Acquisition Of Rules Research Articles

Learning to learn: Single session acquisition of new rules by freely moving mice.

Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.

Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

Impact of Chat GPT on Academic Performance Among Undergraduate Students in the State of Goa

Artificial intelligence refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include the acquisition of information and rules for using the information, using rules to reach approximate or definite conclusions and self-correction. The study aims to understand how the integration of ChatGPT into educational settings affects students' learning outcomes, study habits, and overall academic performance. It focused on exploring the integration of ChatGPT in the Goa's education sector. Descriptive Analysis, Factor Analysis and Regression Analysis has been carried out on the Data collected. The findings showed a significant influence of ChatGPT on various aspects of academic progress, including Learning Dynamics, Academic Support, Skills Enhancement, Academic Growth and Academic Performance Enhancement. The study highlights the importance of integrating AI technologies responsibly to enhance teaching methods, improve learning experiences, and boost student academic performance. The study concluded that ChatGPT play a significant role in shaping the future of education in Goa, emphasizing the need for ethical and responsible use of AI technologies to optimize student academic performance and enhance learning outcomes among undergraduate students. Keywords: Artificial Intelligence, Chat GPT, Education, Undergraduate, Goa

Evaluation method of e-government audit information based on big data analysis

Abstract In order to improve the problems of low efficiency of intelligent auditing and long approval cycles in the evaluation of e-government audit information, this paper proposes a model for the evaluation of e-government audit information. The model covers the acquisition of audit information, evaluation rules, and e-government auditing. The intuitionistic fuzzy roughness model is applied to determine the distinguishing relationship between objects based on the intuitionistic fuzzy values of different objects and to determine the evaluation rules for audit information. An e-government auditing approach based on big data analysis is also proposed, and a genetic algorithm is used to optimize the relevant parameters of a convolutional neural network to enhance the stability of the e-government auditing model. Audit information evaluation activities are carried out at Changchun Housing Provident Fund Center in Jilin Province, China, whose shared approval information data failure records in 2023 accounted for as high as 70.54% of all documents, with low stability. The number of cases and closure of the provident fund center accepting the extranet from 2017 to 2023 only accounted for 4.57% and 3.99% of the whole. In the audit information evaluation indicators, nine secondary indicators, such as credit management and indirect income, have an evaluation value of more than 0.5 in the “medium” evaluation level, and many indicators have an evaluation value of more than 0 in the “poor” evaluation level, so there are many shortcomings in the business.

Reversing Aging: Decline in Complex Olfactory Learning Can be Rectified by Restoring Intrinsic Plasticity of Hippocampal CA1 Pyramidal Neurons.

The acquisition of complex rules requires modifications in intrinsic plasticity of excitatory neurons within relevant brain areas. Olfactory discrimination (OD) rule learning occludes slow calcium-dependent potassium current (sIAHP ) in piriform cortex (PC) pyramidal neurons, which increases their intrinsic neuronal excitability. Similar learning-induced sIAHP changes are demonstrated in hippocampal CA1. The shutdown of sIAHP is mediated by the metabotropic activation of the kainate subtype glutamatergic receptor, GluK2. Here, the duration of training required for OD rule learning increased significantly as the mice matured and aged is first shown, which appears earlier in 5xFAD mice. At the cellular biophysical level, aging is accompanied by reduction in the post-burst AHP in these neurons, while neuronal excitability remains stable. This is in contrast to aging CA1 neurons that exhibit enhanced post-burst AHPs in previous reports. Kainate reduces post-burst AHP in adults, but not in aged PC neurons, whereas it reduces post-burst AHPs in hippocampal CA1 pyramidal neurons of both young and aged mice. Overexpression of GluK2 in CA1 neurons restores OD learning capabilities in aged wild-type and 5xFAD mice, to a level comparable to young adults. Activation of GluK2 receptors in selectively vulnerable neurons can prevent aging-related cognitive decline is suggested.

Dominance-based rule acquisition of multi-scale single-valued neutrosophic decision system

Rule acquisition is significant in real life and extensively utilized in data mining. Currently, most studies have constructed rule acquisition algorithms based on the equivalence relation. However, these algorithms need to be more suitable for dominance-based decision systems and should consider applications in multi-scale environments. In this paper, we establish the dominance relation of the single-valued neutrosophic rough set model using the ranking method with the relative distance favorable degree. We then introduce this approach into a multi-scale environment to obtain the dominance relation of the multi-scale single-valued neutrosophic rough set model, resulting in two discernibility matrices and functions. We propose the algorithm for lower approximation optimal scale reduction and further examine the method of rule acquisition based on the discernibility matrix. Finally, we apply these algorithms to four random data sets to verify their effectiveness.

Grammar in Language and Listening Acquisition

The existence and self-realization of humans are possible due to language, language faculty, and listening acquisition. Anything animate or inanimate perceived in the external world such as an object, phenomenon, entity, etc. acquires a value due to its meaning (semantics). Due to the semantic and syntax structure of a language and its universal dimension, listening plays a key role in the acquisition of language and grammar rules. The present study aimed to discuss the significance of the acquisition of grammar rules in language and listening acquisition based on current literature. The present study data was based on a systematic literature review. In the study, studies on the relationships among language, language acquisition, listening and grammar were reviewed and the acquisition of grammar rules during language and listening acquisition was discussed. The study concluded that listening was effective in the acquisition of grammar rules.

Comparative analysis of morphosyntactic rule learning among monolingual, bilingual, and trilingual speakers: a study on Spanish preterite forms

ABSTRACT The research on language acquisition and retention has primarily focused on monolinguals and bilinguals, with comparatively few studies including trilinguals. To address this gap, the current study compares the acquisition and retention of a novel morphosyntactic rule in Spanish in twelve monolinguals, twelve bilinguals, and twelve trilinguals. The participants underwent training on the preterite verb tense in Spanish, and their ability to correctly identify the rule was tested after two days of training. The study also investigated the effect of verb type on the acquisition of novel morphosyntactic rules in monolinguals and multilinguals. The results revealed that all participants performed better during the delayed retention phase than the other phases. However, bilingual participants demonstrated significantly better accuracy in identifying newly learned tense forms than their monolingual and trilingual counterparts. The study also revealed that regular past tense forms were easier to learn than irregular past tense forms. Overall, the findings from this study suggest that individuals who speak more than two languages may face challenges in acquiring additional languages.

When Children's Production Deviates From Observed Input: Modeling the Variable Production of the English Past Tense.

As children gradually master grammatical rules, they often go through a period of producing form-meaning associations that were not observed in the input. For example, 2- to 3-year-old English-learning children use the bare form of verbs in settings that require obligatory past tense meaning while already starting to produce the grammatical -ed inflection. While many studies have focused on overgeneralization errors, fewer studies have attempted to explain the root of this earlier stage of rule acquisition. In this work, we use computational modeling to replicate children's production behavior prior to the generalization of past tense production in English. We illustrate how seemingly erroneous productions emerge in a model, without being licensed in the grammar and despite the model aiming at conforming to grammatical forms. Our results show that bare form productions stem from a tension between two factors: (1) trying to produce a less frequent meaning (the past tense) and (2) being unable to restrict the production of frequent forms (the bare form) as learning progresses. Like children, our model goes through a stage of bare form production and then converges on adult-like production of the regular past tense, showing that these different stages can be accounted for through a single learningmechanism.

Activation of prefrontal cortex and striatal regions in rats after shifting between rules in a T-maze.

Prefrontal cortical and striatal areas have been identified by inactivation or lesion studies to be required for behavioral flexibility, including selecting and processing of different types of information. In order to identify these networks activated selectively during the acquisition of new reward contingency rules, rats were trained to discriminate orientations of bars presented in pseudorandom sequence on two video monitors positioned behind the goal sites on a T-maze with return arms. A second group already trained in the visual discrimination task learned to alternate left and right goal arm visits in the same maze while ignoring the visual cues still being presented. In each experimental group, once the rats reached criterion performance, the brains were prepared after a 90-min delay for later processing for c-fos immunohistochemistry. While both groups extinguished a prior strategy and acquired a new rule, they differed by the identity of the strategies and previous learning experience. Among the 28 forebrain areas examined, there were significant increases in the relative density of c-fos immunoreactive cell bodies after learning the second rule in the prefrontal cortex cingulate, the prelimbic and infralimbic areas, the dorsomedial striatum and the core of the nucleus accumbens, the ventral subiculum, and the central nucleus of the amygdala. These largely correspond to structures previously identified in inactivation studies, and their neurons fire synchronously during learning and strategy shifts. The data suggest that this dynamic network may underlie reward-based selection for action-a type of cognitive flexibility.

Rule acquisition of three‐way semi‐concept lattices in formal decision context

AbstractThree‐way concept analysis is an important tool for information processing, and rule acquisition is one of the research hotspots of three‐way concept analysis. However, compared with three‐way concept lattices, three‐way semi‐concept lattices have three‐way operators with weaker constraints, which can generate more concepts. In this article, the problem of rule acquisition for three‐way semi‐concept lattices is discussed in general. The authors construct the finer relation of three‐way semi‐concept lattices, and propose a method of rule acquisition for three‐way semi‐concept lattices. The authors also discuss the set of decision rules and the relationships of decision rules among object‐induced three‐way semi‐concept lattices, object‐induced three‐way concept lattices, classical concept lattices and semi‐concept lattices. Finally, examples are provided to illustrate the validity of our conclusions.

Second language acquisition of grammatical rules: The effects of learning condition, rule difficulty, and executive function – CORRIGENDUM

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Preventing Agricultural Non-Point Source Pollution in China: The Effect of Environmental Regulation with Digitization

Environmental regulation (ER) is essential to preventing agricultural non-point source pollution (ANSP). Prior research has focused on the effect of ER on agricultural pollution (AP), but little is known about the impact of ER following digitization on preventing AP, particularly ANSP. Based on the spatial heterogeneity, the effect of ER was examined using a geographic detector tool with provincial panel data from 2010 to 2020 in rural China. The results show that ER is a driver in preventing ANSP, primarily because of the constraint on farmers' behavior. Digitization positively affects the prevention of ANSP, as the new impetus for the infrastructure, technology, and capital is supported. The interaction between ER and digitalization forms a driving effect on the prevention of ANSP, indicating that digitalization constitutes the path dependence of farmers' rule acquisition and perception and addresses the "free riding" dilemma of farmers' participation, thereby enabling the incentive of ER to make agricultural production green and efficient. These findings indicate that the endogenous factor of digitization allowing ER is essential to preventing ANSP.

The Importance of High-Frequency Bandwidth on Speech and Language Development in Children: A Review of Patricia Stelmachowicz's Contributions to Pediatric Audiology.

We review the literature related to Patricia Stelmachowicz's research in pediatric audiology, specifically focusing on the influence of audibility in language development and acquisition of linguistic rules. Pat Stelmachowicz spent her career increasing our awareness and understanding of children with mild to severe hearing loss who use hearing aids. Using a variety of novel experiments and stimuli, Pat and her colleagues produced a robust body of evidence to support the hypothesis that development moderates the role of frequency bandwidth on speech perception, particularly for fricative sounds. The prolific research that came out of Pat's lab had several important implications for clinical practice. First, her work highlighted that children require access to more high-frequency speech information than adults in the detection and identification of fricatives such as /s/ and /z/. These high-frequency speech sounds are important for morphological and phonological development. Consequently, the limited bandwidth of conventional hearing aids may delay the formation of linguistic rules in these two domains for children with hearing loss. Second, it emphasized the importance of not merely applying adult findings to the clinical decision-making process in pediatric amplification. Clinicians should use evidence-based practices to verify and provide maximum audibility for children who use hearing aids to acquire spoken language.

Second language acquisition of grammatical rules: The effects of learning condition, rule difficulty, and executive function

AbstractLearning a new language is an important goal that many individuals find difficult to achieve, particularly during adulthood. Several factors have related this variability to different extrinsic (learning condition, difficulty of the materials) and intrinsic (cognitive abilities) factors, but the interaction between them is barely known. In two experiments, participants learned English grammar rules in intentional (Experiment 1) or explicit (Experiment 2), and incidental learning-contexts. Overall, results of this study indicated that intentional-explicit conditions benefitted rule-learning, as compared to incidental conditions. This benefit was mainly present when participants were learning an easy-rule; explicit and incidental learning did not differ in the case of participants learning a difficult rule (Experiment 2). Moreover, individual differences in executive functioning predicted successful learning in interaction with difficulty. When learning an easy-rule, proactive control facilitated intentional learning. In contrast, when participants were learning a complex-rule, incidental learning was enhanced by lower involvement of proactive control.

Retracted] Multisensor Cross‐Media Data Mining Method Assisted by Expert System

The information generated by the multifaceted and multilevel processing of the data of multiple sensors is more meaningful than the information obtained by a single sensor and provides accurate information and decision‐making basis for various application systems. How to apply data mining theory to the multisensor cross‐media field has become a research hotspot. Through the analysis of multisensor cross‐media data, it is of great significance to dig out the important rules, information, or knowledge hidden in it and use it for cross‐media retrieval engine. The rule acquisition is the “bottleneck” problem of the expert system. This paper adopts the data mining method based on the rough set to acquire the rules and improves the basic algorithm of attribute reduction. Using the attribute reduction algorithm and the heuristic value reduction algorithm, the calculation is simplified and the reduction efficiency is improved. In the presentation, according to the characteristics of cross‐media and the application requirements of expert systems, this paper takes the case representation based on features as the basis and classifies cases according to feature attributes. In case retrieval, according to the hierarchical structure of case features, the entire case database is organized into a multilevel hierarchical index structure. In this paper, a cross‐media retrieval engine is constructed from the perspective of classifier design, and the Euclidean distance is used as the similarity matching model of image content. The mutual retrieval of images and audios preliminarily forms the design process of retrieval from one media type to another and establishes a corresponding cross‐media index. The experimental results show that the algorithm has better processing effect and higher accuracy than other algorithms. Different k‐nearest neighbor values were selected in the experiment, and it reached about 96% in the test environment of libsvm toolbox, which is better than the processing results of LE and LLE algorithms.

Rule acquisition in generalized multi-scale information systems with multi-scale decisions

In this paper, knowledge acquisition in the sense of deriving IF-THEN rules in multi-scale information systems with multi-scale decision attributes is investigated. Specifically, the concept of a generalized multi-scale information system with a multi-scale decision attribute, called generalized multi-scale decision information table (GMDIT for short), is first introduced. Such a system is a multi-scale decision table in which each condition or decision attribute at each object can take different values under different scales. The notion of scale selections for a GMDIT, which is mainly used to determine individual decision tables, is then defined. Information granules and their properties with different scale selections in GMDITs are also described. Optimal scale selections which are used to determine proper decision tables for final decision in inconsistent GMDITs are further formulated. Local optimal scale selections to obtain more concise decision rules for different objects are presented. Finally, attribute reducts based on optimal scale selections are derived and decision rules hidden in inconsistent GMDITs are unraveled.

The acquisition of L2 Hungariangrammar rules and the implicit-explicit debate

Abstract This study contributes to the explicit implicit debate by investigating to which extent adult learners of Hungarian develop knowledge of new grammar rules, and the proficiency to use them, through listening activities complemented with explicit grammar instruction or not. In a quasi-experimental study, we compared the acquisition of three specific rules of Hungarian grammar by 80 Flemish adult students in two conditions. Both methods involved listening activities, but in one condition explicit rule explanation and practice was given and in the other one, meaningful activities were created without grammar explanation. The findings indicate that for teaching simple rules, compared to more complex grammar features, listening activities and exposure to comprehensible input have a significantly positive effect on grammar acquisition at the basic level. The explicit condition did not have an added beneficial effect. Exposure to L2 is the main driver of language development.

A novel approach to attribute reduction and rule acquisition of formal decision context

A novel approach to attribute reduction and rule acquisition of formal decision context

Modulation of Spatial Memory Deficit and Hyperactivity in Dopamine Transporter Knockout Rats via α2A-Adrenoceptors.

Attention deficit hyperactivity disorder (ADHD) is manifested by a specific set of behavioral deficits such as hyperactivity, impulsivity, and inattention. The dopamine neurotransmitter system is postulated to be involved in the pathogenesis of ADHD. Guanfacine, a selective α2A-adrenoceptor agonist, is prescribed for ADHD treatment. ADHD also is known to be associated with impairment of multiple aspects of cognition, including spatial memory, however, it remains unclear how modulation of the norepinephrine system can affect these deficits. Hyperdopaminergic dopamine transporter knockout (DAT-KO) rats are a valuable model for investigating ADHD. The DAT-KO rats are hyperactive and deficient in spatial working memory. This work aimed to evaluate the effects of noradrenergic drugs on the fulfillment of spatial cognitive tasks by DAT-KO rats. The rats were tested in the Hebb – Williams maze during training and following noradrenergic drugs administration. The efficiency of spatial orientation was assessed as to how fast the animal finds an optimal way to the goal box. Testing in a new maze configuration allowed us to evaluate the effects of drug administration after the acquisition of the task rules. The behavioral variables such as the distance traveled, the time to reach the goal box, and the time spent in the error zones were analyzed. It has been observed that α2A-adrenoceptor agonist Guanfacine (0.25 mg/kg) had only a minimal inhibitory effect on hyperactivity of DAT-KO rats in the maze but significantly ameliorated their perseverative pattern of activity and reduced the time spent in the error zones. In contrast, α2A-adrenoceptor antagonist Yohimbine, at the dose of 1 mg/kg, increased the distance traveled by DAT-KO rats and elevated the number of perseverative reactions and the time spent in the error zones. Guanfacine caused minimal effects in wild-type rats, while Yohimbine altered several parameters reflecting a detrimental effect on the performance in the maze. These data indicate that modulation of α2A-adrenoceptor activity potently affects both dopamine-dependent hyperactivity and cognitive dysfunctions. Similar mechanisms may be involved in the beneficial effects of Guanfacine on cognitive deficits in ADHD patients. This study further supports the translational potential of DAT-KO rats for testing new pharmacological drugs.