Do the benefits of learning to write a compare-contrast text transfer to other types of writing task? Analysing the effects of strategy-focused instruction / ¿Es posible transferir el aprendizaje de un texto de comparación-contraste a otro tipo de tareas? Análisis de los efectos de la instrucción estratégica

El programa de Instrucción de la Autorregulación Cognitiva (CSRI) se basa en la instrucción centrada en la estrategia e incluye tres componentes para la mejora del producto textual (enseñanza directa, modelado y práctica entre iguales). Se plantearon como objetivosanalizar si la instrucción con el programa CSRI en un género textual (texto de comparación) conducía a la transferencia espontánea en el producto textual (mejor coherencia, estructura y calidad) en un género no instruido (texto de opinión); y examinar si el orden de los componentes instruccionales presentaba algún efecto. Participaron 126 estudiantes de cuarto de educación primaria que fueron asignados a una de las dos condiciones experimentales en las que se aplicaba el programa CSRI (con diferente secuencia de los componentes instructivos), o a una condición de control en la que se seguía la enseñanza tradicional. Los resultados reflejaron que las dos secuencias de instrucción del programa CSRI mostraban beneficios en la estructura y coherencia del producto textual de los estudiantes en el posttest pero no 8 meses después. Para que los alumnos sean capaces de transferir adecuadamente lo aprendido a géneros textuales no instruidos, necesitan que los profesores les enseñen cómo hacerlo eficazmente.(AU)

Cognitive Self-Regulation Instruction (CSRI) program is a strategy-focused instruction with three instructional components for im-proving students’ writing product (direct teaching, modelling, and peer-practice). The present study aimed to explore whether the CSRI program leads to spontaneous transfer, improving the writing product (in terms of quality, structure, and text coherence) of an uninstructed genre (opinion text); and to examine whether the order in which the instructional compo-nents were implemented had an effect. A total of 126 students in their 4thyear of primary school participated in the study. They were randomly as-signed to one of two experimental conditions which received the CSRI but differed in the order the instructional components were delivered, or to a control condition which followed the traditional teaching approach. Our findings show that both CSRI sequences produced benefits in terms of greater structure and coherence of the writing product in the opinion text at post-test but not 8months after the intervention. In consequence, for students to be able to adequately transfer strategies to uninstructed text genres, they need teachers to teach them how to do it effectively.(AU)

ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments.

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA ( https://enigma-lang.org ) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners' performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331-361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

Potential interactive effect of positive expectancy violation and sleep on memory consolidation in dogs.

In dogs, as in humans, both emotional and learning pretreatment affect subsequent behaviour and sleep. Although learning often occurs in an emotional-social context, the emotion-learning interplay in such context remain mainly unknown. Aims were to assess the effects of Controlling versus Permissive (emotional factors) training (learning factors) styles on dogs' behaviour, learning performance, and sleep. Family dogs (N = 24) participated in two command learning sessions employing the two training styles with each session followed by assessment of learning performance, a 2-h-long non-invasive sleep EEG measurement, and a retest of learning performance. Pre- to post-sleep improvement in learning performance was evident in dogs that received the Permissive training during the second learning session, indicating that dogs that experienced a more rewarding situation than expected (positive expectancy violation) during the second training session showed improved learning success after their afternoon sleep. These results possibly indicate an interactive effect of expectancy violation and sleep on enhancing learning.

Collaborative virtual reality environment in disaster medicine: moving from single player to multiple learners.

BACKGROUND: The use of virtual reality (VR) in healthcare education is on the increase. In disaster medicine, it could be a solution to the cost and logistic constraints for a "full-scale" scenarios. However, VR is mainly designed for single players, which is not appropriate for the objectives pursued in disaster medicine. We decided to evaluate the educational value of using individual VR simulation in disaster medicine on a group of learners. METHODS: The VR scenario used was a reproduction of a major train crash, with 21 victims and whose objectives were START triage and first aid techniques. The sessions were carried out in multi-participant groups with different roles (active and immersed with headset, paper triage without headset, and active for communications not immersed in the headset). Their perceived self-efficacy was assessed before (T0), after (T1) and 2 months (T2) after the training. Satisfaction and confidence in learning were also measured. RESULTS: The median levels of satisfaction and confidence in learning were of 21/25 and 32/40 respectively. Their perceived self-efficacy increased significantly between T0 and T1 (p < 0.001), and remained stable until T2. The different roles of participant showed no difference in terms of satisfaction, confidence in learning or changes in perceived self-efficacy. One third of the participants agreed that the number of participants had interfered with their learning. A significant negative correlation (rS = -0.51, p = 0.002) was found between satisfaction and the fact of having been hindered by the number of participants. Around 90% of participants found the activity entertaining and found the new technologies appropriate for learning technical skills. CONCLUSIONS: This first experience of VR in a group setting is satisfactory and shows its positive effects. The limitations highlighted here will enable areas of improvement to be identified for the use of VR in disaster medicine, pending the development of multi-player tools. It would now be appropriate to analyse the impact of this type of simulation on learning and its retention over time.

'We get to learn as we move': effects and feasibility of lesson-integrated physical activity in a Swedish primary school.

BACKGROUND: Physical activity (PA) promotes health in adults as well as children. At the same time, a large proportion of children do not meet the recommendations for PA, and more school-based efforts to increase PA are needed. This study investigates the effectiveness and feasibility of lesson-integrated PA in a Swedish primary school. METHODS: We evaluate a new method called 'Physical Activity and Lesson in Combination' (abbreviated FALK in Swedish) using a mixed methods approach; a quasi-experimental study followed by qualitative interviews. Two schools participated in the study, one constituting the intervention group (I-school, n = 83) and the other the control group (C-school, n = 81). In addition to regular physical education, the I-school had three 30-minute FALK lessons each week. A total of 164 students aged 7-9 years wore pedometers for a whole week, four times over two semesters, and the number of steps per day (SPD) and the proportion of students with < 10,000 SPD were compared. Statistical differences between the schools were tested with ANOVA, Chi2, t-tests, and ANCOVA. Interviews with students (n = 17), parents (n = 9) and teachers (n = 9) were conducted and analysed using qualitative content analysis. RESULTS: The results show that FALK led to the I-school getting more SPD and fewer students with < 10,000 SPD. Also, FALK was experienced as a positive, clear, and flexible method, simultaneously encouraging PA and learning. Challenges experienced concerned the teachers' work situation, time, finding suitable learning activities, outdoor school environment changes, and extreme weather conditions. CONCLUSIONS: This study indicates that FALK has the desired effects on PA and is a feasible method of integrating PA into theoretical teaching. We conclude that FALK is worth testing at more schools, given that implementation and sustainment of FALK considers both general enablers and barriers, as well as context-specific factors at the individual school.

E-learning-an interventional element of the PRiVENT project to improve weaning expertise.

BACKGROUND: PRiVENT (PRevention of invasive VENTilation) is an evaluation of a bundle of interventions aimed at the prevention of long-term invasive mechanical ventilation. One of these elements is an e-learning course for healthcare professionals to improve weaning expertise. The aim of our analysis is to examine the implementation of the course in cooperating intensive care units. METHODS: The course has been developed through a peer review process by pulmonary and critical care physicians in collaboration with respiratory therapists, supported by health services researchers and a professional e-learning agency. The e-learning platform "weLearn" was made available online to participating healthcare professionals. Feedback on the e-learning programme was obtained and discussed in quality circles (QCs). We measured the acceptance and use of the programme through access statistics. RESULTS: The e-learning course "Joint Prevention of Long-Term Ventilation" consists of 7 separate modules with practice-oriented training units as well as a cross-module area and corresponding interactive case studies. Users can receive 23 CME (continuing medical education) credits. The platform was released on July 1, 2021. By June 28, 2023, 214 users from 33 clinics had registered. Most users (77-98%) completed the modules, thus performing well in the test, where 90-100% passed. In the QCs, the users commended the structure and practical relevance of the programme, as well as the opportunity to earn CME credits. CONCLUSION: Especially for medical staff in intensive care units, where continuous training is often a challenge during shift work, e-learning is a useful supplement to existing medical training. TRIAL REGISTRATION: The PRiVENT study is registered at ClinicalTrials.gov (NCT05260853) on 02/03/2022.

A feeling of not being alone - Patients' with COPD experiences of a group-based self-management education with a digital website: A qualitative study.

AIM: To describe patients' with chronic obstructive pulmonary disease (COPD) experiences of group-based self-management education with a digital website. DESIGN: A qualitative approach with a phenomenologicalmethod. Patients participating in an earlier study, with self-experience of COPD as a special competence, were involved as research partners at the design of this study. METHODS: Eleven individual and two group interviews with five participants in each group were conducted. RESULTS: Group-based self-management education with a digital website supports learning. Sharing experiences with others in similar situations creates security and reduces the feeling of being alone. Based on questions and discussion in the group, and through self-reflection, general information is transformed into useful knowledge and understanding of one's own situation. COPD information on the website provides an opportunity to gain knowledge continuously based on needs that contributes to learning. This research has demonstrated that adapting learning activities to individual learning styles increases sustainability of learning. Sharing experiences reduces feelings of loneliness. It is therefore important to create spaces for sharing experiences and in-depth reflection that support learning over time.

A design thinking-led approach to develop a responsive feeding intervention for Australian families vulnerable to food insecurity: Eat, Learn, Grow.

BACKGROUND: Design thinking is an iterative process that innovates solutions through a person-centric approach and is increasingly used across health contexts. The person-centric approach lends itself to working with groups with complex needs. One such group is families experiencing economic hardship, who are vulnerable to food insecurity and face challenges with child feeding. OBJECTIVE: This study describes the application of a design thinking framework, utilizing mixed methods, including co-design, to develop a responsive child-feeding intervention for Australian families-'Eat, Learn, Grow'. METHODS: Guided by the five stages of design thinking, which comprises empathizing, defining, ideating, prototyping, and testing. We engaged with parents/caregivers of a child aged 6 months to 3 years through co-design workshops (n = 13), direct observation of mealtimes (n = 10), a cross-sectional survey (n = 213) and semistructured interviews (n = 29). Findings across these methods were synthesized using affinity mapping to clarify the intervention parameters. Parent user testing (n = 12) was conducted online with intervention prototypes to determine acceptability and accessibility. A co-design workshop with child health experts (n = 9) was then undertaken to review and co-design content for the final intervention. RESULTS: Through the design thinking process, an innovative digital child-feeding intervention was created. This intervention utilized a mobile-first design and consisted of a series of short and interactive modules that used a learning technology tool. The design is based on the concept of microlearning and responds to participants' preferences for visual, brief and plain language information accessed via a mobile phone. User testing sessions with parents and the expert co-design workshop indicated that the intervention was highly acceptable. CONCLUSIONS: Design thinking encourages researchers to approach problems creatively and to design health interventions that align with participant needs. Applying mixed methods-including co-design- within this framework allows for a better understanding of user contexts, preferences and priorities, ensuring solutions are more acceptable and likely to be engaged.

Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students.

ChatGPT (OpenAI), a cutting-edge natural language processing model, holds immense promise for revolutionizing medical education. With its remarkable performance in language-related tasks, ChatGPT offers personalized and efficient learning experiences for medical students and doctors. Through training, it enhances clinical reasoning and decision-making skills, leading to improved case analysis and diagnosis. The model facilitates simulated dialogues, intelligent tutoring, and automated question-answering, enabling the practical application of medical knowledge. However, integrating ChatGPT into medical education raises ethical and legal concerns. Safeguarding patient data and adhering to data protection regulations are critical. Transparent communication with students, physicians, and patients is essential to ensure their understanding of the technology's purpose and implications, as well as the potential risks and benefits. Maintaining a balance between personalized learning and face-to-face interactions is crucial to avoid hindering critical thinking and communication skills. Despite challenges, ChatGPT offers transformative opportunities. Integrating it with problem-based learning, team-based learning, and case-based learning methodologies can further enhance medical education. With proper regulation and supervision, ChatGPT can contribute to a well-rounded learning environment, nurturing skilled and knowledgeable medical professionals ready to tackle health care challenges. By emphasizing ethical considerations and human-centric approaches, ChatGPT's potential can be fully harnessed in medical education, benefiting both students and patients alike.

Selective plasticity of layer 2/3 inputs onto distal forelimb controlling layer 5 corticospinal neurons with skilled grasp motor training.

Layer 5 neurons of the neocortex receive their principal inputs from layer 2/3 neurons. We seek to identify the nature and extent of the plasticity of these projections with motor learning. Using optogenetic and viral intersectional tools to selectively stimulate distinct neuronal subsets in rat primary motor cortex, we simultaneously record from pairs of corticospinal neurons associated with distinct features of motor output control: distal forelimb vs. proximal forelimb. Activation of Channelrhodopsin2-expressing layer 2/3 afferents onto layer 5 in untrained animals produces greater monosynaptic excitation of neurons controlling the proximal forelimb. Following skilled grasp training, layer 2/3 inputs onto corticospinal neurons controlling the distal forelimb associated with skilled grasping become significantly stronger. Moreover, peak excitatory response amplitude nearly doubles while latency shortens, and excitatory-to-inhibitory latencies become significantly prolonged. These findings demonstrate distinct, highly segregated, and cell-specific plasticity of layer 2/3 projections during skilled grasp motor learning.

Perturbation Variability Does Not Influence Implicit Sensorimotor Adaptation.

Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One compelling line of evidence comes from work suggesting that this form of learning is context-dependent, with the rate of learning modulated by error history. Specifically, learning was attenuated in the presence of perturbations exhibiting high variance compared to when the perturbation is fixed. However, these findings are confounded by the fact that the adaptation system corrects for errors of different magnitudes in a non-linear manner, with the adaptive response increasing in a proportional manner to small errors and saturating to large errors. Through simulations, we show that this non-linear motor correction function is sufficient to explain the effect of perturbation variance without referring to an experience-dependent change in error sensitivity. Moreover, by controlling the distribution of errors experienced during training, we provide empirical evidence showing that there is no measurable effect of perturbation variance on implicit adaptation. As such, we argue that the evidence to date remains consistent with the rigidity assumption.

Specific connectivity optimizes learning in thalamocortical loops.

Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Bringing Real Inquiry-Based Science to Diverse Secondary Educational Environments: A Virtual Zebrafish Laboratory to Investigate Environmental Health.

The goal of the University of Wisconsin-Milwaukee WInSTEP SEPA program is to provide valuable and relevant research experiences to students and instructors in diverse secondary educational settings. Introducing an online experience allows the expansion of a proven instructional research program to a national scale and removes many common barriers. These can include lack of access to zebrafish embryos, laboratory equipment, and modern classroom facilities, which often deny disadvantaged and underrepresented students from urban and rural school districts valuable inquiry-based learning opportunities. An online repository of zebrafish embryo imagery was developed in the Carvan laboratory to assess the effects of environmental chemicals. The WInSTEP SEPA program expanded its use as an accessible online tool, complementing the existing classroom experience of our zebrafish module. This virtual laboratory environment contains images of zebrafish embryos grown in the presence of environmental toxicants (ethanol, caffeine, and nicotine), allowing students to collect data on 19 anatomical endpoints and generate significant amounts of data related to developmental toxicology and environmental health. This virtual laboratory offers students and instructors the choice of data sets that differ in the independent variables of chemical concentration and duration of postfertilization exposure. This enables students considerable flexibility in establishing their own experimental design to match the curriculum needs of each instructor.

Children's cognition and attitudes during long-term cancer treatment: an ethnographic study.

BACKGROUND: Cancer treatment for children is typically long-term and difficult, and the experience is unique for each child. When designing child-centred care, individuals' values and preferences are considered equally important as the clinical evidence; therefore, understanding children's thoughts and attitudes while they receive long-term treatment could offer valuable insights for better clinical practice. METHODS: We conducted long-term consecutive participatory observations and interviews with seven children, who were hospitalised and receiving cancer treatment for the first time. The daily observational data on those children's discourses, behaviours and interactions with health professionals were systematically collected and thematically examined. The analysis was expanded to explore significant narratives for each child to capture their narrative sequence over time. RESULTS: The initial analysis identified 685 narrative indexes for all observation data, which were categorised into 21 sub-codes. Those sub-codes were assembled into five main themes by thematic analysis: making promises with health professionals, learning about the treatment procedures through participation, taking care of oneself, increasing the range of activities one can perform and living an ordinary life. CONCLUSION: We observed a forward-looking attitude toward understanding cancer, accepting treatment and looking forward to the future among children undergoing in-hospital cancer treatment. In addition, the children developed cognitively, affectively and relationally throughout cancer treatment processes. These findings have implications for better clinical practice in child-centred care, including children's participation in shared decision-making in paediatric oncology.

Neuromorphic one-shot learning utilizing a phase-transition material.

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

CD20/MS4A1 is a mammalian olfactory receptor expressed in a subset of olfactory sensory neurons that mediates innate avoidance of predators.

The mammalian olfactory system detects and discriminates between millions of odorants to elicit appropriate behavioral responses. While much has been learned about how olfactory sensory neurons detect odorants and signal their presence, how specific innate, unlearned behaviors are initiated in response to ethologically relevant odors remains poorly understood. Here, we show that the 4-transmembrane protein CD20, also known as MS4A1, is expressed in a previously uncharacterized subpopulation of olfactory sensory neurons in the main olfactory epithelium of the murine nasal cavity and functions as a mammalian olfactory receptor that recognizes compounds produced by mouse predators. While wildtype mice avoid these predator odorants, mice genetically deleted of CD20 do not appropriately respond. Together, this work reveals a CD20-mediated odor-sensing mechanism in the mammalian olfactory system that triggers innate behaviors critical for organismal survival.

Gaze patterns reflect the retrieval and selection of memories in a context-dependent object location retrieval task.

Selective retrieval of context-relevant memories is critical for animal survival. A behavioral index that captures its dynamic nature in real time is necessary to investigate this retrieval process. Here, we found a bias in eye gaze towards the locations previously associated with individual objects during retrieval. Participants learned two locations associated with each visual object and recalled one of them indicated by a contextual cue in the following days. Before the contextual cue presentation, participants often gazed at both locations associated with the given object on the background screen (look-at-both), and the frequency of look-at-both gaze pattern increased as learning progressed. Following the cue presentation, their gaze shifted toward the context-appropriate location. Interestingly, participants showed a higher accuracy of memory retrieval in trials where they gazed at both object-associated locations, implying functional advantage of the look-at-both gaze patterns. Our findings indicate that naturalistic eye movements reflect the dynamic process of memory retrieval and selection, highlighting the potential of eye gaze as an indicator for studying these cognitive processes.

Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals.

As the health care industry increasingly embraces large language models (LLMs), understanding the consequence of this integration becomes crucial for maximizing benefits while mitigating potential pitfalls. This paper explores the evolving relationship among clinician trust in LLMs, the transition of data sources from predominantly human-generated to artificial intelligence (AI)-generated content, and the subsequent impact on the performance of LLMs and clinician competence. One of the primary concerns identified in this paper is the LLMs' self-referential learning loops, where AI-generated content feeds into the learning algorithms, threatening the diversity of the data pool, potentially entrenching biases, and reducing the efficacy of LLMs. While theoretical at this stage, this feedback loop poses a significant challenge as the integration of LLMs in health care deepens, emphasizing the need for proactive dialogue and strategic measures to ensure the safe and effective use of LLM technology. Another key takeaway from our investigation is the role of user expertise and the necessity for a discerning approach to trusting and validating LLM outputs. The paper highlights how expert users, particularly clinicians, can leverage LLMs to enhance productivity by off-loading routine tasks while maintaining a critical oversight to identify and correct potential inaccuracies in AI-generated content. This balance of trust and skepticism is vital for ensuring that LLMs augment rather than undermine the quality of patient care. We also discuss the risks associated with the deskilling of health care professionals. Frequent reliance on LLMs for critical tasks could result in a decline in health care providers' diagnostic and thinking skills, particularly affecting the training and development of future professionals. The legal and ethical considerations surrounding the deployment of LLMs in health care are also examined. We discuss the medicolegal challenges, including liability in cases of erroneous diagnoses or treatment advice generated by LLMs. The paper references recent legislative efforts, such as The Algorithmic Accountability Act of 2023, as crucial steps toward establishing a framework for the ethical and responsible use of AI-based technologies in health care. In conclusion, this paper advocates for a strategic approach to integrating LLMs into health care. By emphasizing the importance of maintaining clinician expertise, fostering critical engagement with LLM outputs, and navigating the legal and ethical landscape, we can ensure that LLMs serve as valuable tools in enhancing patient care and supporting health care professionals. This approach addresses the immediate challenges posed by integrating LLMs and sets a foundation for their maintainable and responsible use in the future.

How children generalize novel nouns: An eye-tracking analysis of their generalization strategies.

Recent research has shown that comparisons of multiple learning stimuli which are associated with the same novel noun favor taxonomic generalization of this noun. These findings contrast with single-stimulus learning in which children follow so-called lexical biases. However, little is known about the underlying search strategies. The present experiment provides an eye-tracking analysis of search strategies during novel word learning in a comparison design. We manipulated both the conceptual distance between the two learning items, i.e., children saw examples which were associated with a noun (e.g., the two learning items were either two bracelets in a "close" comparison condition or a bracelet and a watch in a "far" comparison condition), and the conceptual distance between the learning items and the taxonomically related items in the generalization options (e.g., the taxonomic generalization answer; a pendant, a near generalization item; versus a bow tie, a distant generalization item). We tested 5-, 6- and 8-year-old children's taxonomic (versus perceptual and thematic) generalization of novel names for objects. The search patterns showed that participants first focused on the learning items and then compared them with each of the possible choices. They also spent less time comparing the various options with one another; this search profile remained stable across age groups. Data also revealed that early comparisons, (i.e., reflecting alignment strategies) predicted generalization performance. We discuss four search strategies as well as the effect of age and conceptual distance on these strategies.