Accuracy Of Judgments Research Articles

Stereotypes and Stereotyping: Measuring the Accuracy of Lifestyle-Based Judgments on Political Affiliation

Abstract People often draw inferences about others’ underlying characteristics from single and static samples of their appearance, such as facial features, or attractiveness. Evidence also suggests that these judgments occur spontaneously and rapidly. Are humans also able to detect political preferences based on appearance? This article examines to what extent observable lifestyle characteristics influence people’s judgments about one’s political affiliation and, more importantly, to what degree these judgments are accurate. A conjoint analysis allows for the identification of the specific lifestyle cues that people use to infer one’s political affiliation. These results are contrasted with a large and unique dataset (n = 64,745), enabling the assessment of how accurate these cues are. Results suggest that certain lifestyle characteristics, such as type of car or leisure activities, are clearly associated with different political parties, at least in people’s minds. Results also suggest that, despite the potential detrimental effects of appearance-based judgments, people are generally pretty good at guessing others’ political preferences. This study contributes to a growing body of research on the relationship between lifestyle and political preferences. More generally, it sheds light on the diagnostic value of appearances in everyday social judgments.

Predicting the memorability of scene pictures: Improved accuracy through one's own experience.

There are conflicting findings regarding the accuracy of metamemory for scene pictures. Judgements of stimulus memorability in general (memorability judgements [MJs]) have been reported to be unpredictive of actual image memorability. However, other studies have found that judgements of learning (JOLs)-predictions of one's own later memory performance for recently studied items-are moderately predictive of people's own actual recognition memory for pictures. The current study directly compared the relative accuracy and cue basis of JOLs and MJs for scene pictures. In Experiments 1 and 2, participants completed an MJ task and a JOL task in counterbalanced order. In the MJ task, they judged the general memorability of each picture. In the JOL task, they studied pictures and made JOLs during a learning phase, followed by a recognition memory test. Results showed that MJs were predictive of general scene memorability and relied on the same cues as JOLs, but MJ accuracy considerably improved after the JOL task. Experiment 3 demonstrated that prior learning experiences drove this increase in MJ accuracy. This work demonstrates that people can predict not only their own future memory performance for scene pictures with moderate accuracy but also the general memorability of scene pictures. In addition, experiences with one's own learning and memory support the ability to assess scene memorability in general. This research contributes to our understanding of the basis and accuracy of different metamemory judgements.

Noise-robust pipe wall-thinning discrimination system using convolution recurrent neural network model

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression.

Predict and Optimize Financial Services Risk Using AI-driven Technology

With the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.

Classification of breast cancer data based on neural network algorithm

In today's world, breast cancer has become the most common malignant tumor, so this paper mainly classifies the features of breast cancer through BP neural network to improve the accuracy of research and judgment. This paper mainly adopts 30 breast cancer feature data of 569 cases, and then obtains the feature data used in this BP neural network through correlation analysis, feature selection and principal component analysis. By training, the BP neural network that is most suitable for this data is constructed from aspects such as the number of hidden layers, loss function, and iteration times, ultimately improving the accuracy to 100%.

Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

Developing a quality assessment model (QAM) using logical prediction: Binary validation

This study focuses on evaluating the quality of competency transfer through various assessment methods and results, considering diverse stakeholder perspectives. The research aims to introduce an innovative approach for validating assessment outcomes, leveraging predicted sub-measurements, and transforming Boolean parameters' symbols into a binary coding system. This transformation simplifies the validation process by employing logical equations. The study's sample involves the adaptation of a competency transfer model, which combines internal parameters with the novel logical assessment method. The research findings indicate that the binary 2x system effectively simplifies quantitative and qualitative data representation within the validation process. This system facilitates the early detection of potentially ambiguous results, enabling the creation of validation procedures grounded in organizational cultural dimensions, outcomes, reports, and assessments. The proposed Quality Assessment Model (QAM) serves as a powerful tool for prediction, enhancing the quality of both quantitative and qualitative data outcomes. This approach generates distinct values, precise predictive measurements, and valuable result quality suitable for informed decision-making in various contexts. Ultimately, the study contributes to the advancement of assessment methodologies, enabling stakeholders to make more accurate and reliable judgments based on the quality of competency transfer.

Real-Time Direction Judgment System for Dual-Frequency Laser Interferometer.

Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system's ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion.

Does being emotionally intelligent and empathic predict deception detection accuracy?

ABSTRACT Research into individual differences in deception detection and judgment brought into question the existence of a good liar-catcher. The current study aimed to investigate the role of trait empathy and emotional intelligence (EI) ability in detecting unemotional lies. One hundred and fifty volunteers were given the Interpersonal Reactivity Index and the Mayer-Salovey-Caruso Emotional Intelligence Test, then they watched a sequence of 14 interviews concerning truthful vs. deceptive holidays. For each videotaped interview, detection accuracy, detection confidence, and detection criteria were assessed. Results confirmed the chance-like ability to detect deception. The empathic trait of perspective-taking and the EI ability to perceive emotions predicted detection accuracy, albeit with a modest effect. Receivers’ judgment accuracy was principally determined by the sender to be evaluated, confirming that detection accuracy is mainly explained by the sender, rather than the receiver's characteristics. Confidence appeared unrelated to detection accuracy.

Where experience makes a difference: teachers' judgment accuracy and diagnostic reasoning regarding student learning characteristics.

The concept of teacher professional vision suggests that experienced teachers, compared to novice teachers, might be better at making accurate judgments of students' learning characteristics, which can be explained by their advanced reasoning in diagnostic situations. This study examines experienced and novice teachers' diagnoses of different student characteristic profiles: three inconsistent profiles (overestimating, uninterested, and underestimating) and two consistent profiles (strong and struggling). We examined both experienced (n = 19 in-service mathematics teachers) and novice teachers (n = 24 pre-service mathematics teachers) to determine the extent of differences in their judgment accuracy and their diagnostic reasoning about observable cues when diagnosing student profiles while watching a lesson video. ANOVA results indicate that experienced teachers generally achieved a higher judgment accuracy in diagnosing student profiles compared to novice teachers. Moreover, epistemic network analysis of observable cues in experienced and novice teachers' diagnostic reasoning showed that, compared to novice teachers, experienced teachers make more relations between a broader spectrum of both surface cues (e.g., a student's hand-raising behavior) and deep cues (e.g., a student being interested in the subject). Experienced teachers thereby construct more comprehensive and robust reasoning compared to novice teachers. The findings highlight how professional experience shapes teachers' professional skills, such as diagnosing, and suggest strategies for enhancing teacher training.

Metacognitive accuracy predicts self-reported quality of life following traumatic brain injury

ABSTRACT Objective Metacognition and quality of life (QoL) are both adversely affected by traumatic brain injury (TBI), but the relation between them is not fully understood. As such, the purpose of this study was to determine the degree to which metacognitive accuracy predicts QoL in individuals with TBI. Methods Eighteen participants with moderate-to-severe TBI completed a stimulus-response task requiring the discrimination of emotions depicted in pictures of faces and then provided a retrospective confidence judgment after each response. Metacognitive accuracy was calculated using participants’ response accuracy and confidence judgment accuracy. Participants also completed the Quality of Life After Brain Injury (QOLIBRI) questionnaire to assess QoL in various areas of functioning. Results Performance of a linear regression analysis revealed that higher metacognitive accuracy significantly predicted lower overall QoL. Additionally, higher metacognitive accuracy significantly predicted lower QoL related to cognition and physical limitations. Conclusion The study results provide evidence of an inverse relation between metacognitive performance and QoL following TBI. Metacognitive changes associated with TBI and their relation to QoL have several clinical implications for TBI rehabilitation.

Interactive crowdsourcing to fact-check politicians.

The discourse of political leaders often contains false information that can misguide the public. Fact-checking agencies around the world try to reduce the negative influence of politicians by verifying their words. However, these agencies face a problem of scalability and require innovative solutions to deal with their growing amount of work. While the previous studies have shown that crowdsourcing is a promising approach to fact-check news in a scalable manner, it remains unclear whether crowdsourced judgements are useful to verify the speech of politicians. This article fills that gap by studying the effect of social influence on the accuracy of collective judgements about the veracity of political speech. In this work, we performed two experiments (Study 1: N = 180; Study 2: N = 240) where participants judged the veracity of 20 politically balanced phrases. Then, they were exposed to social information from politically homogeneous or heterogeneous participants. Finally, they provided revised individual judgements. We found that only heterogeneous social influence increased the accuracy of participants compared to a control condition. Overall, our results uncover the effect of social influence on the accuracy of collective judgements about the veracity of political speech and show how interactive crowdsourcing strategies can help fact-checking agencies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A randomised controlled trial investigating the causal role of the medial prefrontal cortex in mediating self-agency during speech monitoring and reality monitoring

Self-agency is the awareness of being the agent of one's own thoughts and actions. Self-agency is essential for interacting with the outside world (reality-monitoring). The medial prefrontal cortex (mPFC) is thought to be one neural correlate of self-agency. We investigated whether mPFC activity can causally modulate self-agency on two different tasks of speech-monitoring and reality-monitoring. The experience of self-agency is thought to result from making reliable predictions about the expected outcomes of one’s own actions. This self-prediction ability is necessary for the encoding and memory retrieval of one’s own thoughts during reality-monitoring to enable accurate judgments of self-agency. This self-prediction ability is also necessary for speech-monitoring where speakers consistently compare auditory feedback (what we hear ourselves say) with what we expect to hear while speaking. In this study, 30 healthy participants are assigned to either 10 Hz repetitive transcranial magnetic stimulation (rTMS) to enhance mPFC excitability (N = 15) or 10 Hz rTMS targeting a distal temporoparietal site (N = 15). High-frequency rTMS to mPFC enhanced self-predictions during speech-monitoring that predicted improved self-agency judgments during reality-monitoring. This is the first study to provide robust evidence for mPFC underlying a causal role in self-agency, that results from the fundamental ability of improving self-predictions across two different tasks.

Relationships between Sleep Quality, Introspective Accuracy, and Confidence Differ among People with Schizophrenia, Schizoaffective Disorder, and Bipolar Disorder with Psychotic Features.

People with schizophrenia-spectrum and bipolar disorders have difficulty accurately estimating their abilities and skills (impaired introspective accuracy [IA]) and tend to over- or underestimate their performance. This discrepancy between self-reported and objective task performance has been identified as a significant predictor of functional impairment. Yet, the factors driving this discrepancy are currently unclear. To date, the relationships between sleep quality and IA have not been examined. The current study aimed to explore the relationships between sleep quality and IA in participants diagnosed with schizophrenia (SCZ; n = 36), schizoaffective disorder (SCZ-A; n = 55), and bipolar disorder with psychotic features (BP; n = 87). Participants completed tasks of emotion recognition, estimated their performance on the tasks (used to calculate IA), and provided confidence ratings for their accuracy judgments. Participants also self-reported their sleep quality. These results suggest significantly greater discrepancies between self-reported and actual task scores for those with SCZ and SCZ-A compared to participants with BP. For those with SCZ, lower confidence on the tasks and underestimation of abilities were associated with lower sleep quality, while for those with SCZ-A, lower sleep quality was associated with higher confidence and overestimation of performance. Results suggest differential relationships between diagnostic groups. Future research is needed to further explore the factors driving these differing relationships, particularly the contrasting relationships between SCZ and SCZ-A.

Exploring clinical judgment ability in second-degree baccalaureate of science nursing students: A mixed methods study

BackgroundAs they have not yet embarked on clinical practice, most students who already have a bachelor's degree but require a bachelor's degree in nursing occasionally perceive the educator's instruction on clinical situations as abstract and challenging for making accurate clinical judgments. ObjectivesThis study aims to implement a clinical judgment model and case scenarios in classroom teaching to evaluate improvements in students' clinical judgment and critical thinking abilities. DesignA mixed-method design. SettingA second-degree Bachelor of Science in Nursing at a university in Taiwan. ParticipantsFirst-year undergraduate nursing students. MethodsThis mixed-methods study featured a survey at the beginning and end of a course, followed by one-on-one online interviews. A purposive sample of sophomore nursing students was recruited from a university in northern Taiwan between March 2020 and May 2021. Semi-structured interviews were conducted after a preliminary analysis of the collected quantitative data. ResultsIn total, 48 participants completed the study questionnaire, and 20 were interviewed. The results show that the students' ability to make clinical judgment and identify individual health problems from case scenarios significantly improved after completing the course. However, critical thinking did not differ significantly after the course. Qualitative data analysis revealed three key themes relevant to the participants' learning experiences: (1) establishing the context of clinical judgment, (2) building a bridge between basic medical science and clinical nursing, and (3) having a broader perspective. ConclusionsIncorporating clinical judgment measurement model and case scenarios in the curriculum may benefit second-degree Bachelor of Science in Nursing students who have not yet begun their clinical practice. Additionally, the result provides educators with valuable learning goals and evaluation strategies in the classroom and clinical practice.

Improving the accuracy of social work judgements: A proof‐of‐concept study of a training programme

AbstractChild and family social workers routinely make professional judgements involving significant legal and moral questions (e.g. whether a child has been abused) and more ‘everyday’ issues (e.g. will the child be re‐referred again if we close the case now?) Yet the world is capricious, and we rarely know with certainty what is going to happen in future or the likely impact of our different choices. Given the consequences of their judgements and decisions, it is imperative that social workers are provided with the best possible support. This paper reports a proof‐of‐concept study of a set of interventions to improve the judgemental accuracy of social workers: (i) a survey to identify respondents with above‐average existing abilities, (ii) training sessions on cognitive debiasing and (iii) structured group working and (iv) three methods for aggregating individual judgements. Findings indicate that it is possible to measure the accuracy of social work judgements in relation to case‐study materials and retrospective questions, while the feedback about the training was largely positive. Any future studies should aim to recruit a more diverse set of respondents, test judgemental accuracy in relation to prospective judgements and explore what types of questions would be most helpful for real‐world decision‐making.

The Misleading count: an identity-based intervention to counter partisan misinformation sharing.

Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US (N = 1709) and UK (N = 804), we found that crowdsourcing accuracy judgements by adding a Misleading count (next to the Like count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared with the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly five times as effective as another popular misinformation intervention (i.e. the accuracy nudge reduced sharing misinformation by 5%). Extreme partisanship did not undermine the effectiveness of the intervention. Our results suggest that identity-based interventions based on the science of social norms can be more effective than identity-neutral alternatives to counter partisan misinformation in politically polarized contexts (e.g. the US). This article is part of the theme issue 'Social norm change: drivers and consequences'.

Abnormal Oculomotor Corollary Discharge Signaling as a Trans-diagnostic Mechanism of Psychosis.

Corollary discharge (CD) signals are "copies" of motor signals sent to sensory areas to predict the corresponding input. They are a posited mechanism enabling one to distinguish actions generated by oneself vs external forces. Consequently, altered CD is a hypothesized mechanism for agency disturbances in psychosis. Previous studies have shown a decreased influence of CD signals on visual perception in individuals with schizophrenia-particularly in those with more severe positive symptoms. We therefore hypothesized that altered CD may be a trans-diagnostic mechanism of psychosis. We examined oculomotor CD (using the blanking task) in 49 participants with schizophrenia or schizoaffective disorder (SZ), 36 bipolar participants with psychosis (BPP), and 40 healthy controls (HC). Participants made a saccade to a visual target. Upon saccade initiation, the target disappeared and reappeared at a horizontally displaced position. Participants indicated the direction of displacement. With intact CD, participants can make accurate perceptual judgements. Otherwise, participants may use saccade landing site as a proxy of pre-saccadic target to inform perception. Thus, multi-level modeling was used to examine the influence of target displacement and saccade landing site on displacement judgements. SZ and BPP were equally less sensitive to target displacement than HC. Moreover, regardless of diagnosis, SZ and BPP with more severe positive symptoms were more likely to rely on saccade landing site. These results suggest that altered CD may be a trans-diagnostic mechanism of psychosis.

Diagnostic accuracy of doctors at the emergency department and radiologists in differentiating between complicated and uncomplicated acute appendicitis

PurposeTo determine the accuracy of final judgements of doctors at the emergency department (ED) and radiologists to differentiate between complicated and uncomplicated acute appendicitis, because these have different treatment options.MethodsThis prospective, multicenter study included adult patients with imaging-confirmed acute appendicitis, operated with intention to appendectomy. Both doctors at ED and radiologists assessed appendicitis severity as a final judgement of “uncomplicated” or “complicated” appendicitis. Doctors at ED integrated clinical, laboratory, and imaging findings. Radiologists relied solely on imaging findings. Outcomes were accuracy of these judgements for diagnosis of complicated appendicitis compared to the reference standard by an adjudication committee.ResultsAfter imaging, 1070 patients with confirmed acute appendicitis were included. Doctors at ED accurately labelled 656 of 701 (93.6%) patients with true uncomplicated appendicitis as uncomplicated, and 163 of 369 (44.2%) patients with true complicated appendicitis were labelled as complicated. Sensitivity, specificity, and positive and negative predictive values (PPV and NPV) for complicated appendicitis were 44.2%, 93.6%, and 78.4% and 76.1%, respectively. Comparable accuracy was found for the radiologist’s assessment in 941 patients, with true positive rates of 92.2% (581 of 630 patients) for uncomplicated appendicitis and 46.6% (145 of 311 patients) for complicated appendicitis.ConclusionMore than half of all patients with true complicated appendicitis is incorrectly classified as uncomplicated appendicitis according to the judgements of doctors at ED, integrating clinical, laboratory, and imaging results, and of radiologists assessing diagnostic imaging. These judgements are thereby not sufficiently reliable in ruling out complicated appendicitis.

An attention-based deep learning method for the detection of electrical status epilepticus during sleep from electroencephalogram waveform analysis in children

Electrical status epilepticus during sleep (ESES) is a special interictal electroencephalographic phenomenon observed in children with epilepsy. ESES refers to continuous or nearly continuous epileptiform discharges of bilateral (or unilateral) spikes and waves induced during non-rapid eye movement (NREM) sleep. It mainly manifests in a series of childhood epileptic syndromes characterized by seizures, ESES, and cognitive impairment, which seriously affect the health of children. Typically, the spike-wave index (SWI), which is the percentage of spikes and waves duration in the total NREM time, is an important criterion for diagnosing ESES. The SWI measures the proportion of the total duration of spikes and waves to the NREM sleep time within several hours. Currently, SWI is obtained primarily by manual measurement and calculation, which has a certain subjective bias, low accuracy, time and labor consumption, and is a waste of medical resources. Therefore, we designed a novel data segmentation model based on a multiresolution convolutional neural network and a self-attention mechanism to calculate the SWI quickly and accurately. This can assist clinicians in making more accurate judgments and providing a better guide for the treatment of children with epilepsy. A large amount of clinical patient data was used to test the proposed model. The results confirmed that this model has an acceptable generalization ability for data from different patients and the same patient at different times. Each performance index of the model was superior to that of current common models, thus fulfilling the requirements for clinical testing.