People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research.
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http://dx.doi.org/10.1037/a0026790 | DOI Listing |
Sci Rep
December 2024
Hangzhou Dianzi University, Baiyang Street, Hangzhou, 310018, China.
To solve the problems of the traditional convolution optimization algorithm (COA), which are its slow convergence speed and likelihood of falling into local optima, a Gaussian mutation convolution optimization algorithm based on tent chaotic mapping (TCOA) is proposed in this article. First, the tent chaotic strategy is employed for the initialization of individual positions to ensure a uniform distribution of the population across a feasible search space. Subsequently, a Gaussian convolution kernel is used for an extensive depth search within the search space to mitigate the likelihood of any individuals converging to a local optimum.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
School of Pharmaceutical Sciences and Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland.
We introduce an enhanced sampling algorithm to obtain converged free energy landscapes of molecular rare events, even when the collective variable (CV) used for biasing is not optimal. Our approach samples a time-dependent target distribution by combining the on-the-fly probability enhanced sampling and its exploratory variant, OPES Explore. This promotes more transitions between the relevant metastable states and accelerates the convergence speed of the free energy estimate.
View Article and Find Full Text PDFAging Clin Exp Res
December 2024
Department of Orthopedics, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Aims: We conducted this study to investigate the impact of muscle loss on musculoskeletal health, fall and fracture risks, and activities of daily living (ADL) in elderly patients with osteoporosis.
Materials And Methods: This age- and sex-matched cross-sectional study analyzed data from a medical center involving patients aged ≥ 50 from 2020 to 2022. The included participants were formed into three groups: 100 with osteoporosis only, 100 with osteosarcopenia, and 50 control individuals without osteoporosis and sarcopenia.
Environ Geochem Health
December 2024
Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological conditions and the consequent health risks posed. Employing EPA health risk assessment methods, the research delved into the implications of pollutant exposure on human health.
View Article and Find Full Text PDFEJNMMI Phys
December 2024
Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, 1111, Hungary.
Background: In the back projection step of the 3D PET reconstruction, all Lines of Responses (LORs) that go through a given voxel need to be identified and included in an integral. The standard Monte Carlo solution to this task samples stochastically the surfaces of the detector crystals and the volume of the voxel to search for valid LORs. To get a low noise Monte Carlo estimate, the number of samples needs to be very high, making the computational cost of the projection significant.
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