Errors In Judgment Research Articles

How human-AI feedback loops alter human perceptual, emotional and social judgements.

Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI's influence, rendering them more susceptible to it. These findings uncover a mechanism wherein AI systems amplify biases, which are further internalized by humans, triggering a snowball effect where small errors in judgement escalate into much larger ones.

Standard Setting for Assessments in Anatomy using the Angoff Method in a Medical School in Sri Lanka

Introduction: Anatomy is a fundamental subject in medical education. A reliable and valid assessment tool is crucial for evaluating students' understanding. The Angoff method, an absolute method of standard-setting has been widely used in various educational contexts. This study aimed to develop and implement a standard-setting process for an assessment in Anatomy using the Angoff method in a Sri Lankan medical school. Methods: Five Anatomy faculty members including two senior lecturers and three temporary assistant lecturers who were recent graduates participated in the standard-setting process. They were presented with each item on the anatomy assessment and asked to estimate the percentage of minimally competent students who would answer the item correctly. Their judgments were then used to determine the cut score for the assessment. Results: The Angoff method yielded a cut score of 56.06 with a cutoff value of 8.41 and a standard error of judgment of 0.18 (1.2%). There was a high interrater reliability (0.89). Conclusion: This study demonstrated the feasibility and effectiveness of using the Angoff method for standard setting in an anatomy assessment in a Sri Lankan medical school. The set cut score provides a benchmark for evaluating students' performance and ensuring that they possess the necessary anatomical knowledge to progress in their medical education.

Cognitive biases in Mohs micrographic surgery.

Mohs surgeons' fallibility is an important topic to explore. There are no studies on the role of cognitive biases in heuristic decision-making within Mohs micrographic surgery (MMS) as a contributing factor in mistakes or errors of clinical judgement. We reviewed established cognitive biases applied to heuristics during the pre-operative, intraoperative and reconstruction stages of MMS, and recommend conscious cognitive bias mitigating strategies for Mohs surgeons. Clinical errors/incidents may be reframed as opportunities to learn and develop, rather than evidence of competency or status. These experiences offer the opportunity to sharpen skills within metacognition and allow for more cognizant Mohs surgeons. We highly recommend all Mohs surgeons develop in-depth knowledge and maintain awareness of cognitive biases throughout clinical practice, to enhance clinical performance.

Enhancing Talent Intelligence Evaluation with Improved Pre-training Models

In the past few years, there has been a noticeable surge in the utilization of artificial intelligence techniques within the realm of talent evaluation, particularly in response to the exponential growth of big data. Conventional talent evaluation approaches, typically relying on the integration of expertise and system-based title appraisal, are plagued with challenges including errors in subjective judgment. The strategic importance of intelligent talent assessment cannot be overemphasized, especially for discovering and fostering high-potential talent across various domains, inclusive of research. This paper introduces an innovative method, termed TE-RCB, auxiliary applications in the evaluation of actual talent titles. The initial stage of this method’s execution involves the meticulous construction of a talent evaluation dataset. This data is sourced from actual talent title applications and standardized scores provided by simulation expert engineers. The subsequent step is the application of the RoBERTa-WWM-large model, employed for the vectorization of pertinent information indispensable for talent evaluation. Further, the TextCNN model is utilized for the extraction of critical features from the talent-related information, succeeded by the application of the BiLSTM model to delve into deeper semantic correlations within the talent attributes. Comprehensive experiments are conducted on both the talent evaluation dataset and the publicly available dataset. The empirical outcomes decisively underline the superior accuracy of the proposed method, asserting its efficacy in addressing the pertinent challenges in the domain of intelligent talent assessment.

Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes

Multi-Criteria Decision Analysis (MCDA) methods are essential for addressing real-world challenges in the dynamic field of decision support technology. As technology advances, the expanding variety of multi-criteria evaluation methods highlights the critical importance of method selection for reliable results. These methods can be applied to scenarios involving both precise data and uncertainties. Expert knowledge is relevant in navigating real-world decision problems, particularly in uncertain environments where judgment inaccuracies can create challenges. Determining criteria weights is a pivotal step, as it significantly influences final outcomes and provides a nuanced understanding of each factor's impact on the evaluation.This paper addresses the pressing need for methods that account for uncertainty and effectively reduce the impact of inaccuracies in expert judgment. To this end, we introduce the Fuzzy RANking COMparison (RANCOM) method, a novel fuzzy subjective weighting approach that extends the advantages of the original RANCOM method to manage uncertainty. Using Triangular Fuzzy Numbers (TFNs), the proposed extension maintains simplicity, rapid execution, and flexibility in establishing criteria relationships. Key contributions of this paper include the development of the Fuzzy RANCOM method and a comparative analysis with the Fuzzy Analytical Hierarchy Process (AHP) method. Through multiple simulations, the study examines the operational dynamics of these methods, offering a valuable tool for decision-makers, especially in contexts where expert judgments may be prone to inaccuracies. The development of the Fuzzy RANCOM method provides a robust solution, equipping decision-makers with an effective tool to determine criteria weights while minimizing the impact of potential judgment errors.

Emotional aftermath of the 2020 U.S. presidential election: a study of hindsight bias in younger and older adults

ABSTRACT Hindsight bias – also known as the knew-it-all-along effect – is a ubiquitous judgment error affecting decision makers. Hindsight bias has been shown to vary across age groups and as a function of contextual factors, such as the decision maker’s emotional state. Despite theoretical reasons why emotions might have a stronger impact on hindsight bias in older than in younger adults, age differences in hindsight bias for emotional events remain relatively underexplored. We examined emotion and hindsight bias in younger and older adults (N = 272) against the backdrop of the 2020 U.S. presidential election. Participants predicted electoral college votes for the two presidential candidates before the election and were asked to remember their predictions approximately three weeks later, after the election results had been finalised. Republicans, for whom the electoral outcome was negatively tinged, exhibited greater hindsight bias for President Biden’s result compared with Democrats, for whom the electoral outcome was positive. The asymmetry in hindsight bias between Republicans and Democrats was similar for younger and older participants. This study suggests that negative emotions may exacerbate hindsight bias, and that adult age differences in hindsight bias observed in laboratory settings may not translate to real-world contexts.

Debiasing Judgements Using a Distributed Cognition Approach: A Scoping Review of Technological Strategies.

To review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field. Distributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised. We conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories. Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed. Through the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application. The study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

Failure in Fully Funded Pensions: Voluntary Exchange and Coercion

Private pension failure for much of the economics mainstream would be regarded as an artifact of markets that are insufficiently regulated, such that suppliers are able to exploit monopoly advantages, and are free to make serious errors of judgment. Market failure analysis is flawed, however, by its own failure to acknowledge salient elements of the institutional context of private pension provision, which, while it has been regulated according to approved public interest ends, has been prone to suboptimal investment performance. Drawing on classical liberal political economy, an alternative account of private pension failure would emphasize the perverse consequences of state intrusion in the market for retirement income protection. The origins of regulation failure can be traced back to a combination of two policy decision dynamics, one of intentional rent creation, a second of imperfect knowledge and understanding of markets resulting in flawed regulatory arrangements. By stifling competition, regulation diminishes the capacity of pension markets to serve pension plan participants by means of improved investment performance. The prevalence of private pension failure is an artifact of regulatory intrusiveness. Given this direction of causality, a better approach to policy would rest centrally on the deregulation of pension markets.

Temporal error monitoring: Does agency matter?

Error monitoring is the ability to report one's errors without relying on feedback. Although error monitoring is investigated mostly with choice tasks, recent studies have discovered that participants parametrically also keep track of the magnitude and direction of their temporal, spatial, and numerical judgment errors. We investigated whether temporal error monitoring relies on internal generative processes that lead to the to-be-judged first-order timing performance. We hypothesized that if the endogenous processes underlie temporal error monitoring, one can monitor timing errors in emitted but not observed timing behaviors. We conducted six experiments to test this hypothesis. The first two experiments showed that confidence ratings were negatively related to error magnitude only in emitted behaviors, but error directionality judgments of observed behaviors were more precise. Experiment 3 replicated these effects even after controlling for the motor aspects of first-order timing performance. The last three experiments demonstrated that belief of agency (i.e., believing that the error belongs to the self or someone else) was critical in accounting for the confidence rating effects observed in the first two experiments. The precision of error directionality judgments was higherin thenon-agency condition. These results show that confidence is sensitive to belief, and short-long judgment is sensitive to the actual agency of timing behavior (i.e., whether the behavior was emitted by the self or someone else).

Complications: skull base and cerebrovascular.

Neurosurgical complications are a rich source for learning, but they are grossly underutilized for the purpose of surgeon education. Details of the complications, which make them all the more powerful as teaching tools, are restricted to morbidity and mortality conferences behind closed doors, and open discussions of the topic are blurred by hypotheticals in order to shield the presenters from medicolegal risks. In this issue of Neurosurgical Focus, 9 neurosurgeons were invited to present complications they encountered along with the details and specific lessons they learned. The contributors were picked for their well-known track record of skill, experience, and candor, so readers can be confident in the lessons. The video and the accompanying written article present the clinical facts. The actions that led to the complications are demonstrated in the surgical video segments. In ancient Western civilization, the seven "deadly sins" categorized and conceptualized human malfeasance, and similarly, the neurosurgical correlate of this set of sins can provide a framework for discussing the errors that lead to unexpectedly poor outcomes. Although errors in judgment and planning grow rarer with experience, errors in execution can still occur, no matter the stage of one's career. Interestingly, even though skill and experience do not eliminate complications, they may affect the lesson that the neurosurgeon takes home. The lack of open discourse on complications may slow progress in the field and set the stage for repeating mistakes. The writing and production teams hope that this work opens a rich source of learning. The video can be found here: https://stream.cadmore.media/r10.3171/2024.7.FOCUS24379.

Minimum loss modulation method for various power factor angles

In recent years, the energy crisis has prompted widespread attention to electric vehicles (EVs). As a core component of electric drive system in EV, motor controller has a significant impact on vehicle energy consumption. In such situation, this paper proposes a minimum loss modulation method for all power factors and reduces current THD by dead-band compensation. Firstly, the mechanism model of the controller is established for loss calculation, and the relationship between the clamping region and the current peak is analyzed based on the traditional modulation methods. Secondly, a discontinuous pulse width modulation considering power factor angle method (CPFA-DPWM) is proposed to ensure that clamping region always falls at the peak current at all power factors. Furthermore, the adoption of current vector method instead of direct sampling for current polarity determination effectively reduces judgment error and avoids false dead-band compensation. The effectiveness of the proposed method is verified by simulation and thermal measurement experiment. Under China Light-duty Vehicle Test Cycle (CLTC), the controller loss of CPFA-DPWM is reduced by 18.61 % compared to SVPWM.

The power of words: the impact of police interviewer’s judgment error and apology on sexual violence victims in simulated interviews

This experiment examines how a police interviewer’s judgment error and apology affect a sexual violence victim’s secondary victimization, trust in the interviewer, rapport, and willingness to provide information. Ninety students from Peru were asked to imagine being sexual violence victims and were interviewed online by a mock police interviewer. Participants were randomized into three conditions: judgment error without apology, judgment error with apology, and a no-error control. The judgment error involved suggesting the victim was partly responsible for their victimhood, citing women’s clothing choices and questioning men’s masculinity. Our findings show judgment errors reduced trust, rapport, and willingness to provide information while increasing secondary victimization; apologizing improved trust and rapport but did not affect secondary victimization or willingness to provide information. These outcomes indicate that judgment errors in sexual violence victim interviews can negatively impact the interviewing process and outcomes and highlight the importance of an apology.

Not our kind of crowd! How partisan bias distorts perceptions of political bots on Twitter (now X).

Social bots, employed to manipulate public opinion, pose a novel threat to digital societies. Existing bot research has emphasized technological aspects while neglecting psychological factors shaping human-bot interactions. This research addresses this gap within the context of the US-American electorate. Two datasets provide evidence that partisanship distorts (a) online users' representation of bots, (b) their ability to identify them, and (c) their intentions to interact with them. Study 1 explores global bot perceptions on through survey data from N = 452 Twitter (now X) users. Results suggest that users tend to attribute bot-related dangers to political adversaries, rather than recognizing bots as a shared threat to political discourse. Study 2 (N = 619) evaluates the consequences of such misrepresentations for the quality of online interactions. In an online experiment, participants were asked to differentiate between human and bot profiles. Results indicate that partisan leanings explained systematic judgement errors. The same data suggest that participants aim to avoid interacting with bots. However, biased judgements may undermine this motivation in praxis. In sum, the presented findings underscore the importance of interdisciplinary strategies that consider technological and human factors to address the threats posed by bots in a rapidly evolving digital landscape.

Probability errors in children's judgements about the likelihood of social characteristics.

The studies reported here investigated mechanisms underlying children's tendency to commit the conjunction fallacy (judging that a conjunction of two events is more likely than one of the events in isolation) when judging people's characteristics. Study 1 investigated these errors in 4- and 5-year-olds (N = 58) using a newly developed social judgement task in which children judged whether a conjunction or one of its elements would apply to a protagonist. Children made conjunction fallacy errors at chance level. Study 2 (N = 71) replicated these findings using an adapted version of the task, in which children separately judged the likelihood of the conjunction and each of its events. Study 3 investigated age-related changes in conjunction fallacy errors in a sample of 148 children aged 4 to 11 years old and 130 adults. This study also investigated how providing background information on the protagonist influenced error rate. Unlike younger children, 10- and 11-year-olds committed the conjunction fallacy at chance level in the absence of background information, but providing information consistent with the likely component of the conjunction significantly increased their error rate. Adults' error rate also significantly increased after the introduction of background information. Across all three studies, conjunction fallacy errors were unrelated to cognitive and social-cognitive abilities, such as verbal ability, theory of mind, and inhibitory control (Studies 1 and 2), and prejudice and hindsight bias (Study 3). These findings suggest that it is only in the second decade of life that children use social information to inform their judgements about people and that social decision-making errors are not determined by core aspects of cognitive and social-cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reinvestigating Thomas Hardy’s Motives in The Characterisation of Alec D’urbervilles and Angel Clare in Tess of the D’urbervilles

One stable error of judgement by critics of Thomas Hardy’s Tess of the d’Urbervilles is regarding Alec D’Urbervilles as a villainous tool of fate instrumental to Tess Durbeyfield’s tragedy. This idea goes with the associated assumption that Tess has all the moral and ethical reasons to reject him and embrace Angel. This paper seeks to redeem the name of Alec in Thomas Hardy’s Tess of the d’Urbervilles by positing that all the odium placed on him result from gross misrepresentation and that he is a hero of love, whose tragedy results from bestowing his love on Tess unrequitedly. It will be proven by comparison that Angel Clare is ironically the evil in the set-up, and the embodiment of all the tragic contents of Victorian moral laws and patriarchal customs. This study is a product of a qualitative research approach, involving interpretive and content analysis aimed at deepening the argument that while Alec’s conduct is almost completely unimpeachable, Angel is the agent of distraction and destruction that single-handedly brings about the tragedy of both Tess and Alec. The key finding is that Tess’s tragedy happens as a result of her rejecting Alec’s love and bestowing her love on the wrong person.

Proxy reports of others’ behaviors: when are they more accurate?

ABSTRACT Surveys often ask people to report about someone else’s behavior. Two approaches to constructing a behavioral frequency judgment are recalling specific episodes of the other person’s behavior or using more general knowledge to infer a frequency judgment. A two-stage study was conducted with 40 roommate-pairs who maintained diaries for 7-days and then reported behavioral frequencies for themself and their roommate. Behavior regularity and relationship closeness were also measured. When general knowledge is cued, judgment error is negatively related to behavior regularity and relationship closeness, while cuing episodic-based recall resulted in less judgment error when regularity and relationship closeness are low.

PH-triggered bilayer film based on carboxymethyl cellulose/zein/Eudragit L100 with purple cabbage anthocyanin for monitoring pork freshness

A novel pH-triggered bilayer film was composed of zein (Z), carboxymethylcellulose (CMC), Eudragit L100 (L100), and purple cabbage anthocyanin (PCA), followed by casting for monitoring pork freshness during storage at 4 °C and 25 °C. This bilayer film was employed to encapsulate anthocyanins, preventing anthocyanins oxidation and photodegradation. Additionally, under pH 6, this film ruptures and releases anthocyanins, inducing a sudden color change in the indicator film, significantly reducing errors in freshness indications. Notably, the ZCLP8% film had excellent stability and pH response properties. The performance of the ZCLP8% film in monitoring pork freshness was evaluated. When the concentration of pork TVB-N reached 15.59 mg/100 g (pH = 6.35), the bilayer film was ruptured, and the release rate of PCA was 85.52 %, which was a significant change in the color of the bilayer film compared with that at pH = 5. Therefore, this work addresses the limitation that anthocyanin-based intelligent films are subject to judgment errors when applied, opening new possibilities for food freshness differentiation monitoring.

Education in Laparoscopic Cholecystectomy: Design and Feasibility Study of the LapBot Safe Chole Mobile Game.

Major bile duct injuries during laparoscopic cholecystectomy (LC), often stemming from errors in surgical judgment and visual misperception of critical anatomy, significantly impact morbidity, mortality, disability, and health care costs. To enhance safe LC learning, we developed an educational mobile game, LapBot Safe Chole, which uses an artificial intelligence (AI) model to provide real-time coaching and feedback, improving intraoperative decision-making. LapBot Safe Chole offers a free, accessible simulated learning experience with real-time AI feedback. Players engage with intraoperative LC scenarios (short video clips) and identify ideal dissection zones. After the response, users receive an accuracy score from a validated AI algorithm. The game consists of 5 levels of increasing difficulty based on the Parkland grading scale for cholecystitis. Beta testing (n=29) showed score improvements with each round, with attendings and senior trainees achieving top scores faster than junior residents. Learning curves and progression distinguished candidates, with a significant association between user level and scores (P=.003). Players found LapBot enjoyable and educational. LapBot Safe Chole effectively integrates safe LC principles into a fun, accessible, and educational game using AI-generated feedback. Initial beta testing supports the validity of the assessment scores and suggests high adoption and engagement potential among surgical trainees.

A Novel Head-following Algorithm for Multi-Joint Articulated Driven Continuum Robots

Abstract Head-following (tracking) issue is a challenge in developing multi-joint continuum robots. However various approaches have been developed in head-following algorithm for articulated-driven mechanism (ADM) continuum robots, problems still exist such as low end-accuracy, large trajectory deviation, and low computational efficiency. This paper presents a novel head-following algorithm(NHF) with high precision, small trajectory deviation, and high computational efficiency for multi-joint ADM continuum robots. The proposed algorithm first uses the follow-the-leader (FTL) method to search for planning points. Secondly, the end-effector errors are calculated, split, and adjusted. Thirdly, the error judgment set is assigned based on the error rate of the end-effector, and also the joints that need to be adjusted are determined. Finally, the joint angles are iteratively adjusted. In this paper, the NHF algorithm is simulated on ADM continuum robots with saparately 10, 20 and 31 joints. The result shows that, compareing with other FTL algorithms, NHF algorithm has the highest end accuracy, and the smallest trajectory deviation.

Theoretical Underpinnings to Establish Fidelity Conditions for Defining Verification Models

AbstractThe current state of the discipline suggests that the desired fidelity of verification models is defined based on qualitative statements, that is, using heuristics, at the onset of an engineering endeavor. While the use of heuristics in systems engineering is well known, many heuristics have not been validated, and therefore there may be errors in judgement that could lead to systems being engineered that are ultimately not fit‐for‐purpose. This bears the question, based on what conditions should we formally define verification models? In this article, we provide insights from our research to discover the sufficient conditions for defining verification models, taking a science‐based approach that leverages the richness of systems‐theoretic constructs.