The gleam-glum effect is a novel sound symbolic finding that words with the /i:/-phoneme (like gleam) are perceived more positive emotionally than matched words with the /K/-phoneme (like glum). We provide data that not only confirm the effect but also are consistent with an explanation that /i:/ and /K/ articulation tend to co-occur with activation of positive versus negative emotional facial musculature respectively. Three studies eliminate selection bias by including all applicable English words from the English Lexicon Project (Balota et al., 2007) and the Warriner et al. ( 2013) database and every possible Mandarin Pinyin combination that differ only in the middle phoneme (/i:/ vs /K/). In Study 1, 61 U.S. undergraduates rated monosyllabic English /i:/ words as robustly more positive than matched /K/ words. Study 2 analyzed the Warriner et al. ( 2013) valence ratings, extending the gleam-glum effect to all applicable words in the database. In Study 3, 38 U.S. participants (using English) and 37 participants in China (using Mandarin Pinyin) rated word pairs under three conditions that moderate musculature activity: Read aloud (Enhance), read silently (Control), and read silently while chewing gum (Interfere). Indeed, the effect was both replicated and was significantly larger when facial musculature was enhanced than when interfered with, and the two language populations did not significantly differ. These findings confirm a robust gleam-glum effect, despite semantic noise, in English and Mandarin Pinyin. Furthermore, these data are consistent with the hypothesis that this type of sound symbolism arises from the overlap in muscles used both in articulation and emotion expression.

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Researchers, educators, and multimedia designers need to better understand how mixing physical tangible objects with virtual experiences affects learning and science identity. In this novel study, a 3D-printed tangible that is an accurate facsimile of the sort of expensive glassware that chemists use in real laboratories is tethered to a laptop with a digitized lesson. Interactive educational content is increasingly being placed online, it is important to understand the educational boundary conditions associated with passive haptics and 3D-printed manipulables. Cost-effective printed objects would be particularly welcome in rural and low Socio-Economic (SES) classrooms. A Mixed Reality (MR) experience was created that used a physical 3D-printed haptic burette to control a computer-based chemistry titration experiment. This randomized control trial study with 136 college students had two conditions: 1) low-embodied control (using keyboard arrows), and 2) high-embodied experimental (physically turning a valve/stopcock on the 3D-printed burette). Although both groups displayed similar significant gains on the declarative knowledge test, deeper analyses revealed nuanced Aptitude by Treatment Interactions (ATIs). These interactions favored the high-embodied experimental group that used the MR device for both titration-specific posttest knowledge questions and for science efficacy and science identity. Those students with higher prior science knowledge displayed higher titration knowledge scores after using the experimental 3D-printed haptic device. A multi-modal linguistic and gesture analysis revealed that during recall the experimental participants used the stopcock-turning gesture significantly more often, and their recalls created a significantly different Epistemic Network Analysis (ENA). ENA is a type of 2D projection of the recall data, stronger connections were seen in the high embodied group mainly centering on the key hand-turning gesture. Instructors and designers should consider the multi-modal and multi-dimensional nature of the user interface, and how the addition of another sensory-based learning signal (haptics) might differentially affect lower prior knowledge students. One hypothesis is that haptically manipulating novel devices during learning may create more cognitive load. For low prior knowledge students, it may be advantageous for them to begin learning content on a more ubiquitous interface (e.g., keyboard) before moving them to more novel, multi-modal MR devices/interfaces.

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