Accuracy Of Judgments Research Articles

Smile variation leaks personality and increases the accuracy of interpersonal judgments.

People ubiquitously smile during brief interactions and first encounters, and when posing for photos used for virtual dating, social networking, and professional profiles. Yet not all smiles are the same: subtle individual differences emerge in how people display this nonverbal facial expression. We hypothesized that idiosyncrasies in people's smiles can reveal aspects of their personality and guide the personality judgments made by observers, thus enabling a smiling face to serve as a valuable tool in making more precise inferences about an individual's personality. Study 1 (N = 303) supported the hypothesis that smile variation reveals personality, and identified the facial-muscle activations responsible for this leakage. Study 2 (N = 987) found that observers use the subtle distinctions in smiles to guide their personality judgments, consequently forming slightly more accurate judgments of smiling faces than neutral ones. Smiles thus encode traces of personality traits, which perceivers utilize as valid cues of those traits.

Effects of real-time adaptivity of scaffolding: Supporting pre-service mathematics teachers’ assessment skills in simulations

BackgroundScaffolding pre-service teachers' assessment process in video-based simulations can enhance their acquisition and refinement of assessment skills, for example, needed for accurate judgments of students' mathematical proof skills. Adapting this scaffolding to learners’ individual learning processes, for example, based on text data during the assessment process, brings potential for increased learning gains. AimsIn this study, we investigated the effectiveness of adaptive scaffolding based on real-time process data, specifically targeting pre-service mathematics teachers' assessment skills regarding students’ mathematical proof skills in geometry. SampleParticipants were 245 pre-service teachers. MethodsIn a pre- and post-test, participants completed a video-based simulation to measure their assessment skills regarding students’ mathematical proof skills. During the intervention, participants were randomly assigned to complete the video-based simulation (i) without scaffolding, (ii) with non-adaptive scaffolding, or (iii) with adaptive scaffolding. ResultsWe did not find significant benefits of adaptive scaffolding in enhancing pre-service teachers’ judgment accuracy, aligning with prior research. For an in-depth analysis, we developed and applied a scheme to systematically validate design decisions for adaptive support. This scheme focuses on the selection and measurement of the source of adaptation and the employed support mechanisms. Applying this scheme pointed towards effects of adaptive scaffolding during the assessment process. ConclusionsThis study highlights the need for proximal measures to describe learning in short interventions, explores the intricacies of adaptive scaffolding, such as overlapping with design-loop adaptivity or the accuracy of automated coding, and provides a scheme for an in-depth evaluation of the adaptivity of scaffolding.

The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion.

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

Active learning-based metamodeling for hybrid uncertainty quantification of hydro-mechatronic-control systems: A case study of EHA systems

The Electro-Hydrostatic Actuator (EHA) is a typical hydro-mechatronic control system. Due to the limited accuracy of measurement, inadequate knowledge, and vague judgments, hybrid uncertainties, including aleatory or epistemic uncertainty, inevitably exist in the performance assessment of EHA systems. Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design. To overcome this drawback, a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process (GP) model. The proposed method is bifurcated into three pillars: (A) Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance (OMMD) algorithm, which aims to maximize the minimum distance between the added samples and original samples, (B) maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy, while updating the original GP model, and (C) judging the convergence by three uncertainty metrics, i.e., area metric, maximum variance metric, and mean value metric. A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method. Meanwhile, the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems. The effects of the uncertainties in the Proportional-Integral-Differential (PID) of the EHA system are also examined.

온라인 학습에서 사후 인출연습과 피드백 유형이 메타인지적 판단의 정확도와 학업성취에 미치는 영향

The purpose of this study is to examine the effects of retrieval practice and various types of feedback on academic achievement and the accuracy of metacognitive judgments in an online learning environment. Participants were divided into a control group, which engaged in standard relearning, and three experimental groups that participated in retrieval practice. These groups were differentiated based on the type and amount of feedback provided: Group 1 received knowledge of results feedback, Group 2 received knowledge of correct response feedback, and Group 3 received elaborate feedback, each designed to test different aspects of learning reinforcement. Participants studied online learning content divided into four segments under these conditions. Metacognitive judgments were assessed through a survey after completing the fourth segment, and academic achievement was measured by a final test consisting of 20 items (9 previously studied items and 11 transfer items), administered one week after the end of the learning period. The results revealed significant differences in academic achievement between the experimental and control groups for both the previously studied and total items. Additionally, the experimental groups demonstrated significantly higher academic achievement and more accurate metacognitive judgments in both item categories compared to the control group. No significant differences were found among the feedback types. Based on these findings, the implications of the study are discussed.

Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling

The social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one’s own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed.

Improving Quantitative Analysis with Cross Instrument-Sparse Bayesian Learning (CI-SBL) Raman Spectroscopy Analysis Algorithm.

Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.

A Bayesian network model integrating organizational, individual and psychological factors for strengthening construction worker safety behavior

Objectives. Construction worker safety behavior (CWSB) may be affected by a confluence of multilevel and interrelated factors. Cultivating and maintaining CWSB is vital for improving construction safety. Current studies focus on organization-level or individual-level CWSB antecedents. However, few studies have examined the influence of psychological factors on CWSB, thereby reducing the joint effects of multilevel factors on CWSB. Methods. To determine effective strategies for strengthening CWSB, this study adopted the Bayesian network technique to explore the interrelationships between CWSB and its antecedent factors. A Bayesian belief network model was developed and trained with data collected from Chinese construction workers, which connected organizational, individual and psychological factors with CWSB. Results. According to the sensitivity analysis, safety knowledge, safety climate and psychological capital are the three most significant influencing factors for CWSB. A combined strategy that enhances safety knowledge, safety climate and communication competence simultaneously is the most effective option for strengthening CWSB. The validation and robustness of the network showed good accuracy for safety behavior judgment. Conclusion. This study proposes an alternative way to improve safety behavior by identifying its interactive causes and illustrates the importance of initiating systematic safety measures, which may help to mitigate the problem of safety plateau.

Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images.

The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet.

Learning with videos: Do task instructions and the availability of a pause button matter?

AbstractBackgroundWhen watching educational online videos, learners need to determine whether the videos' contents are suitable for learning. Whereas this may induce metacognitive monitoring processes, it may also distract learners from the learning materials.ObjectivesIn the current set of experiments, we investigated whether asking participants to rate the suitability of three educational videos to impart knowledge (both experiments) and enabling participants to pause the video (Experiment 2) affects learning.MethodsExperiment 1 was conducted in the laboratory with a sample of university students. Participants were randomly assigned to one of two experimental conditions (rating task vs. viewing task). Experiment 2 was conducted online. Participants were randomly assigned to one of four conditions resulting from the two factors task (rating task vs. viewing task) and pause button (available vs. not available).Results and ConclusionIn Experiment 1, we observed detrimental effects of the rating task on learning outcomes, but more accurate judgements of learning. In Experiment 2, we did not find any effects of the rating task on learning outcomes and the accuracy of the participants' judgements of learning. However, the inclusion of a pause button did result in more accurate judgements of learning. Further, exploratory analyses revealed that older participants reported higher levels of intrinsic and germane cognitive load than the younger participants. This increased effort only translated into better learning outcomes in the condition in that participants could pause the video. Thus, age is introduced as a potential boundary condition that should be investigated in instructional design research.

The Toxic Mix of Multiculturalism and Medicine: The Credentialing and Professional-Entry Experience for Persons of African Descent

This essay is based on a case study of international medical graduates (IMGs) in Canada who migrated from sub-Saharan Africa. The chapter examines how narratives of race are situated and deployed in the field of medicine and can produce some aversive social–psychological landscapes in the credentialing and the professional-entry process as it relates to persons of African descent. It will show that, often without predetermination or intent, professionals of African descent in Canada are highly susceptible to implicit racial associations and implicit racial stereotyping in relation to evaluations of character, credentials, and culture. The article exposes some of the critical intersections of common experience, such as: (a) cultural deficit bias—Whiteness as an institutionalized cultural capital attribute; (b) confirmation bias—reaching a negative conclusion and working backwards to find evidence to support it; (c) repurposed sub-Saharan Blackness stereotypes—binary forms of techno-scamming and fraud; and (d) biased deception judgement—where the accuracy of deception judgements deteriorates when made across cultures. These social psychological phenomena result in significantly disproportionate returns on their foreign education and labour market experience for Black medical professionals that require decisive efforts in changing the narratives.

Seeing the unseen boundary behind you: Predicting the out-of-bounds of flick serves in playing badminton doubles

Previous research on affordance perception has shown that elite athletes can relate the environmental features to their motor abilities to detect the opportunities for action. In playing badminton doubles, experienced players can often anticipate whether a flick serve will go out-of-bounds or not. A field study was performed to examine if such an ability was associated with a developed affordance perception. Twenty-two players with and without badminton playing experience were asked to play a receiver on court to judge without looking back whether a flick serve was out-of-bounds in both standing and retreating conditions. Forty flick serves were randomly delivered to either inner or outer rear corner of the receiver’s box and land adjacent to the back service line. Each player’s ability to reach high and retreat backward with a racquet was assessed on court. The results showed that predictions were better in the inner direction than the outer direction, but experienced players were more sensitive and conservative overall. Playing experience appeared to be a consistent predictor for judgment accuracy. In identifying the affordance information that could have been utilized by players for accurate predictions, only experienced players demonstrated the ability to utilize both body-scaled and action-scaled affordances effectively and reliably for better predictions in both standing and retreating conditions.

Size stable batch mode model updating method

In near-infrared spectroscopy analysis, the predictive performance of models may degrade due to variations in the measurement environment and sample properties. Maintenance or updates to the model are essential to uphold prediction accuracy. The state-of-the-art approach for model updates involves labeling new samples and incorporating them into the calibration set. However, this strategy leads to an escalating size of the calibration dataset, and the low ratio of new samples renders the model update process inefficient. To enhance update efficiency, a size-stable updating strategy is presented which involves the simultaneous addition of new samples and removal of old samples. This is achieved by utilizing the new set as a basis for identifying and deleting significantly different old labeled samples. To improve labeling efficiency, a batch mode update is employed. Initially, calibration samples are clustered to yield multiple clusters with comparable sizes to the new update set. Subsequently, differences between multiple old sample clusters and the new set are calculated. The old sample cluster that most different are removed, accompanied by the addition of the new sample set to maintain calibration set size stability. In calculating set similarity, a multi-indices fusion method is employed to ensure accurate judgments. The efficacy of this method is validated through simulated and real data.

The impact of safety attitude on hazard identification and risk analysis: evidence from event-related potentials

ABSTRACT A questionnaire survey and an event-related potential (ERP) experiment were used to reveal the impact of safety attitudes on risk perception. The results revealed that during hazard identification, the N130 amplitude of subjects with negative safety attitude was significantly higher, which implied that subjects with negative safety attitude were more likely to feel confused. During risk analysis, subjects with positive safety attitude were more inclined to overestimate the probability and damage degree of risks; subjects with positive safety attitudes displayed higher P150 and late positive potential amplitudes, which indicated that subjects with positive safety attitudes devoted more attention to risks in the early stage of risk analysis and had a more intense affective response in the later period. The risk judgment ability of subjects with positive safety attitude was affected by time pressure, and they exhibited higher risk judgment accuracy only under no time pressure.

Development and validation of a text-based instrument measuring teachers’ professional vision of student thinking

Abstract Accurate teacher judgments of student thinking are key to adaptive teaching. Yet, making such judgments represents a particularly challenging task for teachers. Developing instruments that can elicit and capture teachers’ cognitive processes of noticing and interpreting is essential for assessing their skills. These instruments should be sensitive to the teachers’ cognitive processes and specific qualities of student thinking in the respective domain. In this article, we present an approach to develop and validate a text-based vignette test which meets these requirements. We used the example of teachers’ noticing and interpreting of cognitive aspects in learners’ attitudes on creation and evolution. We identified relevant components of student thinking in this domain and included these components systematically varied in written classroom vignettes. Experts confirmed a clear and salient representation of the components of student thinking that we intended to include.

Accuracy in imagining

Recent treatments of imagination have increasingly treated imagining as a skill. Insofar as imaginative accuracy is one of the factors that underwrites this skill, it is important to understand what it means to say that an imagining is accurate. This paper takes up that task. The discussion proceeds in four parts. First, I address two worries that may naturally arise about the coherence of the notion of imaginative accuracy. Second, with those worries addressed, I turn to an exploration of what is meant by imaginative accuracy. My discussion relies on two key points: first, that accuracy is best understood in terms of aim; and second, that imaginings aim at the representation of fictional states of affairs. I call this line of thought the fictionality approach. Third, I look more closely at six different types of imaginings in an effort to develop and clarify the fictionality approach. Finally, I turn to what I call the calibration objection. Given the nature of imagination, there seems to be no way to calibrate one’s judgments of imaginative accuracy. After showing how much of the force of the calibration objection can be defused, I offer some brief concluding remarks.

Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating unaided-eye observational errors with unsupervised machine learning image analysis. By comparing the traditional Gram staining method with the improved method on various bacterial samples, results showed that the improved method offers distinct color contrast. Using multimodal assessment strategies, including unaided-eye observation, manual image segmentation, and advanced unsupervised machine learning automatic image segmentation, the practicality of ethanol pretreatment on Gram staining was comprehensively validated. In our quantitative analysis, the application of the CIEDE2000, and CMC color difference standards confirmed the significant effect of the method in enhancing the discrimination of Gram staining.This study not only improved the efficacy of Gram staining, but also provided a more accurate and standardized strategy for analyzing Gram staining results, which might provide an useful analytical tool in microbiological diagnostics.

Uncovering the connection: how do preservice teachers’ inquiry skills relate to practices of judgment and feedback regarding lower secondary school students’ laboratory reports?

ABSTRACT Recent studies have shown that preservice teachers often struggle to effectively implement formative assessment practices to support students’ inquiry skills development. Therefore, it is crucial to investigate factors related to the successful implementation of these practices by preservice teachers. In this study, the relation between the inquiry skill levels of preservice science teachers and the accuracy of their judgments and the quality and quantity of feedback given on lower secondary school students’ laboratory reports of different levels of achievement were examined. Therefore, 61 preservice science teachers evaluated laboratory reports and provided feedback using a rubric, after their inquiry skills were assessed with a paper-and-pencil test. The results show that preservice teachers’ inquiry skills significantly explained the variance in judgment accuracy for all reports. Additionally, the inquiry skill levels of preservice teachers were positively related to the amount of correct feedback they gave in all reports and the amount of prescriptive feedback they gave in the low-achieving report. Finally, there was a negative correlation between the inquiry skill levels of the preservice teachers and the amount of false feedback they gave in high-achieving reports. The results are discussed in terms of effective formative assessment practices in the context of inquiry skill.

Are visual artists better visual perspective takers? An exploratory study with an unexpected outcome.

Visual perspective taking (VPT) has been argued to elicit image-like representations of other people's visual experiences. Separately, it has been demonstrated that there are inter-individual differences in the ability to successfully take other people's visual perspectives. In the present study, adults were asked to judge how long two lines appeared visually from the point of view of an agent. The lines were of identical length, but the agent was always closer to one of the lines than the other, meaning that the closer line should be judged as appearing visually longer. It was hypothesised that adults with experience in the visual arts would perform better at this task for one or both of two reasons: (1) they should be more familiar with the knowledge that the closer an object is the larger it appears visually (i.e., the retinal image is larger), and (2) they might be able to "draw" an image-like representation that more accurately reflects the effect of distance on perceived size. Consistent with previous experiments with this paradigm, adults generally failed to judge the closest line as appearing longer; indeed, as many judged this line would appear visually shorter. Crucially, increasing experience in the visual arts failed to improve the accuracy of VPT judgements; even a group of professional illustrators failed to recognise that the line closest to the agent would appear longer than the line furthest from the agent. These results are discussed in the context of the processes and representation types potentially involved in VPT.