Environmental Neuroscience

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (10 March 2020) | Viewed by 9633

Special Issue Editor


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Guest Editor
Department of Psychology, University of Chicago, Chicago, IL 60637, USA
Interests: environmental neuroscience; fMRI; multivariate statistics; environmental psychology; cognitive psychology; cognitive neuroscience; social psychology; machine learning

Special Issue Information

Dear Colleagues,

Environmental neuroscience is an emerging field devoted to the scientific study of brain-mediated bi-directional relationships between organisms and their social and physical environments. Environmental neuroscience considers factors that vary across multiple temporal and spatial scales and interact to produce certain behaviors. In order to explicitly model the dynamics of these environments using brain–behavior interactions, environmental neuroscience builds on the multi-level frameworks of social and network neuroscience. These frameworks use multiple levels of analysis to qualitatively guide and interpret research. It is important to model interactions within a level (e.g., brain network connectivity) and also between levels (e.g., the relationship between brain network connectivity and genomic function and also the relationship between brain network connectivity and the network dynamics of a city). Environmental neuroscientists build on these ideas by measuring the spatial and temporal dynamics of the interactions between different levels of analysis, such as how being a carrier of certain genetic polymorphisms may affect an individual’s benefits from interactions with urban greenspace or how different enriched environments affect structural and functional brain networks in non-human species. According to environmental neuroscience, examining all of these levels of analysis at different temporal and spatial scales will lead to greater understanding of behaviors. In addition, the collection of data across these scales and the measurement of their interactions will generate rich datasets that will continue to yield insights as new ways to model these complex multi-level systems are developed, all of which will lead to advances in behavioral understanding.

We invite authors to submit articles to this Special Issue of Brain Sciences, “Environmental Neuroscience”, that examine how the physical and social environment that surrounds us affects brain processes and behavior.

Dr. Marc G. Berman
Guest Editor

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Keywords

  • environmental neuroscience
  • (epi)genetics
  • complex systems
  • hierarchical systems
  • network neuroscience
  • environmental psychology
  • environmental justice
  • cognitive psychology
  • social neuroscience
  • architecture and urban planning

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Published Papers (1 paper)

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Review

28 pages, 2706 KiB  
Review
Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence
by Ryan Paul Badman, Thomas Trenholm Hills and Rei Akaishi
Brain Sci. 2020, 10(6), 396; https://doi.org/10.3390/brainsci10060396 - 20 Jun 2020
Cited by 5 | Viewed by 8119
Abstract
Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive [...] Read more.
Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence. Full article
(This article belongs to the Special Issue Environmental Neuroscience)
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