Credit Assignment during Movement Reinforcement Learning
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{"title"=>"Credit Assignment during Movement Reinforcement Learning", "type"=>"journal", "authors"=>[{"first_name"=>"Gregory", "last_name"=>"Dam", "scopus_author_id"=>"57196566158"}, {"first_name"=>"Konrad", "last_name"=>"Kording", "scopus_author_id"=>"6603812799"}, {"first_name"=>"Kunlin", "last_name"=>"Wei", "scopus_author_id"=>"7203013458"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368325932", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0055352", "scopus"=>"2-s2.0-84873620063", "pmid"=>"23408972", "sgr"=>"84873620063"}, "id"=>"6ed4c214-097e-3718-b267-934738c9298b", "abstract"=>"We often need to learn how to move based on a single performance measure that reflects the overall success of our movements. However, movements have many properties, such as their trajectories, speeds and timing of end-points, thus the brain needs to decide which properties of movements should be improved; it needs to solve the credit assignment problem. Currently, little is known about how humans solve credit assignment problems in the context of reinforcement learning. Here we tested how human participants solve such problems during a trajectory-learning task. Without an explicitly-defined target movement, participants made hand reaches and received monetary rewards as feedback on a trial-by-trial basis. The curvature and direction of the attempted reach trajectories determined the monetary rewards received in a manner that can be manipulated experimentally. Based on the history of action-reward pairs, participants quickly solved the credit assignment problem and learned the implicit payoff function. A Bayesian credit-assignment model with built-in forgetting accurately predicts their trial-by-trial learning.", "link"=>"http://www.mendeley.com/research/credit-assignment-during-movement-reinforcement-learning", "reader_count"=>89, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>17, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>3, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>17, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>3, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>9, "Philosophy"=>1, "Arts and Humanities"=>1, "Business, Management and Accounting"=>2, "Computer Science"=>8, "Engineering"=>15, "Mathematics"=>1, "Medicine and Dentistry"=>6, "Neuroscience"=>14, "Sports and Recreations"=>4, "Psychology"=>23, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>4}, "Psychology"=>{"Psychology"=>23}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>15}, "Neuroscience"=>{"Neuroscience"=>14}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>9}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>6, "Norway"=>1, "Japan"=>2, "United Kingdom"=>2, "France"=>3, "Switzerland"=>1, "Germany"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/491191"], "description"=>"<p>A): Five individual trial trajectories (blue) along with its corresponding hidden target trajectory (red) are shown in separate panels from left to right. The rightmost panel displays the absolute error in trajectory direction (α) and curvature (β). The direction of targets is learned earlier than curvature. B): Panels follow the same format as the upper panels. Curvature learning occurs early but the errors in the direction of targets remain high throughout the session.</p>", "links"=>[], "tags"=>["physiology", "neuroscience"], "article_id"=>161717, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_data_from_typical_participants_from_the_945_reward_condition_upper_panels_A_and_the_946_reward_condition_lower_panels_B_/161717", "title"=>"Learning data from typical participants from the α-reward condition (upper panels, A) and the β-reward condition (lower panels, B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:35:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/491362"], "description"=>"<p>The x- and y-axis denote the absolute errors in two movement properties, respectively. The background temperature plots display the reward functions with the maximum achievable reward centered at zero alpha-beta error. The higher the temperature the higher the reward. White vectors are average parameter changes from one trial to the next across all target trajectories and all participants. Black vectors represent the Bayesian model’s predictions.</p>", "links"=>[], "tags"=>["changes", "properties"], "article_id"=>161894, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quiver_plot_of_average_changes_in_movement_properties_with_a_945_A_and_a_946_B_reward_function_/161894", "title"=>"Quiver plot of average changes in movement properties with a α- (A) and a β- (B) reward function.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:36:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/491032"], "description"=>"<p>A): The cartoon illustration of the setup. Participants sat before a desk and made movement trajectories on the horizontal desktop with a hand-held stylus. The hand displacement was registered by a robot. The feedback was provided via a computer monitor placed on the desk. B): A graphical representation of how trajectories were varied in both direction (α) and curvature (β). C): The learning progress of matching trajectories to a hidden target trajectory within a session of 25 trials.</p>", "links"=>[], "tags"=>["physiology", "neuroscience"], "article_id"=>161556, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g001", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_experimental_setup_/161556", "title"=>"The experimental setup.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:34:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/491265"], "description"=>"<p>A): The monetary rewards, averaged over all target trajectories, are plotted as a function of trials. The two shaded curves stand for mean±SEM over participants for two reward conditions separately. B): The learning curves of two trajectory properties, direction (α) and curvature (β), are plotted in black and red, respectively. The green and the gray lines denote their corresponding exponential fits. The results from the two reward conditions are presented in two separate subplots. C): The same learning curves from B) are re-plotted in the α-β space. The arrows indicate directions of changes and their sizes are proportional to magnitude of changes.</p>", "links"=>[], "tags"=>["physiology", "neuroscience"], "article_id"=>161794, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_related_changes_/161794", "title"=>"Learning-related changes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:35:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/491453"], "description"=>"<p>Displayed in different panels, learning curves are based on α error, β error and actual monetary rewards.</p>", "links"=>[], "tags"=>["curves", "10", "trajectories", "shown"], "article_id"=>161977, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g006", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_learning_curves_over_each_of_10_target_trajectories_are_shown_for_the_945_reward_condition_A_and_the_946_reward_condition_B_separately_/161977", "title"=>"Average learning curves over each of 10 target trajectories are shown for the α-reward condition (A) and the β-reward condition (B) separately.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:37:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/491100"], "description"=>"<p>The implementation assumes a two-dimension probability map that is updated iteratively trial by trial. It is a 200×200 matrix to code the probability of each α-β combination. Each value in the matrix is normalized such that the sum of all possibilities on the map equals 1. The pink cross denotes the current target direction and curvature. The gray dot denotes the best solution before the current trial <i>t</i> and the black dot denotes the best solution after finishing the current trial. The map from a previous trial is degraded by memory decay and it then serves as the prior before the current trial. The prediction error, the difference between the predicted reward based on the direction and curvature used in the current trial and the actual received reward, serves as likelihood distribution to update the probability map. By combining the prior and the likelihood, the probability map is updated to form the posterior distribution. The learning is demonstrated in that the best solution of the posterior, compared to that of the prior, becomes closer to the target solution. The data are from a typical trial (the 4<sup>th</sup> trial in a 25 learning sequence) from a single participant.</p>", "links"=>[], "tags"=>["bayesian"], "article_id"=>161624, "categories"=>["Physiology", "Neuroscience"], "users"=>["Gregory Dam", "Konrad Kording", "Kunlin Wei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055352.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_implementation_of_the_Bayesian_model_/161624", "title"=>"The implementation of the Bayesian model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 15:35:00"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[306, 490, 611, 718, 817, 916, 999, 1091, 1185, 1258, 1341, 1412, 1476]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}, {"subject_area"=>"/Social sciences", "average_usage"=>[289, 475, 593, 703, 805, 902, 990, 1078, 1158, 1250, 1336, 1417, 1482]}, {"subject_area"=>"/Social sciences/Psychology", "average_usage"=>[294, 460, 580, 683, 777, 868, 957, 1044, 1124, 1202, 1276, 1356, 1422]}]}
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