Visual field differences in visual word recognition can emerge purely from perceptual learning: evidence from modeling Chinese character pronunciation

In Chinese orthography, a dominant character structure exists in which a semantic radical appears on the left and a phonetic radical on the right (SP characters); a minority opposite arrangement also exists (PS characters). As the number of phonetic radical types is much greater than semantic radical types, in SP characters the information is skewed to the right, whereas in PS characters it is skewed to the left. Through training a computational model for SP and PS character recognition that takes into account of the locations in which the characters appear in the visual field during learning, but does not assume any fundamental hemispheric processing difference, we show that visual field differences can emerge as a consequence of the fundamental structural differences in information between SP and PS characters, as opposed to the fundamental processing differences between the two hemispheres. This modeling result is also consistent with behavioral naming performance. This work provides strong evidence that perceptual learning, i.e., the information structure of word stimuli to which the readers have long been exposed, is one of the factors that accounts for hemispheric asymmetry effects in visual word recognition.     

A comparison of two flash-card methods for improving sight-word reading

Flash cards have been shown to be useful for teaching sight-word reading. To date, the most effective flash-card instruction method is incremental rehearsal (IR). This method involves the instructor interspersing unknown stimulus items into the presentation of known stimulus items. In this study, we compared IR to a modified IR procedure-strategic incremental rehearsal (SIR)-to determine whether the effects of IR might be improved by incorporating variables likely to increase word acquisition. These included increased opportunities to respond to unknown stimuli, using learner responding as a basis for changing instructional items, and systematic prompting methods. An A-B-A-B design was used to compare the effects of IR and SIR for increasing sight-word reading with 4 elementary school students. Results indicated that students read more words correctly with SIR than with IR. In addition, similar patterns of responding were seen at a 2-week follow-up.     

Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration

Abstract

Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulation the potential under- or over-extraction of latent classes in a growth mixture model when underlying data follow either normal, skewed-normal, or skewed-t distributions. In order to assess this, we implement skewed-t, skewed-normal, and conventional normal (i.e., not skewed) forms of the growth mixture model. The skewed-t and skewed-normal versions of this model have only recently been implemented, and relatively little is known about their performance. Model comparison, fit, and classification of correctly specified and mis-specified models were assessed through various indices. Findings suggest that the accuracy of model comparison and fit measures are dependent on the type of (mis)specification, as well as the amount of class separation between the latent classes. A secondary simulation exposed computation and accuracy difficulties under some skewed modeling contexts. Implications of findings, recommendations for applied researchers, and future directions are discussed; a motivating example is presented using education data.     

Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models

Abstract

Effect partitioning is almost exclusively performed with multilevel models (MLMs) – so much so that some have considered the two to be synonymous. MLMs are able to provide estimates with desirable statistical properties when data come from a hierarchical structure; but the random effects included in MLMs are not always integral to the analysis. As a result, other methods with relaxed assumptions are viable options in many cases. Through empirical examples and simulations, we show how generalized estimating equations (GEEs) can be used to effectively partition effects without random effects. We show that more onerous steps of MLMs such as determining the number of random effects and the structure for their covariance can be bypassed with GEEs while still obtaining identical or near-identical results. Additionally, violations of distributional assumptions adversely affect estimates with MLMs but have no effect on GEEs because no such assumptions are made. This makes GEEs a flexible alternative to MLMs with minimal assumptions that may warrant consideration. Limitations of GEEs for partitioning effects are also discussed.     

Specifying the Interrelationship Between Exposure to Violence and Parental Monitoring for Younger Versus Older Adolescents: A Five Year Longitudinal Test

Five waves of longitudinal data collected from 349 African American youth living in extreme poverty were used to examine the interrelationship between exposure to violence and parenting during adolescence. Semi-parametric group based modeling was used to identify trajectories of parental monitoring and exposure to violence from T1 to T5. Results from these analyses revealed: (1) a trajectory of declining parental monitoring for 48% of youth; and (2) four distinct trajectories of exposure to violence. Multivariate findings were largely consistent with the ecological-transactional model of community violence. Youth with stable and/or increasing trajectories of exposure to violence were more likely than youth with stable-low exposure to violence to have declining parental monitoring, but additional analyses revealed a similar pattern of findings for younger adolescents (age 9–11 T1), but no evidence of linkages between trajectories of exposure to violence and parental monitoring for older adolescents (age 12–16 T1). The theoretical and policy implications of these findings as well as areas for future research are also discussed.     

Time frames and the distinction between affective and cognitive well-being

We examined whether the empirical differences between affective well-being (AWB) and cognitive well-being (CWB) might be due to (a) the use of different time frames in measures of AWB and CWB or (b) structural differences. In Study 1, a multitrait-multimethod (MTMM) analysis indicated that levels of different components are more similar but do not converge completely when the same time frame is used. In Study 2, we found that people are more likely to consider global life circumstances (as opposed to specific events and activities) when they evaluate their CWB, regardless of the specific time frame. In both studies, the time frame did not moderate the associations between AWB and CWB and important correlates (personality, life circumstances).     

Smoking mediates the effect of conscientiousness on mortality: The Veterans Affairs Normative Aging Study

This study examined the relationship between conscientiousness and mortality over 18 years and whether smoking behavior mediated this relationship. We utilized data from the Veterans Affairs Normative Aging Study on 1349 men who completed the Goldberg (1992) adjectival markers of the Big Five. Over the 18-year follow-up, 547 (41%) participants died. Through proportional hazards modeling in a structural equation modeling framework, we found that higher levels of conscientiousness significantly predicted longer life, and that this effect was mediated by current smoking status at baseline. Methodologically, we also demonstrate the effectiveness of using a structural equation modeling framework to evaluate mediation when using a censored outcome such as mortality.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.     

Quantifying the psychopathic stare: Automated assessment of head motion is related to antisocial traits in forensic interviews

Clinicians have long noted that individuals with high psychopathic traits exhibit unique interpersonal style often observable during forensic interviews. Here we develop an automated approach for quantifying head dynamics during video-recorded naturalistic clinical interviews. We expected head dynamics would be related to psychopathic traits. As predicted, dwell times indicate that those with higher levels of psychopathic traits are characterized by more stationary head positions, focused directly towards the camera/interviewer, than were individuals low in psychopathic traits. These associations were primarily driven by developmental/antisocial features of psychopathy, indicating that those with severe and life-course persistent antisocial behavior exhibit more rigid and focused orienting of their head during interpersonal communication. These results encourage more research into the automated quantification of behavioral manifestations of personality to support clinical observations that psychopaths exhibit unique qualities in non-verbal interpersonal communication.