From World to Word and Back: An Empirical and Philosophical Investigation on Syntactical Bootstrapping

This thesis has two connected components: an empirical project and a philosophical project In the empirical project I present an experiment investigating syntactic bootstrapping as a noun learning mechanism. Previous studies have shown that infants as young as 6.5 months of age can perceive the causal relationship in a simple Michotte launching event. Yet it is not until the age of 2 year that children show evidence of reasoning with causality (Gopnik & Sobel, 2000; Gopnik, Sobel, Shulz & Glymour, 2001; Nazzi & Gopnik, 2003; Sobel, Tenenbaum & Gopnik, 2004). One reason for this lag is language. A conceptual understanding of causality requires understanding causal language, and the developmental trajectory of causal language in early childhood remains unclear. Studies have shown that toddlers at 15 months of age can match the transitive structure of the sentence to a causative event through a mechanism known as syntactic bootstrapping (Jin & Fisher, 2014). In the current experiments, we tested whether 12-month-olds and 20-month-olds can rely on the same mechanism to acquire the association between the subject of the sentence and the causal agent of the event. Due to COVID-19 outbreak, the data collection process was interrupted. Only preliminary data was presented in this paper, with discussion focused on the implications of different potential outcomes.In the philosophical project I present a theoretical review on the foundations of infant looking time paradigms. In the past decades, the looking time measurement has been the backbone of developmental psychology. Most of our understanding of infant perceptual and cognitive development came from research using at least one of the three looking time paradigms: habituation, familiarization, and Violation of Expectation. However, the myriad claims supported by infant looking time paradigms suffer from what Richard Aslin called a “many-to-one” problem: there are many different postulated hypotheses, but only one measurement available (Aslin, 2007). The vast underdetermination between the evidence and the interpretation originates from the lack of attention devoted to the theoretical foundations of the looking time paradigms. In this paper, I surveyed and compared the four most prevalent theories in the field: The Comparator Theory, the Multifactor Model, the Object File Theory, and the Dual Process Theory. I analyzed each theory’s strengths and weaknesses in the context of experimental design and data interpretation. I also compared the four theories against each other to assess their explanatory scope. I arrived at the conclusion that none of the theories currently available is sufficient to justify the connection between the evidence and the interpretation. In the future, more systematic investigations are needed to construct a more precise, quantitative interpretation framework to guide empirical research.</div

A synthesis of early cognitive and language development using (meta-)meta-analysis

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes

A synthesis of early cognitive and language development using (meta-)meta-analysis

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes

Habituation reflects optimal exploration over noisy perceptual samples

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context

Habituation reflects optimal exploration over noisy perceptual samples

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context

US-China differences in cognition and perception across 12 tasks: Replicability, robustness, and within-culture variation

Cultural differences between the US and China have been investigated using a broad array of psychological tasks measuring differences between cognition, language, perception, and reasoning. We examine the robustness of several classic experimental paradigms in cross-cultural psychology. Using online convenience samples of adults, we conducted two large-scale replications of 12 tasks previously reported to show cross-cultural differences. Our results showed a heterogeneous pattern of successes and failures: five tasks yielded robust cultural differences across both experiments, while six showed no difference between cultures, and one showed a small difference in the opposite direction. We observed moderate reliability in all of the multi-trial tasks, but there was little shared variation between tasks. Additionally, we did not see within-culture variation across a range of demographic factors in our samples. Finally, as in prior work, cross-cultural differences in cognition (in those tasks showing differences) were not strongly related to explicit measures of cultural identity and behavior. All of our tasks, data, and analyses are available openly online for reuse by future researchers, providing a foundation for future studies that seek to establish a robust and replicable science of cross-cultural difference

Predicting graded dishabituation in a rational learning model using perceptual stimulus embeddings

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance