were already knowledgeable given their prior experience (i.e., common ground). However, 4-year-olds did not show a clear preference. In Experiment 2 (N = 80, age = 4.05–6.99 years), we asked whether children flexibly modulated their own teaching based on learners’ knowledge. Five and 6-year-olds, but not 4-year-olds, were more likely to provide exhaustive demonstrations to naïve learners than to knowledgeable learners. These results suggest that by 5 years of age, children are sensitive to overinformativeness and understand the trade-off between informativeness and efficiency; they reason about what others know based on the presence or absence of common ground and flexibly decide how much information is appropriate both as learners and as teachers.