If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates

Learning new words via reading: The influence of emotional narrative context on learning novel adjectives

People learn new words in narrative contexts, but little is known about how the emotional valence of the narrative influences word learning. In a pre-registered experiment, 76 English-speaking adults read 30 novel adjectives embedded in 60 short narratives (20 positive, 20 negative, and 20 neutral valence). Both immediately after and 24 hr later, participants completed a series of post-tests, including speeded recognition, sentence completion, meaning generation, and valence judgement. Results showed that participants learned both the novel word form and its meaning. Compared with novel words experienced in the neutral contexts, those read in the emotional contexts (both positive and negative) showed better learning of orthographic form in the immediate post-test, but only those read in the negative context were recognised with greater accuracy in the delayed post-test. Furthermore, the valence of the context was reflected in the word meanings participants generated for each novel word, suggesting that word valence can be inferred from the valence of the contexts. Results from sentence completion and valence judgement were mixed, depending on the task demands. These findings are discussed with reference to theories of affective embodiment and the implications for learning abstract words are considered.

Neuroimaging of valence decisions in children and adults

To date, the neural underpinnings of affective components in language processing in children remain largely unknown. To fill this gap, the present study examined behavioural and neural correlates of children and adults performing the same auditory valence decision task with an event-related fMRI paradigm. Based on previous findings in adults, activations in anterior and posterior cingulate cortex, orbitofrontal cortex and left inferior frontal gyrus were expected for both positive and negative valence categories. Recent behavioural findings on valence decisions showed similar ratings and reaction time patterns in children and adults. This finding was successfully replicated in the present study. On a neural level, our analysis of affective language processing showed activations in regions associated with both semantic (superior and middle temporal and frontal) and affective (anterior and posterior cingulate, orbitofrontal and inferior frontal, insula and amygdala) processing. Neural activations in children and adults were systematically different in explicit affective word processing. In particular, adults showed a more distributed semantic network activation while children recruited additional subcortical structures.

Neural correlates of affective contributions to lexical decisions in children and adults

The goal of the present study was to investigate whether 6-9-year old children and adults show similar neural responses to affective words. An event-related neuroimaging paradigm was used in which both age cohorts performed the same auditory lexical decision task (LDT). The results show similarities in (auditory) lexico-semantic network activation as well as in areas associated with affective information. In both age cohorts' activations were stronger for positive than for negative words, thus exhibiting a positivity superiority effect. Children showed less activation in areas associated with affective information in response to all three valence categories than adults. Our results are discussed in the light of computational models of word recognition, and previous findings of affective contributions to LDT in adults.

Redundancy, isomorphism, and propagative mechanisms between emotional and amodal representations of words: A computational study

Some proposals claim that language acts as a link to propagate emotional and other modal information. Thus, there is an eminently amodal path of emotional propagation in the mental lexicon. Following these proposals, we present a computational model that emulates a linking mechanism (mapping function) between emotional and amodal representations of words using vector space models, emotional feature-based models, and neural networks. We analyzed three central concepts within the embodiment debate (redundancy, isomorphism, and propagative mechanisms) comparing two alternative hypotheses: semantic neighborhood hypothesis versus specific dimensionality hypothesis. Univariate and multivariate neural networks were trained for dimensional (N = 11,357) and discrete emotions (N = 2,266), and later we analyzed its predictions in a test set (N = 4,167 and N = 875, respectively). We showed how this computational model could propagate emotional responses to words without a direct emotional experience via amodal propagation, but no direct relations were found between emotional rates and amodal distances. Thereby, we found that there were clear redundancy and propagative mechanisms, but no isomorphism should be assumed. Results suggested that it was necessary to establish complex links to go beyond amodal distances of vector spaces. In this way, although the emotional rates of semantic neighborhoods could predict the emotional rates of target words, the mapping function of specific amodal features seemed to simulate emotional responses better. Thus, both hypotheses would not be mutually exclusive. We also showed that discrete emotions could have simpler relations between modal and amodal representations than dimensional emotions. All these results and their theoretical implications are discussed.

Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA

In recent years, latent semantic analysis (LSA) has reached a level of maturity at which its presence is ubiquitous in technology as well as in simulation of cognitive processes. In spite of this, in recent years there has been a trend of subjecting LSA to some criticisms, usually because it is compared to other models in very specific tasks and conditions and sometimes without having good knowledge of what the semantic representation of LSA means, and without exploiting all the possibilities of which LSA is capable other than the cosine. This paper provides a critical review to clarify some of the misunderstandings regarding LSA and other space models. The historical stability of the predecessors of LSA, the representational structure of word meaning and the multiple topologies that could arise from a semantic space, the computation of similarity, the myth that LSA dimensions have no meaning, the computational and algorithm plausibility to account for meaning acquisition in LSA (in contrast to others models based on online mechanisms), the possibilities of spatial models to substantiate recent proposals, and, in general, the characteristics of classic vector models and their ease and flexibility to simulate some cognitive phenomena will be reviewed. The review highlights the similarity between LSA and other techniques and proposes using long LSA experiences in other models, especially in predicting models such as word2vec. In sum, it emphasizes the lessons that can be learned from comparing LSA-based models to other models, rather than making statements about "the best."