On Domain-Specific Conceptualization and Measurement of Grit in L2 Learning

As a personality trait, second language (L2) grit—a combination of perseverance and passion for L2 learning—has recently been proposed as a meaningful predictor of learners’ motivational behavior and L2 achievement. The results of a growing body of empirical studies carried out in various L2 contexts have substantiated the power of L2 grit in predicting L2 success. In this paper, we contend that grit and its potential effects on L2 outcomes should be conceptualized and measured in a domain-specific fashion. We argue that a domain-specific measure of grit enhances its predictive and construct validity and better captures its differential effects in various domains and across languages. We then briefly review the findings of existing grit research in L2 contexts with respect to their domain-general versus domain-specific conceptualization of grit. Finally, we conclude the paper by discussing several issues raised against domain-general grit and discuss their potential relevance to domain-specific grit research in the context of L2 learning.

Functional organization of social perception in the human brain

Humans can readily perceive a multitude of features from social interactions, but the phenomenological and neural basis of social perception has yet to be solved. Short film clips with rich social content were shown to 97 healthy participants while their haemodynamic brain activity was measured with fMRI. The stimulus clips were annotated for 112 social features yielding the initial stimulus model. Cluster analysis revealed that 13 dimensions were sufficient for describing the social perceptual space. Univariate GLM using these dimensions as predictors was used to map regional neural response profiles to different social features. Multivariate pattern analysis was then utilized to establish the regional specificity of the responses. The results revealed a posterior-anterior gradient in the processing of social information in the brain. Occipital and temporal regions responded to most social dimensions and the classifier revealed that these responses were dimension specific; in contrast Heschl gyri and parietal areas were also broadly tuned to different social signals yet the responses were domain-general and did not differentiate between dimensions. Altogether these results highlight the distributed nature of social processing in the brain as well as the specific contributions of feature-specific versus domain-general social perceptual processes.

Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine’s environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.

EXPRESS: Temporal grouping effects in verbal and musical short-term memory: Is serial order representation domain-general?

The question of the domain-general versus domain-specific nature of the serial order mechanisms involved in short-term memory is currently under debate. The present study aimed at addressing this question through the study of temporal grouping effects in short-term memory tasks with musical material, a domain which has received little interest so far. The goal was to determine whether positional coding—currently the best account of grouping effect in verbal short-term memory—represents a viable mechanism to explain grouping effects in the musical domain. In a first experiment, non-musicians performed serial reconstruction of 6-tone sequences, where half of the sequences was grouped by groups of three items and the other half presented at a regular pace. The overall data pattern suggests that temporal grouping exerts on tone sequences reconstruction the same effects as in the verbal domain, except for ordering errors which were not characterized by the typical increase of interpositions. This pattern has been replicated in two additional experiments with verbal material, using the same grouping structure as in the musical experiment. The findings support that verbal and musical short-term memory domains are characterized by similar temporal grouping effects for the recall of 6-item lists grouped by three, but it also suggests the existence of boundary condition to observe an increase of interposition errors predicted by positional theories.

Machine Learning Strategies for Accurate Log Prediction in Reservoir Characterization: Self-Calibrating Versus Domain-Knowledge

Petrophysical evaluation is a crucial task for reservoir characterization but it is often complicated, time-consuming and associated with uncertainties. Moreover, this job is subjective and ambiguous depending on the petrophysicist's experience. Utilizing the flourishing Artificial Intelligence (AI)/Machine Learning (ML) is a way to build an automating process with minimal human intervention, improving consistency and efficiency of well log prediction and interpretation. Nowadays, the argument is whether AI-ML should base on a statistically self-calibrating or knowledge-based prediction framework! In this study, we develop a petrophysically knowledge-based AI-ML workflow that upscale sparsely-sampled core porosity and permeability into continuous curves along the entire well interval. AI-ML focuses on making predictions from analyzing data by learning and identifying patterns. The accuracy of the self-calibrating statistical models is heavily dependent on the volume of training data. The proposed AI-ML workflow uses raw well logs (gamma-ray, neutron and density) to predict porosity and permeability over the well interval using sparsely core data. The challenge in building the AI-ML model is the number of data points used for training showed an imbalance in the relative sampling of plugs, i.e. the number of core data (used as target variable) is less than 10%. Ensemble learning and stacking ML approaches are used to obtain maximum predictive performance of self-calibrating learning strategy. Alternatively, a new petrophysical workflow is established to debrief the domain experience in the feature selection that is used as an important weight in the regression problem. This helps ML model to learn more accurately by discovering hidden relationships between independent and target variables. This workflow is the inference engine of the AI-ML model to extract relevant domain-knowledge within the system that leads to more accurate predictions. The proposed knowledge-driven ML strategy achieved a prediction accuracy of R2 score = 87% (Correlation Coefficient (CC) of 96%). This is a significant improvement by R2 = 57% (CC = 62%) compared to the best performing self-calibrating ML models. The predicted properties are upscaled automatically to predict uncored intervals, improving data coverage and property population in reservoir models leading to the improvement of the model robustness. The high prediction accuracy demonstrates the potential of knowledge-driven AI-ML strategy in predicting rock properties under data sparsity and limitations and saving significant cost and time. This paper describes an AI-ML workflow that predicts high-resolution continuous porosity and permeability logs from imbalanced and sparse core plug data. The method successfully incorporates new type petrophysical facies weight as a feature augmentation engine for ML domain-knowledge framework. The workflow consisted of petrophysical treatment of raw data includes log quality control, preconditioning, processing, features augmentation and labelling, followed by feature selection to impersonate domain experience.

creativity

Creativity is perhaps what most differentiates humans from other species. Understanding creativity is particularly important in times of accelerated cultural and environmental change such as the present, in which novel approaches and perspectives are needed. The study of creativity is an exciting area that brings together many different branches of psychology: cognitive, social, personality, developmental, organizational, clinical, neuroscience, mathematical models, and computer simulations. The creative process is thought to involve the capacity to shift between divergent and convergent modes of thought in response to task demands. Divergent thought is conventionally characterized as and the kind of thinking needed for open-ended tasks, and measured by the ability to generate multiple solutions, while convergent thought is commonly characterized as the kind of thinking needed for tasks in which there is only one correct solution. More recently, divergent thought has been conceived of as reflecting on the task from unconventional contexts or perspectives, while convergent thought has been conceived of as reflecting on it from conventional contexts or perspectives. Personality traits correlated with creativity include openness to experience, tolerance of ambiguity, impulsivity, and self-confidence. Evidence that creativity is linked with affective disorders is mixed. Neuroscientific research on creativity using electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) suggest that creativity is associated with a loosening of cognitive control and decreased arousal. It has been shown that the distributed, content-addressable structure of associative memory is conducive to bringing task-relevant items to mind without the need for explicit search. Tangible evidence of human creativity date back to the earliest stone tools over three million years ago, with the Middle-Upper Paleolithic marking the onset of art, science and religion, and another surge of creativity in the present. Past and current areas of controversy concern the relative contributions of expertise, chance, and intuition, whether the emphasis should be on process versus product, whether creativity is domain-specific versus domain-general, the extent to which creativity is correlated with affective disorders, and whether divergent thinking entails the generation of multiple ideas or the honing of a single initially ambiguous mental representation that may manifest as different external outputs. Promising areas for further psychological study of creativity include computational modeling, research on the biological basis of creativity, and studies that track specific creative ideation processes over time.

Enhancing our conceptual understanding of state and trait self-efficacy by correlational analysis of four self-efficacy scales in people with spinal cord injury

Background Self-efficacy is an important determinant of adjustment following spinal cord injury. Self-efficacy is defined as the belief that one can successfully execute behavior required to produce the desired outcomes. In its original conceptualization, self-efficacy refers to the confidence that people have in their ability to accomplish specific tasks and behaviors within a specific context. Over the years these situation specific aspects have been unconfined and multiple constructs of self-efficacy have been proposed. The most common is a division in trait and state self-efficacy. Another used division that is utilized is between general, domain-specific and task-specific self-efficacy. The scientific support for these constructs is to date still unclear. The objective of this study was to enhance the understanding of the self-efficacy construct by comparing four self-efficacy scales designed to measure three aspects of self-efficacy (general versus domain-specific versus task-specific) in people with spinal cord injury. Methods Dutch and Australian adults with spinal cord injury (N = 140) completed four frequently used self-efficacy scales; the Moorong Self-efficacy Scale, General Self-efficacy Scale, University of Washington Self-efficacy Scale and a Self-care Self-efficacy Scale approximately 6 months after their inpatient rehabilitation. Pearson correlations examined inter-relationships between the scales. Results Hypothesized strong correlations between scales measuring similar aspects of self-efficacy were found (correlations 0.50–0.65). However, the hypothesized weak to moderate correlations between scales measuring diverging aspects of self-efficacy were only partly found (correlations 0.31–0.74), with 7 out of 12 correlations being strong instead of moderate. Conclusions The expected distinctions between the three aspects of self-efficacy was not demonstrated. All four scales measure a common latent construct, most likely general self-efficacy aspects. Further research is necessary to find ways to improve the measurement of domain-specific and task-specific aspects of SE, so that they are sensitive enough to capture change over time, and thus enhance clinical outcomes of people with SCI as they adjust to their disability.

Temporal grouping effects in tone sequence reconstruction: testing domain-general positional theories of serial order

The question of the domain-general versus domain-specific nature of the serial order mechanisms involved in short-term memory is currently under debate. The present study aimed at addressing this question through the study of temporal grouping effects in short-term memory tasks with musical material, a domain which has received little interest so far. We conducted three experiments with non-musicians who performed serial reconstruction of six-tone sequences, where half of the sequences were temporally grouped and the other half presented at a regular pace. The overall data pattern suggests that temporal grouping exerts the same effects on tone sequences reconstruction as in the verbal domain, except for ordering errors. This pattern was replicated in a fourth experiment with verbal material of the same structure as in musical memory experiments of this study. Our findings support the view that positional markers similar to those described in the verbal domain of short-term memory could be used to represent serial order in the musical domain of short-term memory.

Mantle thermal structure at northern Mid–Atlantic ridge from improved numerical methods and boundary conditions

Summary We computed mantle flow and thermal structure beneath a segment of the northern Mid-Atlantic ridge using numerical simulations adopting asymmetric spreading and ridge migration as boundary conditions. The objective is to obtain new insights on mantle processes acting at this ridge segment. We explored different lateral boundary conditions based on velocity, stress and stress-velocity constraints highlighting differences in the depth of the thermal base of the lithosphere versus domain width. Here, we propose a new formulation of lateral and bottom boundary conditions based on the choice of a proper tangential stress at the bottom and on lateral boundaries of the domain accounting for ridge migration. Moreover, dimensional analysis of governing equations suggests that heat generation due to work of the viscous forces cannot be neglected in the computations. Therefore, we included this thermal contribution into the numerical experiments providing an application to the northern Mid-Atlantic ridge at the reference latitude of 43 ○N. Results are compared with available geophysical data in the area, including also mantle tomography models. Asymmetric spreading and ridge migration in numerical modelling account for an asymmetric accretion of the oceanic lithosphere, supporting the evidence of the asymmetries described by geophysical data across the northern Mid-Atlantic ridge segments.