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{"title"=>"Multiple Choice Neurodynamical Model of the Uncertain Option Task", "type"=>"journal", "authors"=>[{"first_name"=>"Andrea", "last_name"=>"Insabato", "scopus_author_id"=>"36165229100"}, {"first_name"=>"Mario", "last_name"=>"Pannunzi", "scopus_author_id"=>"36133717200"}, {"first_name"=>"Gustavo", "last_name"=>"Deco", "scopus_author_id"=>"7006674531"}], "year"=>2017, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "sgr"=>"85011422762", "scopus"=>"2-s2.0-85011422762", "isbn"=>"1111111111", "pui"=>"614304530", "doi"=>"10.1371/journal.pcbi.1005250", "pmid"=>"28076355"}, "id"=>"ec87b559-5001-3f85-8a34-8cf38a1b39b1", "abstract"=>"The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.", "link"=>"http://www.mendeley.com/research/multiple-choice-neurodynamical-model-uncertain-option-task", "reader_count"=>25, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Researcher"=>5, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Researcher"=>5, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>3, "Medicine and Dentistry"=>1, "Neuroscience"=>11, "Psychology"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>11}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>2}, "Unspecified"=>{"Unspecified"=>5}}, "group_count"=>0}

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• http://doi.org/10.1371/journal.pcbi.1005542

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• {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
• {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}

F1000Prime