Lukas T. Oesch, Michael B. Ryan, Anne K. Churchland
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From innate to instructed: A new look at perceptual decision-making
Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as “naturalistic”) or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals’ pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.
期刊介绍:
Current Opinion in Neurobiology publishes short annotated reviews by leading experts on recent developments in the field of neurobiology. These experts write short reviews describing recent discoveries in this field (in the past 2-5 years), as well as highlighting select individual papers of particular significance.
The journal is thus an important resource allowing researchers and educators to quickly gain an overview and rich understanding of complex and current issues in the field of Neurobiology. The journal takes a unique and valuable approach in focusing each special issue around a topic of scientific and/or societal interest, and then bringing together leading international experts studying that topic, embracing diverse methodologies and perspectives.
Journal Content: The journal consists of 6 issues per year, covering 8 recurring topics every other year in the following categories:
-Neurobiology of Disease-
Neurobiology of Behavior-
Cellular Neuroscience-
Systems Neuroscience-
Developmental Neuroscience-
Neurobiology of Learning and Plasticity-
Molecular Neuroscience-
Computational Neuroscience