Accuracy Of Judgments Research Articles

Prescribing Agreement Improves Judgments and Decisions

ABSTRACTWe investigated whether prescribing agreement improves the quality of judgments and decisions. Participants were first asked to provide judgments or decisions individually. Then, they either revised their initial judgments and decisions based on a partners' response, or they provided a joint judgment agreed upon with their partner. In the latter condition, we allowed for a minimal communication protocol restricted to acceptance and rejection responses to each other's proposals. In the Agreement condition, participants improved both in a cognitive (Experiment 1a) and a perceptual decision task (Experiment 1b). The cognitive task agreement allowed participants to improve above the level of accuracy achieved with revision. Surprisingly, the prescribing agreement improved the quality of the initial independent decisions. In a judgment task (Experiment 2), the prescribing agreement led to more accurate judgments because partners weighed each other's judgments more equally than in the Revision condition where they gave higher weight to their own judgments. We conclude that prescribing agreement reduces egocentric discounting bias and motivates individuals to be more accurate. These results not only demonstrate that collective benefits in judgment and decision making can be accrued without verbal communication but also suggest potential limitations of this approach.

Energy knowledge and public response to restarting nuclear plants in Japan following the fukushima accident

Since the Fukushima accident, Japan's nuclear policy has changed dramatically. However, restarting operations of some reactors has become an option for the government. This study focuses on the role of objective knowledge and, for the first time, develops statements for measuring people's accurate factual judgments and reflecting on energy issues in post-Fukushima Japan. Replies of two surveys conducted in the Kansai region were analyzed. From them, it was observed that having objective knowledge was positively associated with holding a favorable opinion rather than an ambiguous opinion toward restarting the plants, and association differences existed between subjective and objective knowledge.

Comparing the effectiveness of multiple text reading and rereading on knowledge retention and metacognitive accuracy

AbstractAlthough learning approaches are designed to enhance individuals' ability to store and retrieve information, not all of them are considered effective. The goal of the present study was to experimentally compare the test performance as well as the accuracy of metacognitive judgements of a multiple text reading group, rereading group, and single reading group in a one-day vs. one-week delayed test (3 × 2 between subject design). A total of 186 psychology students (Mage = 20.76) participated in the experiment focused on reading comprehension, accuracy of metacognitive judgments and knowledge retention. Results of the knowledge test indicate that in the one-day delayed test, multiple text reading and rereading yield similar results: both deliver slightly better results than single reading. In the one-week delayed test, though, multiple text reading yields better results than rereading, and both these reading approaches outperform single reading. Moreover, multiple text reading results in fairly robust knowledge retention with only a slight decrease in scores between the one-day delayed and one-week delayed test. Regarding metacognitive monitoring, judgements of learning in the multiple text reading group remained relatively stable after each reading and participants were underconfident about their knowledge. In the rereading group, judgements of learning increased after each reading and participants were overconfident about their knowledge, especially on the one-day delayed test. These findings have implications for educational practices aimed at enhancing learning outcomes and promoting effective learning.

Vehicle Lane Changing Game Model Based on Improved SVM Algorithm

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory.

Contribution of metacognitive questions to accuracy of judgment of learning in a digital environment

Primary school pupils have difficulty making accurate judgments of learning (JOL) in a digital environment. In two studies, we examined the contribution of metacognitive self-questioning to JOL accuracy in the course of reading literacy (Study 1) and mathematical literacy (Study 2) digital learning programs. Both studies comprised a six-session intervention. In each session fifth-grade students studied a short text (Study 1, N = 65) or story containing mathematical components (Study 2, N = 72), then judged their comprehension by predicting their performance before completing a test. For the experimental groups, metacognitive questioning was incorporated into the study materials. In both studies the experimental groups improved over the six sessions in both performance and calibration (i.e., reducing the gap between judgment and performance). The findings highlight the contribution of metacognitive support to improved performance and judgment accuracy and strengthen the case for incorporating metacognitive practices when teaching literacy in a digital environment.

Depth Perception in Optical See-Through Augmented Reality: Investigating the Impact of Texture Density, Luminance Contrast, and Color Contrast.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Recommendations on the Use of Structured Expert Elicitation Protocols for Healthcare Decision Making: A Good Practices Report of an ISPOR Task Force

Healthcare decision making, including regulatory and reimbursement decisions, is based on uncertain assessments of clinical and economic value. This arises from the evidence supporting those assessments being uncertain, incomplete, or even absent. Qualitative, structured expert elicitation (SEE) is a valuable tool for extracting expert knowledge about an uncertain quantity and formulating that knowledge as a probability distribution. This creates a useful input to decision modeling and support, particularly in areas with limited evidence, such as advanced therapy products, precision medicine, rare diagnoses, and other areas with high uncertainty.Structured SEE protocols are used to improve the transparency, accuracy, and consistency of quantitative judgments from experts, limiting the effect of heuristics and biases. This task force report introduces 5 commonly used protocols for SEE (Sheffield elicitation framework; modified Delphi method; Cooke’s classical method; investigate, discuss, estimate, aggregate protocol; and the Medical Research Council reference protocol). It describes the common elements of SEE, discusses how these protocols differ in their implementation of those elements and illustrates the use of the protocols.The report then reviews the relevant constraints on implementing SEE within the context of healthcare decision making and considers the strengths and weaknesses of these protocols in light of those considerations. Because this is an introductory report on an emerging topic, specific recommendations on practice are not made. However, there are broad recommendations based on the suitability of the different protocols in various decision contexts. The report concludes with recommendations for further research to better guide future practice.

A Plantar Pressure Detection and Gait Analysis System Based on Flexible Triboelectric Pressure Sensor Array and Deep Learning.

Gait detection is essential for the assessment of human health status and early diagnosis of diseases. The current gait analysis systems are bulky, limited in the scope of use, and cause interference with the movement of the measured person. Hence, it is necessary to develop a wearable gait detection system that is soft, breathable, lightweight, and self-powered. Here, a plantar pressure sensor array and gait analysis system based on a flexible triboelectric pressure sensor (FTPS) array is developed. Soft, breathable, and wearable electrospinning nanofiber film with excellent triboelectric properties is used as the plantar pressure sensor, achieving a high sensitivity of 45.1 mVkPa-1 in the range of 40-200 kPa and 19.4 mVkPa-1 in the range of 200-400 kpa. 32 FTPSs are integrated into an intelligent insole, which has the characteristics of soft, easy production, good air permeability, long-time stability, no external power supply, and etc. Based on the long short-term memory artificial neural network deep learning model, the accuracy of gait judgment can reach 94.23%. This work provides a feasible solution for real-time gait detection, which will have potential applications in human health assessment and early diagnosis of disease.

Naloxone Coprescribing and the Prevention of Opioid Overdoses: Quasi-Experimental Metacognitive Assessment of a Novel Education Initiative.

Critical evaluation of naloxone coprescription academic detailing programs has been positive, but little research has focused on how participant thinking changes during academic detailing. The dual purposes of this study were to (1) present a metacognitive evaluation of a naloxone coprescription academic detailing intervention and (2) describe the application of a metacognitive evaluation for future medical education interventions. Data were obtained from a pre-post knowledge assessment of a web-based, self-paced intervention designed to increase knowledge of clinical and organizational best practices for the coprescription of naloxone. To assess metacognition, items were designed with confidence-weighted true-false scoring. Multiple metacognitive scores were calculated: 3 content knowledge scores and 5 confidence-weighted true-false scores. Statistical analysis examined whether there were significant differences in scores before and after intervention. Analysis of overall content knowledge showed significant improvement at posttest. There was a significant positive increase in absolute accuracy of participant confidence judgments, confidence in correct probability, and confidence in incorrect probability (all P values were <.05). Overall, results suggest an improvement in content knowledge scores after intervention and, metacognitively, suggest that individuals were more confident in their answer choices, regardless of correctness. Implications include the potential application of metacognitive evaluations to assess nuances in learner performance during academic detailing interventions and as a feedback mechanism to reinforce learning and guide curricular design.

The Mechanisms and Neural Signature of Time-averaged Numerosity Perception.

The animal brain is endowed with an innate sense of number allowing to intuitively perceive the approximate quantity of items in a scene, or "numerosity." This ability is not limited to items distributed in space, but also to events unfolding in time and to the average numerosity of dynamic scenes. How the brain computes and represents the average numerosity over time, however, remains unclear. Here, we investigate the mechanisms and EEG signature of the perception of average numerosity over time. To do so, we used stimuli composed of a variable number (3-12) of briefly presented dot arrays (50 msec each) and asked participants to judge the average numerosity of the sequence. We first show that the weight of different portions of the stimuli in determining the judgment depends on how many arrays are included in the sequence itself: the longer the sequence, the lower the weight of the latest arrays. Second, we show systematic adaptation effects across stimuli in consecutive trials. Importantly, the EEG results highlight two processing stages whereby the amplitude of occipital ERPs reflects the adaptation effect (∼300 msec after stimulus onset) and the accuracy and precision of average numerosity judgments (∼450-700 msec). These two stages are consistent with processes involved with the representation of perceived average numerosity and with perceptual decision-making, respectively. Overall, our findings provide new evidence showing how the visual system computes the average numerosity of dynamic visual stimuli, and support the existence of a dedicated, relatively low-level perceptual mechanism mediating this process.

Learning When Not to Measure: Theorizing Ethical Alignment in LLMs

LLMs and other forms of generative AI have shown immense promise in producing highly accurate epistemic judgements in domains as varied as law, education, and medicine – with GPT notably passing the legal Bar exam and various medical licensing exams. The safe extension of LLMs into safety-critical professional domains requires assurance not only of epistemic but ethical alignment. This paper adopts a theoretical and philosophical approach, drawing from metaethical theories to argue for a distinction hinging around quantitative, axiological comparability that separates Kantian ethics from not only the utilitarianism it is well-known to oppose, but from just distribution theories as well, which are key to debiasing LLM models. It presents the novel hypothesis that LLM ethical acquisition from both corpus induction and RLHF may encounter value conflicts between Kantian and just distribution principles that intensify as they come into improved alignment with both theories, hinging around the variability by which self-attention may statistically attend to the same characterizations as more person-like or more resource-like under distinct prompting strategies.

Features are more than just filling in the blanks on body size scales

ABSTRACT Despite widespread misperceptions of body size and shape, the specific visual features influencing such judgments remain unclear. This study assessed the contribution of internal body features (e.g., colour, shading) to the accuracy of female body size judgements and whether that contribution varies with body size. Using a bodyline task, 100 female participants evaluated images of female bodies, either depicted as real bodies including internal features (feature stimuli) or images of bodies as silhouettes only (silhouette stimuli). The results showed greater overall errors in body size estimation for silhouettes compared to full feature images. However, the direction of error differed for large and small bodies: small silhouettes were judged larger than their full-feature counterparts, while large silhouettes were judged smaller. These findings emphasize the importance of internal body features in shaping perceptions of female body size and highlight the complexity of judgement accuracy across different body sizes.

Some novel Dice similarity measures for picture fuzzy sets and their applications

The similarity measures, as an important tool for assessing the similarity between picture fuzzy sets, have attracted the attention of numerous scholars and achieved rich achievements. Although existing similarity measures have exhibited their unique research contributions, they still fail to distinguish between highly similar but different picture fuzzy sets. This phenomenon poses a major challenge in applications, limiting our ability to make accurate judgments in scenarios that necessitate meticulous differentiation of picture fuzzy sets. Furthermore, the current research focuses on proposing individual and specific calculation formulas, particularly in the realm of Dice similarity measures. However, the systematic, uniform construction of Dice similarity measures remains mostly untapped. The main objective of this article is to propose a method for constructing Dice similarity measures. Through this approach, we can not only integrate existing Dice similarity measures into a unified framework, but also generate novel similarity measures. Firstly, we introduce a function that satisfies specific conditions as the basis for constructing Dice similarity measures. Secondly, by selecting different functions, we obtain various similarity measures. Finally, we propose the pattern recognition algorithm, the face recognition algorithm and the cluster analysis algorithm, all based on the proposed similarity measures. These algorithms are then applied to various fields, including pattern recognition, face recognition, and clustering analysis. Experimental results demonstrate that the proposed similarity measures not only compensate for the shortcomings of existing similarity measures but also exhibit high reliability and practicality, offering a powerful new tool for research and applications in related fields.

The effects of special educational needs and socioeconomic status on teachers' and parents' judgements of pupils' cognitive abilities.

Teachers' and parents' judgements of pupils' cognitive abilities influence pupils' daily learning opportunities and experiences, as these judgements affect the difficulty level of materials and instruction that teachers and parents provide. Over time, these judgements thus significantly shape educational success. However, pupils' characteristics, such as special educational needs (SEN), giftedness and socioeconomic status (SES) can influence and bias judgement accuracy. The present study aimed to investigate the relation between pupils' cognitive abilities and their teachers' and parents' judgements of these abilities, and potential bias in these judgements related to SEN, giftedness, and SES. The sample consisted of 1073 primary school pupils from grades 4-6 from 77 classes in 16 schools, and their teachers and parents. Teachers and parents rated their pupils' cognitive abilities. Pupils completed the COVAT-3, a cognitive ability test. Multilevel analyses revealed that parent judgements were significantly higher than teacher judgements, but both informants' judgements were equally strong related to the cognitive ability scores. When controlling for pupils' assessed cognitive abilities, the results revealed small judgement biases: negative for SEN, positive for giftedness, and finally positive for high SES, but only in teachers. Overall, the results indicated that teachers and parents can judge their pupils abilities to a moderate degree, but they also hold judgement biases related to SEN, giftedness and SES. As these biases can affect pupils' opportunities, it is important to increase teachers' and parents' awareness.

Impact of chronic low back pain on implicit motor imagery assessed by a new laterality judgment task

It is clear that implicit motor imagery (IMI) is impaired by chronic pain in peripheral regions (hand, feet), but unclear in axial regions (neck, shoulder, back). Previous IMI tasks displayed small-amplitude movements of axial regions, which limits person-centered IMI processes mobilization. This study aimed to assess the impact of chronic low back pain (CLBP) on IMI processes with a new task displaying large-amplitude whole-body movements mobilizing the lumbar spine. Twenty patients with CLBP and twenty age-matched controls performed a laterality judgment task on four distinct whole-body movements (trunk flexion, trunk rotation, capoeira, kickboxing). Participants viewed images from four different body viewpoints (back, left, right and front), randomly presented. Mixed ANOVAs were used to compare judgment accuracy and response times between groups and conditions. In participants with CLBP, response times were longer than in controls. The response times of participants with CLBP were also associated with DN4 scores, a self-reported questionnaire assessing neuropathic pain. We validated the use of a person-centered IMI because, for all participants, the accuracy decreased and the response times increased for images presented in the front viewpoint, i.e. when a 180° turn in IMI was required, compared to other viewpoints. The laterality judgment task proposed here confirmed that CLBP impacts IMI processes, and that the nature of pain (neuropathic or mechanical) needs to be considered because it seems to modulate IMI processes. PerspectivesA laterality judgment task with large-amplitude lumbar movements is key to show that CLBP alters processing speed of sensorimotor information originating from the painful region. This task could become an objective tool, transferable in clinical settings, for assessing the impact and the progression of CLBP on motor control processes.

Interpersonal Perception of Adult Playfulness at Zero-Acquaintance: A Conceptual Replication Study of Self-Other Agreement and Consensus, and an Extension to Two Accuracy Criteria.

We replicated and extended previous research examining the accuracy of judgments of four facets of adult playfulness (Other-directed, Lighthearted, Intellectual, and Whimsical; OLIW) at zero-acquaintance. We conducted a conceptual replication study. One hundred sixty targets provided self-ratings for the OLIW facets, textual self-descriptions (≤ five sentences), daily self-ratings of playfulness for 14 consecutive days, and ratings by knowledgeable others. Six unacquainted judges provided rated targets' playfulness based on their self-descriptions. We replicated findings on trait-wise self-other agreement (SOA) and consensus and extended prior research by testing SOA for profiles of the four facets and two distinct accuracy criteria (i.e., targets' diary data and aggregates of targets' self-reports and those from knowledgeable others). All interpersonal perception indicators showed that facets of playfulness can be perceived above chance (SOA ≥ 0.26; consensus ≥ 0.29, accuracies ≥ 0.16). SOA extends from single facets to profiles, also when controlling for stereotype effects. Playfulness can be accurately observed from minimal textual information at zero acquaintance. Our study highlights the robustness of findings on the interpersonal perception of playfulness across samples and methods, and degrees of acquaintanceship. We discuss implications for playfulness in social relationships.

Integration of AI Technology and ZPD: Pinpointing the Zone of Student Development to Promote Educational Innovation and Efficiency Improvement

In the era of rapid technological advancement, AI has profoundly penetrated various sectors of society, including education. This paper explores the positive impact of AI on students' Zone of Proximal Development (ZPD), aiming to assist teachers in making more accurate judgments regarding multiple students' ZPDs and thereby enhancing educational teaching. The findings reveal that AI technology, through its application in ZPD, can provide personalized learning resources and assistance to students, as well as offer teachers appropriate teaching content and strategies tailored to students' needs. However, in the actual teaching process, the application of ZPD to teaching still faces difficulties related to students' individualization, sensitive emotions, and trust crises. Based on this, this paper puts forward the following suggestions. Teachers need to be actively trained in AI to overcome potential resistance from older educators. At the same time, teachers should focus on identifying further technological advances in ZPD and related areas to maximize the benefits of AI in education.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited. This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging. This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT-based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT-based DTL signature was combined with independent clinical predictors to construct a ViT-based deep transfer learning nomogram (DTLN). The ViT-based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775-0.867) in the training cohort and 0.825 (95% CI, 0.758-0.891) in the validation cohort. The ViT-based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT-based DTL signature was then used to construct ViT-based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827-0.903) and 0.894 (95% CI, 0845-0942), respectively, surpassing both the clinical factor model and the ViT-based DTL model (p<0.001). This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

How context influences the processing of relevant information and judgment accuracy-the role of information restriction in judgment processes in diagnosing misconceptions.

To adapt teaching to the prerequisites of students, teachers have various options at their disposal to gather and process information as the basis to form a judgment, such as carrying out tests, talking to and observing the behavior of students, or administering tasks. The complexity of such a judgment arises from the multitude of observations and their different possible explanations. This complexity might be reduced when teachers focus on one hypothesis instead of considering multiple hypotheses, interpret information in a confirmatory way, and not collect diagnostically relevant information. However, in this way, they run the risk of undesirable biased judgments. It therefore seems important to improve diagnostic judgments by selecting and processing information in a more reflective way. Research indicates that if information on a student is not easily available but restricted (e.g., by time pressure, difficult access to the student or high effort), a teacher who wants to make a careful decision is forced to rely on more reflective processes in the selection of tasks and in the interpretation of solutions. The present experimental study therefore investigates how the restricted availability of information in a specific diagnostic situation-when diagnostically inexperienced prospective mathematics teachers determine misconceptions in decimal fractions-influences the underlying cognitive processes. We assume that restricting the availability of information on student behavior augments the attentional focus and therefore reduces cognitive biases. Such more reflective processing can be observed by an increased time spent per piece of information, which should lead to the processing of relevant information and further increase judgment accuracy. To investigate these hypotheses, prospective teachers without prior knowledge in diagnosing misconceptions (N = 81) were asked to diagnose misconceptions on decimal fractions of virtual students by collecting information on students' solutions. Data concerning the effects of restricting the availability of information on teachers' cognitive processes were analyzed. The results show that with restricted information, participants indeed select a greater proportion of diagnostically relevant tasks, which positively influences judgment accuracy. These results are discussed with respect to their significance for framing teacher training and for further research.

Salp Swarm Algorithm-Based Kalman Filter for Seamless Multi-Source Fusion Positioning with Global Positioning System/Inertial Navigation System/Smartphones

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.