Measuring transformative virtual reality experiences in children's drawings.

Transformative experiences in an individual's life have a lasting impact on identity, belief system, and values. At the core of these experiences is the complex emotion of awe that promotes learning, making it worthwhile to study from an educational point of view. Drawing studies may provide a useful measure of awe in children-one that is more intuitive and attractive than questionnaires alone. Previous studies conducted with adults indicated that the diminished self, associated with transformative experiences, manifests in an actual decrease in size for figures representing the self in drawings. In the current study, self-representation was investigated in drawings of 10- to 12-year-old primary school children within the context of an immersive virtual reality (VR) experience that elicits the overview effect, known to lead to an intense apperception of awe. We did not replicate the adult findings regarding self-size in this younger age group. However, details and complexity in children's drawings appeared to be impacted by the awe-elicitation procedure in VR. These elements subsequently correlated to learning gains instead of the overview effect, indicating that this measure could be linked to cognitive ability. The findings of the current study contribute to a better understanding of how drawings reflect self-transcendental experiences; however, they also reveal that in younger age groups, they are not necessarily reflected in decreased self-size.

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis.

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer ( https://lingualyzer.com ), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface.

Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning.

Functional near-infrared spectroscopy (fNIRS) is a promising technique for non-invasively assessing cortical brain activity during learning. This technique is safe, portable, and, compared to other imaging techniques, relatively robust to head motion, ocular and muscular artifacts and environmental noise. Moreover, the spatial resolution of fNIRS is superior to electroencephalography (EEG), a more commonly applied technique for measuring brain activity non-invasively during learning. Outcomes from fNIRS measures during learning might therefore be both sensitive to learning and to feedback on learning, in a different way than EEG. However, few studies have examined fNIRS outcomes in learning and no study to date additionally examined the effects of feedback. To address this apparent gap in the literature, the current study examined prefrontal cortex activity measured through fNIRS during visuomotor learning and how this measure is affected by task feedback. Activity in the prefrontal cortex decreased over the course of learning while being unaffected by task feedback. The findings demonstrate that fNIRS in the prefrontal cortex is valuable for assessing visuomotor learning and that this measure is robust to task feedback. The current study highlights the potential of fNIRS in assessing learning even under different task feedback conditions.

Respiratory Biofeedback Does Not Facilitate Lowering Arousal in Meditation Through Virtual Reality.

The current study examined the effectiveness of respiratory biofeedback in lowering subjective and objective arousal after stress. Participants were presented with a meditation session in virtual reality while subjective and objective arousal were measured, the latter measured through ECG and EEG. Three conditions were used: (a) a respiratory biofeedback condition, in which visual feedback was paired to breathing; (b) a control feedback placebo condition, in which visual feedback was not paired to breathing; and (c) a control no-feedback condition, in which no visual feedback was used. Subjective and objective arousal decreased during meditation after stress in all conditions, demonstrating recovery after stress during meditation in virtual reality. However, the reduction in arousal (on all outcome measures combined and heart rate specifically) was largest in the control feedback placebo condition, in which no biofeedback was used, indicating that respiratory biofeedback had no additional value in reducing arousal. The findings of the current study highlight the importance of including a control feedback placebo condition in order to establish the exact additional value of biofeedback and offer insights in applying cost-effective virtual reality meditation training.

Effects of local and global context on processing sentences with subject and object relative clauses.

An eye tracking study investigated the effects of local and global discourse context on the processing of subject and object relative clauses, whereby the contexts favored either a subject relative clause interpretation or an object relative clause interpretation. The fixation data replicated previous studies showing that object relative clause sentences were more difficult to process than subject relative sentences. Crucially, however, the reading difficulty asymmetry between subject and object relative clause sentences disappeared when the sentences were presented with a local or a global discourse context that favored the objects in the object relative clauses. These findings demonstrate that the evidence for a syntax-based account of sentence processing is found when sentences are presented in isolation. However, if sentences are placed more naturally, in context, discourse factors outweigh the initial structural assignment.

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View.

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency-based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition.

Zipf's law revisited: Spoken dialog, linguistic units, parameters, and the principle of least effort.

The ubiquitous inverse relationship between word frequency and word rank is commonly known as Zipf's law. The theoretical underpinning of this law states that the inverse relationship yields decreased effort in both the speaker and hearer, the so-called principle of least effort. Most research has focused on showing an inverse relationship only for written monolog, only for frequencies and ranks of one linguistic unit, generally word unigrams, with strong correlations of the power law to the observed frequency distributions, with limited to no attention to psychological mechanisms such as the principle of least effort. The current paper extends the existing findings, by not focusing on written monolog but on a more fundamental form of communication, spoken dialog, by not only investigating word unigrams but also units quantified on syntactic, pragmatic, utterance, and nonverbal communicative levels by showing that the adequacy of Zipf's formula seems ubiquitous, but the exponent of the power law curve is not, and by placing these findings in the context of Zipf's principle of least effort through redefining effort in terms of cognitive resources available for communication. Our findings show that Zipf's law also applies to a more natural form of communication-that of spoken dialog, that it applies to a range of linguistic units beyond word unigrams, that the general good fit of Zipf's law needs to be revisited in light of the parameters of the formula, and that the principle of least effort is a useful theoretical framework for the findings of Zipf's law.

Corneal reflections and skin contrast yield better memory of human and virtual faces.

Virtual faces have been found to be rated less human-like and remembered worse than photographic images of humans. What it is in virtual faces that yields reduced memory has so far remained unclear. The current study investigated face memory in the context of virtual agent faces and human faces, real and manipulated, considering two factors of predicted influence, i.e., corneal reflections and skin contrast. Corneal reflections referred to the bright points in each eye that occur when the ambient light reflects from the surface of the cornea. Skin contrast referred to the degree to which skin surface is rough versus smooth. We conducted two memory experiments, one with high-quality virtual agent faces (Experiment 1) and the other with the photographs of human faces that were manipulated (Experiment 2). Experiment 1 showed better memory for virtual faces with increased corneal reflections and skin contrast (rougher rather than smoother skin). Experiment 2 replicated these findings, showing that removing the corneal reflections and smoothening the skin reduced memory recognition of manipulated faces, with a stronger effect exerted by the eyes than the skin. This study highlights specific features of the eyes and skin that can help explain memory discrepancies between real and virtual faces and in turn elucidates the factors that play a role in the cognitive processing of faces.

Aviation and neurophysiology: A systematic review.

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

Neurophysiological changes in visuomotor sequence learning provide insight in general learning processes: Measures of brain activity, skin conductance, heart rate and respiration.

Prior research has shown neurophysiological measures of learning yield large effect sizes, suggesting that these measures have high potential in providing insight into learning. Yet, most literature on learning and neurophysiological measures focused on a single outcome measure, neglecting the interplay between different types of measures. Additionally, it is not yet clear which measures change robustly in a way specific to the learning process. The current study assessed implicit visuomotor sequence learning through multiple neurophysiological outcome measures. In two experiments participants were presented with an arm-movement version of the Serial Reaction Time Task with blocks in which targets were selected in a repeating sequence and blocks in which targets were selected randomly. While participants were executing this task, measures of EEG, skin conductance, heart rate (variability) and respiration, in addition to measures of behavioral performance, were collected. Although behavioral performance was sensitive to sequence learning, as demonstrated by faster responses in sequence than in random blocks, neurophysiology was not sensitive to sequence learning. However, in both experiments, skin conductance level and parietal EEG alpha and gamma power were sensitive to task induction and changed during sequence blocks in the direction of a pre-task baseline and were related to behavioral performance. In general, models including only EEG parietal gamma power were just as powerful in explaining behavioral measures during learning as models including a combination of neurophysiological outcome measures. The findings of the current study demonstrate that neurophysiology is not sensitive to implicit sequence learning specifically, but that general learning effects on a visuomotor learning task are reflected in measures of neurophysiology. Additionally, the findings highlight that a combination of neurophysiological outcome measures is not necessarily better in explaining task learning than a single measure.

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis.

Although many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field. The findings of the SWOT analysis indicate that the field of neurophysiology in learning and training is developing rapidly. By leveraging the opportunities of neurophysiology in learning and training (while bearing in mind weaknesses, threats, and strengths) the field can move forward in promising directions. Suggestions for opportunities for future work are provided to ensure valid and effective application of non-invasive neurophysiology in a wide range of learning and training settings.

Creating Ambassadors of Planet Earth: The Overview Effect in K12 Education.

The overview effect is the commonly reported experience of astronauts viewing planet Earth from space and the subsequent reflection on and processing of this experience. The overview effect is associated with feelings of awe, self-transcendence, and a change of perspective and identity that manifest themselves in taking steps toward protecting the fragile ecosystem. In the current study, we investigated whether the overview effect can be obtained in school children when simulated using virtual reality (VR) and whether the effect has a positive impact on learning gains. Using questionnaires and attention data in an existing simulation environment used in the school system, we showed that the VR simulation elicits an overview effect experience. Moreover, the experience yields learning gains in the domain of astrophysics. These findings are in line with past evidence regarding the positive impact of awe on learning and can be used to support further investigations of the relation between the overview effect and behavioral changes, specifically for educational purposes.

Language encodes geographical information.

Population counts and longitude and latitude coordinates were estimated for the 50 largest cities in the United States by computational linguistic techniques and by human participants. The mathematical technique Latent Semantic Analysis applied to newspaper texts produced similarity ratings between the 50 cities that allowed for a multidimensional scaling (MDS) of these cities. MDS coordinates correlated with the actual longitude and latitude of these cities, showing that cities that are located together share similar semantic contexts. This finding was replicated using a first-order co-occurrence algorithm. The computational estimates of geographical location as well as population were akin to human estimates. These findings show that language encodes geographical information that language users in turn may use in their understanding of language and the world.

Non-invasive neurophysiological measures of learning: A meta-analysis.

In a meta-analysis of 113 experiments we examined neurophysiological outcomes of learning, and the relationship between neurophysiological and behavioral outcomes of learning. Findings showed neurophysiology yielding large effect sizes, with the majority of studies examining electroencephalography and eye-related outcome measures. Effect sizes on neurophysiological outcomes were smaller than effect sizes on behavioral outcomes, however. Neurophysiological outcomes were, but behavioral outcomes were not, influenced by several modulating factors. These factors included the sensory system in which learning took place, number of learning days, whether feedback on performance was provided, and age of participants. Controlling for these factors resulted in the effect size differences between behavior and neurophysiology to disappear. The findings of the current meta-analysis demonstrate that neurophysiology is an appropriate measure in assessing learning, particularly when taking into account factors that could have an influence on neurophysiology. We propose a first model to aid further studies that are needed to examine the exact interplay between learning, neurophysiology, behavior, individual differences, and task-related aspects.

Embodied relations are encoded in language.

Studies of embodied cognition have shown that comprehenders process iconic word pairs (attic-basement) more quickly than reverse-iconic pairs (basement-attic) when the pairs are presented to them in a vertical spatial arrangement. This effect disappears in a horizontal spatial arrangement. This has been claimed to show that comprehenders perceptually simulate these word pairs. A complementary explanation is that linguistic conceptualizations (word order) reflect prelinguistic conceptualizations (spatial iconicity), whereby comprehenders use these linguistic conceptualizations in the comprehension process. The results of corpus linguistic, rating, and semantic judgment studies reported here supported this explanation: Iconic word pairs were more frequent than reverse-iconic word pairs; frequency of word order explained response times in a semantic judgment task better than iconicity did; and when iconic word pairs were presented in a horizontal arrangement, the iconicity effect disappeared, but the word order effect remained. These findings show that spatial iconicity patterns are reflected in word order patterns and that comprehenders are sensitive to these word order patterns in language-processing tasks.

Knowing the Meaning of a Word by the Linguistic and Perceptual Company It Keeps.

Debates on meaning and cognition suggest that an embodied cognition account is exclusive of a symbolic cognition account. Decades of research in the cognitive sciences have, however, shown that these accounts are not at all mutually exclusive. Acknowledging cognition is both symbolic and embodied generates more relevant questions that propel, rather than divide, the cognitive sciences: questions such as how computational symbolic findings map onto experimental embodied findings, and under what conditions cognition is relatively more symbolic or embodied in nature. The current paper revisits the Symbol Interdependency Hypothesis, which argues that language encodes perceptual information and that language users rely on these language statistics in cognitive processes. It argues that the claim that words are abstract, amodal, and arbitrary symbols and therefore must always be grounded to become meaningful is an oversimplification of the language system. Instead, language has evolved such that it maps onto the perceptual system, whereby language users rely on language statistics, which allow for bootstrapping meaning also when grounding is limited.

The Bursts and Lulls of Multimodal Interaction: Temporal Distributions of Behavior Reveal Differences Between Verbal and Non-Verbal Communication.

Recent studies of naturalistic face-to-face communication have demonstrated coordination patterns such as the temporal matching of verbal and non-verbal behavior, which provides evidence for the proposal that verbal and non-verbal communicative control derives from one system. In this study, we argue that the observed relationship between verbal and non-verbal behaviors depends on the level of analysis. In a reanalysis of a corpus of naturalistic multimodal communication (Louwerse, Dale, Bard, & Jeuniaux, ), we focus on measuring the temporal patterns of specific communicative behaviors in terms of their burstiness. We examined burstiness estimates across different roles of the speaker and different communicative modalities. We observed more burstiness for verbal versus non-verbal channels, and for more versus less informative language subchannels. Using this new method for analyzing temporal patterns in communicative behaviors, we show that there is a complex relationship between verbal and non-verbal channels. We propose a "temporal heterogeneity" hypothesis to explain how the language system adapts to the demands of dialog.

Archaeology Through Computational Linguistics: Inscription Statistics Predict Excavation Sites of Indus Valley Artifacts.

Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the geographic origin of artifacts from the Indus Valley Civilization, applying methods commonly used in cognitive science to the Indus script. We show that these methods can accurately predict the relative locations of archeological sites on the basis of artifacts of known provenance, and we further apply these techniques to determine the most probable excavation sites of four sealings of unknown provenance. These findings suggest that inscription statistics reflect historical interactions among locations in the Indus Valley region, and they illustrate how computational methods can help localize inscribed archeological artifacts of unknown origin. The success of this method offers opportunities for the cognitive sciences in general and for computational anthropology specifically.

Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance.

Human ratings of valence, arousal, and dominance are frequently used to study the cognitive mechanisms of emotional attention, word recognition, and numerous other phenomena in which emotions are hypothesized to play an important role. Collecting such norms from human raters is expensive and time consuming. As a result, affective norms are available for only a small number of English words, are not available for proper nouns in English, and are sparse in other languages. This paper investigated whether affective ratings can be predicted from length, contextual diversity, co-occurrences with words of known valence, and orthographic similarity to words of known valence, providing an algorithm for estimating affective ratings for larger and different datasets. Our bootstrapped ratings achieved correlations with human ratings on valence, arousal, and dominance that are on par with previously reported correlations across gender, age, education and language boundaries. We release these bootstrapped norms for 23,495 English words.