Simulating single word processing in the classic aphasia syndromes based on the Wernicke-Lichtheim-Geschwind theory

The Wernicke-Lichtheim-Geschwind (WLG) theory of the neurobiological basis of language is of great historical importance, and it continues to exert a substantial influence on most contemporary theories of language in spite of its widely recognized limitations. Here, we suggest that neurobiologically grounded computational models based on the WLG theory can provide a deeper understanding of which of its features are plausible and where the theory fails. As a first step in this direction, we created a model of the interconnected left and right neocortical areas that are most relevant to the WLG theory, and used it to study visual-confrontation naming, auditory repetition, and auditory comprehension performance. No specific functionality is assigned a priori to model cortical regions, other than that implicitly present due to their locations in the cortical network and a higher learning rate in left hemisphere regions. Following learning, the model successfully simulates confrontation naming and word repetition, and acquires a unique internal representation in parietal regions for each named object. Simulated lesions to the language-dominant cortical regions produce patterns of single word processing impairment reminiscent of those postulated historically in the classic aphasia syndromes. These results indicate that WLG theory, instantiated as a simple interconnected network of model neocortical regions familiar to any neuropsychologist/neurologist, captures several fundamental "low-level" aspects of neurobiological word processing and their impairment in aphasia.     

The Social Ecological Model and Physical Activity in African American Women

Little is known about the social and contextual correlates (e.g., social norms, environment, social networks, and organizational support) influencing the adoption and maintenance of regular physical activity among minority and underserved populations. The purpose of this review was to apply the social ecological model to better understand physical activity among African American women. A review of the literature pertaining to correlates of physical activity among African American women was conducted and applied to a social ecological perspective. Understanding and addressing social and contextual correlates of physical activity behavior among African American women are necessary to establish comprehensive programs, particularly within community settings. The social ecological model provides a strong theoretical basis to guide physical activity intervention in this population.     

The relationship between anxiety sensitivity and latent symptoms of emotional problems: A structural equation modeling approach

A large body of research suggests that common and specific psychopathology dimensions underlie the symptoms that occur within mood and anxiety disorders. As of yet, it is unclear precisely how the facets of Anxiety Sensitivity (AS), or fear of the symptoms of fear and anxiety, relate to these latent factors. Using data from 606 adolescents participating in the baseline phase of a longitudinal study on risk factors for emotional disorders, we modeled the facets of AS as measured by the Anxiety Sensitivity Index-Expanded Form (ASI-X) and related these facets to a hierarchical model of latent symptoms of psychological distress. Results suggest that one facet of AS is associated with a broad General Distress factor underlying symptoms of most emotional disorders while others relate to intermediate-level and conceptually-meaningful narrow factors representing aspects of psychological distress specific to particular emotional disorders.     

Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real-life decision making in an experimental context. Busemeyer and Stout [Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253-262] proposed an “Expectancy Valence” reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.     

On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models

Basic results for conditional means and variances, as well as distributional results, are used to clarify the similarities and differences between various extensions of signal detection theory (SDT). It is shown that a previously presented motivation for the unequal variance SDT model (varying strength) actually leads to a related, yet distinct, model. The distinction has implications for other extensions of SDT, such as models with criteria that vary over trials. It is shown that a mixture extension of SDT is also consistent with unequal variances, but provides a different interpretation of the results; mixture SDT also offers a way to unify results found across several types of studies.     

School profiles of at-risk student concentration: Differential growth in oral reading fluency

The present study provides a data-driven approach to identifying groups of schools based on the concentration of at-risk students the school serves. The percentage of English language learners, minority students, and students eligible for free or reduced priced lunch were used as indicators in a latent profile analysis of 569 schools. The goal of the present study was to determine whether school-level average student reading performance varied as a function of the groups identified in the latent profile analysis. To do so, groups extracted by the latent profile analysis were used as school-level predictors of growth in oral reading fluency, which was modeled at the within-student level of a three-level hierarchical growth curve model. Oral reading fluency was measured at four points during the year in a large cross-sectional sample of first-, second-, and third-grade students. Results indicated that schools were able to be classified into four distinct groups based on their concentrations and types of at-risk students. Further, in all three grades, there were significant differences between the four identified groups observed in average reading fluency scores at the beginning of the year, the end of the year, and growth during the year indicating that groups based on school-concentration of at-risk students were significantly related to average student achievement in reading ability.     

Applying memories of reinforcement outcomes mainly to Pavlovian conditioning

A theory devised initially on the basis of instrumental reward schedule data, such as the PREE, was extended to deal with various Pavlovian findings. These Pavlovian findings include blocking, unblocking, relative validity, positive and negative patterning, renewal, reinstatement, reacquisition, and inhibition. In addition, the sequential model was discussed in relation to the rule-learning view of Hulse and Dorsky (1977) employing data from a report by Bloom and Capaldi (1961).     

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

TEM observation of oxide scale formed on a Ti–Al–Zr alloy oxidized at 360°C in alkaline steam

Ti–Al–Zr alloy has been oxidized at 360°C in alkaline steam at a pressure of 10.3?±?0.7?MPa. Cross-sectional transmission electron microscopy (TEM) observations indicated that the oxide scale of Ti–Al–Zr alloy was composed of outer and inner subscales, in which the outer layer consists of anatase-TiO₂ and the inner layer a mixture of TiO and Ti₂O. The thickness of the Ti₂O, TiO and anatase-TiO₂ were approximately 50, 100 and 400?nm, respectively. These results were confirmed by X-ray energy dispersive spectrometry (EDS) measurements. The enhanced corrosion of titanium alloys in LiOH solution is attributed to a high hexagonal Ti₂O to tetragonal TiO₂ phase transformation rate induced by the substitution of Li⁺ for Ti⁴⁺ in the oxide layer.     

Continuum modeling of the reverse Hall–Petch effect in nanocrystalline metals under uniaxial tension: how many grains in a finite element model?

Owing to their very high strength, nanocrystalline metals have been extensively studied over the recent years. The direct Hall–Petch law, empirically proportioning the material strength to the inverse square root of its grain size has been shown to break down below a grain size of the order of tenths of nanometers. This phenomenon has been widely rationalized as a gradual switch from intragrain mediated deformation mechanisms to grain boundary mediated deformation mechanisms. This transition has been observed in many finite element simulations, despite the intrinsic restriction of necessarily limiting the nanocrystalline representative assembly to only a few grains. Such a limitation is generally overlooked, and its influence on an uniaxial tension test – when compared to a complete sample of millions of grains – ignored. We propose here to quantify the approximation done by considering a finite number of grains by means of a simple analytical model based on the early work of Stevens [R.N. Stevens, Philos. Mag. 23 (1971) p. 265]. The finite element approximation is demonstrated to be relatively good, even down to only three grains in width, and a method to “correct” the stress-strain curves of small representative volumes is proposed.