Humans, even from infancy, are capable of unsupervised (“sta- tistical”) learning of linguistic information. However, it re- mains unclear which of the myriad algorithms for unsuper- vised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can ac- tually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most confidence intervals were very large, and few moderators were found to be significant. These findings are consistent with prior work suggesting low power and precision in the litera- ture. Constraining theory will require more, higher-powered studies.

Since the Workshop on Scaling Cognitive Science was held at CogSci 2020, the methodological landscape of the field has undergone a significant shift toward online experiments. The COVID-19 pandemic forced greater numbers of cognitive scientists to collect data over the internet and to explore online alternatives to methods which they had previously used only in the lab. For example, since this time, eye-tracking capabilities have been introduced to a number of software packages for online experiments (Ba ́nki, de Eccher, Falschlehner, Hoehl, & Markova, 2022; Slim & Hartsuiker, 2022; Yang & Krajbich, 2021).

Recent reports have suggested that many published results are unreliable. To increase the reliability and accuracy of published papers, multiple changes have been proposed, such as changes in statistical methods. We support such reforms. However, we believe that the incentive structure of scientific publishing must change for such reforms to be successful. Under the current system, the quality of individual scientists is judged on the basis of their number of publications and citations, with journals similarly judged via numbers of citations. Neither of these measures takes into account the replicability of the published findings, as false or controversial results are often particularly widely cited. We propose tracking replications as a means of post-publication evaluation, both to help researchers identify reliable findings and to incentivize the publication of reliable results. Tracking replications requires a database linking published studies that replicate one another. As any such database is limited by the number of replication attempts published, we propose establishing an open-access journal dedicated to publishing replication attempts. Data quality of both the database and the affiliated journal would be ensured through a combination of crowd-sourcing and peer review. As reports in the database are aggregated, ultimately it will be possible to calculate replicability scores, which may be used alongside citation counts to evaluate the quality of work published in individual journals. In this paper, we lay out a detailed description of how this system could be implemented, including mechanisms for compiling the information, ensuring data quality, and incentivizing the research community to participate.

Humans are born with an intuitive representation of the physics world. How accurate is intuitive physics? Researchers from education focus on the failures, students' errors and misconceptions while cognitive psychologists argue humans anticipate and manipulate physical environments in ways betraying veridical knowledge of classical mechanics. One solution is to hypothesize there are distinct systems of “cognitive physics” with different limitations and deployment in the tasks favored by the two literatures. The goal of current study is to gather evidence from psychometric studies by estimating how many distinct factors explain performance on intuitive physics assessments. We build an augmented concept inventory including several previously-validated concept inventories, around 120 items. The pilot study indicated that participants recruited online from Prolific displayed expected accuracy on the tasks. We are now collecting around 1,000 participants and applying multidimensional item response theory (MIRT) analyzes to identify the latent structure.

Bilingualism varies greatly in terms of language background, age of acquisition, and learning environment, making it complex to study the relationship between language and brain structure. Age of acquisition and linguistic proficiency are both crucial factors in language acquisition research and are closely related. To better understand the relationship between bilingualism and brain structure, we created a dataset of 2,000+ brain 3D images from bilinguals and monolinguals and trained a neural network (Contrastive Variational Autoencoder, CVAE) to distinguish bilingual-specific neuroanatomical variations from those shared by monolingual groups. The initial results reveal that the common variation (e.g. scanner and sex) in brain structure shared by two groups can be identified by CVAE. More importantly, CVAE disentangled linguistic proficiency from the common variation but not age of acquisition. It suggests that compared to age of acquisition, linguistic proficiency is more correlated with brain’s structure.

The Simon, Stroop, and flanker tasks are commonly used to investigate cognitive control. However, it remains unclear whether these three tasks in fact measure the same cognitive abilities and in the same proportion. We take a developmental approach to this question: if the tasks all roughly measure the same capacity, they should show similar patterns of age-related change. We present data from two massive online studies: Study 1 included 9,642 participants 10 to 80 years of age who completed the Simon and Stroop tasks, and Study 2 included 13,448 participants 10 to 79 years of age who completed the flanker task. The results revealed markedly different developmental trajectories among the tasks, with only the flanker task following an inverted U-shaped trajectory. These findings caution against using standard congruency tasks to draw general conclusions about the development of cognitive control and underscore the importance of developing more psychometrically rigorous measures.

The impact of an individual's first language (L1) on their acquisition of a second language (L2) is widely recognized in the field of psycholinguistics. However, previous research has focused on limited L1-L2 pairs, leaving questions about how different linguistic structures, such as genetic relationship, phonology, or syntax, can either facilitate or impede the learning of a new language. In our current study, we aimed to address these gaps by analyzing standardized English assessment scores of English Language Learners (ELLs) from 46 international schools across 30 countries. We compared linguistic similarities between more than 40 L1s and examined how different linguistic structures of the L1 influenced the acquisition of L2, specifically in terms of genetic, phonological, and syntactic similarities. Our findings indicate that older ELLs learn English at a faster rate than younger ELLs, and among older ELLs, those who speak an L1 that shares linguistic similarities with English acquire the language faster than ELLs whose L1 is less closely related to English. These results highlight the transfer of linguistic knowledge from L1 to L2 and emphasize the importance of age of acquisition in L2 learning. Our study has important theoretical and pedagogical implications for research on second language acquisition.