Category Learning Research Articles

ANALYSIS OF STUDENTS' CREATIVE THINKING ABILITY AND LEARNING INDEPENDENCE IN SOLVING STORY PROBLEMS ON THE SUBJECT OF LINEAR EQUATIONS WITH TWO VARIABLES (SPLDV)

The research was motivated by the lack of students' creative thinking abilities and independent learning in solving story problems on SPLDV material. The aim of this research is to describe: (1) Students' thinking abilities in solving story problems in class VIII SPLDV material at SMP Negeri 4 Maniamolo, (2) Students' learning independence in solving story problems in class VIII SPLDV material at SMP Negeri 4 Maniamolo. This type of research is qualitative research with a descriptive approach. The subjects in this research were 25 class VIII students of SMP Negeri 4 Maniamolo. Data collection was carried out using tests, questionnaires and interviews. The data was analyzed in stages, namely data reduction, data presentation and drawing conclusions. The research results show that the ability to think creatively in solving story problems on SPLDV material is classified as poor, namely the very good category at 8%, the good category at 16%, the fair category at 28%, the poor category at 36% and the poor category at 12%. The research results show that learning independence in solving story problems on SPLDV material is classified as moderate, namely the high category at 8%, the medium category at 56% and the low category at 36%. So it can be concluded that students' creative thinking abilities are more dominant in the sufficient category and students' learning independence is also still dominant in the moderate category. The suggestions put forward in this research are that it is hoped that students will be able to develop their level of thinking to be more active and have more practice solving math problems related to SPLDV material, and teachers should be able to develop students' creative thinking abilities and increase students' learning independence in solving story problems on SPLDV material.

Breaking the cycle of poor critical reading comprehension: A strategy-based intervention

Critical reading comprehension is a crucial skill for lifelong learning, but many learners struggle with effectively extracting meaning from texts. This study aimed to address this issue by investigating how a strategy-based instruction intervention could impact learners’ critical reading comprehension competence. The study employed a quantitative approach with a quasi-experimental pre- and post-test control group design. The participants were 155 Grade 9 English first additional language (FAL) learners from two high schools in Gauteng, South Africa. The intervention programme was based on Bloom’s taxonomy categories for learning, teaching and assessing, modified into six strategies: (1) knowledge (pre-reading), (2) comprehension (extracting meaning), (3) application (asking questions), (4) analysis (reflection), (5) evaluation (outlining key concepts and decision-making) and (6) synthesis (summarising). Using a two-way mixed analysis of variance (ANOVA) paired sample t-tests, the study found that while both groups’ critical reading comprehension competence increased, the experimental group that received the strategy-based instruction intervention performed significantly better in the post-test, showing a significant improvement compared to the control group. The findings demonstrated that providing clear explanations, instruction and support on reading strategies can enhance learners’ critical reading comprehension, highlighting the mutual reliance between teaching and learning. The teacher’s instructional approach in guiding learners to effectively use reading strategies is crucial for enhancing critical reading comprehension in the English FAL classroom. In conclusion, the results suggest that the intervention effectively enhanced critical reading comprehension competence, and if implemented by teachers, it could potentially help break the cycle of poor critical reading comprehension among English FAL learners.Contribution: The contribution of this study lies in its provision of empirically supported perspectives and insights into the effectiveness of strategy-based instruction interventions. It emphasises the necessity of employing explicit reading comprehension pedagogies and targeted interventions, along with explicit instructional strategies, to address the reading comprehension challenges encountered by English FAL learners in South Africa and beyond.

Neural correlates of category learning in monkey inferior temporal cortex.

Area TE is required for normal learning of visual categories based on perceptual similarity. To evaluate whether category learning changes neural activity in area TE, we trained two monkeys (both male) implanted with multi-electrode arrays to categorize natural images of cats and dogs. Neural activity during a passive viewing task was compared pre- and post-training. After the category training, the accuracy of abstract category decoding improved. Single units became more category-selective, the proportion of single units with category-selectivity increased, and units sustained their category-specific responses for longer. Visual category learning thus appears to enhance category separability in area TE by driving changes in the stimulus selectivity of individual neurons and by recruiting more units to the active network.Significance statement Neurons in Area TE are known to respond selectively to a small number of visual stimuli. Here we demonstrate that the neural activity in area TE is modulated by category learning of natural images (cats and dogs), thus demonstrating that this region is capable of undergoing rapid plastic changes in adult primates.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.

A Coupled Hidden Markov Model framework for measuring the dynamics of categorization

We introduce a new framework for measuring the dynamics of category learning using Coupled Hidden Markov Models (CHMMs). The key assumptions of the framework are that people maintain a latent assignment of every stimulus to a category, and that they can update the assignments for all stimuli whenever they encounter any stimulus. These assumptions contrast with many existing accounts of category learning, which either do not allow for what is learned about one stimulus to influence the category association of others, or allow only for indirect influence. The CHMM framework allows tailored models to be developed for specific category learning tasks, taking as input the stimulus sequence and category responses people make, and producing as output inferences about the underlying dynamics of category assignments and the mechanics of the response processes. We demonstrate the framework by applying it to a categorization task considered by Lee and Navarro (2002), showing how the model measures the change in participants’ latent category assignments as they learn the category structure. We conclude by discussing potential applications of the CHMM framework to category learning situations involving prior knowledge, changing category structures, and category learning tasks that involve the consideration of multiple stimuli at one time.

Systematic changes in neural selectivity reflect the acquired salience of category-diagnostic dimensions.

Humans and other animals develop remarkable behavioral specializations for identifying, differentiating, and acting on classes of ecologically important signals. Ultimately, this expertise is flexible enough to support diverse perceptual judgments: a voice, for example, simultaneously conveys what a talker says as well as myriad cues about her identity and state. Mature perception across complex signals thus involves both discovering and learning regularities that best inform diverse perceptual judgments, and weighting this information flexibly as task demands change. Here, we test whether this flexibility may involve endogenous attentional gain to task-relevant dimensions. We use two prospective auditory category learning tasks to relate a complex, entirely novel soundscape to four classes of "alien identity" and two classes of "alien size." Identity, but not size, categorization requires discovery and learning of patterned acoustic input situated in one of two simultaneous, frequency-delimited bands. This allows us to capitalize on the coarsely segregated frequency-band-specific channels of auditory tonotopic maps using fMRI to ask whether category-relevant perceptual information is prioritized relative to simultaneous, uninformative information. Among participants expert at alien identity categorization, we observe prioritization of the diagnostic frequency band that persists even when the diagnostic information becomes irrelevant in the size categorization task. Tellingly, the neural selectivity evoked implicitly in categorization aligns closely with activation driven by explicit, sustained selective attention to other sounds presented in the same frequency band. Additionally, we observe fingerprints of individual differences in the learning trajectories taken to achieve expert-level categorization in patterns of neural activity associated with the diagnostic dimension. In all, this indicates that acquiring categories can drive the emergence of acquired attentional salience to dimensions of acoustic input.

Application of Digital Find Me Pair Evaluation on Student Learning Outcomes of Class V Social Sciences Learning Cultural Diversity Material Elementary School

This research is motivated by the low learning outcomes in social studies learning. So the learning outcomes of class V students are low and can be said to be insufficient (KKM). By implementing the Find Me Pair digital evaluation. The aim of this research is to describe the application of digital find me pair evaluation on the learning outcomes of students learning social sciences material on cultural diversity in class V elementary schools. This research includes descriptive quantitative research. Data collection in this research consisted of 30 students in the VA class at SDN Sedatigede II. The data collection method used is observation using an assessment tool in the form of an observation sheet and a test using an assessment tool in the form of a test sheet. The results of this research show that student activities and educator activities in implementing digital evaluation are in the very good category and student learning outcomes are in the very good category

Enhancing the utilization of uncertain pixels in semi-supervised semantic segmentation

In semi-supervised semantic segmentation, determining the correct label for uncertain pixels is crucial yet challenging. The recently proposed virtual category (VC) learning achieves excellent performance by creating a potential category (PC) set to exploit the valuable part of the uncertain pixels effectively. However, the potential category set in the existing method is not accurate enough at the beginning of iterations, which could lead to less accurate segmentation results. To better utilize uncertain pixels, we propose to improve the strategy of constructing the potential category set. Specifically, we adjust the threshold based on the model’s optimization status and split the pseudo-labels into low/high-confidence areas to create a more appropriate PC set. At the same time, due to uncertainty in boundary pixels, it is challenging to achieve precise object segmentation, and there is currently no method available to optimize these areas in semi-supervised segmentation tasks. Therefore, we propose a entropy-based method to identify boundary areas and design a novel boundary refinement network to process labeled and unlabeled data separately to optimize segmentation. The experimental results demonstrate that our proposed method excels in accurately segmenting the boundary areas of the targets. Furthermore, it achieves state-of-the-art semi-supervised segmentation on the Pascal VOC and Cityscapes datasets.

A neural network model of how category learning alters perceptual similarity

A neural network model of how category learning alters perceptual similarity

Geometric changes in monkey V4 and IT neural responses during visual category learning

Geometric changes in monkey V4 and IT neural responses during visual category learning

Comparing auditory and visual category learning

Comparing auditory and visual category learning

Assessing the Health Belief Model of Students with Regard to Breast Cancer: A Descriptive Study

Background: Breast cancer continues to be a substantial worldwide health issue, impacting millions of individuals and families annually. Gaining insight into individuals' perception and reaction to the risk of breast cancer is crucial for advancing early detection and successful prevention methods. The Health Belief Model (HBM) is a commonly utilised theoretical framework for examining health behaviours associated with breast cancer. According to the Health Belief Model (HBM), individuals are more inclined to take measures to prevent or manage a health threat if they believe that they are at risk of developing the condition, perceive the condition to have severe consequences, believe that taking action would be advantageous in reducing the threat, and perceive minimal obstacles to taking action. The study seeks to evaluate the Health Belief Model of students in relation to breast cancer. Methods: A descriptive research design was adopted. Self-administered semi-structured questionnaires were used to elicit information from 365 female participants. Purposive sampling approach was used to choose Tamale Girls and Ghana Senior High Schools from the Tamale Metropolis and simple random technique was used to identify the study respondents. Data was analysed using SPSS. Statistics like frequency and percentages were used to represent the findings. Results: The study revealed that students possessed a moderate-to-high level of information regarding the causes, effects, and preventions of breast cancer. This knowledge was mostly influenced by the type or category of learning. Overall, a minimal number of students participated in the screening process for breast cancer. Among the three techniques, breast self-examination was the most commonly utilised, followed by mammography and clinical breast examination. Factors such as age, family size, religion, and social class were found to be associated with breast cancer screening. Conclusion: Students generally exhibited a modest degree of perceived vulnerability to breast cancer. They also demonstrated a favourable disposition towards the advantages of breast cancer prevention and early detection measures.

Interleaved pretesting enhances category learning and classification skills.

Interleaved pretesting enhances category learning and classification skills.

Comparison of virtual reality perceptions of teachers working in Türkiye and South Africa

With the study reported on here we aimed to determine and compare teacher perceptions of the concept of virtual reality between Turkish and South African teachers using metaphors. We adopted a phenomenological approach, and a questionnaire was administered to a total of 100 teachers in both countries, using random sampling. The study included 4 schools – 2 secondary schools selected from each country. The data obtained were analysed and categorised into themes using the metaphor mismatch technique. The following themes were identified and interpreted, learning, entertainment, technology, art, travel, and imagination. Teachers presented metaphors based on relevance to their area of practice and perceived potential use of virtual reality in teaching and learning. Based on the number of metaphors identified, teachers held a narrow perception in the categories, entertainment, technology, and travel, while more metaphors, with varied sample explanation responses were identified in the categories of learning, art, and imagination. The findings of this study can be used to develop a user road map for teacher professional development and to inform policy on the use of virtual reality in teaching and learning.

Crossmodal Correspondence Mediates Crossmodal Transfer from Visual to Auditory Stimuli in Category Learning.

This article investigated whether crossmodal correspondence, as a sensory translation phenomenon, can mediate crossmodal transfer from visual to auditory stimuli in category learning and whether multimodal category learning can influence the crossmodal correspondence between auditory and visual stimuli. Experiment 1 showed that the category knowledge acquired from elevation stimuli affected the categorization of pitch stimuli when there were robust crossmodal correspondence effects between elevation and size, indicating that crossmodal transfer occurred between elevation and pitch stimuli. Experiments 2 and 3 revealed that the size category knowledge could not be transferred to the categorization of pitches, but interestingly, size and pitch category learning determined the direction of the pitch-size correspondence, suggesting that the pitch-size correspondence was not stable and could be determined using multimodal category learning. Experiment 4 provided further evidence that there was no crossmodal transfer between size and pitch, due to the absence of a robust pitch-size correspondence. These results demonstrated that crossmodal transfer can occur between audio-visual stimuli with crossmodal correspondence, and multisensory category learning can change the corresponding relationship between audio-visual stimuli. These findings suggest that crossmodal transfer and crossmodal correspondence share similar abstract representations, which can be mediated by semantic content such as category labels.

Adaptively triggered comparisons enhance perceptual category learning: evidence from face learning

Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner’s performance, show that adaptively triggered comparisons improve category learning.

Exploratory analysis of students’ open-ended responses describing their perception of course inclusivity in an introductory physics course

In this exploratory study, we examined students’ perceptions of inclusion in a calculus-based, introductory physics 1 course for science and engineering majors. This course, offered at a large R1 institution in the United States, was conducted remotely due to the COVID-19 pandemic. Via a survey given at the end of the semester, students rated their course inclusion and provided open-text explanations for their ratings. On average, students rated the course as moderately inclusive. Using inductive qualitative content analysis, six categories emerged: academic, identity, lack of understanding, nonspecific, other, and remote learning. The top three categories were academic (41%), nonspecific (33%), and remote learning (18%). The remote learning category included phrases containing remote learning, Zoom, or COVID-19, along with a second idea explaining the student’s level of inclusion, leading to remote learning subcategories. These subcategories were similar to the other primary categories and the academic subcategories. Many students cited academic reasons for their inclusivity scores, including course structure, teaching practices, instructor-student interaction, student-student interaction, and overall course environment. Importantly, many of these factors are within the instructor’s influence. Chi-square tests indicated that students perceiving high inclusion emphasized academic factors, while those feeling low inclusion focused on the remote learning aspect of the course. Overall, our findings suggest that instructors can significantly influence students’ perceptions of inclusion through various teaching strategies, interactions between instructors and peers, and a welcoming environment. These insights contribute to the ongoing discussion about creating inclusive classrooms by incorporating student perspectives. Published by the American Physical Society 2024

Exploration, Distributed Attention, and Development of Category Learning

Category learning is a crucial aspect of cognition that involves organizing entities into equivalence classes. Whereas adults tend to focus on category-relevant features, young children often distribute attention between relevant and irrelevant ones. The reasons for children’s distributed attention are not fully understood. In two category-learning experiments with adults and with children aged 4, 5, and 6 (N = 201), we examined potential drivers of distributed attention, including (a) immature filtering of distractors and (b) the general tendency for exploration or broad information sampling. By eliminating distractor competition, we reduced filtering demands. Despite identifying the features critical for accurate categorization, children, regardless of their categorization performance, continued sampling more information than was necessary. These results indicate that the tendency to sample information extensively contributes to distributed attention in young children. We identify candidate drivers of this tendency that need to be examined in future research.

Exploring the effectiveness of reward-based learning strategies for second-language speech sounds.

Adults struggle to learn non-native speech categories in many experimental settings (Goto, Neuropsychologia, 9(3), 317-323 1971), but learn efficiently in a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, Cognitive Science, 35(7), 1390-1405 2011). Behavioral and neural evidence from this and other paradigms point toward the involvement of reinforcement learning mechanisms in speech category learning (Harmon, Idemaru, & Kapatsinski, Cognition, 189, 76-88 2019; Lim, Fiez, & Holt, Proceedings of the National Academy of Sciences, 116, 201811992 2019). We formalize this hypothesis computationally and implement a deep reinforcement learning network to map between environmental input and actions. Comparing to a supervised model of learning, we show that the reinforcement network closely matches aspects of human behavior in two experiments - learning of synthesized auditory noise tokens and improvement in speech sound discrimination. Both models perform comparably and the similarity in the output of each model leads us to believe that there is little inherent computational benefit to a reward-based learning mechanism. We suggest that the specific neural circuitry engaged by the paradigm and links between striatum and superior temporal areas play a critical role in effective learning.

Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction With Extremely Limited Labels.

Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the issue of confirmation bias caused by the resulting inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a Virtual Category (VC) is assigned to each confusing sample in such a way that it can safely contribute to the model optimisation even without a concrete label. This provides an upper bound for inter-class information sharing capacity, which eventually leads to a better embedding space. Extensive experiments on two mainstream dense prediction tasks - semantic segmentation and object detection, demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially when only very few labels are available. Our intriguing findings highlight the usage of VC learning in dense vision tasks.