Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments
Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accou...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00991/full |
| _version_ | 1828368803981950976 |
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| author | Chris F Westbury Cyrus eShaoul Geoff eHollis Lisa eSmithson Benny B. Briesemeister Markus J. Hofmann Arthur M Jacobs |
| author_facet | Chris F Westbury Cyrus eShaoul Geoff eHollis Lisa eSmithson Benny B. Briesemeister Markus J. Hofmann Arthur M Jacobs |
| author_sort | Chris F Westbury |
| collection | DOAJ |
| description | Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a word’s context and the emotional associations of the word. We outline an algorithmic method for predicting imageability judgments using co-occurrence distances in a large corpus. Our computed judgments account for 58% of the variance in a set of nearly two thousand human imageability judgments, for words that span the entire range of imageability. The two factors account for 43% of the variance in lexical decision reaction times (LDRTs) that is attributable to imageability in a large database of 3697 LDRTs spanning the range of imageability. We document variances in the distribution of our measures across the range of imageability that suggest that they will account for more variance at the extremes, from which most imageability-manipulating stimulus sets are drawn. The two predictors account for 100% of the variance that is attributable to imageability in newly-collected LDRTs using a previously-published stimulus set of 100 items. We argue that our model of imageability is neurobiologically plausible by showing it is consistent with brain imaging data. The evidence we present suggests that behavioral effects in the lexical decision task that are usually attributed to the abstract/concrete distinction between words can be wholly explained by objective characteristics of the word that are not directly related to the semantic distinction. We provide computed imageability estimates for over 29,000 words. |
| first_indexed | 2024-04-14T06:16:25Z |
| format | Article |
| id | doaj.art-f980fb95eebf41d7999e2ba988b7e718 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-14T06:16:25Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-f980fb95eebf41d7999e2ba988b7e7182022-12-22T02:08:12ZengFrontiers Media S.A.Frontiers in Psychology1664-10782013-12-01410.3389/fpsyg.2013.0099168986Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgmentsChris F Westbury0Cyrus eShaoul1Geoff eHollis2Lisa eSmithson3Benny B. Briesemeister4Markus J. Hofmann5Arthur M Jacobs6University of AlbertaUniversity of TuebingenUniversity of AlbertaUniversity of AlbertaFree UniversityFree UniversityFree UniversityMany studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a word’s context and the emotional associations of the word. We outline an algorithmic method for predicting imageability judgments using co-occurrence distances in a large corpus. Our computed judgments account for 58% of the variance in a set of nearly two thousand human imageability judgments, for words that span the entire range of imageability. The two factors account for 43% of the variance in lexical decision reaction times (LDRTs) that is attributable to imageability in a large database of 3697 LDRTs spanning the range of imageability. We document variances in the distribution of our measures across the range of imageability that suggest that they will account for more variance at the extremes, from which most imageability-manipulating stimulus sets are drawn. The two predictors account for 100% of the variance that is attributable to imageability in newly-collected LDRTs using a previously-published stimulus set of 100 items. We argue that our model of imageability is neurobiologically plausible by showing it is consistent with brain imaging data. The evidence we present suggests that behavioral effects in the lexical decision task that are usually attributed to the abstract/concrete distinction between words can be wholly explained by objective characteristics of the word that are not directly related to the semantic distinction. We provide computed imageability estimates for over 29,000 words.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00991/fullEmotionslexical accesscontext effectsco-occurrence statisticsImageability |
| spellingShingle | Chris F Westbury Cyrus eShaoul Geoff eHollis Lisa eSmithson Benny B. Briesemeister Markus J. Hofmann Arthur M Jacobs Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments Frontiers in Psychology Emotions lexical access context effects co-occurrence statistics Imageability |
| title | Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments |
| title_full | Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments |
| title_fullStr | Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments |
| title_full_unstemmed | Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments |
| title_short | Now you see it, now you don’t: On emotion, context, & the algorithmic prediction of human imageability judgments |
| title_sort | now you see it now you don t on emotion context amp the algorithmic prediction of human imageability judgments |
| topic | Emotions lexical access context effects co-occurrence statistics Imageability |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00991/full |
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