Implicit learning of semantic preferences of English words by Chinese learners

The aim of the present study was to explore whether Chinese learners could implicitly learn the semantic preferences of novel English words. In training, participants learned four novel verbs and were exposed to a set of verb-noun phrases that included these new words. What the participants were not told was that the use of the verbs depended on the concreteness of the nouns (i.e., the semantic preference rule). In testing, participants were required to choose between two possible verbs (one of which violated the semantic preference rule) for nouns that never occurred in training. The results showed that participants acquired unconscious knowledge of semantic preferences under incidental learning conditions, as measured by verbal reports and structural knowledge attributions. Our results provide further evidence for implicit learning of semantic preferences, suggesting that implicit learning is an important mechanism in the acquisition of L2 collocations.     

Surface features can deeply affect artificial grammar learning

Three experiments explored the extent to which surface features explain discrimination between grammatical and non-grammatical strings in artificial grammar learning (AGL). Experiment 1 replicated Knowlton and Squire’s (1996) paradigm using either letter strings as in the original study, or an analogous set of color strings to further explore if learning was affected by type of stimuli. Learning arose only with letter strings, but the results were mostly due to the discrimination of non-grammatical strings containing highly salient illegal features. Experiments 2 and 3 tested a new grammar devised to control for those features. Experiment 2 showed reduced grammar learning effects, and again only for letter materials. Experiment 3 explored the effect of additional practice with letter stimuli, and found increased learning only in the spaced practice condition, though additional practice also produced more explicit knowledge. These findings call for further research on the boundary conditions of learning in AGL paradigms.     

The use of mixed modeling to evaluate the impact of treatment integrity on learning

Treatment integrity, or the degree to which an intervention is implemented as intended, is a critical feature of skill acquisition tasks. Single‐case design consistently demonstrates that low treatment integrity slows or inhibits learning, but the relative impact of different types of instructional errors, or the presence of multiple errors, is less clear. The present study utilized a multilevel modeling approach to evaluate the impact of type of error (omission versus omission and commission) and error component (reinforcer delivery versus feedback) on learning. Findings revealed that learning outcomes worsened based on the type of error and as the complexity of errors increased; more specifically, participants performed better when only a single type of error occurred and when only a single error component was manipulated. Additionally, individual characteristics contributed to learning outcomes, highlighting the use of multilevel modeling as a helpful tool to supplement single‐case design. The differential impact of integrity errors on learning may be due to timing of errors (i.e., commission errors more likely to occur early in learning) or how errors affect reinforcement schedule versus discriminative control.     

Children learn words easier when they are interested in the category to which the word belongs

The overall pattern of vocabulary development is relatively similar across children learning different languages. However, there are considerable differences in the words known to individual children. Historically, this variability has been explained in terms of differences in the input. Here, we examine the alternate possibility that children's individual interest in specific natural categories shapes the words they are likely to learn – a child who is more interested in animals will learn a new animal name easier relative to a new vehicle name. Two‐year‐old German‐learning children (N = 39) were exposed to four novel word–object associations for objects from four different categories. Prior to the word learning task, we measured their interest in the categories that the objects belonged to. Our measure was pupillary change following exposure to familiar objects from these four categories, with increased pupillary change interpreted as increased interest in that category. Children showed more robust learning of word–object associations from categories they were more interested in relative to categories they were less interested in. We further found that interest in the novel objects themselves influenced learning, with distinct influences of both category interest and object interest on learning. These results suggest that children's interest in different natural categories shapes their word learning. This provides evidence for the strikingly intuitive possibility that a child who is more interested in animals will learn novel animal names easier than a child who is more interested in vehicles.     

“I don't know but I know who to ask”: 12‐month‐olds actively seek information from knowledgeable adults

Active social communication is an effective way for infants to learn about the world. Do pre‐verbal and pre‐pointing infants seek epistemic information from their social partners when motivated to obtain information they cannot discover independently? The present study investigated whether 12‐month‐olds (N = 30) selectively seek information from knowledgeable adults in situations of referential uncertainty. In a live experiment, infants were introduced to two unfamiliar adults, an Informant (reliably labeling objects) and a Non‐Informant (equally socially engaging, but ignorant about object labels). At test, infants were asked to make an impossible choice—locate a novel referent among two novel objects. When facing epistemic uncertainty—but not at other phases of the procedure—infants selectively referred to the Informant rather than the Non‐Informant. These results show that pre‐verbal infants use social referencing to actively and selectively seek information from social partners as part of their interrogative communicative toolkit. A video abstract of this article can be viewed at https://youtu.be/23dLPsa-fAY     

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes‐optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes‐optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6‐ to 7‐year‐old children when tasked with localizing a ‘hidden’ target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6‐ to 7‐year‐olds learn task‐relevant statistics at a rate on par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young children's behavior to a greater extent than that of adults, leading their behavior to look sub‐optimal. Our findings have important implications for how we should interpret failures in young children's ability to carry out sophisticated computations. These ‘failures’ need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.     

Motion tracking of parents’ infant‐ versus adult‐directed actions reveals general and action‐specific modulations

Parents tend to modulate their movements when demonstrating actions to their infants. Thus far, these modulations have primarily been quantified by human raters and for entire interactions, thereby possibly overlooking the intricacy of such demonstrations. Using optical motion tracking, the precise modulations of parents’ infant‐directed actions were quantified and compared to adult‐directed actions and between action types. Parents demonstrated four novel objects to their 14‐month‐old infants and adult confederates. Each object required a specific action to produce a unique effect (e.g. rattling). Parents were asked to demonstrate an object at least once before passing it to their demonstration partner, and they were subsequently free to exchange the object as often as desired. Infants’ success at producing the objects’ action‐effects was coded during the demonstration session and their memory of the action‐effects was tested after a several‐minute delay. Indicating general modulations across actions, parents repeated demonstrations more often, performed the actions in closer proximity and demonstrated action‐effects for longer when interacting with their infant compared to the adults. Meanwhile, modulations of movement size and velocity were specific to certain action‐effect pairs. Furthermore, a ‘just right’ modulation of proximity was detected, since infants’ learning, memory, and parents’ prior evaluations of their infants’ motor abilities, were related to demonstrations that were performed neither too far from nor too close to the infants. Together, these findings indicate that infant‐directed action modulations are not solely overall exaggerations but are dependent upon the characteristics of the to‐be learned actions, their effects, and the infant learners.     

Children can optimally integrate multisensory information after a short action‐like mini game training

Combining information across different sensory modalities is of critical importance for the animal's survival and a core feature of human's everyday life. In adulthood, sensory information is often integrated in a statistically optimal fashion, so that the combined estimates of two or more senses are more reliable than the best single one. Several studies have shown that young children use one sense to calibrate the others, which results in unisensory dominance and undermines their optimal multisensory integration abilities. In this study we trained children aged 4–5 years with action‐like mini games, to determine whether it could improve their multisensory as well as their visuo‐spatial skills. Multisensory integration abilities were assessed using a visuo‐haptic size discrimination task, while visuo‐spatial attention skills were investigated using a multiple object tracking task (MOT). We found that 2‐weeks training were sufficient to observe both optimal multisensory integration and visuo‐spatial enhancements selectively in the group trained with action‐like mini games. This plastic change persisted up to 3 months, as assessed in a follow‐up. Our novel findings reveal that abilities that are commonly known to emerge in late childhood can be promoted in younger children through action‐like mini games and have long‐lasting effects. Our data have clinical implications, in that they suggest that specific trainings could potentially help children with multisensory integration deficits.     

The ontogeny of cumulative culture: Individual toddlers vary in faithful imitation and goal emulation

The success of human culture depends on early emerging mechanisms of social learning, which include the ability to acquire opaque cultural knowledge through faithful imitation, as well as the ability to advance culture through flexible discovery of new means to goal attainment. This study explores whether this mixture of faithful imitation and goal emulation is based in part on individual differences which emerge early in ontogeny. Experimental measurements and parental reports were collected for a group of 2‐year‐old children (N = 48, age = 23–32 months) on their imitative behavior as well as other aspects of cognitive and social development. Results revealed individual differences in children's imitative behavior across trials and tasks which were best characterized by a model that included two behavioral routines; one corresponding to faithful imitation, and one to goal emulation. Moreover, individual differences in faithful imitation and goal emulation were correlated with individual differences in theory of mind, prosocial behavior, and temperament. These findings were discussed in terms of their implications for understanding the mechanisms of social learning, ontogeny of cumulative culture, and the benefit of analyzing individual differences for developmental experiments.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.