Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning
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{"title"=>"Bayesian cue integration as a developmental outcome of reward mediated learning", "type"=>"journal", "authors"=>[{"first_name"=>"Thomas H.", "last_name"=>"Weisswange", "scopus_author_id"=>"57190842494"}, {"first_name"=>"Constantin A.", "last_name"=>"Rothkopf", "scopus_author_id"=>"15078701200"}, {"first_name"=>"Tobias", "last_name"=>"Rodemann", "scopus_author_id"=>"16550602800"}, {"first_name"=>"Jochen", "last_name"=>"Triesch", "scopus_author_id"=>"6701435912"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-79959991433", "pui"=>"362084485", "doi"=>"10.1371/journal.pone.0021575", "sgr"=>"79959991433", "pmid"=>"21750717"}, "id"=>"8e013e1a-db0a-3fb4-aaa1-631f7178f61d", "abstract"=>"Average human behavior in cue combination tasks is well predicted by bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference.", "link"=>"http://www.mendeley.com/research/bayesian-cue-integration-developmental-outcome-reward-mediated-learning", "reader_count"=>73, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>19, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>27, "Other"=>4, "Student > Master"=>6, "Student > Bachelor"=>1, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>19, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>27, "Other"=>4, "Student > Master"=>6, "Student > Bachelor"=>1, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>6, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>4, "Philosophy"=>1, "Neuroscience"=>6, "Physics and Astronomy"=>4, "Psychology"=>32, "Computer Science"=>9, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Psychology"=>{"Psychology"=>32}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>9}, "Linguistics"=>{"Linguistics"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>4, "Japan"=>2, "United Kingdom"=>2, "Switzerland"=>1, "Germany"=>5}, "group_count"=>5}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/759267"], "description"=>"<p>Reward obtained by the learner when choosing the action with highest predicted reward (black) compared to the different Bayesian observers. At each dotted vertical line visual reliability changes. Each data point is the sliding average of 1000 trials. </p>", "links"=>[], "tags"=>["rl", "outputs", "temporally"], "article_id"=>429640, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g008", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_RL_Model_for_two_outputs_and_temporally_changing_reliabilities_/429640", "title"=>"Performance of the RL Model for two outputs and temporally changing reliabilities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:40:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/759418"], "description"=>"<p>Average fraction of maximum reward received in 100,000 steps after learning (, ) for different variation the task and the model. Results of the different Bayesian observers for comparison.</p>", "links"=>[], "tags"=>["neuroscience"], "article_id"=>429807, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_performance_for_different_set_ups_/429807", "title"=>"Model performance for different set-ups.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-07-05 02:43:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/758883"], "description"=>"<p>The distribution of errors over 100,000 orienting actions carried out by the RL model after 10 runs with each 100,000 training steps (black), compared with Bayesian optimal integration (red) and the best single cue predictions (dashed) for a single audio-visual object. Errorbars show standart deviation of 10 runs. (, ).</p>", "links"=>[], "tags"=>["estimation"], "article_id"=>429264, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_position_estimation_errors_/429264", "title"=>"Distribution of position estimation errors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/758992"], "description"=>"<p>Reward obtained by the learner when choosing the action with highest predicted reward (black) compared to the different Bayesian observers. Signals can originate from one or two objects. Left: Change of performance during learning. Each data point is the sliding average of 1000 trials. Right: Barplot of the mean reward over 100,000 trials after learning. Standard error of the means is smaller than 0.5% for all bars. (, ).</p>", "links"=>[], "tags"=>["rl", "bayesian", "observers"], "article_id"=>429372, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_RL_model_and_Bayesian_observers_for_a_single_output_/429372", "title"=>"Performance of the RL model and Bayesian observers for a single output.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:36:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/759056"], "description"=>"<p>Expected reward for visual signals (x-axis) and auditory signals (y-axis) for the action of orienting towards the center. Red colors represent high, blue low predicted rewards. Left: Visual cue is more reliable (, ); Right: Both cues have the same variance (). For a detailed explanation see main text.</p>", "links"=>[], "tags"=>["subsections", "learned", "q-value"], "article_id"=>429433, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exemplary_subsections_of_two_learned_Q_value_functions_/429433", "title"=>"Exemplary subsections of two learned Q-value functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:37:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/381743", "https://ndownloader.figshare.com/files/381770", "https://ndownloader.figshare.com/files/381790", "https://ndownloader.figshare.com/files/381820"], "description"=>"<div><p>Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference.</p> </div>", "links"=>[], "tags"=>["bayesian", "mediated"], "article_id"=>135415, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0021575.s001", "https://dx.doi.org/10.1371/journal.pone.0021575.s002", "https://dx.doi.org/10.1371/journal.pone.0021575.s003", "https://dx.doi.org/10.1371/journal.pone.0021575.s004"], "stats"=>{"downloads"=>4, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Bayesian_Cue_Integration_as_a_Developmental_Outcome_of_Reward_Mediated_Learning/135415", "title"=>"Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-07-05 01:30:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/758839"], "description"=>"<p>Top: Sketch of the orienting task used in this study. The learner receives an auditory () and a visual () signal, which are probabilistically related to the true position . The task is to orient towards this true position. Left: A case where the visual and auditory signals have a common cause. Right: The signals originate from different locations. Bottom: The generative models for the task. The noisy sensory signals ( and ) are either generated by a single () or two independent causes () having different spatial positions.</p>", "links"=>[], "tags"=>["layout", "orienting", "tasks", "generative"], "article_id"=>429212, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g001", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scene_layout_of_orienting_tasks_and_generative_models_/429212", "title"=>"Scene layout of orienting tasks and generative models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:33:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/759197"], "description"=>"<p>Axes are the same as Fig. 5, but for three different actions with constant auditory reliability () and space varying visual reliability. L-R: Actions towards a peripheral(), intermediate () and central position ().</p>", "links"=>[], "tags"=>["subsections", "learned", "q-value", "foveation"], "article_id"=>429569, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g007", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exemplary_subsections_of_the_learned_Q_value_function_for_the_foveation_setup_/429569", "title"=>"Exemplary subsections of the learned Q-value function for the foveation setup.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:39:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/759372"], "description"=>"<p>A: Overlay of contour plots of the Q-value functions for one action after unbiased training (filled areas) and after training with a -position shift in the mean of the auditory noise (empty areas). The contours include areas with predicted reward values higher than (red) and (yellow). B: Unisensory tuning curves of the same output neuron for biased (red) and unbiased conditions. The maximum visual response (top) does not change, whereas it is shifted by positions in the auditory domain (bottom). ().</p>", "links"=>[], "tags"=>["neurons", "auditory"], "article_id"=>429746, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g009", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Responses_of_output_neurons_after_training_with_auditory_shift_data_/429746", "title"=>"Responses of output neurons after training with auditory shift data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:42:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/758932"], "description"=>"<p>A: Psychometric curves for the proportion of ‘right’-actions in an audio-visual 2-AFC position estimation task. The input positions of the standard were mismatched, with the auditory signal positioned at 12 and the visual signal at 18. Probe inputs were matched and tested 1,000 times at each position (0–30). The point at which the curves cross the black vertical line is the PSE. The curves differ in the variance of the visual noise (see legend), auditory noise was kept constant with . B: Plot using data from a psychophysical experiment by Ernst and Banks <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021575#pone.0021575-Ernst2\" target=\"_blank\">[9]</a>. They used a visuo-haptic 2-AFC size discrimination task and count the proportion of ‘taller’-actions. The standard inputs were mismatched (haptic at 50, visual at 60), probe inputs were matched and varied between 45 and 65. The visual reliability was varied by adding external noise to the display.</p>", "links"=>[], "tags"=>["curves", "2-afc"], "article_id"=>429311, "categories"=>["Neuroscience"], "users"=>["Thomas H. Weisswange", "Constantin A. Rothkopf", "Tobias Rodemann", "Jochen Triesch"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021575.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Psychometric_curves_for_2_AFC_task_in_comparison_to_human_psychophysics_/429311", "title"=>"Psychometric curves for 2-AFC task in comparison to human psychophysics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-05 02:35:11"}

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