There is little consensus about the nature of logical reasoning and, equally important, about how it develops. To address this, we looked at the early origins of deductive reasoning in preschool children. We examined the contribution of two factors to the reasoning ability of very young children: inhibitory capacity and the capacity to generate alternative ideas. In a first study, a total of 32 preschool children were all given generation, inhibition, and logical reasoning measures. Logical reasoning was measured using knowledge-based premises such as "All dogs have legs," and two different inferences: modus ponens and affirmation of the consequent. Results revealed that correctly reasoning with both inferences is not related to the measure of inhibition, but is rather related to the capacity to generate alternative ideas. In a second study, 32 preschool children were given either the generation or the inhibition task before the logical reasoning measure. Results showed that receiving the generation task beforehand significantly improved logical reasoning compared to the inhibition task given beforehand. Overall, these results provide evidence for the greater importance of idea generation in the early development of logical reasoning.
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http://dx.doi.org/10.3758/s13421-016-0653-4 | DOI Listing |
Sensors (Basel)
January 2025
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Old Aberdeen AB24 3UE, UK.
Background/objectives: A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.
Methods: We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level.
Forensic Sci Res
December 2024
National Forensic Laboratory, Ljubljana, Slovenia.
Like other pattern recognition disciplines, forensic handwriting examination relies on various human factors. Expert opinions in the field are based on visual analysis and comparison, and the evaluation of findings is generally conducted without reference to tabulated data. This high level of subjectivity may contribute to bias and error in the examination process.
View Article and Find Full Text PDFSci Rep
January 2025
Changchun Automobile Economic & Technological Development Zone Employment Service Bureau, Jilin City, China.
The permanent magnet synchronous motor control system is characterized by its nonlinear and strongly coupled complexity, presenting significant challenges for control performance optimization. To address these challenges, a Fuzzy adaptive fractional order [Formula: see text] control strategy based on torque observation compensation is proposed. The parameters of the fractional order [Formula: see text] controller are optimized real time using fuzzy logic reasoning, in order to enhance the speed of parameters tuning, a graphical design method of the fractional order [Formula: see text] controller parameters based on frequency domain performance indicators is proposed to obtain the initial values of the fuzzy adaptive fractional order [Formula: see text] controller parameters graphically and intuitively.
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