Various studies have recently shown that the long-held claim that the relation between the sound of a word and its meaning is arbitrary needs to be revisited. In two computational studies we investigated whether word valence can be derived from sound features in English, Dutch and German. In Study 1, we identified the extent to which individual phonological features explained valence scores per language separately. In Study 2, we aimed to determine the optimal combination of cues that can predict valence scores across the three languages using two statistical classifiers and four machine learning classifiers. Our results showed that frequency and word complexity were the most reliable shared cues to predict valence for all three languages, obtaining a correct valence classification of about 60%. This percentage could be enhanced for individual or pairs of languages using additional relevant cues. These findings demonstrated that the claim that the relation between the sound of a word and its meaning is arbitrary is too strong.

Language provides an important source of information to pre-dict human personality. However, most studies that have pre-dicted personality traits using computational linguistic meth-ods have focused on lexicon-based information. We investigateto what extent the performance of lexicon-based and grammar-based methods compare when predicting personality traits. Weanalyzed a corpus of student essays and their personality traitsusing two lexicon-based approaches, one top-down (Linguis-tic Inquiry and Word Count (LIWC)), one bottom-up (topicmodels) and one grammar-driven approach (Biber model), aswell as combinations of these models. Results showed thatthe performance of the models and their combinations demon-strated similar performance, showing that lexicon-based top-down models and bottom-up models do not differ, and neitherdo lexicon-based models and grammar-based models. More-over, combination of models did not improve performance.These findings suggest that predicting personality traits fromtext remains difficult, but that the performance from lexicon-based and grammar-based models are on par.

There is a considerable amount of literature on the role ofimmersion and presence in virtual reality learningenvironments. Far less is known about the interaction ofimmersion and presence with the important individualcharacteristics that influence learning behavior, particularly,dispositional awe. Dispositional awe is manifested by anemotional response to information that defies existing mentalschemas in a given domain and by a need to accommodate thisexperience. In a virtual reality study with eight elementaryschool classes, we investigated the interaction of immersivetendencies with dispositional awe and compassion on learninggains in the domain of nature conservation. We tested thisinteraction using a novel virtual reality concept in whichchildren are sent to virtually simulated space to experience theoverview effect, a cognitive shift in awareness reported byastronauts. The findings of the study showed that participantsexperienced strong feelings of awe and scored highly onoverview effect constructs. Importantly, their learning gainswere influenced by the overview effect which was, in turn,supported by presence, dispositional awe, and compassion.This study shows the potential of using immersive virtualreality experiences in educational programs, combiningwonder and learning.

Previous research has shown that perceptual relations, social affiliations, and geographical locations can be predicted using distributional semantics. We investigated whether this extends to chronological relations. In several computational studies we demonstrated that the chronological order of days, months, years, and the chronological sequence of historical figures can be predicted using language statistics. In fact, both the leaders of the Soviet Union and the presidents of the United States can be ordered chronologically based on the cooccurrences of their names in language. An experiment also showed that the bigram frequency of US president names predicted the response time of participants in their evaluation of the chronology of these presidents. These findings are explained by the Symbol Interdependency Hypothesis which predicts that as a function of language use, language encodes relations in the world around us. Language users can then use language as a cognitive short-cut for mental representations

We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of consecutive trials was manipulated in order to produce an Auditory-to-visual switch condition, a Haptic-to- visual switch condition, and a Visual-to-visual, no-switch condition. Event-Related Potentials (ERPs) were time-locked to the onset of the first word (property) in the target trials so as to measure the effect online and to avoid a within-trial confound. A switch effect was found, characterized by more negative ERP amplitudes for modality switches than no- switches. It proved significant in four typical time windows from 160 to 750 milliseconds post word onset, with greater strength in posterior brain regions, and after 350 milliseconds. These results suggest that conceptual processing may be modality-specific in certain tasks, but also that the early stage of processing is relatively amodal.