Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments

Abstract

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 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.

The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics

“…Our results suggest that further understanding of the construct of arousal may be made by interpreting its relationship to concreteness. Westbury et al (2013) demonstrated that affective measures can account for a large portion of the psychologically relevant variation contained within human judgments of imageability. Computer-estimated imageability judgments (from Westbury et al, 2013), in turn, are closely related to Brysbaert, Warriner, and Kuperman's (2014) concreteness judgments (r = .77 over 5,278 words).…”

“…Westbury et al (2013) demonstrated that affective measures can account for a large portion of the psychologically relevant variation contained within human judgments of imageability. Computer-estimated imageability judgments (from Westbury et al, 2013), in turn, are closely related to Brysbaert, Warriner, and Kuperman's (2014) concreteness judgments (r = .77 over 5,278 words). The explanation we propose for why judgments of concreteness are so strongly related to PC2 is that, like imageability judgments, concreteness judgments are affective in nature.…”

“…For instance, the similarity of meaning between two words can be assessed by measuring the similarity between their co-occurrence vectors. Cooccurrence vectors contain enough information to pass tests for basic verbal ability (e.g., Landauer & Dumais, 1997), to accurately predict human judgments of valence and arousal (e.g., Hollis & Westbury, 2016;Mandera, Keuleers, & Brysbaert, 2015;Recchia & Louwerse, 2015;Westbury, Keith, Briesemeister, Hofmann, & Jacobs, 2015), and to account for behavioral effects of high-level lexical properties such as subjective familiarity (Westbury, 2014) and imageability (Westbury et al, 2013).…”

Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions

“…Note, however, that the radical and highly reliable difference in predictive power between the imageability-specific affect model developed in Westbury et al (2013) (r = .57 for the same 2532 words) and the two valence measures suggests that it is not valence per se that is predictive of imageability, but rather that valence is correlated with a different measure of affective force that is itself very highly predictive of imageability.…”

The effect of extroversion on communication: Evidence from an interlocutor visibility manipulation

“…Concreteness coding was done by word type and concreteness ratings were calculated using a database of 3814 words that had been assigned imageability judgments (database complied by Westbury et al, 2013). The average percentage of word types for which concreteness ratings were available in the screen condition was 68.0% (SD = 2.9%) and in the screen-down condition 69.5% (SD = 3.0%), a difference that was significant on a repeated measures ANOVA, F (1, 31) = 6.15, p = .019, eta squared = .166.…”