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The Journal of Nursing Education Mar 2024
Topics: Humans; Students, Nursing; Judgment; Clinical Reasoning
PubMed: 38442396
DOI: 10.3928/01484834-20240212-01 -
Frontiers in Sociology 2024Do social classes differ in moral judgment? Previous research showed that upper-class actors have a greater inclination toward utilitarian judgments than lower-class...
Do social classes differ in moral judgment? Previous research showed that upper-class actors have a greater inclination toward utilitarian judgments than lower-class actors and that this relationship is mediated by empathic concern. In this paper, we take a closer look at class-based differences in moral judgment and use the psychometric technique of process dissociation to measure utilitarian and deontological decision inclinations as independent and orthogonal concepts. We find that upper-class actors do indeed have a greater inclination toward decisions consistent with utilitarian principles, albeit only to a quite small extent. Class-related differences are more pronounced with respect to deontological judgments, in so far as upper-class actors are less inclined to judgments consistent with deontological principles than lower-class actors. In addition, it is shown that class-based differences in utilitarian judgments are mediated by cognitive styles and not so much by empathic concern or moral identity. None of these potential mediators explains class-based differences in the inclination toward deontological judgments.
PubMed: 38745822
DOI: 10.3389/fsoc.2024.1391214 -
Medical Law Review Nov 2023For many purposes in England and Wales, the Court of Protection determines whether a person has or lacks capacity to make a decision, by applying the test within the... (Review)
Review
For many purposes in England and Wales, the Court of Protection determines whether a person has or lacks capacity to make a decision, by applying the test within the Mental Capacity Act 2005. This test is regularly described as a cognitive test with cognitive processes discussed as internal characteristics. However, it is unclear how the courts have framed interpersonal influence as negatively impacting upon a person's decision-making processes in a capacity assessment context. We reviewed published court judgments in England and Wales in which interpersonal problems were discussed as relevant to capacity. Through content analysis, we developed a typology that highlights five ways the courts considered influence to be problematic to capacity across these cases. Interpersonal influence problems were constructed as (i) P's inability to preserve their free will or independence, (ii) restricting P's perspective, (iii) valuing or dependence on a relationship, (iv) acting on a general suggestibility to influence, or (v) P denying facts about the relationship. These supposed mechanisms of interpersonal influence problems are poorly understood and clearly merit further consideration. Our typology and case discussion are a start towards more detailed practice guidelines, and raise questions as to whether mental capacity and influence should remain legally distinct.
Topics: Humans; Judgment; England; Wales; Mental Competency; Decision Making
PubMed: 37295959
DOI: 10.1093/medlaw/fwad017 -
American Journal of Respiratory and... Jan 2024
Topics: Humans; Early Detection of Cancer; Patient Selection; Judgment; Lung Neoplasms; Hospitalization
PubMed: 38051108
DOI: 10.1164/rccm.202310-1858ED -
Npj Mental Health Research Jun 2024Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous...
Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.
PubMed: 38890545
DOI: 10.1038/s44184-024-00074-x -
Cognition Jul 2023How we judge the similarity between objects in the world is connected ultimately to how we represent those objects. It has been argued extensively that object...
How we judge the similarity between objects in the world is connected ultimately to how we represent those objects. It has been argued extensively that object representations in humans are 'structured' in nature, meaning that both individual features and the relations between them can influence similarity. In contrast, popular models within comparative psychology assume that nonhuman species appreciate only surface-level, featural similarities. By applying psychological models of structural and featural similarity (from conjunctive feature models to Tversky's Contrast Model) to visual similarity judgements from adult humans, chimpanzees, and gorillas, we demonstrate a cross-species sensitivity to complex structural information, particularly for stimuli that combine colour and shape. These results shed new light on the representational complexity of nonhuman apes, and the fundamental limits of featural coding in explaining object representation and similarity, which emerge strikingly across both human and nonhuman species.
Topics: Adult; Animals; Humans; Hominidae; Judgment; Pan troglodytes; Models, Psychological; Pattern Recognition, Visual
PubMed: 37104894
DOI: 10.1016/j.cognition.2023.105419 -
Cognition Jun 2024Repeating information increases people's belief that the repeated information is true. This truth effect has been widely researched and is relevant for topics such as...
Repeating information increases people's belief that the repeated information is true. This truth effect has been widely researched and is relevant for topics such as fake news and misinformation. Another effect of repetition, which is also relevant to those topics, has not been extensively studied so far: Do people believe they knew something before it was repeated? We used a standard truth effect paradigm in four pre-registered experiments (total N = 773), including a presentation and judgment phase. However, instead of "true"/"false" judgments, participants indicated whether they knew a given trivia statement before participating in the experiment. Across all experiments, participants judged repeated information as "known" more often than novel information. Participants even judged repeated false information to know it to be false. In addition, participants also generated sources of their knowledge. The inability to distinguish recent information from well-established knowledge in memory adds an explanation for the persistence and strength of repetition effects on truth. The truth effect might be so robust because people believe to know the repeatedly presented information as a matter of fact.
PubMed: 38593568
DOI: 10.1016/j.cognition.2024.105791 -
Scientific Reports Dec 2023While earlier investigations into thermal perception focused on measuring the detection of temperature changes across distinct bodily regions, the complex nature of...
While earlier investigations into thermal perception focused on measuring the detection of temperature changes across distinct bodily regions, the complex nature of thermal perception throughout the entire body remains a subject of ongoing exploration. To address this, we performed an experiment using four climate chambers with oscillating temperatures between 24 °C ± 1 °C. Our study involved 26 participants who moved between these chambers and had the task of reporting whether the second chamber entered was warmer or colder than the previous one. We collected 3120 temperature judgments, which we analysed via generalised linear mixed-effects models. The results showed surprisingly accurate temperature discrimination abilities and limited variation between individuals. Specifically, the Point of Subjective Equality stood at - 0.13 °C (± 0.02 °C), the Just Noticeable Difference (JND) was 0.38 °C (± 0.02 °C), the JND (indicating 95% accuracy) 0.92 °C (± 0.05 °C), the negative ceiling performance level (CPL) was - 0.91 °C (± 0.28 °C) and the positive CPL 0.80 °C (± 0.34 °C). The implications of the JND and the CPLs are particularly noteworthy, as they hold potential to significantly contribute to the advancement of intelligent algorithms for temperature control systems within building environments.
Topics: Humans; Temperature; Climate; Linear Models; Cold Temperature; Judgment
PubMed: 38049468
DOI: 10.1038/s41598-023-47880-5 -
Journal of Gastric Cancer Jul 2023Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments... (Review)
Review
Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.
PubMed: 37553126
DOI: 10.5230/jgc.2023.23.e31 -
NPJ Digital Medicine Aug 2023While the literature on putting a "human in the loop" in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid... (Review)
Review
While the literature on putting a "human in the loop" in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML's use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission.
PubMed: 37626155
DOI: 10.1038/s41746-023-00906-8