Direct Versus Indirect Causation as a Semantic Linguistic Universal: Using a Computational Model of English,Hebrew, Hindi,Japanese, and K'iche’ Mayan to Predict Grammaticality Judgments in Balinese

The aim of this study was to test the claim that languages universally employ morphosyntactic marking to differentiate events of more- versus less-direct causation, preferring to mark them with less- and more- overt marking, respectively (e.g., Somebody broke the window vs. Somebody MADE the window break; *Somebody cried the boy vs. Somebody MADE the boy cry). To this end, we investigated whether a recent computational model which learns to predict speakers’ by-verb relative preference for the two causatives in English, Hebrew, Hindi, Japanese, and K'iche’ Mayan is able to generalize to a sixth language on which it has never been trained: Balinese. Judgments of the relative acceptability of the less- and more-transparent causative forms of 60 verbs were collected from 48 native-speaking Balinese adults. The composite crosslinguistic computational model was able to predict these judgments, not only for verbs that it had seen, but also––in a split-half validation test––to verbs that it had never seen in any language. A “random-semantics” model showed only a relatively small decrement in performance with seen verbs, whose behavior can be learned on a verb-by-verb basis, but achieved zero correlation with human judgments when generalizing to unseen verbs. Together, these findings suggest that Balinese conceptualizes directness of causation in a similar way to these unrelated languages, and therefore constitute support for the view that the distinction between more- versus less-distinct causation constitutes a morphosyntactic universal.     

Contrasting single and multi-component working-memory systems in dual tasking

Working memory can be a major source of interference in dual tasking. However, there is no consensus on whether this interference is the result of a single working memory bottleneck, or of interactions between different working memory components that together form a complete working-memory system. We report a behavioral and an fMRI dataset in which working memory requirements are manipulated during multitasking. We show that a computational cognitive model that assumes a distributed version of working memory accounts for both behavioral and neuroimaging data better than a model that takes a more centralized approach. The model’s working memory consists of an attentional focus, declarative memory, and a subvocalized rehearsal mechanism. Thus, the data and model favor an account where working memory interference in dual tasking is the result of interactions between different resources that together form a working-memory system.     

Effects of task constraint on action dynamics

The actualization of action possibilities (i.e., affordances) can often be accomplished in numerous ways. For instance, an individual could walk over to a rubbish bin to drop an item in or throw the piece of rubbish into the bin from some distance away. The aim of the current study was to investigate the action dynamics that emerge from such under-constrained task or action spaces using an object transportation task. Participants were instructed to transport balls between a starting location and a large wooden box located 9 m away. The temporal interval between the sequential presentation of balls was manipulated as a control parameter and was expected to influence the distance participants moved prior to throwing or dropping the ball into the target box. A two-parameter state space derived from the Cusp Catastrophe Model was employed to illustrate how behavioral variability emerged as a consequence of the under-constrained task context. Two follow-up experiments demonstrated direct correspondence between model predictions and observed action dynamics as a function of increasing task constraints. Implications for modeling, the theory of affordances, and empirical studies more generally are discussed.     

A practical guide to understanding reliability in studies of within-person variability

This article concerns how to estimate reliability (defined as the internal consistency of responses to a scale) in designs that are commonly used in studies of within-person variability. I present relevant issues, describe common errors, make recommendations for best practice, and discuss unresolved issues and future directions. I describe how to estimate the reliability of scales administered in studies in which observations are nested within persons, such as daily diary and “beeper” studies and studies of social interaction. Multilevel modeling analyses that include a measurement level can estimate the occasionlevel (e.g., days or beeps or interactions) reliability of scales. In such models, items on a scale are nested within occasions of measurement and occasions of measurement are nested within persons.     

Factors affecting perceived safety and enjoyment based on driver experience

A positive driving experience, especially for older drivers, increases the attention to the emotional dimensions of driving, such as the driver’s perceived safety. Therefore, this study empirically presents factors affecting driver experience and compares them between older and younger drivers. Consequently, we conducted a face-to-face survey on elderly and young drivers and analyzed the data of 246 drivers using structural equation modeling. The analysis presented measurements and structural model evaluations. Considering the analysis, it was found that driving-related information and car-exterior context affect the perceived safety and enjoyment of a driver’s experience. Additionally, car-exterior context exerts a greater influence on the perceived safety and enjoyment of the elderly drivers’ group than the young drivers’ group. The results of this study will empirically contribute to the satisfaction of driver experience and perceived safety improvement in the future. It also provides a basis for the development of driving interfaces to improve the quality of the driving experience of the elderly.     

I’m lonely,can’t you tell? Convergent validity of self- and informant ratings of loneliness

To what degree do self-ratings of loneliness converge with informant ratings? In this study, we obtained self-ratings of loneliness from 463 young adults and informant ratings from their parents, friends, and romantic partners. Convergence among these ratings was estimated using structural equation models for multitrait-multimethod data and compared to self-informant convergence of life-satisfaction ratings. Self- and informant ratings were moderately correlated and comparable to self-informant correlations obtained for life satisfaction. Romantic partners were more accurate in their judgments than both friends and parents, who did not differ significantly from each other in terms of accuracy. Together, these findings indicate that informant ratings of loneliness can be used as valid indicators of loneliness in applied contexts and in future research.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.     

Highlights as an Early Predictor of Student Comprehension and Interests

When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides little evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into her mental state—her level of comprehension, grasp of the key ideas, reading goals, and so on. We explore this hypothesis via an experiment in which 400 participants read three sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. Nonetheless, highlighting patterns may allow us to infer reader comprehension and interests. Using multiple representations of the highlighting patterns, we built probabilistic models to predict quiz performance and matrix factorization models to predict what content would be highlighted in one passage from highlights in other passages. We find that quiz score prediction accuracy reliably improves with the inclusion of highlighting data (by about 1%–2%), both for held-out students and for held-out student questions (i.e., questions selected randomly for each student), but not for held-out questions. Furthermore, an individual’s highlighting pattern is informative of what she highlights elsewhere. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the reader’s mind but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.