Do Health Care Workplaces Affect Treatment Environments?

Previous research has demonstrated that characteristics of the health care workplace influence staff outcomes such as morale and burnout, but the potential effect of health care workplaces on the treatment environment has been little studied. Building on a model proposed by Schaefer and Moos (1993), we propose that the workplace factors of supervisory work environment (e.g. support from supervisors, managerial control) and programme philosophical orientation (e.g. disease model of addiction, psychosocial learning model of addiction) predict four treatment environment elements: patient autonomy, staff control, staff sensitivity, and patient alienation. Multiple regression analysis of data drawn from a survey (response rate = 86%) of 327 staff members at 15 Veterans Affairs (VA) inpatient substance abuse treatment programmes revealed that greater managerial control over staff predicted greater patient alienation, lower staff sensitivity towards patients, and greater staff control over patients. Stronger disease model programme orientations predicted less patient alienation and greater staff sensitivity, whereas stronger psychosocial model programme orientations predicted less staff control. These results suggest that health care workplaces may influence treatment environments. Implications for further research and practice are discussed. © 1997 John Wiley & Sons, Ltd.     

Western and East Asian sociocultural learning models: Evidence from cross‐cultural and immigrant research

We review Li's research and theory of cultural learning models in light of Chirkov's (2019) theory of sociocultural models that draw on previous theories and research across social sciences. We recast Li's models as sociocultural learning models (SCLMs) by incorporating Chirkov's three emphases: (a) the inseparability between the social and the cultural, (b) the public versus individuals’ internalization of SCLMs and their mutual enforcement, and (c) collective intentionality and intersubjectivity as embedded in the enactment of SCLMs. As an initial attempt to document how public SCLMs become internalized, we further examine the role of joint intentionality and intersubjectivity at the person‐to‐person level. We use discourse analysis to look at two mother–child conversations about learning from our current European American and Chinese immigrant research data. We show evidence that both joint intentionality and intersubjectivity are prevalently and deeply present in the process of parental socialization (transmitting SCLMs to their young). We conclude that intentionality and intersubjectivity are indispensable to enable SCLMs to exist and to continue by enabling the public to become internalized. The internalization is then enacted and repeated by members across the culture, who in turn function to uphold and renew SCLMs.     

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.     

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.     

多水平研究中的概念转换模式--以气氛研究为例

在组织环境中开展多水平研究会涉及到对处于组织中不同水平七的概念、变量或过程进行界定的问题。在已有概念基础上提出新的概念必须保证其有效性。本文以气氛研究为例介绍了与此问题相应的五种模式。     

Learning to divide the labor: an account of deficits in light and heavy verb production

Theories of sentence production that involve a convergence of activation from conceptual‐semantic and syntactic‐sequential units inspired a connectionist model that was trained to produce simple sentences. The model used a learning algorithm that resulted in a sharing of responsibility (or “division of labor”) between syntactic and semantic inputs for lexical activation according to their predictive power. Semantically rich, or “heavy”, verbs in the model came to rely on semantic cues more than on syntactic cues, whereas semantically impoverished, or “light”, verbs relied more on syntactic cues. When the syntactic and semantic inputs were lesioned, the model exhibited patterns of production characteristic of agrammatic and anomic aphasic patients, respectively. Anomic models tended to lose the ability to retrieve heavy verbs, whereas agrammatic models were more impaired in retrieving light verbs. These results obtained in both sentence production and single‐word naming simulations. Moreover, simulated agrammatic lexical retrieval was more impaired overall in sentences than in single‐word tasks, in agreement with the literature. The results provide a demonstration of the division‐of‐labor principle, as well as general support for the claim that connectionist learning principles can contribute to the understanding of non‐transparent neuropsychological dissociations.     

From human to artificial cognition and back: New perspectives on cognitively inspired AI systems

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.