Linking the cognitive performance of wild animals with fitness consequences is crucial for understanding evolutionary processes that shape individual variation in cognition. However, the few studies that have examined these links revealed differing relationships between various cognitive performance measures and fitness proxies. To contribute additional comparative data to this body of research, we linked individual performance during repeated problem-solving and spatial learning ability in a maze with body condition and survival in wild grey mouse lemurs (). All four variables exhibited substantial inter-individual variation. Solving efficiency in the problem-solving task, but not spatial learning performance, predicted the magnitude of change in body condition after the harsh dry season, indicating that the ability to quickly apply a newly discovered motor technique might also facilitate exploitation of new, natural food resources. Survival was not linked with performance in both tasks, however, suggesting that mouse lemurs' survival might not depend on the cognitive performances addressed here. Our study is the first linking cognition with fitness proxies in a wild primate species, and our discussion highlights the importance and challenges of accounting for a species' life history and ecology in choosing meaningful cognitive and fitness variables for a study in the wild.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6107563PMC
http://dx.doi.org/10.1098/rstb.2017.0295DOI Listing

Publication Analysis

Top Keywords

spatial learning
12
linking cognition
8
cognition fitness
8
wild primate
8
learning ability
8
cognitive performance
8
fitness proxies
8
body condition
8
fitness
6
performance
6

Similar Publications

Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.

View Article and Find Full Text PDF

Drought is one of the most detrimental natural calamities to the economy. Despite its significant consequences, the evolution from meteorological to agricultural and hydrological droughts still needs to be explored. A thorough investigation was carried out in India's eastern hills and plateau region to determine the extent of drought's impact through indices.

View Article and Find Full Text PDF

Background: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.

View Article and Find Full Text PDF

Detecting autism in children through drawing characteristics using the visual-motor integration test.

Health Inf Sci Syst

December 2025

Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.

This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.

View Article and Find Full Text PDF

The spatial arrangement of cells plays a pivotal role in shaping tissue functions in various biological systems and diseased microenvironments. However, it is still under-investigated of the topological coordinating rules among different cell types as tissue spatial patterns. Here, we introduce the Triangulation cellular community motif Neural Network (TrimNN), a bottom-up approach to estimate the prevalence of sizeable conservative cell organization patterns as Cellular Community (CC) motifs in spatial transcriptomics and proteomics.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!