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{"title"=>"The child as econometrician: A rational model of preference understanding in children", "type"=>"journal", "authors"=>[{"first_name"=>"Christopher G.", "last_name"=>"Lucas", "scopus_author_id"=>"57197345292"}, {"first_name"=>"Thomas L.", "last_name"=>"Griffiths", "scopus_author_id"=>"35409148500"}, {"first_name"=>"Fei", "last_name"=>"Xu", "scopus_author_id"=>"36350399600"}, {"first_name"=>"Christine", "last_name"=>"Fawcett", "scopus_author_id"=>"35306798100"}, {"first_name"=>"Alison", "last_name"=>"Gopnik", "scopus_author_id"=>"7004056086"}, {"first_name"=>"Tamar", "last_name"=>"Kushnir", "scopus_author_id"=>"15760251500"}, {"first_name"=>"Lori", "last_name"=>"Markson", "scopus_author_id"=>"7004833532"}, {"first_name"=>"Jane", "last_name"=>"Hu", "scopus_author_id"=>"57198194896"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84899845613", "doi"=>"10.1371/journal.pone.0092160", "sgr"=>"84899845613", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"24667309", "issn"=>"19326203", "pui"=>"373020707"}, "id"=>"eed53033-2581-35a7-a7c6-989ba3ae8c13", "abstract"=>"Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.", "link"=>"http://www.mendeley.com/research/child-econometrician-rational-model-preference-understanding-children", "reader_count"=>56, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>5, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>5, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Neuroscience"=>2, "Design"=>1, "Psychology"=>32, "Social Sciences"=>2, "Computer Science"=>5, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>2}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>2}, "Psychology"=>{"Psychology"=>32}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "Hungary"=>1, "United States"=>1, "Japan"=>1, "Luxembourg"=>1, "Germany"=>2}, "group_count"=>2}

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• http://doi.org/10.1016/j.cognition.2019.06.006
• http://doi.org/10.1111/cdev.13290
• http://doi.org/10.1016/j.cognition.2016.07.004
• http://doi.org/10.1371/journal.pcbi.1005422
• http://doi.org/10.1126/science.aag2132
• http://doi.org/10.1080/15248372.2018.1528976
• http://doi.org/10.1111/cogs.12480
• http://doi.org/10.1016/j.cognition.2017.06.017
• http://doi.org/10.1016/j.cognition.2015.01.011
• http://doi.org/10.1016/j.cognition.2015.05.006
• http://doi.org/10.1016/j.cobeha.2019.04.010
• http://doi.org/10.1002/wcs.1330
• http://doi.org/10.1111/cogs.12596
• http://doi.org/10.1016/j.tics.2016.05.011
• http://doi.org/10.1038/s41562-019-0748-6
• http://doi.org/10.1111/cogs.12548
• http://doi.org/10.1016/j.cogdev.2019.05.005
• http://doi.org/10.1016/j.cognition.2014.11.026
• http://doi.org/10.1016/j.cogdev.2015.09.012
• http://doi.org/10.1038/s41562-017-0064

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• {"id"=>"541840644302323712", "text"=>"we used to be fairly rational back when we were children. then we grow more irrational as time goes by http://t.co/8sjoOFJROv", "created_at"=>"2014-12-08T06:24:11Z", "user"=>"lurino", "user_name"=>"Kamen", "user_profile_image"=>"http://pbs.twimg.com/profile_images/538611202566287360/Y3gfIyCf_normal.jpeg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1434422"], "description"=>"<div><p>Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.</p></div>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "cognition", "Decision making", "Cognitive psychology", "Cognitive neuroscience", "psychology", "Developmental psychology", "Systems science", "Operations research", "Management engineering", "Decision analysis", "mathematics", "Applied mathematics", "Decision theory", "Probability theory", "Bayes theorem", "Statistics (mathematics)", "Economics", "Economic models"], "article_id"=>973704, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Christopher G. Lucas", "Thomas L. Griffiths", "Fei Xu", "Christine Fawcett", "Alison Gopnik", "Tamar Kushnir", "Lori Markson", "Jane Hu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0092160", "stats"=>{"downloads"=>15, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Child_as_Econometrician_A_Rational_Model_of_Preference_Understanding_in_Children_/973704", "title"=>"The Child as Econometrician: A Rational Model of Preference Understanding in Children", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-25 03:27:58"}
• {"files"=>["https://ndownloader.figshare.com/files/1434417"], "description"=>"<p>(A) Predicted and observed proportions of children's offers under the default model. (B) Predicted and observed proportions of offers under the assumption that squirrel can decline to choose any object. Error bars represent 95% confidence intervals.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "cognition", "Decision making", "Cognitive psychology", "Cognitive neuroscience", "psychology", "Developmental psychology", "Systems science", "Operations research", "Management engineering", "Decision analysis", "mathematics", "Applied mathematics", "Decision theory", "Probability theory", "Bayes theorem", "Statistics (mathematics)", "Economics", "Economic models", "predictions"], "article_id"=>973699, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Christopher G. Lucas", "Thomas L. Griffiths", "Fei Xu", "Christine Fawcett", "Alison Gopnik", "Tamar Kushnir", "Lori Markson", "Jane Hu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0092160.g001", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_predictions_and_data_for_Kushnir_Xu_and_Wellman_s_study_2_/973699", "title"=>"Model predictions and data for Kushnir, Xu, and Wellman's study [2].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-25 03:27:58"}
• {"files"=>["https://ndownloader.figshare.com/files/1434419"], "description"=>"<p>(A) Results for children who showed a preference for 4 interesting toys. (B) Results for children who only showed a preference for 3 of 4 toys. The first character for each pair of bars denotes whether the actors showed a positive (P) reaction to the hidden toys versus a negative (N) reaction. The second character reflects whether the hidden object was said to be in a similar (S) or different (D) category from those seen in training. is the probability of selecting Actor 1's novel object. Error bars represent 95 percent confidence intervals. Cases where children had fewer than 4 chances to play with the training objects are excluded. For (A), there were 17, 17, 11 and 11 participants in the PS, PD, NS, and ND groups, respectively. For (B), there were 26, 26, 32, and 32 participants in the PS, PD, NS, and ND groups, respectively.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "cognition", "Decision making", "Cognitive psychology", "Cognitive neuroscience", "psychology", "Developmental psychology", "Systems science", "Operations research", "Management engineering", "Decision analysis", "mathematics", "Applied mathematics", "Decision theory", "Probability theory", "Bayes theorem", "Statistics (mathematics)", "Economics", "Economic models", "predictions", "fawcett", "markson"], "article_id"=>973701, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Christopher G. Lucas", "Thomas L. Griffiths", "Fei Xu", "Christine Fawcett", "Alison Gopnik", "Tamar Kushnir", "Lori Markson", "Jane Hu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0092160.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_predictions_for_data_in_Experiment_1_of_Fawcett_and_Markson_1_/973701", "title"=>"Model predictions for data in Experiment 1 of Fawcett and Markson [1].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-25 03:27:58"}
• {"files"=>["https://ndownloader.figshare.com/files/1434420"], "description"=>"<p>Each line shows the mean across 15 simulations, with standard errors. In both plots, the upper dashed line marks the proportion of 14-month-olds who offered the actor goldfish over broccoli (7 of 8), while the lower dashed line marks the proportion of 18-month-olds who did so (8 of 26), with standard errors. Plot (a) assumes equal prior belief in each model, while (b) assumes that the simpler model has a prior probability of 0.9.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "cognition", "Decision making", "Cognitive psychology", "Cognitive neuroscience", "psychology", "Developmental psychology", "Systems science", "Operations research", "Management engineering", "Decision analysis", "mathematics", "Applied mathematics", "Decision theory", "Probability theory", "Bayes theorem", "Statistics (mathematics)", "Economics", "Economic models", "simulations", "unmatched", "repacholi", "gopnik"], "article_id"=>973702, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Christopher G. Lucas", "Thomas L. Griffiths", "Fei Xu", "Christine Fawcett", "Alison Gopnik", "Tamar Kushnir", "Lori Markson", "Jane Hu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0092160.g003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_simulations_of_the_unmatched_condition_from_Repacholi_and_Gopnik_3_/973702", "title"=>"Results of simulations of the unmatched condition from Repacholi and Gopnik [3].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-25 03:27:58"}
• {"files"=>["https://ndownloader.figshare.com/files/1434421"], "description"=>"<p>Predicted probability that objects will be selected, plotted against observed proportions, where A was chosen over C 7 of 10 times, B was chosen over C 5 of 5 times, and D was a novel alternative.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "cognition", "Decision making", "Cognitive psychology", "Cognitive neuroscience", "psychology", "Developmental psychology", "Systems science", "Operations research", "Management engineering", "Decision analysis", "mathematics", "Applied mathematics", "Decision theory", "Probability theory", "Bayes theorem", "Statistics (mathematics)", "Economics", "Economic models", "predictions", "hu", "et"], "article_id"=>973703, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Christopher G. Lucas", "Thomas L. Griffiths", "Fei Xu", "Christine Fawcett", "Alison Gopnik", "Tamar Kushnir", "Lori Markson", "Jane Hu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0092160.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_predictions_for_Hu_et_al_s_experiment_/973703", "title"=>"Model predictions for Hu et al.'s experiment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-25 03:27:58"}

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