Linguistic alignment (LA) is the tendency during a conversation to reuse each other's linguistic expressions, including lexical, conceptual, or syntactic structures. LA is often argued to be a crucial driver in reciprocal understanding and interpersonal rapport, though its precise dynamics and effects are still controversial. One barrier to more systematic investigation of these effects lies in the diversity in the methods employed to analyze LA, which makes it difficult to integrate and compare results of individual studies. To overcome this issue, we have developed ALIGN (Analyzing Linguistic Interactions with Generalizable techNiques), an open-source Python package to measure LA in conversation (https://pypi.python.org/pypi/align) along with in-depth open-source tutorials hosted on ALIGN's GitHub repository (https://github.com/nickduran/align-linguistic-alignment). Here, we first describe the challenges in the study of LA and outline how ALIGN can address them. We then demonstrate how our analytical protocol can be applied to theory-driven questions using a complex corpus of dialogue (the Devil's Advocate corpus; Duran & Fusaroli, 2017). We close by identifying further challenges and point to future developments of the field.

Objective: The Cognition Battery of the National Institutes of Heath Toolbox is a commonly utilized set of assessments of neuropsychological abilities, evaluating executive function, attention, working memory, processing speed, and episodic memory. We highlight the utility of an advanced statistical model in providing nuanced characterization of neurocognition in an adolescent population. We propose that partially ordered set (POSET) models are well suited to analyze polyfactorial tasks and identify distinct profiles of cognitive functioning. Method: Two models were considered using POSET classification. The first modeled 5 distinct cognitive functions and allowed for multiple functions to contribute to task performance. The second simpler model involved only 2 broader-based functions without polyfactorial task specifications. Existing performance data from 745 adolescents aged 14-17 years were analyzed. Posterior probabilities of classification performance and the discriminatory properties of the estimated response distributions indicated how well the modeling approaches fit the data. Results: The larger first model resulted in 8 profiles or states characterized by combinations of high or low functioning in 5 distinct functions. The simpler second model involved 2 broader-based functions that resulted in 4 states. Comparing model fit criteria, we believe that the finer-grained first model may better reflect the cognitive constructs associated with the tasks. Notably, POSET modeling did not always provide adequate classification of working memory because of the limited design of the Cognition Battery. Conclusions: We demonstrate that the use of POSET models is a feasible approach for detailed analysis of neurocognitive data that can extract information on cognitive functions, even when provided with limited task batteries.

Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although this method is commonly used in textbooks and classrooms. theoretically it remains uncertain whether feature descriptions should advantage learning complex natural-science categories. In three experiments, participants were trained on 12 categories of rocks, either without or with a brief description highlighting key features of each category. After training, they were tested on their ability to categorize both old and new rocks from each of the categories. Providing feature descriptions as a caption under a rock image failed to improve category learning relative to providing only the rock image with its category label (Experiment 1). However, when these same feature descriptions were presented such that they were explicitly linked to the relevant parts of the rock image (feature highlighting), participants showed significantly higher performance on both immediate generalization to new rocks (Experiment 2) and generalization after a 2-day delay (Experiment 3). Theoretical and practical implications are discussed.

A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention-which teaches that intellectual abilities can be developed-improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.

Logical properties such as negation, implication, and symmetry, despite the fact that they are foundational and threaded through the vocabulary and syntax of known natural languages, pose a special problem for language learning. Their meanings are much harder to identify and isolate in the child's everyday interaction with referents in the world than concrete things (like spoons and horses) and happenings and acts (like running and jumping) that are much more easily identified, and thus more easily linked to their linguistic labels (spoon, horse, run, jump). Here we concentrate attention on the category of symmetry [a relation R is symmetrical if and only if (iff) for all x, y: if R(x,y), then R(y,x)], expressed in English by such terms as similar, marry, cousin, and near. After a brief introduction to how symmetry is expressed in English and other well-studied languages, we discuss the appearance and maturation of this category in Nicaraguan Sign Language (NSL). NSL is an emerging language used as the primary, daily means of communication among a population of deaf individuals who could not acquire the surrounding spoken language because they could not hear it, and who were not exposed to a preexisting sign language because there was none available in their community. Remarkably, these individuals treat symmetry, in both semantic and syntactic regards, much as do learners exposed to a previously established language. These findings point to deep human biases in the structures underpinning and constituting human language.

The doctoral advisor-typically the principal investigator (PI)-is often characterized as a singular or primary mentor who guides students using a cognitive apprenticeship model. Alternatively, the cascading mentorship model describes the members of laboratories or research groups receiving mentorship from more senior laboratory members and providing it to more junior members (i.e., Pls mentor postdocs, postdocs mentor senior graduate students, senior students mentor junior students, etc.). Here we show that Pis' laboratory and mentoring activities do not significantly predict students' skill development trajectories, but the engagement of postdocs and senior graduate students in laboratory interactions do. We found that the cascading mentorship model accounts best for doctoral student skill development in a longitudinal study of 336 PhD students in the United States. Specifically, when postdocs and senior doctoral students actively participate in laboratory discussions, junior PhD students are over 4 times as likely to have positive skill development trajectories. Thus, postdocs disproportionately enhance the doctoral training enterprise, despite typically having no formal mentorship role. These findings also illustrate both the importance and the feasibility of identifying evidence-based practices in graduate education.

Links between electricity consumption and economic growth are fairly well documented for national economies, but less so for urban economies. The analysis of such relationships at the sub-national level of aggregation can potentially offer a useful complement to national-level research. This study examines the electricity-growth nexus in El Paso, Texas, while also considering the roles of capital stocks and employment. Testing suggests the presence of cointegrating relationships and a vector error correction model is estimated. Granger causality tests reveal the absence of causality between electricity consumption and personal income, implying that energy conservation efforts will have a neutral effect on economic growth. Furthermore, the results indicate that causality runs from the capital stock and employment to both personal income and electricity consumption. This echoes previous research regarding the importance of accounting for capital and labour factors of production in studies of aggregate electricity utilization and economic performance. The methodology used in this analysis to develop a broad synthetic measure of the urban capital stock, including various categories of public infrastructure, can also be applied to other regions and urban economies.

A growing body of research suggests that interventions promoting students' utility value for a subject can improve their academic outcomes. However, numerous questions remain regarding how much to adapt prior intervention materials to promote utility value in new educational contexts and how implementation constraints of an educational context may impact the success of these interventions. In this study, using a design-based process we developed and tested three utility value interventions in a new educational context (online high school math). We found that one of the interventions increased utility value compared to control conditions, but we also encountered constraints on intervention implementation that limited the effectiveness of our intervention and the conclusions we could draw from this work. We use our experience as a case study to illustrate the costs and benefits of making certain implementation choices when partnering with practitioners to administer utility value interventions in new contexts.

Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student's degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has been little work to forecast how far a student is from mastery or predict how much more practice the student will need before he or she will reach mastery. This article presents a method for predicting the point at which a student will reach skill mastery within an adaptive learning system, based on current approaches to estimating student knowledge. We apply this technique to two popular methods of modeling student learning - Bayesian knowledge tracing and performance factors analysis - and compare prediction correctness. Potential applications and future steps for improving the method are discussed.

We investigated how affective states influence expository text comprehension and whether text valence moderates the effects (i.e., mood congruency). In Experiment 1 participants were randomly assigned to a happy or sad affective state (elicited via films) before reading a positive or negative version of a scientific text on animal adaptations. Participants (n = 79) in the sad (film) group had higher scores on deep-reasoning (d = .312) but not surface-level questions on a subsequent multiple-choice comprehension assessment; there was also no evidence for mood congruence. Using a neutral version of the same text, in Experiment 2 participants (n = 52) in a fearful condition performed better on surface-level comprehension questions (d = .594) compared with a sad condition, but the groups were on par for deep-reasoning questions. Experiment 3 (n = 595) did not replicate the findings from Experiment 2 (no comprehension differences between the sad and fear groups) and there were no differences between the fear and happy groups. However, the sad group outperformed the happy group on deep-reasoning questions (d = .210), thereby replicating Experiment 1. The overall findings were confirmed after pooling the data from the three experiments to increase power.