title: Insights into decision making using choice probability.
abstract: A long-standing question in systems neuroscience is how the activity of single neurons gives rise to our perceptions and actions. Critical insights into this question occurred in the last part of the 20th century when scientists began linking modulations of neuronal activity directly to perceptual behavior. A significant conceptual advance was the application of signal detection theory to both neuronal activity and behavior, providing a quantitative assessment of the relationship between brain and behavior. One metric that emerged from these efforts was choice probability (CP), which provides information about how well an ideal observer can predict the choice an animal makes from a neuron's discharge rate distribution. In this review, we describe where CP has been studied, locational trends in the values found, and why CP values are typically so low. We discuss its dependence on correlated activity among neurons of a population, assess whether it arises from feedforward or feedback mechanisms, and investigate what CP tells us about how many neurons are required for a decision and how they are pooled to do so.
authors.affiliation: Joaquin Fuster Laboratory of Cognitive Neuroscience, Departments of Psychiatry and Biobehavioral Sciences and Neurobiology, The Semel Institute for Neuroscience and Human Behavior and the Brain Research Institute, University of California, Los Angeles, Los Angeles, California.
authors.forename: Trinity B
authors.initials: TB
authors.lastname: Crapse
authors.affiliation: Joaquin Fuster Laboratory of Cognitive Neuroscience, Departments of Psychiatry and Biobehavioral Sciences and Neurobiology, The Semel Institute for Neuroscience and Human Behavior and the Brain Research Institute, University of California, Los Angeles, Los Angeles, California mbasso@mednet.ucla.edu.
authors.forename: Michele A
authors.initials: MA
authors.lastname: Basso
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D002755:Choice Behavior; D025461:Feedback, Physiological; D006801:Humans; D008959:Models, Neurological; D011336:Probability
keywords: correlated variability; eye movements; multiple neuron recording; neurophysiology; perception; sensation; signal detection theory; vision