Search for: All records

Adult speakers rarely produce a verb that does not agree with its subject in number, unless the sentence contains nouns with clashing pluralities. For example, a sentence such as “The key next to the cabinets…”, sometimes elicits a plural verb, and such attraction errors are more common with singular than plural heads (the attraction asymmetry). Both attraction and attraction asymmetry have been instrumental in understanding the computations underlying agreement production. Interestingly, developmental studies of agreement have often found very different patterns of agreement errors in children, leading to the conclusion of different mechanisms for agreement production in children and adults. Using a referential communication game, we demonstrate that Englishspeaking children as young as 5 years of age show robust agreement attraction. Children 6 years and older also demonstrate the attraction asymmetry. These findings support similar mechanisms underlying agreement production in children and adults. 

One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g.,Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial. 

Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra. 

Successful climate change adaptation depends on the spread and maintenance of adaptive behav- iors. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority-majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout popula- tions better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs. 

Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children. 

Many computational models of reasoning rely on explicit relation representations to account for human cognitive capacities such as analogical reasoning. Relational luring, a phenomenon observed in recognition memory, has been interpreted as evidence that explicit relation representations also impact episodic memory; however, this assumption has not been rigorously assessed by computational modeling. We implemented an established model of recognition memory, the Generalized Context Model (GCM), as a framework for simulating human performance on an old/new recognition task that elicits relational luring. Within this basic theoretical framework, we compared representations based on explicit relations, lexical semantics (i.e., individual word meanings), and a combination of the two. We compared the same alternative representations as predictors of accuracy in solving explicit verbal analogies. In accord with previous work, we found that explicit relation representations are necessary for modeling analogical reasoning. In contrast, preliminary simulations incorporating model parameters optimized to fit human data reproduce relational luring using any of the alternative representations, including one based on non-relational lexical semantics. Further work on model comparisons is needed to examine the contributions of lexical semantics and relations on the luring effect in recognition memory. 

Source-goal events involve an object moving from the Source to the Goal. In this work, we focus on the representation of the object, which has received relatively less attention in the study of Source-goal events. Specifically, this study aims to investigate the mapping between language and mental representations of object locations in transfer-of-possession events (e.g. throwing, giving). We investigate two different grammatical factors that may influence the representation of object location in transfer-of-possession events: (a) grammatical aspect (e.g. threw vs. was throwing) and (b) verb semantics (guaranteed transfer, e.g. give vs. no guaranteed transfer, e.g. throw). We conducted a visual-world eye-tracking study using a novel webcam-based eye-tracking paradigm (Webgazer; Papoutsaki et al., 2016) to investigate how grammatical aspect and verb semantics in the linguistic input guide the real-time and final representations of object locations. We show that grammatical cues guide the real-time and final representations of object locations. 

We describe UMA (Unified Model of Arithmetic), a theory of children’s arithmetic implemented as a computational model. UMA extends a theory of fraction arithmetic (Braithwaite et al., 2017) to include arithmetic with whole numbers and decimals. We evaluated UMA in the domain of decimal arithmetic by training the model on problems from a math textbook series, then testing it on decimal arithmetic problems that were solved by 6th and 8th graders in a previous study. UMA’s test performance closely matched that of children, supporting three assumptions of the theory: (1) most errors reflect small deviations from standard procedures, (2) between-problem variations in error rates reflect the distribution of input that learners receive, and (3) individual differences in strategy use reflect underlying variation in learning parameters. 

Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra. 

One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g., Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial.