Where’s the Advantage? Mutual Exclusivity Promotes Children’s Initial Mapping, but Not Long-Term Memory, for Words Compared to Other Strategies

Children frequently apply a novel label to a novel object, a behavior known as the mutual exclusivity bias (MEB). This study examined how MEB affects children’s retention for word mappings. In Experiment 1, preschoolers (N = 39; Mage = 46.62 months) and adults (N = 24; Mage = 21.63 years) completed an immediate word mapping task and a delayed retention test. Both samples used MEB during referent selection, but neither group displayed higher retention for words mapped via MEB than words mapped via other referent selection strategies at test. Experiment 2 replicated Experiment 1 with preschoolers (N = 85; Mage = 47.78 months) and provided evidence against the possibility that interference from multiple words contributed to children’s faster forgetting of word mappings when using MEB. Experiment 3 presented children (N = 30; Mage = 51.13 months) with an abbreviated version of the task, providing evidence against the alternative hypothesis that cognitive load during learning caused the forgetting observed in Experiments 1 and 2. Taken together, these experiments suggest that MEB supports initial word mapping but may not provide an advantage for long-term retention.

Queries and Cues: Textual Stimuli for Reflective Thinking in Digital Mind-Mapping

Mind-mapping is useful for externalizing ideas and their relationships surrounding a central problem. However, balancing between the exploration of different aspects (breadth) of the problem with respect to the detailed exploration of each of its aspects (depth) can be challenging, especially for novices. The goal of this paper is to investigate the notion of “reflection-in-design” through a novel interactive digital mind-mapping workflow that we call “QCue”. The idea behind this workflow is to incorporate the notion of reflective thinking through two mechanisms: (1) offering suggestions to promote depth exploration through user's queries (Q), and (2) asking questions (Cue) to promote reflection for breadth exploration. This paper is an extension of our prior work where our focus was mainly on the algorithmic development and implementation of a cognitive support mechanism behind QCue enabled by ConceptNet (a graph-based rich ontology with “commonsense” knowledge). In this extended work, we first present a detailed summary of how QCue facilitated the breadth-depth balance in a mind-mapping task. Second, we present a comparison between QCue and conventional digital mind-mapping i.e. without our algorithm through a between-subjects user study. Third, we present new detailed analysis on the usage of different cognitive mechanisms provided by QCue. We further consolidate our prior quantitative analysis and build a connection with our observational analysis. Finally, we discuss in detail the different cognitive mechanisms provided by QCue to stimulate reflection in design.

Exploring the Linguistic, Cognitive, and Social Skills Underlying Lexical Processing Efficiency as Measured by the Looking-while-Listening Paradigm

The looking-while-listening (LWL) paradigm is frequently used to measure toddlers’ lexical processing efficiency (LPE). Children's LPE is associated with vocabulary size, yet other linguistic, cognitive, or social skills contributing to LPE are not well understood. It also remains unclear whether LPE measures from two types of LWL trials (target-initial versus distractor-initial trials) are differentially associated with the abovementioned potential correlates of LPE. We tested 18- to 24-month-olds and found that children's word learning on a fast-mapping task was associated with LPE measures from all trials and distractor-initial trials but not target-initial trials. Children's vocabulary and pragmatic skills were both associated with their fast-mapping performance. Executive functions and pragmatic skills were associated with LPE measures from distractor-initial but not target-initial trials. Hence, LPE as measured by the LWL paradigm may reflect a constellation of skills important to language development. Methodological implications for future studies using the LWL paradigm are discussed.

The Role of Audiovisual Speech in Fast-Mapping and Novel Word Retention in Monolingual and Bilingual 24-Month-Olds

Three experiments examined the role of audiovisual speech on 24-month-old monolingual and bilinguals’ performance in a fast-mapping task. In all three experiments, toddlers were exposed to familiar trials which tested their knowledge of known word–referent pairs, disambiguation trials in which novel word–referent pairs were indirectly learned, and retention trials which probed their recognition of the newly-learned word–referent pairs. In Experiment 1 (n = 48), lip movements were present during familiar and disambiguation trials, but not retention trials. In Experiment 2 (n = 48), lip movements were present during all three trial types. In Experiment 3 (bilinguals only, n = 24), a still face with no lip movements was present in all three trial types. While toddlers succeeded in the familiar and disambiguation trials of every experiment, success in the retention trials was only found in Experiment 2. This work suggests that the extra-linguistic support provided by lip movements improved the learning and recognition of the novel words.

Transformer: Linking Atom Mapping and Neural Machine Translation

<div><br></div><div><p> Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective.</p></div>

Transformer: Linking Atom Mapping and Neural Machine Translation

<div><br></div><div><p> Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective.</p></div>

MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.