Category Learning Research Articles

On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using frequency selectivity to examine category-informative dimension-selective attention

In everyday situations, listeners may selectively attend to acoustic dimensions (e.g., frequency) within complex sounds and ignore other simultaneous dimensions. Speech sound categories are defined over multiple dimensions that vary in informativeness; thus, speech perception may demand selective attention to diagnostic dimensions. Here, we examine how selective attention affects cortical representations of informative acoustic dimensions during category learning. Participants completed a five-day training regimen wherein they learned four novel non-speech categories to criterion (Obasih et al., in prep). Crucially, stimuli were designed such that for two categories, categorization required reliance on – and perhaps selective attention to – acoustic patterns within high-frequency bands; the other two categories were differentiated by acoustic patterns in low-frequency bands. After training, listeners completed an fMRI session using the same non-speech categorization task. Best frequency for each auditory cortex voxel was obtained using tonotopic mapping. We hypothesized that when categorization depended on distinctions in certain frequency ranges (e.g., high frequencies), listeners would selectively attend to those frequencies, resulting in greater recruitment of voxels preferring those frequencies and possibly attenuation of signal in voxels preferring other frequencies. We present preliminary data from our study, with the goal of clarifying the cortical mechanisms supporting dimension-based auditory selective attention.

As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

Two fundamental difficulties when learning novel categories are deciding (a) what information is relevant and (b) when to use that information. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal order of information sampling influences learning. Here, we use the adaptive attention representation model (AARM) to demonstrate that a common set of mechanisms can be used to specify: (a) How the distribution of attention is updated between trials over the course of learning and (b) how attention dynamically shifts among dimensions within a trial. We validate our proposed set of mechanisms by comparing AARM's predictions to observed behavior in four case studies, which collectively encompass different theoretical aspects of selective attention. We use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact during category learning. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence which dimensions are attended to via gaze fixations? (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Robot learning towards smart robotic manufacturing: A review

Robotic equipment has been playing a central role since the proposal of smart manufacturing. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy workloads significantly. Towards the next generation of manufacturing, this review first introduces the comprehensive background of smart robotic manufacturing within robotics, machine learning, and robot learning. Definitions and categories of robot learning are summarised. Concretely, imitation learning, policy gradient learning, value function learning, actor-critic learning, and model-based learning as the leading technologies in robot learning are reviewed. Training tools, benchmarks, and comparisons amongst different robot learning methods are delivered. Typical industrial applications in robotic grasping, assembly, process control, and industrial human-robot collaboration are listed and discussed. Finally, open problems and future research directions are summarised.

Assessment of a Large-Scale Peer Learning Program's Value by Manual Review of Case Submissions.

Prior studies used submission numbers or report addendum rates to measure peer learning programs' (PLP) impact. We assessed the educational value of a PLP by manually reviewing cases submitted to identify factors correlating with meaningful learning opportunities (MLOs). This institutional review board-exempted, retrospective study was performed in a large academic radiology department generating >800,000 reports annually. A PLP facilitating radiologist-to-radiologist feedback was implemented May 1, 2017, with subsequent pay-for-performance initiatives encouraging increasing submissions, >18,000 by 2019. Two radiologists blinded to submitter and receiver identity categorized 336 randomly selected submissions as a MLO, not meaningful, or equivocal, resolving disagreements in consensus review. Primary outcome was proportion of MLOs. Secondary outcomes included percent engagement by subspecialty clinical division and comparing MLO and report addendum rates via Fisher's exact tests. We assessed association between peer learning category, pay-for-performance interventions, and subspecialty division with MLOs using logistic regression. Of 336 PLP submissions, 65.2% (219 of 336) were categorized as meaningful, 27.4% (92 of 336) not meaningful, and 7.4% (25 of 336) equivocal, with substantial reviewer agreement (86.0% [289 of 336], κ= 0.71, 95% confidence interval 0.64-0.78). MLO rate (65.2% [219 of 336]) was five times higher than addendum rate (12.9% [43 of 333]) for the cohort. MLO proportion (adjusted odds ratios 0.05-1.09) and percent engagement (0.5%-3.6%) varied between subspecialty divisions, some submitting significantly fewer MLOs (P < .01). MLO proportion did not vary between peer learning categories. Educational value of a large-scale PLP, estimated through manual review of case submissions, is likely a more accurate measure of program impact. Incentives to enhance PLP use did not diminish the program's educational value.

Analysis indicators of the networks of collaboration for learning in environments virtual

This paper presents the importance of collaborative learning in current education, mainly in higher education, which is where the least research has been done on the use of ICT in the classroom. The experience and use of networks in virtual learning spaces was analyzed in an exploratory way, in order to verify the real advantages of their use and how they favor learning. The research was considered from the training framework of the graduate programs of the Virtual University System. The methodological procedure carried out was to analyze collaboration networks in virtual environments and how they influence the learning of postgraduate students, dimensions and categories were determined. Its approach was both qualitative and qualitative, an intentional sampling was chosen, the dimensions of analysis included collaboration for learning as a central axis.

The attentional boost effect facilitates visual category learning.

Increasing evidence has shown that summary visual statistics, such as the mean size or centroid of locations, can be perceived without focal attention. Here, we tested the role of attention in visual category learning - rapid learning of visual similarities among paintings of the same artist. Participants encoded paintings from two famous artists into memory while simultaneously monitoring a rapid serial visual presentation (RSVP) stream of colored squares, pressing the spacebar for target colors and making no response to distractor colors. Paintings encoded with the RSVP targets were better remembered than those encoded with the RSVP distractors, demonstrating an Attentional Boost Effect. Importantly, pairing one artist's paintings with the RSVP targets led to better visual category learning - participants were more accurate at recognizing novel paintings from this artist, relative to another artist whose paintings were presented with the RSVP distractors. Thus, visual category learning is subjected to the same constraint of attention as exemplar memory, demonstrating common mechanisms for exemplar and category learning.

The benefits of immature cognitive control: How distributed attention guards against learning traps

Cognitive control allows one to focus one’s attention efficiently on relevant information while filtering out irrelevant information. This ability provides a means of rapid and effective learning, but using this control also brings risks. Importantly, useful information may be ignored and missed, and learners may fall into “learning traps” (e.g., learned inattention) wherein they fail to realize that what they ignore carries important information. Previous research has shown that adults may be more prone to such traps than young children, but the mechanisms underlying this difference are unclear. The current study used eye tracking to examine the role of attentional control during learning in succumbing to these learning traps. The participants, 4-year-old children and adults, completed a category learning task in which an unannounced switch occurred wherein the feature dimensions most relevant to correct categorization became irrelevant and formerly irrelevant dimensions became relevant. After the switch, adults were more likely than children to ignore the new highly relevant dimension and settle on a suboptimal categorization strategy. Furthermore, eye-tracking analyses reveal that greater attentional selectivity during learning (i.e., optimizing attention to focus only on the most relevant sources of information) predicted this tendency to miss important information later. Children’s immature cognitive control, leading to broadly distributed attention, appears to protect children from this trap—although at the cost of less efficient and slower learning. These results demonstrate the double-edged sword of cognitive control and suggest that immature control may serve an adaptive function early in development.

Category learning is shaped by the multifaceted development of selective attention

Categories are a fundamental building block of cognition that simplify the multitude of entities we encounter into equivalence classes. By simplifying this barrage of inputs, categories support reasoning about and interacting with their members. For example, despite differences in size, color, and other features, we can treat members of the category of dogs as equivalent, and thus generalize information about any given dog to other dogs. Simplifying entities into categories in adulthood is supported by selective attention, in which people focus on category-relevant attributes, while filtering out category-irrelevant attributes. However, much category learning takes place in infancy and early childhood, when selective attention undergoes substantial development. We designed two experiments to disentangle the contributions of the focusing and filtering aspects of selective attention to category learning over development. Experiment 1 provided evidence that learning simple categories was accompanied by selective attention in both 4- and 5- year-old children and adults. Experiment 2 provided evidence that only focusing contributed to selective attention in 4-year-olds, whereas both focusing and filtering contributed to selective attention in 5-year-olds and adults. Thus, category learning may recruit different aspects of selective attention across development.

Learning Patterns in STEAM Education: A Comparison of Three Learner Profiles

A learner profile is a method of classifying learners through their characteristics. Much of the current research on learner profiles has focused on online learning environments; there is a lack of in-depth category profiling and learning behavior analysis of student profiles in the STEAM context. To address this research demand, this study conducted a cluster analysis based on observed higher-order thinking behaviors, leadership behaviors, and verbal and non-verbal interaction behaviors of 81 primary school students in a STEAM project to explore the differences in learning outcomes, learning perception, and social recognition among different types of learners. The results revealed that STEAM students can be divided into three categories: Thinkers, Speakers, and Followers. There are significant differences between Thinkers and Followers in terms of positive emotions. Speakers and Followers have notable differences in their contributions and active participation. The research results can help teachers acquire a deeper understanding of student types in STEAM and thus provide more relevant and personalized instructional facilitation and class management.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning.

To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

Mathematical problem-solving ability: The impact of self-regulated learning on the system of linear inequalities in two variables

This research aims to analyze the mathematical problem-solving ability of students in terms of self-regulated learning. The method used in this research is the qualitative descriptive method. There are 4 indicators of problem-solving ability in this research, namely understanding the problem, formulating the problem, solving the problem, and re-examining the answers. The four indicators of problem-solving ability can be met by subjects who have a high self-regulated learning category. Subjects with medium self-regulated learning category were only able to fulfill 3 indicators of problem-solving ability. Meanwhile, subjects with low self-regulated learning were only able to fulfill 2 indicators of problem-solving ability. The results showed that the ability to solve problems with high categories was owned by subjects with high self-regulated learning.

Analysis of Students Mathematical Reasoning Abilities in Terms of Self-Regulated Learning

The implementation of research activities in this paper is intended to provide an analysis related to the ability of students to reason mathematically in terms of self-regulated learning using qualitative methods and descriptive approaches. Data collection techniques will be obtained through filling out 6 test questions, 28 questionnaire statements, and interview activities. The subjects to be observed were carried out through a technique called purposive sampling so that 6 students were obtained which were divided into two students in each category. The datta will be arranged sequentially using data analysis technique consisting of data reduction, data presentation, and drawing conclusions. The results of the data found identify that students in the high self-regulated learning category have mathematical reasoning abilities as well. Furthermore, students in the medium categori have the ability to do moderate mathematical reasoning. Finally, students who are in the low category have low mathematical reasoning abilities. So based on the data above, it can be concluded that the higher the level of self-regulated learning. The less likely it is to make mistakes in solving problem related to the ability to do mathematical reasoning. The gives an indication that the better the level of intelligence and critical thinking that a person has, the mathematical reasoning ability will follow in the same.

Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning

This paper presented a review on the capabilities of machine learning algorithms toward Earth observation data modelling and information extraction. The main purpose was to identify new trends in the application of or research on machine learning and Earth observation—as well as to help researchers positioning new development in these domains, considering the latest peer-reviewed articles. A review of Earth observation concepts was presented, as well as current approaches and available data, followed by different machine learning applications and algorithms. Special attention was given to the contribution, potential and capabilities of Earth observation-machine learning approaches. The findings suggested that the combination of Earth observation and machine learning was successfully applied in several different fields across the world. Additionally, it was observed that all machine learning categories could be used to analyse Earth observation data or to improve acquisition processes and that RF, SVM, K-Means, NN (CNN and GAN) and A2C were among the most-used techniques. In conclusion, the combination of these technologies could prove to be crucial in a wide range of fields (e.g., agriculture, climate and biology) and should be further explored for each specific domain.

Substantive Bias and Variation in the Acquisition of /n/~/l/ Alternation

Phonological biases suppose that language learners are predisposed toward certain sound patterns. A widely studied phonological bias is substantive bias, a bias that favors phonetically natural or motivated patterns. Experimental results for substantive bias have been mixed. Against this background, this study explores the possible factors influencing the effect of substantive bias, and the focus is on the input variability. An artificial language learning study on /n/~/l/ alternation by native speakers of Hong Kong Cantonese was conducted. There were two categorical learning conditions and two variable learning conditions. In the categorical learning conditions, all the initial /n/ changed to /l/ either in phonetically motivated or unmotivated contexts. In the variable learning conditions, the dominant percentage of initial /n/ changed to /l/ in phonetically motivated or unmotivated context. Substantive bias arose only in variable learning conditions, which suggests that the substantive bias effect is not global, but it is activated under certain circumstances. This study found that the shape and distribution of input could influence the effect of substantive bias.

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.

The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment.

When a population exhibits collective cognitive alignment, such that group members tend to perceive, remember, and reproduce information in similar ways, the features of socially transmitted variants (i.e., artifacts, behaviors) may converge over time towards culture-specific equilibria points, often called cultural attractors. Because cognition may be plastic, shaped through experience with the cultural products of others, collective cognitive alignment and stable cultural attractors cannot always be taken for granted, but little is known about how these patterns first emerge and stabilize in initially uncoordinated populations. We propose that stable cultural attractors can emerge from general principles of human categorization and communication. We present a model of cultural attractor dynamics, which extends a model of unsupervised category learning in individuals to a multiagent setting wherein learners provide the training input to each other. Agents in our populations spontaneously align their cognitive category structures, producing emergent cultural attractor points. We highlight three interesting behaviors exhibited by our model: (1) noise enhances the stability of cultural category structures; (2) short 'critical' periods of learning early in life enhance stability; and (3) larger populations produce more stable but less complex attractor landscapes, and cliquish network structure can mitigate the latter effect. These results may shed light on how collective cognitive alignment is achieved in the absence of shared, innate cognitive attractors, which we suggest is important to the capacity for cumulative culturalevolution.

Kemampuan Komunikasi Matematis Ditinjau dari Self Regulated Learning pada Materi Sistem Persamaan Linear Dua Variabel

Mathematical communication skills are still low. An analysis is needed to describe mathematical communication skills, including in terms of students' self-regulated learning of the material of a two-variable linear equation system. The research was conducted qualitatively with a descriptive method. The research subjects were three grade VIII junior high school students in Cimuncang, selected purposively. Data collection techniques used mathematical communication skills tests, student self-regulated learning questionnaires, and interviews. The data analysis technique was carried out by data reduction, data presentation, and conclusion. The results showed that in two subjects with a high self-regulated learning category, one of them did not meet the four indicators applied in the mathematical communication ability test. While the other one can meet two indicators. One student with moderate self-regulated learning has not met any of the indicators of mathematical communication skills. Self-regulated learning is a factor that needs to be considered related to students' mathematical communication skills.

Implementation of project-based learning technology within the educational process of higher military institutions

The article defines the category of project-based learning. It is an efficient pedagogical technology, oriented towards integration of theoretical knowledge from the different spheres of science, formation of professional skills, development of creativity, critical thinking, communication, leadership and decision-making. Also, project-based learning is a productive searching activity of cadets that is organized by an instructor to solve particular cognitive and practical tasks that is closely connected with future professional activity of learners.&#x0D; We found that project-based learning technology is an important component of professional training of future military officers and it gives the possibility to use the person-centered approach, activate independent work and enhance cadets’ motivation to future professional activity.&#x0D; The article investigates the types of projects in details. It is stated, that a number of projects are used, while implementing project-based learning technology during the educational process at the higher military institution. They are the following: construction and practical projects, role-plays and game projects, information and research projects, survey projects, productive projects, organization and creative projects. We have proved that the projects can also be divided into individual, personal, team and pair ones on the basis of participants; number. In addition, the projects can be short-term, middle-term, and long-term because of the duration of the activity.&#x0D; The authors found that the project learning technology is realized in the four stages at the higher military institution: substantiation stage, searching stage, technological stage, and concluding stage. The article explains the kinds of work for each stage in details.&#x0D; The notion of didactic conditions was outlined. The authors have proved that these conditions concern reasons and circumstances of realization of the educational process. Didactic conditions contribute to positive results of the process of professional training of future military officers. We decided that didactic conditions for implementation of project-based learning technology within the educational process at the higher military institution can be divided into the four stages: motivation, content, technological, and evaluation conditions. Each of these groups consists of specific didactic conditions, oriented towards the efficiency of project-based learning technology at the higher military institution

DEEP REVIEW ON ALOPECIA AREATA DIAGNOSIS FOR HAIR LOSS-RELATED AUTOIMMUNE DISORDER PROBLEM

Lots of women all over the globe are affected by thinning hair, and the number of females suffering from the disease is growing per year. Another important component in the development of thinning hair is genetics. One of the most important goals is to make a clinical condition. For example, in the area of medicine, categorization is critical since one of the primary goals of the doctor is to determine whether or not a patient suffers from an illness. Alopecia areata is a kind of chronic illness that causes baldness in the affected region. AA may cause baldness for a variety of causes thus, testing may be essential to confirm if it is the source of the loss of hair. Machine learning approaches have shown promise in a variety of fields, including dermatology, and may be useful in identifying alopecia areata for better prediction and diagnosis. Proper detection of an illness is also influenced by the fluctuating character of illness signs. Deep learning algorithms for identifying hair loss levels in males using facial pictures in this research. In this situation, a special training database, including face photos with varying degrees of baldness, has been generated. Furthermore, despite the limited accessibility of hairs in such images, a matching approach for mechanically categorizing face images to design categorization tables of male baldness from the medical field is provided. The outcomes of the experiments demonstrate the potential and efficiency for medical, security, and business apps. Related work in machine learning for hair illness categorization has also been addressed. The main objective of this study to analyze several machine learning and deep learning strategies for the identification of alopecia as well as in humans, as well as to determine the accuracy of extracting features methodologies.