Category Learning Research Articles

Auditory category learning is robust across training regimes

Multiple lines of research have developed training approaches that foster category learning, with important translational implications for education. Increasing exemplar variability, blocking or interleaving by category-relevant dimension, and providing explicit instructions about diagnostic dimensions each have been shown to facilitate category learning and/or generalization. However, laboratory research often must distill the character of natural input regularities that define real-world categories. As a result, much of what we know about category learning has come from studies with simplifying assumptions. We challenge the implicit expectation that these studies reflect the process of category learning of real-world input by creating an auditory category learning paradigm that intentionally violates some common simplifying assumptions of category learning tasks. Across five experiments and nearly 300 adult participants, we used training regimes previously shown to facilitate category learning, but here drew from a more complex and multidimensional category space with tens of thousands of unique exemplars. Learning was equivalently robust across training regimes that changed exemplar variability, altered the blocking of category exemplars, or provided explicit instructions of the category-diagnostic dimension. Each drove essentially equivalent accuracy measures of learning generalization following 40 min of training. These findings suggest that auditory category learning across complex input is not as susceptible to training regime manipulation as previously thought.

Auditory and visual category learning in children and adults.

Categories are fundamental to everyday life and the ability to learn new categories is relevant across the lifespan. Categories are ubiquitous across modalities, supporting complex processes such as object recognition and speech perception. Prior work has proposed that different categories may engage learning systems with unique developmental trajectories. There is a limited understanding of how perceptual and cognitive development influences learning as prior studies have examined separate participants in a single modality. The current study presents a comprehensive assessment of category learning in 8-12-year-old children (12 female; 34 white, 1 Asian, 1 more than one race; M household income $85-$100 K) and 18-61-year-old adults (13 female; 32 white, 10 Black or African American, 4 Asian, 2 more than one race, 1 other; M household income $40-55 K) in a broad sample collected online from the United States. Across multiple sessions, participants learned categories across modalities (auditory, visual) that engage different learning systems (explicit, procedural). Unsurprisingly, adults outperformed children across all tasks. However, this enhanced performance was asymmetrical across categories and modalities. Adults far outperformed children in learning visual explicit categories and auditory procedural categories, with fewer differences across development for other types of categories. Adults' general benefit over children was due to enhanced information processing, while their superior performance for visual explicit and auditory procedural categories was associated with less cautious correct responses. These results demonstrate an interaction between perceptual and cognitive development that influences learning of categories that may correspond to the development of real-world skills such as speech perception and reading. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Hierarchical categorization learning is associated with representational changes in the dorsal striatum and posterior frontal and parietal cortex.

Learning and recognition can be improved by sorting novel items into categories and subcategories. Such hierarchical categorization is easy when it can be performed according to learned rules (e.g., "if car, then automatic or stick shift" or "if boat, then motor or sail"). Here, we present results showing that human participants acquire categorization rules for new visual hierarchies rapidly, and that, as they do, corresponding hierarchical representations of the categorized stimuli emerge in patterns of neural activation in the dorsal striatum and in posterior frontal and parietal cortex. Participants learned to categorize novel visual objects into a hierarchy with superordinate and subordinate levels based on the objects' shape features, without having been told the categorization rules for doing so. On each trial, participants were asked to report the category and subcategory of the object, after which they received feedback about the correctness of their categorization responses. Participants trained over the course of a one-hour-long session while their brain activation was measured using functional magnetic resonance imaging. Over the course of training, significant hierarchy learning took place as participants discovered the nested categorization rules, as evidenced by the occurrence of a learning trial, after which performance suddenly increased. This learning was associated with increased representational strength of the newly acquired hierarchical rules in a corticostriatal network including the posterior frontal and parietal cortex and the dorsal striatum. We also found evidence suggesting that reinforcement learning in the dorsal striatum contributed to hierarchical rule learning.

LINGUISTIC ASPECT OF TEACHING THE ENGLISH VERB IN THE CONTEXT OF COMPETENCE-ORIENTED EDUCATION

The strategic direction of secondary education is a reorientation to forming students’ key competencies, which are the basis of harmonious personality development. The problem of teaching English attracts the special attention of scientists because the mentioned language is an effective means of international communication in today’s multilingual world. Therefore, this paper states the difficulty of implementing the modern competence paradigm in teaching the English language in general and during the study of verbs, as it’s one of the most challenging categories in learning and teaching. We analyzed some approaches to interpreting the concept of “verb” in linguistic literature and came to the conclusion that in addition to assigning a verb to parts of speech, researchers consider its following aspects: it acts as a predicate in a sentence, has a general grammatical meaning, identifies time frames of communication. Consequently, taking into consideration the proposed definition of this category by researchers (D. Hacker, G. Mulderig, A Nesvit, A. Raimes, О. Selivanova, C. Weaver) and the current concept of education in Ukraine, we distinguish such necessary aspects that we should hold in mind for defining the English verb: (1) accessibility, (2) reflects the essential features of the verb (3) characterizes the role and function performed by the verb in the sentence, (4) corresponds to the competence paradigm of education. Hence, we suggest the following definition of the English verb in general secondary educational institutions: a verb is a part of speech that describes an action or a condition, usually functions as a predicate in a sentence, and indicates a time frame within which an action or condition occurs. Among the promising areas for further study, we distinguish the justification of the lingua-didactic aspect of teaching the English verb at secondary schools, taking into account the competence-oriented concept of education.

Эффект называемости и его значение для категориального научения

<p style="text-align: justify;">In this review, a new and important nameability effect — the effect of the ease of naming parts of objects on categorical learning — is discussed: new categories are more easily learned when the related objects consist of easy-to-name parts. Although the effect has only recently been described, studies have revealed its importance not only for understanding the nature of category learning, but also for research on the mechanisms of categorization, memory, and cognitive development in general. The origins of the nameability effect, methods of measuring it, and limitations in its manifestation related to task type and category type were also reviewed. Part of the review contains a description of the study of age-specific regularities of the nameability effect and the specifics of its use in adult-guided learning. The nameability effect is compared to the memorability of the stimulus. It is assumed that the nameability can correlate not only with memorability, but alsocausally determine the degree of ease of memorizing stimuli. It is emphasized that higher memorability can be an important factor in categorical learning. It seems important in the future to study the contribution of verbalization processes to the effect of memorability and categorical learning at the same time. The main conclusion of the review is that the nameability effect is an important factor in the success of categorical learning in adults and children from a certain age. This effect needs to be taken into account both in explaining the regularities of learning and in designing the material, for example, by assessing it for the level of nameability.</p>

A computational model of prefrontal and striatal interactions in perceptual category learning

Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system’s response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson’s disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.

Vocabulary and automatic attention: The relation between novel words and gaze dynamics in noun generalization.

Words direct visual attention in infants, children, and adults, presumably by activating representations of referents that then direct attention to matching stimuli in the visual scene. Novel, unknown, words have also been shown to direct attention, likely via the activation of more general representations of naming events. To examine the critical issue of how novel words and visual attention interact to support word learning we coded frame-by-frame the gaze of 17- to 31-month-old children (n = 66, 38 females) while generalizing novel nouns. We replicate prior findings of more attention to shape when generalizing novel nouns, and a relation to vocabulary development. However, we also find that following a naming event, children who produce fewer nouns take longer to look at the objects they eventually select and make more transitions between objects before making a generalization decision. Children who produce more nouns look to the objects they eventually select more quickly following the naming event and make fewer looking transitions. We discuss these findings in the context of prior proposals regarding children's few-shot category learning, and a developmental cascade of multiple perceptual, cognitive, and word-learning processes that may operate in cases of both typical development and language delay. RESEARCH HIGHLIGHTS: Examined how novel words guide visual attention by coding frame-by-frame where children look when asked to generalize novel names. Gaze patterns differed with vocabulary size: children with smaller vocabularies attended to generalization targets more slowly and did more comparison than those with larger vocabularies. Demonstrates a relationship between vocabulary size and attention to object properties during naming. This work has implications for looking-based tests of early cognition, and our understanding of children's few-shot category learning.

Quantitative land price analysis via computer vision from street view images

Land price is an important economic factor in producing meaningful references for regional planners by assisting them in urban planning, economic decision-making, and land resource allocation. However, related studies in land price analysis were mainly focused on the factors of site area and plot ratio, analysis of the potential impact of streetscape factors and human subjective perception on the land price has been lacking, regardless of the impact on supply and demand relationship. Therefore, this study developed a new approach for estimating and analyzing land prices through deep learning that considered the streetscape and human subjective perception factors. In the estimation part, we developed a fine-grained end-to-end deep learning model, the input is street view images, and the output is land prices. In the analysis part, we extracted the semantic segmentation results and human subjective perception scores from the images and combined them with the results of land price estimation. We then introduced a combination of quantitative analysis using the gradient-weighted class activation mapping and L1-based sparse linear regression to model the relationship between the streetscape and the human subjective perception quantitatively. The gradient-weighted class activation mapping was used to determine which categories of pixels deep learning relied on to output the results of land price estimation quantitatively. Combined with segmentation results, we implemented L1-based sparse linear regression and quantitatively determined the importance of the streetscape factors for land prices. Overall, our deep learning model achieved 77.99 % accuracy by only using street views in estimating the land price, and we illustrated the impacts of the streetscape and perception on the land price by showing that perception scores are more important than streetscape factors such as road and mountain in the streetscape. Comfortable, lived-in feel in perception have the most important impact on land price estimation.

A Computational Model Reveals Learning Dynamics During Interpretation Bias Training With Clinical Applications

BackgroundSome psychopathologies, including anxiety and irritability, are associated with biases when judging ambiguous social stimuli. Interventions targeting these biases, or interpretation bias training (IBT), are amenable to computational modeling to describe their associative learning mechanisms. Here, we translated ALCOVE (attention learning covering map), a model of category learning, to describe learning in youths with affective psychopathology when training on more positive judgments of ambiguous face emotions. MethodsA predominantly clinical sample comprised 71 youths (age range, 8–22 years) representing broad distributions of irritability and anxiety symptoms. Of these, 63 youths were included in the test sample by completing an IBT task with acceptable performance for computational modeling. We used a separate sample of 28 youths to translate ALCOVE for individual estimates of learning rate and generalization. In the test sample, we assessed associations between model learning estimates and irritability, anxiety, their shared variance (negative affectivity), and age. ResultsAge and affective symptoms were associated with category learning during IBT. Lower learning rates were associated with higher negative affectivity common in anxiety and irritability. Lower generalization, or improved discrimination between face emotions, was associated with increasing age. ConclusionsThis work demonstrates a functional consequence of age- and symptom-related learning during interpretation bias. Learning measured by ALCOVE also revealed learning types not accounted for in the prior literature on IBT. This work more broadly demonstrates the utility of measurement models for understanding trial-by-trial processes and identifying individual learning styles.

Learning Category Distribution for Text Classification

Label smoothing has a wide range of applications in the machine learning field. Nonetheless, label smoothing only softens the targets by adding a uniform distribution into a one-hot vector, which cannot truthfully reflect the underlying relations among categories. However, learning category relations is of vital importance in many fields such as emotion taxonomy and open set recognition. In this work, we propose a method to obtain the label distribution for each category (category distribution) to reveal category relations. Furthermore, based on the learned category distribution, we calculate new soft targets to improve the performance of model classification. Compared with existing methods, our algorithm can improve neural network models without any side information or additional neural network module by considering category relations. Extensive experiments have been conducted on four original datasets and 10 constructed noisy datasets with three basic neural network models to validate our algorithm. The results demonstrate the effectiveness of our algorithm on the classification task. In addition, three experiments (arrangement, clustering, and similarity) are also conducted to validate the intrinsic quality of the learned category distribution. The results indicate that the learned category distribution can well express underlying relations among categories.

Equivalence partition based morphological similarity clustering for large-scale time series

Data clustering belongs to the category of unsupervised learning and plays an important role in the dynamic systems and big data. The clustering problem of sampled time-series data is undoubtedly much more challenging than that of repeatable sampling data. Most of the existing time-series clustering methods stay at the level of algorithm design, lacking rigorous theoretical foundation and being inefficient in dealing with large-scale time series. To address this issue, in this paper, we establish the mathematical theory for the large-scale time series clustering of dynamic system. The main contributions of this paper include proposing the concept of time series morphological isomorphism, proving that translation isomorphism and stretching isomorphism are equivalent relations, developing the calculation method of morphological similarity measure, and establishing a new time series clustering method based on equivalent partition and morphological similarity. These contributions provide a new theoretical foundation and practical method for the clustering of large-scale time series. Simulation results in typical applications verify the validity and practicability of the aforementioned clustering methods.

Indonesian EFL Students’ Autonomus Learning Implementation in Online Learning

Although studies on online learning have been widely discussed, most of the studies still focus on the students’ experiences adjusting to online learning. However, few studies have analyzed students’ experiences in online learning regarding autonomous learning implementation. This study aimed at identifying Indonesian EFL students' autonomous learning implementation in online learning. A questionnaire adapted from Khotimah et al. (2019) and Hidayati and Husna (2020) was used as a single instrument to obtain data from EFL students from 22 universities around Indonesia. This study sample was selected using a random sampling technique, and the data was analyzed using a simple percentage statistics formula. The results showed how students learned English online in four areas: listening, reading, speaking, and writing. They also showed how the students felt about online learning on their own. These experiences relate to students’ experiences selecting learning materials and learning activity preferences. These preferences are divided into learning English for academic purposes and learning English for pleasure. However, regarding students’ efforts to foster their autonomous online learning, the students had implemented three categories of autonomous learning, namely the initiating process, monitoring process, and evaluating process in their learning process. Hence, the research results are expected to provide new insights for the readers about implementing online learning, which can support the students to be more autonomous learners.

Rule and Exemplar-based Transfer in Category Learning.

We compared the neural systems involved in transfer to novel stimuli via rule application versus exemplar processing. Participants learned a categorization task involving abstraction of a complex rule and then categorized different types of transfer stimuli without feedback. Rule stimuli used new features and therefore could only be categorized using the rule. Exemplar stimuli included only one of the features necessary to apply the rule and therefore required participants to categorize based on similarity to individual previously learned category members. Consistent and inconsistent stimuli were formed so that both the rule and feature similarity indicated the same category (consistent) or opposite categories (inconsistent). We found that all conditions eliciting rule-based transfer recruited a medial prefrontal-anterior hippocampal network associated with schematic memory. In contrast, exemplar-based transfer recruited areas of the intraparietal sulcus associated with learning and executing stimulus-category mappings along with the posterior hippocampus. These results support theories of categorization that postulate complementary learning and generalization strategies based on schematic and exemplar mechanisms.

The Effectiveness of the Use of Assistive Technology Malitung for Learning Outcomes of Students with Mild Intellectual Disabilities

The purposes of this research were to test the effectiveness of the use of assistive technology malitung (manik, tali, hitung) for learning outcomes of students with mild intellectual disabilities in basic arithmetic operations topic and describe their learning outcomes when using assistive technology malitung at Ungaran Public Special School. The method used in this research was Single Subject Research (SSR) with A-B-A design followed by qualitative descriptive research. Data collection techniques used in this research were observation, interviews, questionnaires, and tests. The data analysis techniques used in this research were descriptive statistics, data reduction, data presentation, data interpretation, and conclusion drawing. There were 3 students as research subjects who were students of class IX-C at Ungaran Public Special School. The results of this study are that the learning outcomes of the three research subjects in terms of cognitive, affective, and psychomotor aspects increased and reached category pretty good, good, and very good categories in learning using assistive technology malitung. These results were reinforced by very positive responses from Research Subjects 1 and 3 and positive responses from Research Subjects 2. The conclusions obtained were 1) the use of assistive technology malitung effective on the learning outcomes of students with mild intellectual disabilities on basic arithmetic operations material at Ungaran Public Special School, 2) basic arithmetic operations ability, curiosity, accuracy, responsibility, never give up, enthusiasm for learning, confidence, and interest in learning, problem-solving skills related to addition, subtraction, multiplication, and division operations were achieved in pretty good, good, and very good categories in learning by using assistive technology malitung.

APLICATION OF THE PROBLEM BASED LEARNING MODEL TO IMPROVE MATHEMATICS LEARNING OUTCOMES FOR 4TH GRADE STUDENTS OF SDN NO. 48 INPRES GALUNG UTARA BANGGAE DISTRICT MAJENE REGENCY

The problem behind this research is the low learning outcomes of students in learning Mathematics for class IV SDN No. 48 Inpres Galung Utara, Banggae District, Majene Regency. This study aims to describe the application of the Problem Based Learning model to improve mathematics learning outcomes for fourth grade students of SDN No. 48 Inpres Galung Utara, Banggae District, Majene Regency. The approach used is a qualitative approach and the type of classroom action research (CAR) which consists of 2 cycles, each cycle consisting of 2 meetings with the stages of activities in the form of planning, implementing, observing, and reflecting. The focus of this research is the application of the Problem Based Learning model and student learning outcomes. The research subjects are teachers and fourth grade students at SDN No. 48 Inpres Galung Utara, Banggae District, Majene Regency. In the even semester of the 2022/2023 academic year, there are 18 students. Collecting data using the format of observation, tests and documentation. The data analysis used is qualitative. The results showed that there was an increase in learning both in teacher and student activities and student learning outcomes. The results of this study are teacher teaching activities and student learning activities have increased, in the first cycle the second meeting is in the sufficient category while in the second cycle the second meeting is in the good category. Student learning outcomes in cycle I meeting II are in the high category but student learning outcomes in Mathematics learning have not been completed classically, while in cycle II meeting II the category is very high, student learning outcomes have increased and have been completed classically. The conclusion of this study is that the application of the Problem Based Learning model in learning Mathematics can improve the learning outcomes of fourth grade students at SDN No. 48 Inpres Galung Utara, Banggae district Majene Regency.

Exploring the Intersection of Data Science and Machine Learning: A Comprehensive Review

Data science and machine learning have become increasingly important fields as businesses and industries seek to gain insights, automate processes, and make informed decisions. The intersection of data science and machine learning has produced a range of techniques and algorithms that are capable of processing and analysing vast amounts of data to extract valuable insights. In this comprehensive review paper, we aim to explore the intersection of data science and machine learning by examining the current state of the field and recent advancements. We begin by defining key terms and concepts before delving into studies and literature reviews that provide insight into the effectiveness and limitations of machine learning algorithms. We provide a detailed overview of the three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. For each category, we review several commonly used algorithms, their strengths, and their limitations. We also discuss recent advancements in the field, including deep learning techniques such as convolutional neural networks and recurrent neural networks. Practical examples of how to implement machine learning algorithms in Python are provided using the scikit-learn library. We demonstrate how to use decision trees for credit card fraud detection and Naive Bayes for email spam filtering. We provide step-by- step instructions and code snippets to make it easy for readers to replicate the results. We also review recent studies and literature reviews that have been conducted to evaluate the effectiveness of machine learning algorithms in various applications such as fraud detection, email spam filtering, and image recognition. Finally, we discuss the limitations and ethical considerations that must be taken into account when using machine learning algorithms. We conclude by emphasizing the importance of continued research and development in the field of data science and machine learning to ensure that these powerful tools are used ethically and responsibly. Keywords: Machine learning algorithms, Supervised learning, Unsupervised learning, Reinforcement learning, Scikit-learn library, Deep learning,

Analysis of Learning Effectiveness During the Covid-19 Pandemic at Korwilcam Bidik Elementary School, Adimulyo District, Kebumen Regency

The objectives of this study were (1) to analyze the learning approach in the primary school Korwilcam Bidik, Adimulyo District, Kebumen Regency in the Academic Year 2020/2021 during the Covid-19 pandemic, (2) to analyze learning management at the Korwilcam Bidik primary school, Adimulyo District, Kebumen Regency in Academic Year 2020/2021 during the Covid-19 pandemic (3) to analyze the obstacles that arise and how to overcome them in learning at the Korwilcam Bidik elementary school, Adimulyo District, Kebumen Regency in the 2020/2021 Academic Year during the Covid-19 pandemic (4) to analyze the effectiveness of learning in the primary school Korwilcam Bidik, Adimulyo Subdistrict, Kebumen Regency in the Academic Year 2020/2021 during the Covid-19 pandemic. This research method uses qualitative research, using interview techniques, observation and documentation. In this study the researchers explored and found the teaching approach chosen by the teacher, why the teacher chose that approach, analyzed learning management, the obstacles that arise in learning and how to overcome these obstacles and analyzed qualitatively the effectiveness of learning carried out by teachers at Korwicam Bidik, Adimulyo District, Kebumen Regency. . Analysis using the Miles and Hubermen model. Researchers collect data, reduce, present, verify data and draw conclusions. The results of the study (1) teachers at Korwilcam Bidik, Adimulyo District during the Covid-19 pandemic, their learning used online, offline and a combination of online and offline approaches. (2) good category learning management (3) learning constraints in the form of device availability, network disruption, availability internet packages, student discipline and inadequate competence in technology and teacher information, the constraints are overcome by the teacher by paying attention to the causes of these constraints. (4) based on the analysis of the learning effectiveness indicators, the learning carried out by the teachers at the Korwilcam Bidik, Adimulyo District was effective.

Memory for incidentally learned categories evolves in the post-learning interval.

Humans generate categories from complex regularities evolving across even imperfect sensory input. Here, we examined the possibility that incidental experiences can generate lasting category knowledge. Adults practiced a simple visuomotor task not dependent on acoustic input. Novel categories of acoustically complex sounds were not necessary for task success but aligned incidentally with distinct visuomotor responses in the task. Incidental sound category learning emerged robustly when within-category sound exemplar variability was closely yoked to visuomotor task demands and was not apparent in the initial session when this coupling was less robust. Nonetheless, incidentally acquired sound category knowledge was evident in both cases one day later, indicative of offline learning gains and, nine days later, learning in both cases supported explicit category labeling of novel sounds. Thus, a relatively brief incidental experience with multi-dimensional sound patterns aligned with behaviorally relevant actions and events can generate new sound categories, immediately after the learning experience or a day later. These categories undergo consolidation into long-term memory to support robust generalization of learning, rather than simply reflecting recall of specific sound-pattern exemplars previously encountered. Humans thus forage for information to acquire and consolidate new knowledge that may incidentally support behavior, even when learning is not strictly necessary for performance.

Leveraging conscious and nonconscious learning for efficient AI.

Various interpretations of the literature detailing the neural basis of learning have in part led to disagreements concerning how consciousness arises. Further, artificial learning model design has suffered in replicating intelligence as it occurs in the human brain. Here, we present a novel learning model, which we term the "Recommendation Architecture (RA) Model" from prior theoretical works proposed by Coward, using a dual-learning approach featuring both consequence feedback and non-consequence feedback. The RA model is tested on a categorical learning task where no two inputs are the same throughout training and/or testing. We compare this to three consequence feedback only models based on backpropagation and reinforcement learning. Results indicate that the RA model learns novelty more efficiently and can accurately return to prior learning after new learning with less computational resources expenditure. The final results of the study show that consequence feedback as interpretation, not creation, of cortical activity creates a learning style more similar to human learning in terms of resource efficiency. Stable information meanings underlie conscious experiences. The work provided here attempts to link the neural basis of nonconscious and conscious learning while providing early results for a learning protocol more similar to human brains than is currently available.

A novel meta-learning-based hyperspectral image classification algorithm

Aimed at the hyperspectral image (HSI) classification under the condition of limited samples, this paper designs a joint spectral–spatial classification network based on metric meta-learning. First, in order to fully extract HSI fine features, the squeeze and excitation (SE) attention mechanism is introduced into the spectrum dimensional channel to selectively extract useful HSI features to improve the sensitivity of the network to information features. Second, in the part of spatial feature extraction, the VGG16 model parameters trained in advance on the HSRS-SC dataset are used to realize the transfer and learning of spatial feature knowledge, and then, the higher-level abstract features are extracted to mine the intrinsic attributes of ground objects. Finally, the gated feature fusion strategy is introduced to connect the extracted spectral and spatial feature information on HSI for mining more abundant feature information. In this paper, a large number of experiments are carried out on the public hyperspectral dataset, including Pavia University and Salinas. The results show that the meta-learning method can achieve fast learning of new categories with only a small number of labeled samples and has good generalization ability for different HSI datasets.