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Optimally generate policy-based evidence before scaling

Social scientists have increasingly turned to the experimental method to understand human behaviour. One critical issue that makes solving social problems difficult is scaling up the idea from a small group to a larger group in more diverse situations. The urgency of scaling policies impacts us every day, whether it is protecting the health and safety of a community or enhancing the opportunities of future generations. Yet, a common result is that, when we scale up ideas, most experience a ‘voltage drop’—that is, on scaling, the cost–benefit profile depreciates considerably. Here I argue that, to reduce voltage drops, we must optimally generate policy-based evidence. Optimality requires answering two crucial questions: what information should be generated and in what sequence. The economics underlying the science of scaling provides insights into these questions, which are in some cases at odds with conventional approaches. For example, there are important situa ....

National Bureau Of Economic Research , United States , Washington Square , United Kingdom , City Of , The Commons , Robert Rosenthal , A Strahant Cadell , Naturalis Principia Mathematica , Ralphl Rosnow , Experimental Designs For Research Rand Mcnally Company , Paper National Bureau Of Economic Research , Parent Social Networks , Universe Washington Square , Rl Artifacts In Behavioral Research , Child Development , A New Preschool Education Program Combining Parent , Getting Out , Chicago Press , Collective Action , Voltage Effect , Make Good Ideas Great , Great Ideas Scale , Part One , Straight Talk , Human Perils ,

How deep is the brain? The shallow brain hypothesis | Nature Reviews Neuroscience

Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today’s dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning ....

United Kingdom , East Sussex , Jordan General , Dj Van Essen , C Van Noorden , Dj Van Essen , Th Organization , Cognitive Development Ch , Computer Vision , Pattern Recognition , Machine Learning , Van Essen , Computational Geometry , Annual Conference , Learning Theory , Its Role , Cortical Function , Brain Res , Neural Circuits , Prefrontal Cortex , Frontal Lobe , Cell Neurosci , Fundamental Neuroscience , Neural Circuit , Cognitive Development , Brain Sci ,

From fossils to mind | Communications Biology

Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections i ....

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