Semantic Size of Abstract Concepts: It Gets Emotional When You Can’t See It
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{"title"=>"Semantic Size of Abstract Concepts: It Gets Emotional When You Can't See It", "type"=>"journal", "authors"=>[{"first_name"=>"Bo", "last_name"=>"Yao", "scopus_author_id"=>"55862812400"}, {"first_name"=>"Milica", "last_name"=>"Vasiljevic", "scopus_author_id"=>"55087993000"}, {"first_name"=>"Mario", "last_name"=>"Weick", "scopus_author_id"=>"24336743000"}, {"first_name"=>"Margaret E.", "last_name"=>"Sereno", "scopus_author_id"=>"7005050484"}, {"first_name"=>"Patrick J.", "last_name"=>"O'Donnell", "scopus_author_id"=>"35976666700"}, {"first_name"=>"Sara C.", "last_name"=>"Sereno", "scopus_author_id"=>"6604078970"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"24086421", "sgr"=>"84884575002", "doi"=>"10.1371/journal.pone.0075000", "scopus"=>"2-s2.0-84884575002", "pui"=>"369883069", "issn"=>"19326203"}, "id"=>"11a7b4c4-c054-399a-8edc-6494b4611f12", "abstract"=>"Size is an important visuo-spatial characteristic of the physical world. In language processing, previous research has demonstrated a processing advantage for words denoting semantically \"big\" (e.g., jungle) versus \"small\" (e.g., needle) concrete objects. We investigated whether semantic size plays a role in the recognition of words expressing abstract concepts (e.g., truth). Semantically \"big\" and \"small\" concrete and abstract words were presented in a lexical decision task. Responses to \"big\" words, regardless of their concreteness, were faster than those to \"small\" words. Critically, we explored the relationship between semantic size and affective characteristics of words as well as their influence on lexical access. Although a word's semantic size was correlated with its emotional arousal, the temporal locus of arousal effects may depend on the level of concreteness. That is, arousal seemed to have an earlier (lexical) effect on abstract words, but a later (post-lexical) effect on concrete words. Our findings provide novel insights into the semantic representations of size in abstract concepts and highlight that affective attributes of words may not always index lexical access.", "link"=>"http://www.mendeley.com/research/semantic-size-abstract-concepts-it-gets-emotional-cant-see-it", "reader_count"=>37, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>8, "Other"=>4, "Student > Master"=>3, "Student > Bachelor"=>8, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>8, "Other"=>4, "Student > Master"=>3, "Student > Bachelor"=>8, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Arts and Humanities"=>2, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Psychology"=>22, "Social Sciences"=>1, "Computer Science"=>3, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>22}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>2, "Japan"=>1, "Luxembourg"=>1, "Poland"=>1, "France"=>2}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1215991"], "description"=>"<p>The left panel illustrates the Size × Arousal interaction at a high concreteness rating level (M+SD). The right panel illustrates the same interaction but at a low concreteness level (M−SD). The dotted lines with circles at both ends represent a low arousal level (M−SD). The solid lines with diamonds at both ends represent high arousal level (M+SD). The slopes of the two lines indicate the strength and direction of the Size effects on RTs at the different levels of Arousal.</p>", "links"=>[], "tags"=>["concreteness", "arousal"], "article_id"=>808352, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Concreteness_215_Size_215_Arousal_interaction_/808352", "title"=>"The Concreteness × Size × Arousal interaction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215992"], "description"=>"<p>Panel A illustrates the basic mediation model where Size can either directly or indirectly influence RTs via Arousal. Panel B, C, and D illustrate three possibilities where Concreteness (CnC) can moderate the direct or indirect effect of Size on RTs. The relative spatial layout does not imply an absolute time frame for processing. Our analyses lend greatest support to Model 14 (Panel D).</p>", "links"=>[], "tags"=>["illustrations", "moderated", "mediation"], "article_id"=>808353, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_illustrations_of_the_moderated_mediation_models_29_30_under_testing_/808353", "title"=>"Schematic illustrations of the moderated mediation models [29], [30] under testing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215993"], "description"=>"<p>The solid red line represents the mean effect of Size across values of Concreteness. The five filled circles correspond to the mean Size effect at the 10<sup>th</sup>, 25<sup>th</sup>, 50<sup>th</sup>, 75<sup>th</sup>, and 90<sup>th</sup> percentiles of the Concreteness ratings (see also <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075000#pone-0075000-t008\" target=\"_blank\">Table 8</a>). The upper and lower dotted lines represent the 95% confidence intervals around the means. The curves were fit using 3<sup>rd</sup> and 4<sup>th</sup> degree polynomial functions. A horizontal line crossing the 0 value on the y-axis is displayed as a reference point to visualize the significance of the effect. Panels A, B, and C correspond to Models 5, 7, and 14 (and Panels B, C, and D of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075000#pone-0075000-g002\" target=\"_blank\">Figure 2</a>), respectively. The data pattern lends greatest support to Model 14 (Panel C).</p>", "links"=>[], "tags"=>["moderation", "concreteness"], "article_id"=>808354, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustrations_of_the_moderation_conditional_effect_of_Concreteness_by_model_/808354", "title"=>"Illustrations of the moderation (conditional) effect of Concreteness by model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215994"], "description"=>"<p>Reported are the Effects (beta values), the bootstrap-estimated Standard Errors (SEs), and the lower and higher boundaries of the bootstrap-estimated Confidence Intervals (CIs). <i>t-</i> and <i>p-</i>values are also reported for direct effects.</p>", "links"=>[], "tags"=>["moderated", "mediation"], "article_id"=>808355, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_for_moderated_mediation_analyses_by_model_/808355", "title"=>"Results for moderated mediation analyses by model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215995"], "description"=>"<p>Reported are the slopes (Bs) for each regressor, their associated 95% confidence intervals (CIs), and <i>p</i>-values. Also shown are their zero-order correlation coefficients (<i>r</i>s) and variance inflation factors (VIFs).</p>", "links"=>[], "tags"=>["regression", "semantic"], "article_id"=>808356, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_regression_on_semantic_Size_with_Concreteness_Arousal_and_their_interaction_as_predictors_/808356", "title"=>"Linear regression on semantic Size with Concreteness, Arousal, and their interaction as predictors.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215996"], "description"=>"<p>Reported are the slopes (Bs) for each regressor, the associated 95% confidence intervals (CIs), <i>p</i>-values, and whether they survived the False Discovery Rate (FDR) correction (<i>p</i><0.05) for multiple comparison (significant effects are marked with 1s). Also reported are the regressors’ semi-partial correlation coefficients (R<sup>2</sup>s), the associated 95% CIs, and variance inflation factors (VIFs).</p>", "links"=>[], "tags"=>["regression"], "article_id"=>808357, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t005", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multiple_regression_results_/808357", "title"=>"Multiple regression results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215997"], "description"=>"<p>Reported are the slopes (Bs) for each regressor, the associated 95% confidence intervals (CIs), <i>p</i>-values, and intercepts.</p>", "links"=>[], "tags"=>["putative", "concreteness"], "article_id"=>808358, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_Size_effects_slopes_at_putative_high_and_low_levels_of_Concreteness_and_Arousal_/808358", "title"=>"Summary of the Size effects (slopes) at putative high and low levels of Concreteness and Arousal.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215998"], "description"=>"<p>Reported are the slopes (Bs) for each regressor, the associated 95% confidence intervals (CIs), <i>p</i>-values, and whether they survived the False Discovery Rate (FDR) correction (<i>p</i><0.05) for multiple comparison (significant effects are marked with 1s). Also reported are the regressors’ semi-partial correlation coefficients (R<sup>2</sup>s), the associated 95% CIs, and variance inflation factors (VIFs).</p>", "links"=>[], "tags"=>["regression", "median"], "article_id"=>808359, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multiple_regression_results_using_a_median_split_of_Concreteness_/808359", "title"=>"Multiple regression results using a median split of Concreteness.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1215999"], "description"=>"<p>Ratings for the following factors were based on separate 100-point scales (low to high): Concreteness (abstract to concrete), Semantic Size (small to large), Arousal (unarousing to arousing), Raw Valence (negative to positive), and Age of Acquisition (early to late). Absolute Valence was calculated via the following transformations: (a) shifting the 0 to 100 scale to a −50 to +50 scale (to more appropriately represent valence); (b) taking the absolute value of each rating (resulting in a 50-point scale); and (c) doubling each value to obtain a 100-point scale (from low to high unsigned valence). Word Frequency is expressed in occurrences per million and Word Length in number of letters.</p>", "links"=>[], "tags"=>["words", "deviations"], "article_id"=>808360, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Specifications_of_the_experimental_words_with_standard_deviations_in_parentheses_/808360", "title"=>"Specifications of the experimental words with standard deviations in parentheses.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216000"], "description"=>"<p>Mean RTs (in ms) and %Error (with standard deviations in parentheses) across experimental conditions.</p>", "links"=>[], "tags"=>["rts", "deviations"], "article_id"=>808361, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_RTs_in_ms_and_Error_with_standard_deviations_in_parentheses_across_experimental_conditions_/808361", "title"=>"Mean RTs (in ms) and %Error (with standard deviations in parentheses) across experimental conditions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216001"], "description"=>"<p>Reported are the slopes (Bs) for each regressor, their associated 95% confidence intervals (CIs), and <i>p</i>-values. Also shown are their zero-order correlation coefficients (<i>r</i>s) and variance inflation factors (VIFs; a VIF indexes the extent to which the variance of an estimated regression coefficient is increased because of collinearity).</p>", "links"=>[], "tags"=>["regression", "semantic"], "article_id"=>808362, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0075000.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_regression_on_semantic_Size_with_Arousal_Absolute_Valence_and_their_interaction_term_as_predictors_/808362", "title"=>"Linear regression on semantic Size with Arousal, Absolute Valence, and their interaction term as predictors.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-25 02:04:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216003", "https://ndownloader.figshare.com/files/1216004", "https://ndownloader.figshare.com/files/1216005"], "description"=>"<div><p>Size is an important visuo-spatial characteristic of the physical world. In language processing, previous research has demonstrated a processing advantage for words denoting semantically “big” (e.g., <i>jungle</i>) versus “small” (e.g., <i>needle</i>) concrete objects. We investigated whether semantic size plays a role in the recognition of words expressing abstract concepts (e.g., <i>truth</i>). Semantically “big” and “small” concrete and abstract words were presented in a lexical decision task. Responses to “big” words, regardless of their concreteness, were faster than those to “small” words. Critically, we explored the relationship between semantic size and affective characteristics of words as well as their influence on lexical access. Although a word’s semantic size was correlated with its emotional arousal, the temporal locus of arousal effects may depend on the level of concreteness. That is, arousal seemed to have an earlier (lexical) effect on abstract words, but a later (post-lexical) effect on concrete words. Our findings provide novel insights into the semantic representations of size in abstract concepts and highlight that affective attributes of words may not always index lexical access.</p></div>", "links"=>[], "tags"=>["gets"], "article_id"=>808364, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Yao", "Milica Vasiljevic", "Mario Weick", "Margaret E. Sereno", "Patrick J. O’Donnell", "Sara C. Sereno"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0075000.s001", "https://dx.doi.org/10.1371/journal.pone.0075000.s002", "https://dx.doi.org/10.1371/journal.pone.0075000.s003"], "stats"=>{"downloads"=>12, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Semantic_Size_of_Abstract_Concepts_It_Gets_Emotional_When_You_Can_8217_t_See_It_/808364", "title"=>"Semantic Size of Abstract Concepts: It Gets Emotional When You Can’t See It", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-09-25 02:04:31"}

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Relative Metric

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