A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from (...) state‐of‐the‐art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co‐occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words. (shrink)

How many words—and which ones—are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns (...) out to be its Core, a “Strongly Connected Subset” of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest—the graph's “minimum feedback vertex set” or MinSet—is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. (shrink)

Despite increased focus on emotional language, research lacks for the most emotional language: Swearing. We used event-related potentials to investigate whether swear words have content dist...

The hypothesis that word representations are emotionally impoverished in a second language (L2) has variable support. However, this hypothesis has only been tested using tasks that present words in isolation or that require laboratory-specific decisions. Here, we recorded eye movements for 34 bilinguals who read sentences in their L2 with no goal other than comprehension, and compared them to 43 first language readers taken from our prior study. Positive words were read more quickly than neutral words in the L2 across (...) first-pass reading time measures. However, this emotional advantage was absent for negative words for the earliest measures. Moreover, negative words but not positive words were influenced by concreteness, frequency and L2 proficiency in a manner similar to neutral words. Taken together, the findings suggest that only negative words are at risk of emotional disembodiment during L2 reading, perhaps because a positivity bias in L2 experiences ensures that positive words are emotionally grounded. (shrink)

The Affective grounding hypothesis suggests that affective experiences play a crucial role in abstract concepts’ processing (Kousta et al. 2011). Vigliocco and colleagues test the role of affective experiences as well as the role of language in learning words denoting abstract concepts, comparing children with typical and atypical development. They conclude that besides the affective experiences also language plays a critical role in the processing of words referring to abstract concepts.

Affective information can be retrieved simply by measuring words co‐occurrences in linguistic contexts. Lenci and colleagues demonstrate that the affective measures retrieved from linguistic occurrences predict words’ concreteness: abstract words are more heavily loaded with affective information than concrete ones. These results challenge the Affective grounding hypothesis, suggesting that abstract concepts may be ungrounded and coded only linguistically, and that their affective load may be a linguistic factor.

There are considerable gaps in our knowledge of how children develop abstract language. In this paper, we tested the Affective Embodiment Account, which proposes that emotional information is more essential for abstract than concrete conceptual development. We tested the recognition memory of 7- and 8-year-old children, as well as a group of adults, for abstract and concrete words which differed categorically in valence. Word valence significantly interacted with concreteness in hit rates of both children and adults, such that effects of (...) valence were only found in memory for abstract words. The pattern of valence effects differed for children and adults: children remembered negative words more accurately than neutral and positive words, whereas adults remembered negative and positive words more accurately than neutral words. In addition, signal detection analysis revealed that children were better able to discriminate negative than positive words, regardless of concreteness. The findings suggest that the memory accuracy of 7- and 8-year-old children is influenced by emotional information, particularly for abstract words. The results are in agreement with the Affective Embodiment Account and with multimodal accounts of children’s lexical development. (shrink)

The brain processes underlying the distinction between emotion-label words (e.g. happy, sad) and emotion-laden words (e.g. successful, failed) remain inconclusive in bilingualism research. The present study aims to directly compare the processing of these two types of emotion words in both the first language (L1) and second language (L2) by recording event-related potentials (ERP) from late Chinese-English bilinguals during a lexical decision task. The results revealed that in the early word processing stages, the N170 emotion effect emerged only for L1 (...) negative emotion-laden words and L2 negative emotion-label words. In addition, larger early posterior negativity (EPN) was elicited by emotion-laden words than emotion-label words in both L1 and L2. In the later processing stages, the N400 emotion effect was evident for L1 emotion words, excluding positive emotion-laden words, while it was absent in L2. Notably, L1 emotion words elicited enhanced N400 and attenuated late positive complex (LPC) compared to those in L2. Taken together, these findings confirmed the engagement of emotion, and highlighted the modulation of emotion word type and valence on word processing in both early and late processing stages. Different neural mechanisms between L1 and L2 in processing written emotion words were elucidated. (shrink)