Inferior parietal cortex represents relational structures for explicit transitive inference.

The human brain is distinguished by its ability to perform explicit logical reasoning like transitive inference. This study investigated the functional role of the inferior parietal cortex in transitive inference with functional MRI. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, the inferior parietal cortex, but not the hippocampus or lateral prefrontal cortex, was associated with transitive inference. The inferior parietal activity and functional connectivity were modulated by inference (new versus old pairs) and discrimination (true versus false pairs). Moreover, the new/old and true/false pairs were decodable from the inferior parietal representation. Second, the inferior parietal cortex represented an integrated relational structure (ordered and directed series). The inferior parietal activity was modulated by serial position (larger end versus center pairs). The inferior parietal representation was modulated by symbolic distance (adjacent versus distant pairs) and direction (preceding versus following pairs). It suggests that the inferior parietal cortex may flexibly integrate observed relations into a relational structure and use the relational structure to infer unobserved relations and discriminate between true and false relations.

The effects of Urban Identity on Entrepreneurial Choice.

This study investigate how the Urban identity(UI) influence the entrepreneurial choice of the migrants. Drawing on the identity economics theory in combination with microscopic perspective on entrepreneurship, we conclude that the UI increases the odds of self-employment of the migrants by 19.91% after solving endogenous problem in our sample of 126385 individuals from the China Migrants Dynamic Survey. We test the moderating effect of medical insurance and find that the interaction coefficient is positive. This study further reveals that the expanding social networks, improving urban integration, and increasing income are the three main mechanisms through which the UI influences the entrepreneurial choice of the migrants. So, we derive results consistent with our hypotheses. The findings have implications for both the entrepreneurship and national policy literature.

"It is very hard to just accept this" - a qualitative study of palliative care teams' ethical reasoning when patients do not want information.

BACKGROUND: The aim of this study was to explore how palliative care staff reason about the autonomy challenge that arises when a patient who has first said he wants full information appears to change his mind and rejects being informed. METHODS: The study had a qualitative and exploratory design. Participants (physicians, registred nurses, social workers, physiotherapists and occupational therapists) were recruited from palliative care teams in southern Sweden. Six separate focus group interviews with a total number of 33 participants were conducted. The teams were asked to discuss a fictional case of a man who first wants, then rejects, information about his situation. The interviews were audiotaped and transcribed verbatim. Reflexive thematic analysis following Braun and Clarke was undertaken to analyse data. RESULTS: The analysis resulted in three themes: Patients have a right to reject information, Questioning whether this patient WANTS to reject information and There are other values at stake, too. Although participants endorsed a right to reject information, they were unsure whether this right was relevant in this situation, and furthermore felt that it should be balanced against counteracting factors. The effect of such balancing was that participants would aim to find a way to present relevant information to the patient, but in a probing and flexible way. CONCLUSIONS: In their work with dying patients, palliative care staff meet many autonomy challenges. When faced with a choice to withhold information as per a patient's wishes, or to provide information with the patient's best interest in mind, staff find it hard to balance competing values. Staff also find it hard to balance their own interests against a purely professional stance. The overall strategy seems to be to look for caring ways to impart the information.

[Relationship between Moral Concerns and Problem-Solving Behaviors for Outpatient Nurses Working in Palliative Care Accredited Educational Facilities].

The purpose of this study was to examine the moral concerns and problem-solving behavior for outpatient nurses in palliative cancer care. The target of this study was 284 outpatient nurses(22.9%)out of 1,241 respondents. As a result, it was concluded that outpatient nurses providing palliative cancer care have higher ethical concerns than nurses working in acute care hospitals. In addition, the more moral concerns there were, the more nurses manage their care according to patient's individual circumstances. In the future, it is necessary to provide education on the moral concerns of outpatient nurses and the problem-solving behavior for nurses so that patients in the final stages of life and their families can spend a better time.

A noise audit of human-labeled benchmarks for machine commonsense reasoning.

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single 'ground-truth'). However, much recent work in psychology has suggested that most tasks involving significant human judgment can have non-trivial degrees of noise. In his book, Kahneman suggests that noise may be a much more significant component of inaccuracy compared to bias, which has been studied more extensively in the AI community. This article proposes a detailed noise audit of human-labeled benchmarks in machine commonsense reasoning, an important current area of AI research. We conduct noise audits under two important experimental conditions: one in a smaller-scale but higher-quality labeling setting, and another in a larger-scale, more realistic online crowdsourced setting. Using Kahneman's framework of noise, our results consistently show non-trivial amounts of level, pattern, and system noise, even in the higher-quality setting, with comparable results in the crowdsourced setting. We find that noise can significantly influence the performance estimates that we obtain of commonsense reasoning systems, even if the 'system' is a human; in some cases, by almost 10 percent. Labeling noise also affects performance estimates of systems like ChatGPT by more than 4 percent. Our results suggest that the default practice in the AI community of assuming and using a 'single' ground-truth, even on problems requiring seemingly straightforward human judgment, may warrant empirical and methodological re-visiting.

Probabilistic causal reasoning under time pressure.

While causal reasoning is a core facet of our cognitive abilities, its time-course has not received proper attention. As the duration of reasoning might prove crucial in understanding the underlying cognitive processes, we asked participants in two experiments to make probabilistic causal inferences while manipulating time pressure. We found that participants are less accurate under time pressure, a speed-accuracy-tradeoff, and that they respond more conservatively. Surprisingly, two other persistent reasoning errors-Markov violations and failures to explain away-appeared insensitive to time pressure. These observations seem related to confidence: Conservative inferences were associated with low confidence, whereas Markov violations and failures to explain were not. These findings challenge existing theories that predict an association between time pressure and all causal reasoning errors including conservatism. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize that causal judgements are the result of multiple processes.

A design for life: Predicting cognitive performance from lifestyle choices.

Maintaining cognitive capacity through adulthood has been the target of many recent studies that have examined the influence of lifestyle choices such as exercise, diet, and sleeping habits. Many of these studies have focused on a single factor (e.g., diet) and its effect on cognitive abilities; however, humans make numerous lifestyle choices every single day, many of which interact and influence each other. Here, we investigated whether combinations of lifestyle choices can predict better or worse cognitive performance in the general population, and whether optimal combinations of choices existed depending on the cognitive domain. Specifically, we examined 20 self-reported lifestyle choices, such as playing video games, drinking alcohol, and amount of exercise taken, in a sample of almost 10,000 participants. All participants also completed 12 cognitive tests that have been shown to generate three composite cognitive domain scores pertaining to short-term memory, verbal abilities, and reasoning. Using recursive feature elimination and random forest regression, we were able to explain 9% of the variance in short-term memory scores, 8% of the variance in reasoning scores, and 7% of the variance in verbal ability scores. While the regression model provided predictive power in all three domains, these levels indicate that even when considering a large number of lifestyle choices, there remains a considerable degree of variability in predicting short-term memory, reasoning and verbal abilities. Thus, while some modifiable lifestyle factors may have an impact on cognitive capacity, there likely exists no single optimal design for life.

Enhancing Robotic Perception through Synchronized Simulation and Physical Common-Sense Reasoning.

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.

People see more of their biases in algorithms.

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.

Peripheral vision and crowding in mental maze-solving.

Solving a maze effectively relies on both perception and cognition. Studying maze-solving behavior contributes to our knowledge about these important processes. Through psychophysical experiments and modeling simulations, we examine the role of peripheral vision, specifically visual crowding in the periphery, in mental maze-solving. Experiment 1 measured gaze patterns while varying maze complexity, revealing a direct relationship between visual complexity and maze-solving efficiency. Simulations of the maze-solving task using a peripheral vision model confirmed the observed crowding effects while making an intriguing prediction that saccades provide a conservative measure of how far ahead observers can perceive the path. Experiment 2 confirms that observers can judge whether a point lies on the path at considerably greater distances than their average saccade. Taken together, our findings demonstrate that peripheral vision plays a key role in mental maze-solving.

Components related to ethical decision making in medical science students: A structural equation model.

BACKGROUND: Evaluating moral principles in the Society of Medical Sciences and health care workers (HCWs) is imperative due to their direct contact with the community and the significant impact of their attitudes and decisions on people's lives. This study aimed to determine the components related to ethical decisions in medical sciences students. METHODS: One thousand two hundred thirty-five eligible students in the Alborz University of Medical Sciences participated in this descriptive study. We gathered their socio-demographic information, assessed their moral reasoning, and used the ethical decisions questionnaire, Lutsen moral sensitivity questionnaire, and general health questionnaire (GHQ) for data gathering. The data were analyzed with SPSS software version 25 and LISREL version 8.8. RESULTS: According to the path analysis test findings, ethical reasoning significantly correlated with ethical decision-making (B = 0.40). The number of clinical courses passed, moral sensation (moral sensitivity), and the total number of passed academic semesters had the greatest positive and negative association with ethical decision-making, respectively. (B = 0.54), (B = 0.524) and (B = -0.11). CONCLUSION: Based on the findings of the moral reasoning test, the moral sensation was associated with ethical decision-making, which indicates the necessity of attending to ethical aspects, promoting moral reasoning, sensitivity, and students' accuracy.

Teach Design to Surgical Trainees: One Small Step for Surgeons, One Giant Leap for Surgery.

The field of surgery faces complex, systemic challenges that will require new academic frameworks. In this paper, we propose design thinking as a useful problem-solving technique to apply to such challenges. We define design thinking and provide a brief history of this practice. Finally, we offer suggestions to introduce design thinking to surgical trainees, drawing from the experience of innovation programs that have incorporated this technique.

Improving mental health in chronic care in general practice: study protocol for a cluster-randomised controlled trial of the Healthy Mind intervention.

BACKGROUND: Mental health issues are common among patients with chronic physical conditions, affecting approximately one in five patients. Poor mental health is associated with worse disease outcomes and increased mortality. Problem-solving therapy (PST) may be a suitable treatment for targeting poor mental health in these patients. This study protocol describes a randomised controlled trial of the Healthy Mind intervention, a general practice-based intervention offering PST to patients with type 2 diabetes and/or ischaemic heart disease and poor mental well-being. METHODS: A stepped-wedge cluster-randomised controlled trial with 1-year follow-up will be conducted in Danish general practice. At the annual chronic care consultation, patients with type 2 diabetes and/or chronic ischaemic heart disease will be screened for poor mental well-being. Patients in the control group will be offered usual care while patients in the intervention group will be offered treatment with PST provided by general practitioners (GPs) or general practice staff, such as nurses, who will undergo a 2-day PST course before transitioning from the control to the intervention group. The primary outcome is change in depressive symptoms after 6 and 12 months. Secondary outcomes include change in mental well-being, anxiety, and diabetes distress (patients with type 2 diabetes) after 6 and 12 months as well as change in total cholesterol levels, low-density lipoprotein (LDL) levels, and blood glucose levels (patients with diabetes) after 12 months. Process outcomes include measures of implementation and mechanisms of impact. We aim to include a total of 188 patients, corresponding to approximately 14 average-sized general practices. DISCUSSION: The Healthy Mind trial investigates the impact of PST treatment for patients with chronic disease and poor mental well-being in general practice. This will be the first randomised controlled trial determining the effect of PST treatment for patients with chronic diseases in general practice. The results of this study will provide relevant insights to aid GPs, and general practice staff manage patients with poor mental well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT05611112. Registered on October 28, 2022.

Looking at Mental Images: Eye-Tracking Mental Simulation During Retrospective Causal Judgment.

How do people evaluate causal relationships? Do they just consider what actually happened, or do they also consider what could have counterfactually happened? Using eye tracking and Gaussian process modeling, we investigated how people mentally simulated past events to judge what caused the outcomes to occur. Participants played a virtual ball-shooting game and then-while looking at a blank screen-mentally simulated (a) what actually happened, (b) what counterfactually could have happened, or (c) what caused the outcome to happen. Our findings showed that participants moved their eyes in patterns consistent with the actual or counterfactual events that they mentally simulated. When simulating what caused the outcome to occur, participants moved their eyes consistent with simulations of counterfactual possibilities. These results favor counterfactual theories of causal reasoning, demonstrate how eye movements can reflect simulation during this reasoning and provide a novel approach for investigating retrospective causal reasoning and counterfactual thinking.

Structural explanations lead young children and adults to rectify resource inequalities.

Decisions about how to divide resources have profound social and practical consequences. Do explanations regarding the source of existing inequalities influence how children and adults allocate new resources? When 3- to 6-year-old children (N = 201) learned that inequalities were caused by structural forces (stable external constraints affecting access to resources) as opposed to internal forces (effort), they rectified inequalities, overriding previously documented tendencies to perpetuate inequality or divide resources equally. Adults (N = 201) were more likely than children to rectify inequality spontaneously; this was further strengthened by a structural explanation but reversed by an effort-based explanation. Allocation behaviors were mirrored in judgments of which allocation choices by others were appropriate. These findings reveal how explanations powerfully guide social reasoning and action from childhood through adulthood.

Collective intelligence: A unifying concept for integrating biology across scales and substrates.

A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes.

Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.

Ontological pluralism and social values.

There seems to be an emerging consensus among many philosophers of science that non-epistemic values ought to play a role in the process of scientific reasoning itself. Recently, a number of philosophers have focused on the role of values in scientific classification or taxonomy. Their claim is that a choice of ontology or taxonomic scheme can only be made, or should only be made, by appealing to non-epistemic or social values. In this paper, I take on this "argument from ontological choice," claiming that it equivocates on the notion of choice. An ontological choice can be understood either in terms of determining which taxonomic scheme is valid, or in terms of deciding which taxonomic scheme to deploy in a given context. I try to show that while the latter can be determined in part by social values, the former ought not to be so determined.

Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study.

Collaborative cooperation (CC) and division of labor cooperation (DLC) are two prevalent forms of cooperative problem-solving approaches in daily life. Despite extensive research on the neural mechanisms underlying cooperative problem-solving approaches, a notable gap exists between the neural processes that support CC and DLC. The present study utilized a functional near-infrared spectroscopy (fNIRS) hyperscanning technique along with a classic cooperative tangram puzzle task to investigate the neural mechanisms engaged by both friends and stranger dyads during CC versus DLC. The key findings of this study were as follows: (1) Dyads exhibited superior behavioral performance in the DLC task than in the CC task. The CC task bolstered intra-brain functional connectivity and inter-brain synchrony (IBS) in regions linked to the mirror neuron system (MNS), spatial perception (SP) and cognitive control. (2) Friend dyads showed stronger IBS in brain regions associated with the MNS than stranger dyads. (3) Perspective-taking predicted not only dyads' behavioral performance in the CC task but also their IBS in brain regions associated with SP during the DLC task. Taken together, these findings elucidate the divergent behavioral performance and neural connection patterns between the two cooperative problem-solving approaches. This study provides novel insights into the various neurocognitive processes underlying flexible coordination strategies in real-world cooperative contexts.

Does volunteering impact refugee women's life satisfaction, empowerment, and wellbeing? Experimental evidence, local knowledge, and causal reasoning.

BACKGROUND: There are gaps in the evidence base addressing whether volunteering programs enhance the wellbeing, empowerment, and life satisfaction of individual volunteers. Program impacts are seldom rigorously evaluated, whilst construct meanings remain largely unspecified, especially in the Middle East. This study tested the impacts of We Love Reading, a program training volunteers to read aloud in their local communities. It also mapped local knowledge representation. METHODS: We conducted a mixed-method program evaluation based on a randomized cluster trial with 105 Syrian refugee women from poor households in Amman, Jordan. At three time points (baseline, 5-month and 12-month-follow-up), we implemented a survey to measure levels of life satisfaction (Cantril), psychological empowerment (PE), and psychological wellbeing (PWB). We used regression models on panel data to estimate individual-level impacts, adjusting for women's characteristics and the moderating effects of their social networks. We also conducted net-mapping sessions to clarify local concepts and their causal connections, generating thematic analyses and fuzzy cognitive maps (FCMs) to represent local knowledge and causal influences. RESULTS: Life satisfaction was the only outcome variable showing a significant impact for We Love Reading (Cantril, ß = 3.00, p = 0.002). Thematic analyses and FCMs made explicit the multi-dimensional aspects of lived experiences: emphasis was placed on reaching goals, having "the full right to act," the freedom to take decisions, willingness and determination. Women explained that building their empowerment and agency was a main driver of life satisfaction, and that volunteering boosted the resolve of "not giving up" on life goals. CONCLUSION: This program evaluation integrates scientifically-rigorous and culturally-relevant methodologies to identify impacts, local knowledge systems, and causal pathways of influence. This helps clarify how and why volunteering works in real-life situations across cultural contexts, calling attention to what programs seek to achieve, how they avoid volunteer burden, and why they generate social change.