In contrast to the ample research that shows a positive relationship between the need for closure (NFC) and heuristic information processing, this research examines the hypothesis that this relationship is moderated by the ability to achieve closure (AAC), that is, the ability to use information-processing strategies consistent with the level of NFC. Three different operationalizations of heuristic information processing were used: recall of information consistent with the impression (Study 1); pre-decisional information search (Study 2); and stereotypic impression formation (Study 3). The results of the studies showed that there were positive relationships between NFC and heuristic information processing when participants assessed themselves as being able to use cognitive strategies consistent with their level of NFC (high AAC). For individuals with low AAC, the relationships were negative. Our data show that motivation-cognition interactions influence the information-processing style.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/bjop.12001 | DOI Listing |
Sensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFMar Drugs
January 2025
Institute for Bioengineering, The School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, UK.
Fucoidan is a sulfated polysaccharide found in brown seaweed. Due to its reported biological activities, including antiviral, antibacterial and anti-inflammatory activities, it has garnered significant attention for potential biomedical applications. However, the direct relationship between fucoidan extracts' chemical structures and bioactivities is unclear, making it extremely challenging to predict whether an extract will possess a given bioactivity.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
RoboticsLab, Universidad Carlos III de Madrid, 28911 Madrid, Spain.
Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to be learned. To address this challenge, this work presents an algorithm for acquiring robotic skills through automatic and unsupervised segmentation.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
Department of Computer Science, Durham University, Durham DH1 3LE, UK.
The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Electronic and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Artificial intelligence plays an indispensable role in improving productivity and promoting social development, and causal discovery is one of the extremely important research directions in this field. Acyclic directed graphs (DAGs) are the most commonly used tool in causal modeling because of their excellent interpretability and structural properties. However, in the face of insufficient data, the accuracy and efficiency of DAGs learning are greatly reduced, resulting in a false perception of causality.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!