Theory of Mind: Did Evolution Fool Us?
Publication Date
February 05, 2014
Journal
PLOS ONE
Authors
Marie Devaine, Guillaume Hollard & Jean Daunizeau
Volume
9
Issue
2
Pages
e87619
DOI
https://dx.plos.org/10.1371/journal.pone.0087619
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0087619
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24505296
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3914827
Europe PMC
http://europepmc.org/abstract/MED/24505296
Web of Science
000330829200055
Scopus
84895515803
Mendeley
http://www.mendeley.com/research/theory-mind-evolution-fool
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Mendeley | Further Information

{"title"=>"Theory of mind: Did evolution fool us?", "type"=>"journal", "authors"=>[{"first_name"=>"Marie", "last_name"=>"Devaine", "scopus_author_id"=>"55326115600"}, {"first_name"=>"Guillaume", "last_name"=>"Hollard", "scopus_author_id"=>"23469566500"}, {"first_name"=>"Jean", "last_name"=>"Daunizeau", "scopus_author_id"=>"21742214400"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84895515803", "pui"=>"372535213", "doi"=>"10.1371/journal.pone.0087619", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84895515803", "pmid"=>"24505296"}, "id"=>"b4901c5a-514f-3aa6-983c-9d486750c5c0", "abstract"=>"Theory of Mind (ToM) is the ability to attribute mental states (e.g., beliefs and desires) to other people in order to understand and predict their behaviour. If others are rewarded to compete or cooperate with you, then what they will do depends upon what they believe about you. This is the reason why social interaction induces recursive ToM, of the sort \"I think that you think that I think, etc.\". Critically, recursion is the common notion behind the definition of sophistication of human language, strategic thinking in games, and, arguably, ToM. Although sophisticated ToM is believed to have high adaptive fitness, broad experimental evidence from behavioural economics, experimental psychology and linguistics point towards limited recursivity in representing other's beliefs. In this work, we test whether such apparent limitation may not in fact be proven to be adaptive, i.e. optimal in an evolutionary sense. First, we propose a meta-Bayesian approach that can predict the behaviour of ToM sophistication phenotypes who engage in social interactions. Second, we measure their adaptive fitness using evolutionary game theory. Our main contribution is to show that one does not have to appeal to biological costs to explain our limited ToM sophistication. In fact, the evolutionary cost/benefit ratio of ToM sophistication is non trivial. This is partly because an informational cost prevents highly sophisticated ToM phenotypes to fully exploit less sophisticated ones (in a competitive context). In addition, cooperation surprisingly favours lower levels of ToM sophistication. Taken together, these quantitative corollaries of the \"social Bayesian brain\" hypothesis provide an evolutionary account for both the limitation of ToM sophistication in humans as well as the persistence of low ToM sophistication levels.", "link"=>"http://www.mendeley.com/research/theory-mind-evolution-fool", "reader_count"=>120, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>10, "Researcher"=>19, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>7, "Other"=>7, "Student > Master"=>20, "Student > Bachelor"=>8, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>10, "Researcher"=>19, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>7, "Other"=>7, "Student > Master"=>20, "Student > Bachelor"=>8, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Agricultural and Biological Sciences"=>12, "Arts and Humanities"=>2, "Chemistry"=>1, "Computer Science"=>10, "Decision Sciences"=>2, "Engineering"=>4, "Mathematics"=>2, "Medicine and Dentistry"=>7, "Neuroscience"=>17, "Physics and Astronomy"=>1, "Psychology"=>42, "Social Sciences"=>6, "Linguistics"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>6}, "Decision Sciences"=>{"Decision Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>42}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>11}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>17}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>10}, "Linguistics"=>{"Linguistics"=>3}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>2, "Japan"=>3, "Luxembourg"=>1, "Brazil"=>1, "United Kingdom"=>4, "Italy"=>1, "France"=>4, "Portugal"=>1, "Switzerland"=>1, "Germany"=>2}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1378067"], "description"=>"<p>The behavioural prediction of ToM players (y-axis) is plotted against her opponent’s true behavioural tendency (x-axis) for each trial of a simulated repeated game with trials. The grey line indicates the best-fitting straight line in the data. Upper half: “Hide and Seek”. Lower half: “Battle of the Sexes”. Left: accuracy of 1-ToM predictions when playing against 0-ToM. Right: accuracy of 0-ToM predictions when playing against 1-ToM.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "behavioural", "predictions", "0-tom", "playing"], "article_id"=>927681, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g002", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_behavioural_predictions_in_competitive_and_cooperative_contexts_example_of_0_ToM_playing_against_1_ToM_/927681", "title"=>"Accuracy of behavioural predictions in competitive and cooperative contexts: example of 0-ToM playing against 1-ToM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378068"], "description"=>"<p>This figure depicts the MCMC average of the linear trend between the behavioural prediction of ToM players and their opponent’s true behavioural tendency . In other words, this corresponds to the slope of the best-fitting straight line in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087619#pone-0087619-g002\" target=\"_blank\">Figure 2</a>. The figure uses the same format as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087619#pone-0087619-g001\" target=\"_blank\">Figure 1</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "pairs", "tom"], "article_id"=>927682, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MCMC_average_prediction_accuracy_of_all_pairs_of_ToM_agents_/927682", "title"=>"MCMC average prediction accuracy of all pairs of ToM agents.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378071"], "description"=>"<p>Each bar gives the number of MCMC simulations (z-axis) that led to each particular combination of belief , both agents had on each other’s ToM sophistication level (x/y-plane). Histograms are truncated to the upper-left triangle for visualization purposes (they are symmetrical by construction). Upper half: “Hide and Seek”. Lower half: “battle of the Sexes”. Left: “twin” pairs of 2-ToM agents, Middle: “twin” pairs of 3-ToM agents, right: “twin” pairs of 4-ToM agents.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "empirical", "learned", "sophistication", "pairs", "tom"], "article_id"=>927685, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g004", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MCMC_empirical_distribution_of_learned_opponent_8217_s_sophistication_level_for_8220_twin_8221_pairs_of_ToM_agents_/927685", "title"=>"MCMC empirical distribution of learned opponent’s sophistication level for “twin” pairs of ToM agents.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378073"], "description"=>"<p>The frequency of each ToM phenotype (y-axis) is plotted against evolutionary time (x-axis), for 128 different simulations with different initial conditions. Different ToM traits correspond to different colours (see legend). Pie charts depict the evolutionary stale states, i.e. the equilibrium or fixed point, replicator dynamics converge to (the colour coding is the same). Upper half: “Hide and Seek”. Lower half: “battle of the Sexes”.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "purely"], "article_id"=>927687, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g005", "stats"=>{"downloads"=>4, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Replicator_dynamics_for_purely_cooperative_and_competitive_social_interactions_/927687", "title"=>"Replicator dynamics for purely cooperative and competitive social interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378074"], "description"=>"<p>Each pie chart depict the evolutionary stable state that is induced by a particular combination of amount of learning τ (x-axis) and proportion ω of cooperative interactions (y-axis).</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "diagram", "tom"], "article_id"=>927688, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g006", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phase_diagram_of_ToM_evolution_/927688", "title"=>"Phase diagram of ToM evolution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378075"], "description"=>"<p>This figure uses the same format as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087619#pone-0087619-g006\" target=\"_blank\">Fig. 6</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "diagram", "tom", "rl", "nash"], "article_id"=>927689, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g007", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phase_diagram_of_ToM_evolution_Impact_of_RL_and_Nash_phenotypes_/927689", "title"=>"Phase diagram of ToM evolution: Impact of RL and Nash phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378077"], "description"=>"<p>Numbers inside brackets indicate the payoffs; the number on the left (resp. on the right) indicates the payoff player 1 (resp. player 2) gets when making decision while player 2 chooses .</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology"], "article_id"=>927691, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Payoffs_for_each_player_in_the_8220_hide_and_seek_8221_game_left_and_8220_battle_of_the_sexes_8221_right_/927691", "title"=>"Payoffs for each player in the “hide and seek” game (left) and “battle of the sexes” (right).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378078"], "description"=>"<div><p>Theory of Mind (ToM) is the ability to attribute mental states (e.g., beliefs and desires) to other people in order to understand and predict their behaviour. If others are rewarded to compete or cooperate with you, then what they will do depends upon what they believe about you. This is the reason why social interaction induces recursive ToM, of the sort “I think that you think that I think, etc.”. Critically, recursion is the common notion behind the definition of sophistication of human language, strategic thinking in games, and, arguably, ToM. Although sophisticated ToM is believed to have high adaptive fitness, broad experimental evidence from behavioural economics, experimental psychology and linguistics point towards limited recursivity in representing other’s beliefs. In this work, we test whether such apparent limitation may not in fact be proven to be adaptive, i.e. optimal in an evolutionary sense. First, we propose a meta-Bayesian approach that can predict the behaviour of ToM sophistication phenotypes who engage in social interactions. Second, we measure their adaptive fitness using evolutionary game theory. Our main contribution is to show that one does not have to appeal to biological costs to explain our limited ToM sophistication. In fact, the evolutionary cost/benefit ratio of ToM sophistication is non trivial. This is partly because an informational cost prevents highly sophisticated ToM phenotypes to fully exploit less sophisticated ones (in a competitive context). In addition, cooperation surprisingly favours <i>lower</i> levels of ToM sophistication. Taken together, these quantitative corollaries of the “social Bayesian brain” hypothesis provide an evolutionary account for both the limitation of ToM sophistication in humans as well as the persistence of low ToM sophistication levels.</p></div>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "did"], "article_id"=>927692, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619", "stats"=>{"downloads"=>5, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Theory_of_Mind_Did_Evolution_Fool_Us_/927692", "title"=>"Theory of Mind: Did Evolution Fool Us?", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-05 04:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1378065"], "description"=>"<p>This figure depicts the MCMC average of the payoff matrices for both “hide and seek” (left) and “battle of the sexes” (right) after learning has occurred. The i<sup>th</sup> line gives the accumulated payoff of the i<sup>th</sup> type of agent, when playing against each and every other ToM phenotype. Note that the absolute payoff levels of both types of games cannot be compared.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary modeling", "Evolutionary biology", "Evolutionary processes", "adaptation", "Organismal evolution", "Human evolution", "evolutionary theory", "Applied mathematics", "game theory", "Probability theory", "Bayes theorem", "information science", "information theory", "psychology", "Cognitive psychology", "Human intelligence", "Social psychology", "payoffs", "pairs", "tom"], "article_id"=>927679, "categories"=>["Biological Sciences", "Mathematics", "Sociology"], "users"=>["Marie Devaine", "Guillaume Hollard", "Jean Daunizeau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087619.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MCMC_average_payoffs_of_all_pairs_of_ToM_agents_/927679", "title"=>"MCMC average payoffs of all pairs of ToM agents.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-05 04:00:40"}

PMC Usage Stats | Further Information

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Relative Metric

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