Emergence of Collective Memories
Publication Date
September 01, 2010
Journal
PLOS ONE
Authors
Sungmin Lee, Verónica C. Ramenzoni & Petter Holme
Volume
5
Issue
9
Pages
e12522
DOI
https://dx.plos.org/10.1371/journal.pone.0012522
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0012522
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/20824141
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2931705
Europe PMC
http://europepmc.org/abstract/MED/20824141
Web of Science
000281456100020
Scopus
77958616349
Mendeley
http://www.mendeley.com/research/emergence-collective-memories
Events
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Emergence of collective memories", "type"=>"journal", "authors"=>[{"first_name"=>"Sungmin", "last_name"=>"Lee", "scopus_author_id"=>"36767798500"}, {"first_name"=>"Verónica C.", "last_name"=>"Ramenzoni", "scopus_author_id"=>"6508162451"}, {"first_name"=>"Petter", "last_name"=>"Holme", "scopus_author_id"=>"7003326827"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"19326203 (ISSN)", "pmid"=>"20824141", "doi"=>"10.1371/journal.pone.0012522", "pui"=>"359838947", "issn"=>"19326203", "arxiv"=>"1008.2489", "sgr"=>"77958616349", "scopus"=>"2-s2.0-77958616349"}, "id"=>"fe702d64-38c7-319d-be08-5745d5894290", "abstract"=>"We understand the dynamics of the world around us as by associating pairs of events, where one event has some influence on the other. These pairs of events can be aggregated into a web of memories representing our understanding of an episode of history. The events and the associations between them need not be directly experienced-they can also be acquired by communication. In this paper we take a network approach to study the dynamics of memories of history.", "link"=>"http://www.mendeley.com/research/emergence-collective-memories", "reader_count"=>63, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>7, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>8, "Other"=>5, "Student > Bachelor"=>6, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>7, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>8, "Other"=>5, "Student > Bachelor"=>6, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>6, "Arts and Humanities"=>5, "Business, Management and Accounting"=>2, "Computer Science"=>7, "Mathematics"=>3, "Medicine and Dentistry"=>1, "Design"=>1, "Neuroscience"=>2, "Physics and Astronomy"=>8, "Psychology"=>11, "Social Sciences"=>11, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>11}, "Physics and Astronomy"=>{"Physics and Astronomy"=>8}, "Psychology"=>{"Psychology"=>11}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>5}, "Design"=>{"Design"=>1}, "Neuroscience"=>{"Neuroscience"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>7}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Linguistics"=>{"Linguistics"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Poland"=>1, "United Kingdom"=>1, "Malaysia"=>1, "Slovenia"=>1, "France"=>2, "Portugal"=>1, "Germany"=>2, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/832744"], "description"=>"<p>In panel (<i>A</i>), we plot the number of groups <i>g</i> for various system sizes as functions of the communication rate. (<i>B</i>) shows the <i>r</i>-values maximizing , , as a function of <i>N</i>. The solid line follows a power-law with exponent 1.14 (i.e. a functional form ), a value obtained from linear regression. (<i>C</i>) displays the number of groups against , confirming the size-scaling of the peak's position. (<i>D</i>) shows as a function of the number of agents. Parameter values are and . The values are sampled after 440 time steps (when the system has reached equilibrium). We use 50 averages. Errorbars are smaller than the symbol size in all panels except (<i>D</i>).</p>", "links"=>[], "tags"=>["relationships"], "article_id"=>503120, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scaling_relationships_in_the_community_structure_and_collective_memories_/503120", "title"=>"Scaling relationships in the community structure and collective memories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:52:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/832431"], "description"=>"<p>The group structure reflects the similarities of memory webs as described in the text. This is exemplified by the memory webs of three agents—two in the same cluster and one in another cluster.</p>", "links"=>[], "tags"=>["configuration", "webs", "agents", "simulation"], "article_id"=>502800, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_configuration_of_the_memory_webs_of_the_agents_after_a_simulation_with__and_/502800", "title"=>"Example configuration of the memory webs of the agents after a simulation with , , , and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:46:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/832259"], "description"=>"<p>(<i>A</i>) displays cumulative in- and outdegree distributions. (<i>B</i>) shows the clustering coefficient as a function of the in and outdegrees. In panel (<i>C</i>) we investigate the degree–degree correlation by plotting the average neighbor degree as a function of the in- and outdegree of an agent. (<i>D</i>) is a plot of the largest connected component of the aggregated memory web, highlighting the modular structure. A weighted-network clustering scheme identifies 48 smaller groups. If these are treated as vertices and the same clustering scheme is applied to that network of groups, we discover four supergroups indicated by different colors.</p>", "links"=>[], "tags"=>["properties", "empirical", "aggregated"], "article_id"=>502626, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structural_properties_of_an_empirical_aggregated_memory_web_/502626", "title"=>"Structural properties of an empirical aggregated memory web.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:43:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/832552"], "description"=>"<p>Example of the merging and splitting of memory groups in a run with parameter values with , , and .</p>", "links"=>[], "tags"=>["merging", "splitting", "groups", "parameter"], "article_id"=>502917, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g004", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_the_merging_and_splitting_of_memory_groups_in_a_run_with_parameter_values_with__and_/502917", "title"=>"Example of the merging and splitting of memory groups in a run with parameter values with , , and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:48:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/414558"], "description"=>"<div><h3>Background</h3><p>We understand the dynamics of the world around us as by associating pairs of events, where one event has some influence on the other. These pairs of events can be aggregated into a web of memories representing our understanding of an episode of history. The events and the associations between them need not be directly experienced—they can also be acquired by communication. In this paper we take a network approach to study the dynamics of memories of history.</p><h3>Methodology/Principal Findings</h3><p>First we investigate the network structure of a data set consisting of reported events by several individuals and how associations connect them. We focus our measurement on degree distributions, degree correlations, cycles (which represent inconsistencies as they would break the time ordering) and community structure. We proceed to model effects of communication using an agent-based model. We investigate the conditions for the memory webs of different individuals to converge to collective memories, how groups where the individuals have similar memories (but different from other groups) can form.</p><h3>Conclusions/Significance</h3><p>Our work outlines how the cognitive representation of memories and social structure can co-evolve as a contagious process. We generate some testable hypotheses including that the number of groups is limited as a function of the total population size.</p></div>", "links"=>[], "tags"=>["emergence", "memories"], "article_id"=>141864, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Emergence_of_Collective_Memories/141864", "title"=>"Emergence of Collective Memories", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-01 00:31:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/832672"], "description"=>"<p>(<i>A</i>) shows the number of detected groups <i>g</i> against communication rate <i>r</i> for , 0.2, and 0.3. (<i>B</i>) displays the Jaccard index measure of similarity averaged over all pairs of vertices and pairs of vertices in the same community of the social network. In (<i>C</i>) we graph the average value of <i>J</i>'s components— and against <i>r</i> for —for all pairs. We omit errorbars as they are smaller than the symbol size.</p>", "links"=>[], "tags"=>["memory-similarity", "systems", "1000", "agents", "simulation", "averages", "50", "runs"], "article_id"=>503024, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Group_size_and_memory_similarity_statistics_for_systems_with_1000_agents_and_at_the_final_time_step_of_the_simulation_we_use_averages_over_50_runs_of_the_algorithm_/503024", "title"=>"Group-size and memory-similarity statistics for systems with 1000 agents and at the final time step of the simulation , we use averages over 50 runs of the algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:50:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/832348"], "description"=>"<p>(<i>A</i>) shows an illustration of the social network of agents. The thickness of the lines are proportional to the strength of the social connection—the familiarity <i>F</i>. The social networks can have communities (encircled in the figure)—groups of agents that are more strongly connected (in terms of familiarity) within than between each other. (<i>B</i>) illustrates the communication between two agents <i>i</i> and <i>j</i>. The initial memory webs are derived from the same seed graph . At simulation time step 1 agent <i>i</i> and <i>j</i> are selected to communicate (this selection is random with a probability proportional to the strength of their social ties). Then an association tie between two events is selected as a topic of communication between <i>i</i> and <i>j</i>. As a result of the communication, one event vertex and one arc with weight one is added to agent <i>j</i>'s memory web, whereas the corresponding arc of <i>i</i>'s memory web increases in weight by one unit. (<i>C</i>) illustrates the randomness introduced to the memory webs by rewiring of arcs.</p>", "links"=>[], "tags"=>["neuroscience/cognitive neuroscience", "physics/interdisciplinary physics", "mental health/psychology"], "article_id"=>502719, "categories"=>["Mental Health", "Neuroscience", "Physics"], "users"=>["Sungmin Lee", "Verónica C. Ramenzoni", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012522.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustrations_of_the_model_/502719", "title"=>"Illustrations of the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-01 00:45:19"}

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Relative Metric

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