The knapping experiments with Kanzi, a bonobo, are among the most insightful experiments into Oldowan technology ever undertaken. Comparison of his artifacts against archeological material, however, indicated he did not produce Oldowan lithic attributes precisely, prompting suggestions that this indicated cognitive or biomechanical impediments. The literature describing the learning environment provided to Kanzi, we suggest, indicates alternative factors. Based on consideration of wild chimpanzee learning environments, and experiments with modern knappers that have looked at learning environment, we contend that Kanzi's performance was impeded by an impoverished learning environment compared to those experienced by novice Oldowan knappers. Such issues are precisely those that might be tested via a repeat study, but in this case, practical and ethical constraints likely impede this possibility. We propose experiments that may be relevant to drawing conclusions from Kanzi's experiments that may not need to use non-human primates, thus bypassing some of these issues.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/evan.21858 | DOI Listing |
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Sci Rep
January 2025
Department of Psychology, Faculty of Psychology and Sport Science, Justus Liebig University, Otto-Behaghel-Str. 10F, 35394, Gießen, Germany.
Adapting movements to rapidly changing conditions is fundamental for interacting with our dynamic environment. This adaptability relies on internal models that predict and evaluate sensory outcomes to adjust motor commands. Even infants anticipate object properties for efficient grasping, suggesting the use of internal models.
View Article and Find Full Text PDFSci Rep
January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, 10300, Thailand.
Attention mechanisms such as the Convolutional Block Attention Module (CBAM) can help emphasize and refine the most relevant feature maps such as color, texture, spots, and wrinkle variations for the avocado ripeness classification. However, the CBAM lacks global context awareness, which may prevent it from capturing long-range dependencies or global patterns such as relationships between distant regions in the image. Further, more complex neural networks can improve model performance but at the cost of increasing the number of layers and train parameters, which may not be suitable for resource constrained devices.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!