Solving Bongard Problems With a Visual Language and Pragmatic Constraints.

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.

Question-asking as a mechanism of information seeking.

Ivancovsky et al. explore the relationship between curiosity and creativity, by suggesting they align through novelty-seeking mechanisms. We argue that a general mechanism linking both capacities together is question-asking: Curiosity drives question-asking that leads to creative problem solving. Yet, current findings from our lab suggest that question complexity relates to creativity, but not necessarily to curiosity, warranting further investigation.

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.

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.

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 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.

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.

[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.

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.

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.

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.

"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.

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.

Students' performance, attitude, and classroom observation data to assess the effect of problem-based learning approach supplemented by YouTube videos in Ugandan classroom.

In response to global demands, Uganda's Vision 2040 seeks to transform the country into a modern and prosperous nation by implementing Sustainable Development Goal (SDG) 4, focusing on equitable and quality education. The 21st-century workforce requires individuals who can effectively navigate complex workplace challenges. This dataset was gathered from Form-2 Ugandan secondary school students (aged 12 to 15) across 12 schools in the Sheema District. The dataset comprises three types of data: students' performance in a physics topic (simple machines), their attitudes toward problem-solving and critical thinking when learning physics using Problem-Based Learning (PBL) supplemented by YouTube videos, and classroom observations documented with the reformed teaching observational protocol (RTOP). The intervention of teaching using PBL was executed in 2022, collecting data from 973 lower secondary school students. The intervention involved three approaches: one group (144 students) received PBL along with YouTube videos, another group of 482 students received PBL alone, and a third group (347 students) was taught using the traditional method. This data article explains the study's data creation, collection, and analysis process. The dataset holds significance for secondary school teachers, policymakers, and researchers, offering insights into the impact of PBL with and without ICT resources on learning physics and students' attitudes toward these learner-centered approaches.

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.

From integers to fractions: The role of analogy in transfer and long-term learning.

Fractions are the gatekeepers to advanced mathematics but are difficult to learn. One powerful learning mechanism is analogy, which builds fraction understanding on a pre-existing foundation of integer knowledge. Indeed, a short intervention that aligned fractions and integers on number lines improved children's estimates of fractions (Yu et al., 2022). The breadth and durability of such gains, however, are unknown, and analogies to other sources (such as percentages) may be equally powerful. To investigate this issue, we randomly assigned 109 fourth and fifth graders to one of three experimental conditions with different analogical sources (integers, percentages, or fractions) or a control condition. During training, children in the experimental conditions solved pairs of aligned fraction number line problems and proportionally-equivalent problems expressed in integers, percentages, or fractions (e.g., 3/8 on a 0-1 number line aligned with 3 on a 0-8 number line). Children in the control group solved fraction number-line problems sequentially. At pretest and a two-week delayed posttest, children completed a broad fraction knowledge battery, including estimation, comparison, categorization, ordering, and arithmetic. Results showed that aligning integers and fractions on number lines facilitated better estimation of fractional magnitudes, and the training effect transferred to novel fraction problems after two weeks. Similar gains were not observed for analogies using percentages. These findings highlight the importance of building new mathematical knowledge through analogies to familiar, similar sources.

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.

Investigating low intelligence stereotype threat in adults with developmental dyslexia.

Stereotype threat (ST) is a phenomenon that leads to decreased test performance and occurs when one deals with added pressure of being judged on the basis of stereotyped group membership. The ST effect has been previously investigated in many contexts but not in individuals with dyslexia who are often stereotyped as less intelligent. Prevalent use of intelligence tests in job selection processes and employment gap between people with dyslexia and those without warrants this investigation. Sixty-three participants (30 with dyslexia and 33 without dyslexia; mean age = 33.7; SD = 13.7; 47 F, 13 M, three non-binary) were asked to complete intelligence test typically used in selection processes. All participants were randomly assigned to one of three test instruction conditions: (1) they were told the test was diagnostic of their intelligence (ST triggering instruction); (2) test was a measure of their problem-solving skills (reduced threat); (3) or they were simply asked to take the test (control). Results showed that participants with dyslexia in ST condition performed poorer than those in other conditions and those in the same condition who did not have dyslexia. This study provides preliminary evidence for diminishing effects of ST in individuals with dyslexia.

The effect of episodic specificity inductions on cognitive tasks involving episodic retrieval: A quantitative review.

A growing body of research suggests that an episodic specificity induction (ESI), that is, training in recalled details of a (recent) past event, impacts performance on subsequent tasks that require episodic retrieval processes. The constructive episodic simulation hypothesis (Schacter and Addis, 2007) posits that various tasks which require, at least partially, episodic retrieval processes rely on a single, flexible episodic memory system. As such, a specificity induction activates that episodic memory system and improves subsequent performance on tasks that require use of that memory system. The present quantitative review analyzed the literature demonstrating that the Episodic Specificity Induction (ESI) improves performance on subsequence cognitive tasks that require (at least partial) episodic retrieval processes. Twenty-three studies met criteria for measuring the impact of ESI, compared to a non-specificity control induction(s), on subsequent tasks requiring edpisodic retrieval, including memory, imagination, problem solving, divergent thinking. The results of this review demonstrate a strong, positive effect of ESI on episodic memory, imagination, divergent thinking, and problem-solving tasks.

The involvement of decomposition and composition processes in restructuring during problem solving.

Decomposition of chunks has been widely accepted as a critical proxy of restructuring, but the role of composition in forming new representations has been largely neglected. This study aims to investigate the roles of both decomposition and composition processes in chunk restructuring, as well as their relationships with "aha" experiences during problem-solving. Participants were asked to move a part of a character to another character to create two new characters. Across three experiments, the characters to be decomposed or composed were varied in terms of tight or loose chunks. The results showed that decomposition or composition of tight chunks led to lower success rates, longer response times, and significantly stronger "Aha!" emotional experiences (mainly in terms of surprise and suddenness). This study provides evidence for the contribution of both decomposition and composition processes to restructuring in creative insight.