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Novel Noun Generalization And Stimulus Comparisons In Children: Manipulating The Number And Structure Of Learning Stimuli.
Get PDFStudies in novel word learning show that comparison settings (i.e., several stimuli introduced simultaneously) favor taxonomically-based generalization. Most comparison studies have been done with forced-choice designs. Here, we investigated, in a free-choice comparison design the type of stimuli four and five-year-old children chose in a novel noun generalization task, either from the same basic level category, or a near superordinate category, or a distant superordinate category, but also perceptual, thematic, and unrelated lures. Same basic level category items were more chosen than other taxonomically related items. Perceptual lures and near superordinate items did not differ, suggesting that children did not arbitrate between perception and taxonomy. Results are discussed in terms of different theoretical perspectives on stimulus generalization, lexical constraints, stimulus comparison and finally Bayesian approaches. They suggest that children integrate the results of their comparison rather than sampling probabilistic regularitie
Representational similarity analysis between ADHD and SCZ based on functional brain network
Get PDFAttention deficit hyperactivity disorder (ADHD) and schizophrenia (SCZ) have complex neural mechanisms. This study used fMRI data from resting state and 2-back working memory tasks to analyze the abnormal brain network characteristics of the two. The results showed that ADHD patients had dispersed functional connections under task conditions, and increased node degrees in the prefrontal cortex, insula, anterior cingulate gyrus, and cerebellum, indicating compensatory network reorganization. SCZ patients showed abnormal connections between the default mode network and the limbic cortex, and weakened coupling between the parietal lobe and the attention network, reflecting cognitive integration and emotion regulation defects. Through representational similarity analysis (RSA), it was found that the two diseases had shared abnormal connections in the prefrontal cortex, middle temporal gyrus, and hippocampus, which may be related to working memory regulation disorders. These findings provide potential biomarkers for targeted intervention
Mandarin-Speaking Late Talkers and Gesture Production at 24 Months
Get PDFWe studied gesture and language production in 21 Mandarin-speaking late talkers at 24 months of age and compared them with 28 age-matched typically developing children to determine their gestural and cross-modal communicative abilities. Spontaneous cross-modal data were collected during naturalistic mother-child interactions. Results from the Words and Sentences survey of the MCDI-T showed that late talkers had underdeveloped vocabulary and grammatical complexity. Nonetheless, their gestural competence was intact and comparable to that of typically developing children. Both groups demonstrated similar patterns in using declarative pointing, imperative pointing, showing, giving, representational, and conventional gestures to achieve communicative goals. Among these, declarative pointing was the most common for establishing joint attention and sharing interests or information with the addressee. Although late talkers were capable of reinforcing, clarifying, and supplementing speech with gestures, they did so less frequently than their typically developing peers. In sum, late talkers used gestures effectively to support communication; however, they showed limitations in integrating varied information for cross-modal communication
Dual-Branch EEG Decoding Method for Collaborative Multi-Brain Motor Imagery
Get PDFCollaborative multi-brain motor imagery is an innovative brain-computer interface (BCI) paradigm that records and decodes brain signals from multiple individuals to collectively complete motor imagery tasks. However, existing decoding methods often rely on techniques such as averaging, concatenating, or cross-brain coupling of data or features, and lack coordination between single-brain and multi-brain decision-making in this context. To address this, we propose a dual-branch electroencephalogram (EEG) decoding method that jointly learns private and shared domain information. The method employs a Siamese network for private common spatial pattern (CSP) learning and a feature-sharing network for shared features, then combines the outputs for classification. Experiments with EEG data demonstrated a 10.27% improvement over the single-brain scenario and a 9% improvement over state-of-the-art methods. This approach effectively integrates private and shared domain learning, advancing collaborative BCI technology
From Infants to AI: Incorporating Infant-like Learning in Models Boosts Efficiency and Generalization in Learning Social Prediction Tasks
Get PDFEarly in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning
Modeling a network of beliefs surrounding parents' endorsement of COVID vaccines for children
Get PDFCognitive science offers powerful tools for addressing pressing public health needs. Here we apply the cognitive science of intuitive theories and the tools of Bayesian networks to shed light on why so few children in the US have received COVID vaccines and how we might encourage caregivers to seek out these and other life-saving vaccines for their children. 1700 US parents completed 13 belief scales on a range of topics likely to influence their endorsement of pediatric COVID vaccines. We deployed structure learning techniques to develop a cognitive model of the relationships among beliefs and their influence on endorsement of vaccines for children, which accounted for 70% of the variance in participants' beliefs in a held-out testing split. Model-based simulations suggested that educational interventions focused on the effectiveness of COVID vaccines for supporting individual and community health may be most effective in increasing uptake of the COVID vaccine for children
Does Precision Affect Categorization? Magnitude Categorization and Measurement Scales
Get PDFHow do systems of measurement influence our conceptualization of relative magnitudes? This study investigates the cognitive interplay between measurement precision and magnitude categorization. By employing morphed shapes organized by an arbitrary dimension, we examine whether exposure to high- vs low-precision numerical systems affects conceptual judgments and well-known phenomena such as semantic distance and semantic congruity effects as found for familiar dimensions. Participants trained on novel scales revealed differences in their sensitivity that depended on the precision of the trained measurement system, consistent with high-precision systems leading to relatively expanded dimensional encodings compared to low-precision systems. Our findings also shed light on other topics such as the interplay of perception and language in learning novel dimensions and the association of directionality with a mental number line
Sexual Selection Preferences in Anthropomorphized Imagery of Interpretative Graphics in Quantitative Visualization
Get PDFWhen choosing what we find visually attractive, men and women tend to focus on different features, even for simple shapes. This study investigates gender differences in visual feature preferences during the anthropomorphization of graphics in the context of sexual selection. We constructed a feature set consisting of 48 geometric attributes to explore how these elements affect sexual selection preferences across genders. In Study 1, we quantitatively visualized these features using genetic algorithms, GANs, and manual design. Study 2 assessed gender preferences through an online survey of 288 participants, revealing the most significant features and differences in male and female preferences.Finally, in Study 3, we applied these findings to real-world art (Chinese calligraphy) to verify the explanatory power of the features. Our results provide new insights into the role of visual features in sexual selection and have practical applications in art, product design, and user experience optimization
The effect of learning Chinese Sign Language on spatial conceptualisation of time in hearing Mandarin speakers
Get PDFTemporal-spatial metaphors can be differed across languages, and such cross-linguistic influences may affect people's spatial conceptualization of time. Mandarin (including gestures) has different spatial metaphors for time than Chinese Sign Language (CSL). This paper investigated whether native Mandarin speakers' mental space-time mappings change after learning CSL for 14 weeks. Sixty native Mandarin speakers who had no prior knowledge of sign language took a pretest and posttest of space-time mappings before and after taking a CSL course. The results showed that participants changed their temporal-spatial mappings after learning CSL. Specifically, they had more sagittal space-time mappings and fewer lateral ones than before. They also had more "future-in-front/ past-in-back" mappings consistent with CSL space-time mappings. Furthermore, these changes were more significant in high-proficiency learners than in low-proficiency learners. Our results not only demonstrate an effect of bodily experience on time conceptions, but also suggest that sign language can impact spatial temporal reasoning
Multi-Option Polarization: How Deliberating More Options Both Increases and Decreases Polarization
Get PDFFormal models in social epistemology explore why rational agents might polarize. While paradigmatic models focus on binary topics, e.g., "Is H true or false?", many real-world issues involve multi-option topics: "Which of n > 2 options is true/best?" This paper introduces a model of rational deliberation on multi-option topics to address the following question: As a group discusses more options, should we expect their beliefs to polarize more or less? We find a dual effect: as the number of options increases, agents are more likely to disagree on which option is most likely correct. This makes it harder to reach consensus on a single position. At the same time, their beliefs—and thus their disagreements—become less extreme. Hence, while agents are more likely to disagree, these disagreements are less intense. Since each trend aligns with a familiar concept of polarization, more options can increase or decrease polarization, depending on one's measurement