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.

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.

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.

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.

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.

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.

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.

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.

"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.

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.

Collaborative clinical reasoning: a scoping review.

Background: Collaborative clinical reasoning (CCR) among healthcare professionals is crucial for maximizing clinical outcomes and patient safety. This scoping review explores CCR to address the gap in understanding its definition, structure, and implications. Methods: A scoping review was undertaken to examine CCR related studies in healthcare. Medline, PsychInfo, SciVerse Scopus, and Web of Science were searched. Inclusion criteria included full-text articles published between 2011 to 2020. Search terms included cooperative, collaborative, shared, team, collective, reasoning, problem solving, decision making, combined with clinical or medicine or medical, but excluded shared decision making. Results: A total of 24 articles were identified in the review. The review reveals a growing interest in CCR, with 14 articles emphasizing the decision-making process, five using Multidisciplinary Team-Metric for the Observation of Decision Making (MDTs-MODe), three exploring CCR theory, and two focusing on the problem-solving process. Communication, trust, and team dynamics emerge as key influencers in healthcare decision-making. Notably, only two articles provide specific CCR definitions. Conclusions: While decision-making processes dominate CCR studies, a notable gap exists in defining and structuring CCR. Explicit theoretical frameworks, such as those proposed by Blondon et al. and Kiesewetter et al., are crucial for advancing research and understanding CCR dynamics within collaborative teams. This scoping review provides a comprehensive overview of CCR research, revealing a growing interest and diversity in the field. The review emphasizes the need for explicit theoretical frameworks, citing Blondon et al. and Kiesewetter et al. The broader landscape of interprofessional collaboration and clinical reasoning requires exploration.

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.

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.

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.

An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph.

Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based or logical rule-based. Embedding-based methods depend on learned entity and relation embeddings for prediction, but they suffer from the lack of interpretability due to the opaque reasoning process. On the other hand, logical rule-based methods face scalability challenges as they heavily rely on predefined logical rules. To overcome these limitations, we propose a hybrid model that combines embedding-based and logical rule-based methods to capture deep causal logic. Our model, called the Inductive Reasoning Model based on Interpretable Logical Rule (ILR-IR), aims to provide interpretable insights while effectively predicting future events in TKGs. ILR-IR delves into historical information, extracting valuable insights from logical rules embedded within relations and interaction preferences between entities. By considering both logical rules and interaction preferences, ILR-IR offers a comprehensive perspective for predicting future events. In addition, we propose the incorporation of a one-class augmented matching loss during optimization, which serves to enhance performance of the model during training. We evaluate ILR-IR on multiple datasets, including ICEWS14, ICEWS0515, and ICEWS18. Experimental results demonstrate that ILR-IR outperforms state-of-the-art baselines, showcasing its superior performance in TKG extrapolation reasoning. Moreover, ILR-IR demonstrates remarkable generalization capabilities, even when applied to related datasets that share a common relation vocabulary. This suggests that our proposed model exhibits robust zero-shot reasoning abilities. For interested parties, we have made our code publicly available at https://github.com/mxadorable/ILR-IR.

Radical Collective Intelligence and the Reimagining of Cognitive Science.

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem-solving. Other papers focus on the cultural and institutional practices that make radical CI possible.

A National Survey of Problem-Solving Court Staff Perceptions of In-Person versus Virtual Hearings.

During the COVID-19 pandemic, problem-solving courts adopted virtual hearings. We conducted an online nationwide survey with a convenience sample of court staff to elicit their perceptions of court participants' attendance, engagement, willingness to talk, and ability to form connection with judges during in-person versus virtual hearings. Sign tests compared ordinal ratings for perceptions of court participant outcomes during in-person versus virtual hearing modalities, and for audiovisual technology versus audio-only technology. The final analysis included 146 staff. Staff felt that during in-person hearings judges could form closer relationships with participants, quality of information exchanged was higher, and participants were more willing to talk. Staff rated attendance as high regardless of the modality. Staff felt participant engagement was higher with audiovisual technology than audio-only technology. Our results suggest that staff have concerns about effects of virtual hearings on court participant engagement and ability to form relationships with judges. Courts should address these potential negative effects of virtual hearings. We are concerned that staff perceived participants more negatively when participants used audio-only versus audiovisual technology, because technology access could be associated with participant demographic characteristics. Further research is needed to examine court participant perceptions and outcomes.

Variations in Student Approaches to Problem Solving in Undergraduate Biology Education.

Existing research has investigated student problem-solving strategies across science, technology, engineering, and mathematics; however, there is limited work in undergraduate biology education on how various aspects that influence learning combine to generate holistic approaches to problem solving. Through the lens of situated cognition, we consider problem solving as a learning phenomenon that involves the interactions between internal cognition of the learner and the external learning environment. Using phenomenography as a methodology, we investigated undergraduate student approaches to problem solving in biology through interviews. We identified five aspects of problem solving (including knowledge, strategy, intention, metacognition, and mindset) that define three qualitatively different approaches to problem solving; each approach is distinguishable by variations across the aspects. Variations in the knowledge and strategy aspects largely aligned with previous work on how the use or avoidance of biological knowledge informed both concept-based and nonconcept-based strategies. Variations in the other aspects revealed intentions spanning complete disengagement to deep interest with the course material, different degrees of metacognitive reflections, and a continuum of fixed to growth mindsets. We discuss implications for how these characterizations can improve instruction and efforts to support development of problem-solving skills.