Hey! I’m a postdoctoral researcher in the Psycholinguistics group at the Faculty of Language Science and Technology in the University of the Saarland in Germany. I recently graduated from the Ph. D. program at the Department of Psychologgy, Cornell University, under the supervision of Morten H. Christiansen. My main research interests lie in computational modeling, machine learning and other data analysis techniques applied to the study of language learning and processing. Having a background in Philosophy of Science, I’m also interested in data-driven exploration of scientific activity. I like to tackle these topics with a firm eye on theoretical considerations and the predictions of overarching frameworks.
Ph. D. in Psychology, 2023
MA in Philosophy, 2016
Universidad de Chile
BA in Philosophy, 2012
Universidad de Chile
To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language.
It is not clear to what extent these divisions are reflected in the organization of work done within the field, as expressed by patterns of communication between authors. To assess this, we analyze the phenomenology literature using bibliometric methods (Osareh, 1996). This makes it possible to supplement an intuitive understanding of its structure with an empirically grounded analysis of citation patterns. In particular, we extract an author-wise citation network (Radicchi et al., 2012) for the published phenomenology literature, a network of nodes and connections, where nodes correspond to authors of articles or books about phenomenology, and connections correspond to citations from one author to another. The resulting graph has 11,980 nodes and 69,324 connections.1 We then study clusters in this network, that is, groups of authors who cite each other more than they cite authors in other clusters and compare this more bottom-up image of communication dynamics withinthe field to the different sub-groups identified in expert historical reconstructions.
What predicts individual differences in children’s acquisition of consonant production across languages? Considerations of children’s development of early speech production have traditionally emphasized inherent physiological constraints of the vocal apparatus that speakers generally have in common (i.e., articulatory complexity). In contrast, we propose a statistical learning account of phonological development, in which phonological regularities of the ambient language guide children’s learning of those regularities in production. Across four languages (English, Spanish, Japanese, and Korean), we utilized recent meta-analytic dataset of age of consonant acquisition spanning 28 studies. High-density measures of children’s ambient language environment from over 8,000 transcripts of speech directed to over 1,000 children were used to assess how well the frequency of consonants in child-directed speech predict the age of consonant acquisition. Our results suggest that both frequency and articulatory complexity are related to age of acquisition, with similar results found for English, Spanish, Japanese, and Korean. Consonants heard frequently by children tended to be incorporated into their production repertoires earlier and consonants heard less frequently are incorporated into production repertoires later in development. We discuss future directions that incorporate a statistical learning pathway towards learning to produce the sound patterns of the ambient language.
In recent years, the “science of science” has combined computational methods with novel data sources to understand the dynamics of research communities. Many of the questions investigated by science of science are also relevant to academic philosophy. To what extent can the discipline be divided into subfields with different methods and topics? How are prestige and credit distributed across the discipline? And how do these factors interact with other factors, such as gender, to shape job market outcomes? Using job market data for anglophone academic philosophy, this paper finds, first, evidence that is consistent with the analytic-continental divide but is also consistent with other, more complex ways of organizing academic philosophy into distinct intellectual traditions; second, a clear prestige hierarchy, dividing Ph.D. programs into two distinct prestige categories; and, third, evidence that gender, prestige, and country have notable effects on academic job market outcomes for recent philosophy Ph.Ds.
The study by Kidd and Garcia is long overdue. Their analyses of published research on language acquisition highlight the lack of typological diversity in studies of how children acquire their native tongue. We concur with their conclusion that more research on understudied languages is urgently needed. However, we argue that what the field needs is not just wider cross-linguistic coverage but a systematic comparative approach to language acquisition – one in which investigations of well-studied languages still has much to contribute.