Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement
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{"title"=>"Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement", "type"=>"journal", "authors"=>[{"first_name"=>"Katrien", "last_name"=>"Beuls", "scopus_author_id"=>"55569199900"}, {"first_name"=>"Luc", "last_name"=>"Steels", "scopus_author_id"=>"7003942451"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84875074136", "pmid"=>"23527055", "issn"=>"19326203", "pui"=>"368539602", "doi"=>"10.1371/journal.pone.0058960", "sgr"=>"84875074136"}, "id"=>"6c111479-7be6-3ed3-814e-17922c5f37ea", "abstract"=>"Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.", "link"=>"http://www.mendeley.com/research/agentbased-models-strategies-emergence-evolution-grammatical-agreement", "reader_count"=>64, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>10, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>10, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Mathematics"=>6, "Agricultural and Biological Sciences"=>4, "Medicine and Dentistry"=>1, "Arts and Humanities"=>3, "Philosophy"=>5, "Physics and Astronomy"=>2, "Psychology"=>5, "Social Sciences"=>3, "Computer Science"=>23, "Linguistics"=>7, "Business, Management and Accounting"=>1, "Environmental Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>5}, "Mathematics"=>{"Mathematics"=>6}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>23}, "Linguistics"=>{"Linguistics"=>7}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Philosophy"=>{"Philosophy"=>5}}, "reader_count_by_country"=>{"New Zealand"=>1, "Netherlands"=>2, "Turkey"=>1, "Belgium"=>2, "Luxembourg"=>1, "Japan"=>1, "Brazil"=>2, "United Kingdom"=>2, "France"=>3, "Germany"=>3, "Spain"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/990255"], "description"=>"<p>The situation contains three objects: which has the properties , , and , with properties , , and and with , , and . The speaker has chosen and as topic and expresses the set of distinctive properties , to identify and to identify . After vocabulary lookup the speaker finds that the word <i>shuqfon</i> covers , <i>sizhic</i> covers and <i>iqvu</i> covers . The utterance is therefore <i>shuqfon iqvu sizhic</i>. The hearer looks up these words in his own vocabulary and recovers , , , where , and are variables to be bound to objects in the situation model. In the current situation model, the hearer finds that the topic can be either (a) and (implying that and ) or (b) , , and (so that , , ), or (c) and (so that and )</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654429, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_language_game_/654429", "title"=>"The language game.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:53:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1007374"], "description"=>"<p>Overview of different strategies with their main advantages and drawbacks. The advantages are cumulative and the drawbacks of one strategy are resolved by the next one.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>667998, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_/667998", "title"=>"Summary.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-18 02:13:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/990257"], "description"=>"<p><i>Left</i>: The number of possible hypotheses grows exponentially with the number of words as predicted by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058960#pone.0058960.e054\" target=\"_blank\">equation (1</a>). <i>Right</i>: Percentage of remaining hypotheses and remaining semantic ambiguity after the application of selection restrictions (left) or the consultation of the situation model (right), for 50 game series with 10 agents and 5000 games in total (1000 per agent). Average values are shown with standard deviations. Both sources of information reduce the set of possible hypotheses in the search space but significant semantic ambiguity remains.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654431, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g002", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combinatorial_complexity_of_interpretation_/654431", "title"=>"Combinatorial complexity of interpretation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:53:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/990258"], "description"=>"<p>The production of an utterance by the speaker now involves an extra step (step iii) to identify words that are about the same object and add markers to them. In parsing, the hearer uses the markers to reconstruct the co-reference relations, and makes the variables for properties refering to the same object equal. In the present example, y and x are co-referential because the words that introduce properties with these variables contain the same markers. Only one possible interpretation, namely that the topic is and then remains.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654432, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Formal_marker_strategy_/654432", "title"=>"Formal marker strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:53:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/990260"], "description"=>"<p>i) Computer simulations for 50 game series involving 10 agents playing 1000 language games (200 per agent). Average values and standard deviations are shown. At each time point only two agents play a game, although the model works just as well with parallel interactions. Usage inventory reaches a peak after 50 games (10 per agent) after which it gets damped to an optimum of three markers (because the maximum number of objects chosen as topic is 3) due to the lateral inhibition dynamics, before increasing slightly when new agents come into the population. Variation gets damped quickly and efficiency is close to 0.3. The inventory is maintained despite population turnover () although new inventions may arise and in rare cases occur where a new invention overtakes existing markers. ii) The average preference scores for all invented markers in the memories of all agents for a single experiment. There is one marker with the highest score and two others with lower scores. When a new agent comes in, the average scores go down (see circles) but move back up as the new agent acquires the existing preferences.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654434, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_formal_marker_strategy_/654434", "title"=>"Performance of the formal marker strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:54:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/990261"], "description"=>"<p>A feature matrix has rows for the different attributes (, , ) and columns for the possible values of these attributes () which could correspond for example to , and for the attribute . The cells contain + when the relevant attribute value pair is true, - when it is not, and ? when it is open. Two feature matrices are compared using the standard logical unification operator to see whether they fit. Open values may thus be determined. For example, the open values for in the feature matrix of <i>fracksup</i> become constrained by those specified for the marker <i>-tjikaboem</i> and conversely the gender values of the marker become constrained by the word.</p>", "links"=>[], "tags"=>["matrices"], "article_id"=>654435, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_matrices_in_agreement_systems_/654435", "title"=>"Feature matrices in agreement systems.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:54:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/990262"], "description"=>"<p>When the speaker invents new markers he selects an attribute which is distinctive for the different objects in the utterance (in this case this is ) and creates new markers for each value (in this case “-ti” and “-ta” for the two values (medium) and (large) respectively). The hearer is able to guess the semantic features of these markers by using again the principle of distinctiveness.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654436, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g006", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Meaningful_marker_strategy_/654436", "title"=>"Meaningful marker strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:55:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/990264"], "description"=>"<p>Averages over 50 game series with 10 agents for 5000 games (average of 1000 per agent). There is no population change. i) Agents end up with an inventory markers. Compared to the formal marker strategy there is more variation which gets damped more slowly and there is considerably less efficiency (). ii) The evolution of marker preferences for a single experiment shows that many markers () get invented due to the many possible meanings that can potentially be used, but that a subset becomes dominant. The insert displays the frequency of marker usage which shows a long tail Zipfian distribution.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654438, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g007", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_meaningful_marker_strategy_/654438", "title"=>"Performance of the meaningful marker strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:55:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/990265"], "description"=>"<p>Instead of inventing new markers, speakers use existing words which have already the meaning that the marker should express. In this case, the words “sizhic” and “goapi” can be used to express the two feature values for . The hearer is now able to infer with more certainty the semantic features for these markers.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654439, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g008", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reuse_strategy_/654439", "title"=>"Reuse strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:55:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/990267"], "description"=>"<p>Averages over 50 game series with 10 agents for 5000 games (average of 1000 per agent). i) Agents reach an inventory of a similar size as without reuse (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058960#pone-0058960-g007\" target=\"_blank\">Figure 7</a>) but they reach it in a more efficient way (). There is less variation that gets damped more quickly. ii) The evolution of marker preferences (single experiment) illustrates that fewer markers are considered and the shared subset becomes dominant more quickly compared to the no-reuse strategy. Below the graph is the list of markers and the properties they express.</p>", "links"=>[], "tags"=>["strategies"], "article_id"=>654441, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g009", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_meaningful_marker_strategies_with_reuse_/654441", "title"=>"Performance of meaningful marker strategies with reuse.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:56:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/990269"], "description"=>"<p>i) 50 game series for 5000 games (1000 per agents). Average values are shown with standard deviation. We see clearly that despite population turnover (increasing ), the marker system is transmitted in a stable way once it has emerged. Variation and the size of the marker inventory stay at the same level. Efficiency is higher compared to a reuse strategy without population turn over (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058960#pone-0058960-g009\" target=\"_blank\">Figure 9</a>) because in a changing population, markers that were invented early on do not survive. ii) Evolution of marker preferences for a single experiment. The marker preferences are averages for all agents. Coherence dips every time a new agent enters before recovering.</p>", "links"=>[], "tags"=>["reuse"], "article_id"=>654443, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g010", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_population_change_for_reuse_strategy_/654443", "title"=>"Effect of population change for reuse strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:56:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/990270"], "description"=>"<p>This graph compares the efficiency of the formal marker strategy, the meaningful strategy without reuse, and the reuse strategy for the last 10 games of a run with 5000 games. Left is without population change and right with population change. Average for 50 runs with standard deviation. The reuse strategy is significantly more efficient, even in the case of population turnover. Unsurprisingly, a changing population (shown on the right) makes it harder for the agents to bootstrap an initial system.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654444, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g011", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_strategies_/654444", "title"=>"Comparison of strategies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:56:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/990271"], "description"=>"<p>Human natural language systems historically build agreement systems by reusing existing words. The form of these words then undergoes erosion by phonological reduction processes until the marker may get lost entirely. Their meaning becomes more abstract and semantic features become purely conventional rather than grounded in the meaning of the word, through coercion.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654445, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g012", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Grammaticalization_/654445", "title"=>"Grammaticalization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:56:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/990272"], "description"=>"<p>Speakers construct a new variant by phonologically reducing a marker's form with a probability . The hearer is able to recognize a variant if it deviates only for one consonant or vowel and will adopt it as the new norm for the marker when the variant is encountered twice.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654446, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g013", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phonological_reduction_strategy_/654446", "title"=>"Phonological reduction strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:57:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/990273"], "description"=>"<p>i) Average performance values for 50 game series for a total of 50,000 games (average of 10,000 per agent). The probability that the speaker phonologically reduces a marker is per game. We see that the length of the markers progressively diminishes, thus reducing articulatory effort, but variation does not increase, implying that the system is stable. The marker inventory size remains constant as well. ii) Trace of the changes to a marker in a single experiment. The marker <i>-uinbui</i> erodes progressively to <i>-u</i>. A truncated variant is typically present for a while in the population until a phase transition happens and it becomes dominant.</p>", "links"=>[], "tags"=>["phonological"], "article_id"=>654447, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g014", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_phonological_reduction_strategy_/654447", "title"=>"Performance of the phonological reduction strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:57:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/990275"], "description"=>"<p>Suppose that the speaker wants to use the marker <i>-ti</i> but that the relevant word has no values for gender. The feature matrix of the word can then be ‘coerced’ by assigning it the values of the feature matrix of <i>-ti</i> so that now there is a semantic fit.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654449, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g015", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coercion_/654449", "title"=>"Coercion.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:57:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/990277"], "description"=>"<p>i) Performance summary for 50 game series of 1000 games (200 per agent), average values with standard deviation. We see that variation, which is unavoidable due to the different possible ways in which re-categorization can be deployed, gets damped quickly. The number of markers is reduced compared to the plain reuse strategy (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058960#pone-0058960-g009\" target=\"_blank\">figure 9</a> i). ii) Development of which feature matrix becomes associated with the controller <i>epwui</i>. This controller started out with an empty feature matrix, i.e. no semantically grounded features. There are different ways in which it can be re-categorized with respect to the existing marker inventory. In this experimental run, a feature matrix with a positive value for has become dominant.</p>", "links"=>[], "tags"=>["coercion"], "article_id"=>654451, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g016", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_coercion_strategy_/654451", "title"=>"Performance of coercion strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:57:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/990278"], "description"=>"<p>This graph compares the main strategies presented here with respect to the size of the marker inventory. It shows that meaningful markers require a larger marker inventory (because they have semantic constrains). However, using a coercion strategy, agents manage to function with a similar inventory size for meaningful markers as for formal markers, thus significantly reducing effort for learning and memory.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Evolutionary biology", "mathematics"], "article_id"=>654452, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960.g017", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_inventory_size_/654452", "title"=>"Comparison of inventory size.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-19 08:58:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/990279"], "description"=>"<div><p>Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.</p> </div>", "links"=>[], "tags"=>["agent-based", "strategies", "emergence", "grammatical", "agreement"], "article_id"=>654453, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Evolutionary Biology"], "users"=>["Katrien Beuls", "Luc Steels"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058960", "stats"=>{"downloads"=>10, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Agent_Based_Models_of_Strategies_for_the_Emergence_and_Evolution_of_Grammatical_Agreement__/654453", "title"=>"Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-19 08:58:12"}

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  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"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/Linguistics", "average_usage"=>[290, 461, 572, 671, 770, 847, 934, 1000, 1096, 1189, 1263, 1319, 1382]}]}
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