Perceptual salience of derivational suffixes in visual word recognition

This study analyzes the role of derivational suffixes in visual word recognition, tracking the eye movements of 31 participants in a sentence-reading task in Spanish. Perceptual salience of suffixes was operationalized as the proportion of letters represented by the suffixes with respect to the full words, that is, we relate the number of letters comprising the suffixes to the number of letters in the words in which they appear. The results reveal a significant role in first fixation duration of both word frequency – the more frequent the word, the shorter the fixations, and perceptual salience – the more salient the suffix, the longer the fixations. Moreover, in gaze duration, our results show a main effect of word length – the longer the word, the longer the fixations; word frequency; and significant interactions between word frequency and perceptual salience of suffixes on the one hand – the effect of word frequency is only significant when perceptual salience of suffixes is high, and between word frequency and word length on the other hand – the frequency effect decreases as word length increases. Overall results are interpreted in the light of the dual route models by which full-form and morphological processing interactively cooperate in visual word recognition.     

Constructing Semantic Models From Words,Images, and Emojis

A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from state-of-the-art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co-occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words.     

Predictors of aggressive behavior

A multiple factor approach was used to test additive and multiplicative models as well as to isolate a best predictive model of physical aggression. The variables of aggressive learning history, provocation, sex of target, sex of subject, sex-role orientation, and aggressive tendencies were selected. Eighty-three males and 117 females participated in the experimental session. Multiple regression analyses indicated that multiple predictor models were able to account for significantly more variance than were single predictor models; however, multiplicative models were unable to increase predictive efficacy. A model composed of sex of target, masculinity, and aggressive tendencies was established as the best predictive model for unprovoked aggression; provocation, masculinity, and aggressive tendencies made up the best predictive model of provoked aggression. © 1992 Wiley-Liss, Inc.     

Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.     

Behavioral Health Consultation and Primary Care: Lessons Learned

This article provides an overview of 20 years of professional experiences with developing and implementing a model for integrating behavioral health services into primary care. The Primary Care Behavioral Health (PCBH) model is designed to provide immediate access to behavioral care for a large number of primary care patients by positioning a behavioral health consultant in the exam room area to function as a core member of the primary care team. In an initial era of discovery, the authors were directly involved in developing and testing a variety of new approaches to providing behavioral health services in general medicine. In a second era focused on feasibility, the authors worked with Kaiser Permanente, the United States Air Force and Navy, the Veteran’s Administration, and the Bureau of Primary Care to system test this innovative model of integrated care. Now in an era devoted to dissemination, the authors review the various roles formal research, system level quality improvement initiatives and stakeholder analysis play in promoting integrated care. The authors also describe current efforts to (1) create a tool that helps systems develop integration targets and (2) use the PCBH model as a platform for teaching medical residents and behavioral health providers to work together in a redesigned primary care team model.     

Post‐traumatic functional recovery and reorganization in animal models: A theoretical and methodological challenge

Studies addressing cerebral functional localization face methodological and theoretical problems. Lesion experiments expect that when a functionally specialized structure is missing, its function can be deduced from the resulting impairments. Mostly, however, initial impairments are partially or fully eliminated through functional recovery. Apparently, such a recovery contradicts the notion of functional localization. In order to understand the mechanisms of recovery, improved methodology and a new theoretical framework are required. Insights into the mechanisms of recovery can be achieved by using “challenge” techniques, where functionally recovered individuals are exposed to organic and behavioral challenges, e.g. pharmacological manipulations or additional lesions, as well as modified test situations. Using such methods, a number of principles of functional recovery have emerged. We evaluate some of the available theories of post‐traumatic recovery against these principles and find that none of them can account for the principles. Finally, we present a new conceptual framework – the Reorganization of Elementary Functions (REF) model. This model reconceptualizes the term “function”, suggests mechanisms of post‐traumatic reorganizations, and resolves the contradiction between localization and functional recovery.     

Analysis of multinomial models under inequality constraints: Applications to measurement theory

Multinomial random variables are used across many disciplines to model categorical outcomes. Under this framework, investigators often use a likelihood ratio test to determine goodness-of-fit. If the permissible parameter space of such models is defined by inequality constraints, then the maximum likelihood estimator may lie on the boundary of the parameter space. Under this condition, the asymptotic distribution of the likelihood ratio test is no longer a simple χ² distribution. This article summarizes recent developments in the constrained inference literature as they pertain to the testing of multinomial random variables, and extends existing results by considering the case of jointly independent mutinomial random variables of varying categorical size. This article provides an application of this methodology to axiomatic measurement theory as a means of evaluating properly operationalized measurement axioms. This article generalizes Iverson and Falmagne’s [Iverson, G. J. & Falmagne, J. C. (1985). Statistical issues in measurement. Mathematical Social Sciences, 10, 131-153] seminal work on the empirical evaluation of measurement axioms and provides a classical counterpart to Myung, Karabatsos, and Iverson’s [Myung, J. I., Karabatsos, G. & Iverson, G. J. (2005). A Bayesian approach to testing decision making axioms. Journal of Mathematical Psychology, 49, 205-225] Bayesian methodology on the same topic.     

Self-Interest, Symbolic Attitudes, and Support for Public Policy: A Multilevel Analysis

This paper examines the role of self-interest and symbolic attitudes as predictors of support for two domestic policy issues—guaranteed jobs and incomes and national health insurance—in the American National Election Survey (ANES) between 1972 and 2004. As was the case in 1976 when Sears, Lau, Tyler, and Allen (1980 ) first explored this topic, symbolic attitudes continue to be much more important predictors of policy attitudes than various indicators of self-interest over the 30 years we analyze. We explore this finding further to determine whether any individual/internal and external/contextual variables affect the magnitude of self-interest effects on policy support. Five possible internal moderators of self-interest effects are examined: (1) political knowledge, (2) issue publics, (3) political values, (4) social identifications, and (5) emotions, but none are found to boost the magnitude of the self-interest effect. However, we do find some evidence that contextual variables representing the social/information environment moderate the impact of self-interest on public opinion.     

The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person’s linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.