Judgment Process Research Articles

Una valutazione col pilota automatico? Una riflessione sulle cose che possiamo guadagnare e quelle che rischiamo di perdere impiegando l’intelligenza artificiale nei processi valutativi

EVALUATION ON AUTOPILOT? A REFLECTION ON THE THINGS WE CAN GAIN AND THOSE WE RISK LOSING BY USING ARTIFICIAL INTELLIGENCE IN EVALUATION PROCESSES Abstract The paper analyses pros and cons of using generative artificial intelligence in the students’ learning assessment process. In particular, the article focuses on the possibility offered by programs such as ChatGPT to reduce the time devoted to complex operations such as construction of multiple-choice questions or classification of open-ended answers. Thus, it is possible to use the generated content to support a phase of the evaluation process that normally dilutes time and makes the workload difficult to sustain. Although the benefits from this side are significant, the contribution emphasises that it is quite inappropriate to claim that generative artificial intelligence can take over the responsibility for judgement processing. Firstly, since there are no objective evaluations, such a choice would merely conceal the necessarily subjective criteria used to make well-founded value judgements. Secondly, such a choice would imply a deresponsibility with respect to judgements that have relevant practical consequences and a renunciation of the possibility of actively and consciously employing the evaluation process for the enrichment of future experiences.

How does language modulate the association between number and space? A registered report of a cross-cultural study of the spatial-numerical association of response codes effect.

Past investigations into the connection between space and numbers have revealed its potential vulnerability to external influences such as cultural factors, including language. This study aims to examine whether language moderates the association between space and number in the spatial-numerical association of response codes (SNARC) effect, which is demonstrated in an interaction between number magnitude and response side. The SNARC effect has been observed across various stimuli. However, research on the influence of linguistic factors, such as reading direction, on the SNARC effect has yielded contradictory findings. We systematically examined the moderating effect of language on the SNARC effect in a cross-cultural design. A group of British English speakers and a group of Iranian Farsi speakers performed four SNARC tasks including both explicit (magnitude classification) and implicit (parity judgment) processing of number magnitude in two modalities of visual and auditory presentations. To the best of our knowledge, this is the first systematic investigation of language, magnitude processing, and sensory modalities altogether. While our registered analyses found no group differences in SNARC tasks, nonregistered analyses using a Bayesian ex-Gaussian framework revealed novel findings: a stronger SNARC effect in slower responses and auditory tasks. These findings challenge the idea of a substantial language role in shaping the SNARC effect but also indicate large uncertainty regarding the exact nature of language-induced effects, highlighting the need for further investigations of spatial-numerical interactions that may be differently influenced by linguistic and cultural factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Propiedades psicométricas y validación de la escala GADA para medir el Grado de Conciencia de Género en Adolescentes

Introduction. In contrast to the wide range of possibilities for measuring gender awareness, there are fewer validated instruments with a broad, global and holistic vision in relation to gender and far from the adult-centred perspective. For this reason, a scale that measures the Degree of Gender Awareness in Adolescents is created, validated and developed: GADA (Gender Awareness Degree in Adolescents). Method. GADA has been developed after a pilot test and an expert judgment process and has been validated through factor analysis based on the latest statistical advances in the social sciences following post-Little Jiffy psychometrics. The FACTOR program has been used to obtain two factors, the personal and the social, with a constructionist vision of gender. The population belongs to 10 secondary education institutes in Córdoba (Spain) with a sample of 510 students (55% women and 45% men) between 14 and 16 years old. Results. The scale shows high reliability and internal consistency, with Cronbach's Omega and alpha coefficient being .84 and optimal goodness adjustments (GFI =.992, CFI =.992, NNFI =.987, RMSEA [95% CI] =.032 (.0101-.0378) and RMCR =.0404). Furthermore, sex is an invariant variable. Discussion or Conclusion: The GADA Scale is a reliable instrument with a global and comprehensive vision of gender and feminism with relevant issues for adolescence that addresses gender awareness with educational applications.

Semi-Open Set Object Detection Algorithm Leveraged by Multi-Modal Large Language Models

Currently, closed-set object detection models represented by YOLO are widely deployed in the industrial field. However, such closed-set models lack sufficient tuning ability for easily confused objects in complex detection scenarios. Open-set object detection models such as GroundingDINO expand the detection range to a certain extent, but they still have a gap in detection accuracy compared with closed-set detection models and cannot meet the requirements for high-precision detection in practical applications. In addition, existing detection technologies are also insufficient in interpretability, making it difficult to clearly show users the basis and process of judgment of detection results, causing users to have doubts about the trust and application of detection results. Based on the above deficiencies, we propose a new object detection algorithm based on multi-modal large language models that significantly improves the detection effect of closed-set object detection models for more difficult boundary tasks while ensuring detection accuracy, thereby achieving a semi-open set object detection algorithm. It has significant improvements in accuracy and interpretability under the verification of seven common traffic and safety production scenarios.

Basis is also explanation: Interpretable Legal Judgment Reasoning prompted by multi-source knowledge

The task of Legal Judgment Prediction (LJP) aims to forecast case outcomes by analyzing fact descriptions, playing a pivotal role in enhancing judicial system efficiency and fairness. Existing LJP methods primarily focus on improving representations of fact descriptions to enhance judgment performance. However, these methods typically depend on the superficial case information and neglect the underlying legal basis, resulting in a lack of in-depth reasoning and interpretability in the judgment process of long-tail or confusing cases. Recognizing that the basis for judgments in real-world legal contexts encompasses both factual logic and related legal knowledge, we introduce the interpretable legal judgment reasoning framework with multi-source knowledge prompted. The essence of this framework is to transform the implicit factual logic of cases and external legal knowledge into explicit basis for judgment, aiming to enhance not only the accuracy of judgment predictions but also the interpretability of the reasoning process. Specifically, we design a chain prompt reasoning module that guides a large language model to elucidate factual logic basis through incremental reasoning, aligning the model prior knowledge with task-oriented knowledge in the process. To match the above fact-based information with legal knowledge basis, we propose a contrastive knowledge fusing module to inject external statutes knowledge into the fact description embedding. It pushes away the distance of similar knowledge in the semantic space during the encoding of external knowledge base without manual annotation, thus improving the judgment prediction performance of long-tail and confusing cases. Experimental results on two real datasets indicate that our framework significantly outperforms existing LJP baseline methods in accuracy and interpretability, achieving new state-of-the-art performance. In addition, tests on specially constructed long-tail and confusing case datasets demonstrate that the proposed framework possesses improved generalization abilities for predicting these complex cases.

Class-based differences in moral judgment: A bayesian approach

This study employs Bayesian inference to explore class-based differences in moral judgment. Based on the dual-process perspective in interdisciplinary action theory, we estimate in a first step a process model which differentiates parametrically between emotionally driven deontological, deliberatively driven utilitarian, and residual judgmental inclinations. In a second step, our estimates of these parameters are correlated via beta regressions with indicators of social class and thinking dispositions. We find a considerable association between social class, specifically income, and deontological inclinations, whereas consequentialist inclinations correlate with thinking dispositions but not with social class. This research underscores the utility of Bayesian estimation in closing the gap between theoretical and statistical modelling. Employing this approach enhances our understanding of the nuanced interplay between intuitive and deliberative processes in moral judgment and, more generally, offers a promising direction for advancing sociological action theory.

The two-stage processing of judgment of confidence: evidence from ERP

BackgroundThe judgment of confidence (JOC) refers to the confidence in the accuracy of the target item individuals have just retrieved and is a typical retrospective metacognitive monitoring process. In the classical paradigm of JOC, JOC occurs after the recognition or recall task. While initially viewed as a single-stage monitoring process, recent research on JOC suggests its internal mechanisms may be more complex, potentially encompassing both retrieval and monitoring processes. This study aims to delve into these mechanisms concerning neural temporal processes.MethodsIn this study, event-related potential (ERP) was used to compare N400 and slow-wave ERPs of high and low JOCs at different time windows using a classic JOC paradigm.ResultsBehavioral results showed an inverted-U shaped relationship between response time (RT) and JOCs, peaking at magnitude 3 before declining. There were significantly longer RT for low JOCs compared with high JOCs, along with lower recognition scores. The ERP results showed that low JOCs induced larger N400 in the right frontal lobe and right central area, while high JOCs induced larger slow-wave components (500 ~ 700ms) in the right frontal lobe.ConclusionsBased on these findings, the present study suggests that JOC involves two processing stages. N400 reflects the process of cue acquisition, while the slow-wave component reflects the process of cue application. Furthermore, a two-stage model was proposed and validated, enriching the study of metacognition monitoring mechanisms, offering insights into the processing mechanisms of retrospective metacognitive monitoring.

Multivariate Decision Tree-Oriented Early Warning Method for College Students’ Psychological Crisis Behavior

To prevent the occurrence of psychological crisis behavior and help college students grow from the crisis is not only an important issue related to college students’ mental health, but also a very necessary and urgent practical issue related to college students’ family happiness, college talent training, and social harmony and stability. Therefore, a multivariable decision tree-oriented early warning method for college students’ psychological crisis behavior is proposed. Using the laser beam emitted by the sensor as a reference standard, the psychological crisis behavior data is extracted, converted into different formats, divided into linear regions, and used to build a multivariate decision tree model. The model is used as the judgment processing target, and the judgment equation of mental health status data is established. The status data is divided into different processing groups as the processing object, calculate the maximum state value of the state data in the two data combinations to obtain the optimal early warning result, and finally realize the early warning of psychological crisis behavior. The experimental results show that the final success rate of early warning obtained by this method is high, which can improve the efficiency of early warning; the number of missing data sets was the least, and most of the subjects’ psychological risk tolerance was at the medium level.

An applied cognitive task analysis investigation of professional judgement and decision-making in a small group of high-level, UK-based caving instructors

ABSTRACT The professional judgement and decision-making processes of adventure sports coaches’ leadership practice is a growing area of research. However, the PJDM and underpinning epistemology of caving instructors has recived limited research focus. The decision-making practice of professionals working in complex and constrained environments has significant transferability to domains such as rescue and emergency services. Consequently, this paper investigates the leadership practice of a purposive sample of 3 UK based caving instructors, utilising an Applied Cognitive Task Analysis to access expert cognition. The study aimed specifically to examine the decision-making processes of this sub-group of outdoor professionals who work in difficult, multifaceted and demanding environments. Results supported and built upon previous work in adventure sports, specifically in confirming the complexity of judgement and decision-making underground. In particular, decision-making creates high cognitive loads because of environmental factors inherent in extended caving journeys. Loads are assuaged by the application of heuristics and planning, but naturalistic decision-making processes remain numerous and multifaceted. Findings have implications for National Governing Body instructor training programme design, with particular reference to the development of decision-making expertise in complex environments.

Multimodal Consistency Suppression Factor for Fake News Detection

Recent multi-modal fake news detection methods often use the consistency between textual and visual contents to determine the truth or fake of a news information. Higher levels of textual-visual consistency typically lead to a greater likelihood of classifying a news item as real. However, a critical observation reveals that creators of most fake news intentionally select images that align with the textual content, thereby enhancing the credibility of the news. Consequently, high consistency between textual and visual contents alone cannot guarantee the authenticity of the information. To address this problem, we introduce a novel approach termed Multimodal Consistency-based Suppression Factor to modulate the significance of textual-visual consistency in information assessment. When the textual-visual matching is high, this suppression factor reduces the influence of consistency during the judgment process. Moreover, we use Contrastive Language-Image Pre-training (CLIP) model to extract features and measure the consistency level between modalities to guide multimodal fusion. In addition, we also use a method of compressing and fusing modal information based on Variational Autoencoder (VAE) to reconstruct CLIP features, learning the shared representation of different modal information of CLIP. Finally, extensive experiments were conducted on three publicly datasets, Weibo, Twitter and Weibo21, and the results confirmed that our method outperformed the state-of-the-art methods in the field, and had 0.8%, 2.6% and 4.1% effect improvement on the accuracy rate.

Ground fault protection algorithm of active distribution network based on energy extremum direction

AbstractAfter large‐scale distributed power sources are connected to the distribution network, the fault current undergoes noticeable changes, affecting the accuracy of traditional single‐phase grounding fault algorithms. Therefore, the objective of this paper is to address the impact of distributed power integration on the grounding algorithms of distribution networks. The main contribution is: by establishing an electrical system structure and model for distribution network grounding faults that include distributed generation (DG), theoretically deriving and calculating the transient zero‐sequence current frequency changes at the moment of fault, analysing the directional characteristics of zero‐sequence currents under DG connection conditions, designing a local grounding fault judgment algorithm based on energy extremum direction, and providing a fault judgment and isolation process for grounding fault monitoring devices. The results show: through simulation and experimentation, the algorithm was tested, and the method can reliably judge grounding faults under various transition resistances, different numbers and capacities of connected distributed power sources, and different grounding switch‐on angles. The applicability of the algorithm covers both methods of neutral grounding through an arc suppression coil and ungrounded neutrals, adapting to scenarios with or without DG connections.

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.

Using artificial intelligence in trademark registration in light of the UAE trademark Law

When a commercial project is established to produce certain goods or services of high quality to compete and attract consumers, the project owner must adopt a trademark for the good, service or product in order to distinguish what he does from other competing services, goods and production. This requires registering the trademark with the relevant institutions in the country of registration, which requires procedures, requirements and attachments to be submitted. Then comes the role of those institutions in examining the submitted application in accordance with the law in force in the country of registration. Hence, we are discussing the special procedures for registration in the United Arab Emirates, which is one of the most advanced countries in government procedures. We are proposing the idea of introducing artificial intelligence software into the examination and judgment process to speed up the registration process for fear of it being registered by another person in another country that has an agreement with the UAE. Therefore, ownership of the trademark will be for the first to register.

Development of a Core Outcome Measure Instrument; "LeishCOM_LCL", for Localised Cutaneous Leishmaniasis.

Localized cutaneous leishmaniasis (LCL) is a chronic ulcerating disease. A literature review identified inconsistencies in clinical trials. The aims of this study were to reach a consensus on the most important domains to measure when assessing LCL, agree on parameters to measure the domains, and develop a tool representing a Core Outcome Set (COS), for use in clinical assessment of LCL. A literature review was conducted to identify any existing COS for LCL embracing agreed Outcome Domains, i.e. what to measure and any Outcome Measurement Instruments (OMIs). As no COS was available, potential outcome domains for assessment of LCL were identified through an international collaborative approach using e-consultations and virtual discussions with expert stakeholders (n = 20) from geographically different LCL endemic countries. Subsequent judgmental validation process included a face-to-face multidisciplinary stakeholders' meeting adopting the Nominal Group Technique. A final consensual agreement on outcome domains and items required to measure these domains was established. "Clinical Cure" was defined as the ideal overall "General Concept". The five Core Outcome Domains included Signs capturing clinical morphology, diameter, and induration of an index lesion with the aid of a palpability score, Treatment Efficacy assessing percentage change in size of the lesion and re-epithelialization compared to baseline, Treatment Impact which included an investigator and patient visual analogue score, and Clinical Sequelae rating pigment change, atrophic and hypertrophic/keloid scars. It was agreed that two open-ended questions should be included to capture some aspects of Health-Related Quality of Life as a means of capturing a patient-focused approach. LeishCOM_LCL was generated to reflect a COS for LCL. This captured demographic details, agreed outcome domains and measures to assess these domains. Validation of LeishCOM_LCL will be reported in a separate paper. Development of a Patient Reported Outcome Measure will be considered in the future.

An Overview of Moral Psychology

Moral process has been a complex process since ancient times, and studying and understanding morality is beneficial for us to solve most of the moral dilemmas in our society, therefore, this paper mainly focuses on the introduction of moral emotions as well as the process of moral judgment, and discusses the relevant research and theories of moral psychology.

When the Heart Meets the Mind: Exploring the Brain-Heart Interaction during Time Perception.

Recent studies suggest that time estimation relies on bodily rhythms and interoceptive signals. We provide the first direct electrophysiological evidence suggesting an association between the brain's processing of heartbeat and duration judgment. We examined heartbeat-evoked potential (HEP) and contingent negative variation (CNV) during an auditory duration-reproduction task and a control reaction-time task spanning 4, 8, and 12 s intervals, in both male and female participants. Interoceptive awareness was assessed with the Self-Awareness Questionnaire (SAQ) and interoceptive accuracy through the heartbeat-counting task (HCT). Results revealed that SAQ scores, but not the HCT, correlated with mean reproduced durations with higher SAQ scores associating with longer and more accurate duration reproductions. Notably, the HEP amplitude changes during the encoding phase of the timing task, particularly within 130-270 ms (HEP1) and 470-520 ms (HEP2) after the R-peak, demonstrated interval-specific modulations that did not emerge in the control task. A significant ramp-like increase in HEP2 amplitudes occurred during the duration-encoding phase of the timing but not during the control task. This increase within the reproduction phase of the timing task correlated significantly with the reproduced durations for the 8 s and the 4 s intervals. The larger the increase in HEP2, the greater the under-reproduction of the estimated duration. CNV components during the encoding phase of the timing task were more negative than those in the reaction-time task, suggesting greater executive resources orientation toward time. We conclude that interoceptive awareness (SAQ) and cortical responses to heartbeats (HEP) predict duration reproductions, emphasizing the embodied nature of time.

Behind the grades: co-constructing fairness to reach agreement in evaluative judgement

Assessment in higher education is often a balancing act between fairness and the inherent complexity of standards, with evaluative judgement playing a crucial role in determining the quality of student work. This study employs a sociomaterial perspective to investigate the often-obscured justifications used by examiners during real-time moderation meetings, as well as their role in shaping judgements. Through participant observation and focus group interviews, a two-stage analysis was conducted. Thematic analysis identified four categories of justifications – predefined, personal, norm-based and other justifications – highlighting a range of rationales behind examiners’ grading decisions. Subsequent interaction analysis explored how these justifications inform the evaluative judgement process, revealing their role in co-constructing an understanding of fairness to reach a compromise. A key finding is that different perspectives on fairness significantly impact evaluative judgements. The study recommends the use of the concepts ‘tinkering’ and ‘rupturing’ as analytical tools to promote flexibility and harness the strengths of subjectivity while maintaining the integrity of judgements.

Behavioral Biases in Investment Decisions: An Extensive Literature Review and Pathways for Future Research

In the realm of investment decisions, the influence of behavioral biases has emerged as a captivating area of exploration. This article embarks on a comprehensive journey through the landscape of behavioral biases in investment choices, delving into their profound impact on financial markets. Contrary to traditional finance theories assuming rationality, a multitude of empirical evidence attests to the pervasive effects of cognitive and emotional biases. Through an extensive literature review, this article elucidates the intricacies of key biases such as overconfidence, loss aversion, anchoring, confirmation bias, herding behavior, disposition effect, framing effects, and regret aversion. By examining the distinct ways these biases distort investors' judgment and decision-making processes, we unveil the often unexpected deviations from rationality. Each bias, rooted in human psychology, can lead to suboptimal investment behaviors, portfolio misalignments, and heightened market volatility. However, recognizing the impact of these biases provides opportunities for transformative insights. As investment professionals, policymakers, and individuals alike comprehend the subtle nuances of behavioral biases, tailored interventions, educational initiatives, and adaptive strategies can be devised to mitigate their adverse effects. This article not only synthesizes the prevailing research but also charts a course for future investigations. The implications of understanding and addressing behavioral biases extend beyond financial realms, offering a bridge between finance and psychology. As interdisciplinary collaboration gains momentum, pathways for future research become evident, beckoning scholars to delve deeper into the uncharted territories of human behavior and its intricate relationship with investment decisions. Through the exploration of these biases and their potential remedies, this article illuminates the evolving landscape of investment decision-making in a world where cognitive fallacies intersect with financial choicest.

Budgetary Cycle and late accountability: diagnosis regarding the political judgment of government financial statements within the scope of the States

The normative scenario configured by the 1988 Constitution, in matters of public finances, reveals a dynamic structured based on the notion of budget cycle. However, the materialization of the processes that make up this cycle is permeated by decision-making operated from a perspective of supervision of social bodies and institutionalized control. In this sense, the theoretical framework regarding horizontal accountability emerges, according to O'Donnell's (1998) categorization, which translates in the issuance of the preliminary opinion by the Audit Courts and the subsequent political judgment issued of Parliament. Having as its object of work the temporal profile of the political judgment of government accounts, this paper proposes to investigate the existence of empirical distortions concerning the process of assessment and judgment of the accounts of the head of the Executive Branch of the States, and where possible delays arise – whether in the assessment and sending of the preliminary opinion by the accounting body or in the processing of the document in the legislative houses. Therefore, in methodological terms, for the purpose of articulating the diagnosis on a legal-exploratory basis, we opted for a sample selection stratified into subgroups referring to the five historically recognized regions, choosing the most populous federated entity in each region as a critical case for analysis, which takes place through the mobilization of qualitative data, extracted from primary sources (legislative decrees, draft legislative decrees and previous opinions), with a documentary analytical focus, taking as a focus the period referring to the accounts from 2010 to 2020.

Structured expert judgement approach of the health impact of various chemicals and classes of chemicals.

Chemical contamination and pollution are an ongoing threat to human health and the environment. The concern over the consequences of chemical exposures at the global level continues to grow. Because resources are constrained, there is a need to prioritize interventions focused on the greatest health impact. Data, especially related to chemical exposures, are rarely available for most substances of concern, and alternate methods to evaluate their impact are needed. A Structured Expert Judgment (Research Outreach, 2021) process was performed to provide plausible estimates of health impacts for 16 commonly found pollutants: asbestos, arsenic, benzene, chromium, cadmium, dioxins, fluoride, highly hazardous pesticides (HHPs), lead, mercury, polycyclic-aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), Per- and Polyfluorinated Substances (PFAs), phthalates, endocrine disrupting chemicals (EDCs), and brominated flame retardants (BRFs). This process, undertaken by sector experts, weighed individual estimations of the probable global health scale health impacts of each pollutant using objective estimates of the expert opinions' statistical accuracy and informativeness. The foremost substances, in terms of mean projected annual total deaths, were lead, asbestos, arsenic, and HHPs. Lead surpasses the others by a large margin, with an estimated median value of 1.7 million deaths annually. The three other substances averaged between 136,000 and 274,000 deaths per year. Of the 12 other chemicals evaluated, none reached an estimated annual death count exceeding 100,000. These findings underscore the importance of prioritizing available resources on reducing and remediating the impacts of these key pollutants. Based on the evidence available, experts concluded some of the more notorious chemical pollutants, such as PCBs and dioxin, do not result in high levels of human health impact from a global scale perspective. However, the chemical toxicity of some compounds released in recent decades, such as Endocrine Disrupters and PFAs, cannot be ignored, even if current impacts are limited. Moreover, the impact of some chemicals may be disproportionately large in some geographic areas. Continued research and monitoring are essential; and a preventative approach is needed for chemicals. These results, and potential similar analyses of other chemicals, are provided as inputs to ongoing discussions about priority setting for global chemicals and pollution management. Furthermore, we suggest that this SEJ process be repeated periodically as new information becomes available.