Contemporary descriptions of human intelligence hold that this trait influences a broad range of cognitive abilities, including learning, attention, and reasoning. Like humans, individual genetically heterogeneous mice express a "general" cognitive trait that influences performance across a diverse array of learning and attentional tasks, and it has been suggested that this trait is qualitatively and structurally analogous to general intelligence in humans. However, the hallmark of human intelligence is the ability to use various forms of "reasoning" to support solutions to novel problems. Here, we find that genetically heterogeneous mice are capable of solving problems that are nominally indicative of inductive and deductive forms of reasoning, and that individuals' capacity for reasoning covaries with more general learning abilities. Mice were characterized for their general learning ability as determined by their aggregate performance (derived from principal component analysis) across a battery of five diverse learning tasks. These animals were then assessed on prototypic tests indicative of deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping") and inductive reasoning (execution of an efficient search strategy in a binary decision tree). The animals exhibited systematic abilities on each of these nominal reasoning tasks that were predicted by their aggregate performance on the battery of learning tasks. These results suggest that the coregulation of reasoning and general learning performance in genetically heterogeneous mice form a core cognitive trait that is analogous to human intelligence.
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http://dx.doi.org/10.1037/a0027355 | DOI Listing |
Oper Neurosurg (Hagerstown)
July 2024
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal , Quebec , Canada.
Background And Objectives: Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
Computational Clinical Imaging Group (CCIG), Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.
View Article and Find Full Text PDFNanomicro Lett
January 2025
Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education, School of Chemistry, Beihang University, Beijing, 100191, People's Republic of China.
The proliferation of wearable biodevices has boosted the development of soft, innovative, and multifunctional materials for human health monitoring. The integration of wearable sensors with intelligent systems is an overwhelming tendency, providing powerful tools for remote health monitoring and personal health management. Among many candidates, two-dimensional (2D) materials stand out due to several exotic mechanical, electrical, optical, and chemical properties that can be efficiently integrated into atomic-thin films.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
Sleep Breath
January 2025
Department of Internal Medicine II (Cardiology, Pneumology, and Intensive Care), University Medical Centre Regensburg, Regensburg, Germany.
Purpose: In heart failure (HF) and chronic obstructive pulmonary disease (COPD) populations, sleep-disordered breathing (SDB) is associated with impaired health outcomes. We evaluated whether in patients with HF, concomitant HF and COPD or COPD, the number of hospitalizations would be reduced in the year after testing for SDB with and without treatment initiation compared to the year before.
Methods: We performed a multicentre retrospective study of 390 consecutive sleep-clinic patients who had a primary diagnosis of chronic HF, HF and COPD or COPD and a secondary diagnosis of SDB.
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