An astonishing regularity in student learning rate.

Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or, do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial prepractice performance, at about 65% accuracy. Despite being in the same course, students' initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.

A shared temporal window of integration across cognitive control and reinforcement learning paradigms: A correlational study.

Cognitive control refers to the ability to override prepotent response tendencies to achieve goal-directed behavior. On the other hand, reinforcement learning refers to the learning of actions through feedback and reward. Although cognitive control and reinforcement learning are often viewed as opposing forces in driving behavior, recent theories have emphasized possible similarities in their underling processes. With this study, we aimed to investigate whether a similar time window of integration could be observed during the learning of control on the one hand, and the learning rate in reinforcement learning paradigms on the other. To this end, we performed a correlational analysis on a large public dataset (n = 522) including data from two reinforcement learning tasks, i.e., a probabilistic selection task and a probabilistic Wisconsin Card Sorting Task (WCST), and data from a classic conflict task (i.e., the Stroop task). Results showed expected correlations between the time scale of control indices and learning rate in the probabilistic WCST. Moreover, the learning-rate parameters of the two reinforcement learning tasks did not correlate with each other. Together, these findings suggest a reliance on a shared learning mechanism between these two traditionally distinct domains, while at the same time emphasizing that value updating processes can still be very task-specific. We speculate that updating processes in the Stroop and WCST may be more related because both tasks require task-specific updating of stimulus features (e.g., color, word meaning, pattern, shape), as opposed to stimulus identity.

WI-TMLEGA: Weight Initialization and Training Method Based on Entropy Gain and Learning Rate Adjustment.

Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using the multilayer perceptron (MLP) model as an example. Initially, entropy gain was used to replace random initial values for weight initialization. Subsequently, an incremental learning rate strategy was employed for weight updates. The model was trained and validated using the MNIST handwritten digit dataset. The experimental results showed that, compared to random initialization, the proposed initialization method improves training effectiveness by 39.8% and increases the maximum recognition accuracy by 8.9%, demonstrating the feasibility of this method in large model applications.

Stimulating human prefrontal cortex increases reward learning.

Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it has been proposed that depressed individuals believe that negative events are more informative than positive events, resulting in higher learning rates from negative outcomes (Pulcu and Browning, 2019). In this proof-of-concept study, we investigated whether transcranial direct current stimulation (tDCS) applied to dorsolateral prefrontal cortex, as commonly used in depression treatment trials, might change learning rates for affective outcomes. Healthy adults completed an established reinforcement learning task (Pulcu and Browning, 2017) in which the information content of reward and loss outcomes was manipulated by varying the volatility of stimulus-outcome associations. Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) increased learning rates specifically for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: tDCS applied during task performance increased learning rates for wins; tDCS applied before task performance decreased both win and loss learning rates. A replication study confirmed the key finding that tDCS to DLPFC during task performance increased learning rates specifically for rewards. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning that are relevant to mood disorders.

Prolonged development of forced-choice recognition when targets are paired with non-corresponding lures.

Previous research suggests that mnemonic discrimination (i.e., the ability to discriminate between previously encountered and novel stimuli even when they are highly similar) improves substantially during childhood. To further understand the development of mnemonic discrimination during childhood, the current study had 4-year-old children, 6-year-old children, and young adults complete the forced-choice Mnemonic Similarity Task (MST). The forced-choice MST offers a significant advantage in the context of developmental research because it is not sensitive to age-related differences in response criteria and includes three test formats that are theorized to be supported by different cognitive processes. A target (i.e., a previously encountered item) is paired with either a novel item (A-X), a corresponding lure (A-A'; i.e., an item mnemonically similar to the target), or a non-corresponding lure (A-B'; i.e., an item mnemonically similar to a different previously encoded item). We observed that 4-year-olds performed more poorly than 6-year-olds on the A-X and A-A' test formats, whereas both 4- and 6-year-olds performed more poorly than young adults on the A-B' test format. The MINERVA 2.2 computational model effectively accounted for these age-related differences. The model suggested that 4-year-olds have a lower learning rate (i.e., probability of encoding stimulus features) than 6-year-olds and young adults and that both 4- and 6-year-olds have greater encoding variability than young adults. These findings provide new insight into possible mechanisms underlying memory development during childhood and serve as the basis for multiple avenues of future research.

The comparison of the effect of two methods of face-to-face and E-learning education on learning, retention, and interest in English language course.

This study compared the effect of two face-to-face(F2F) and e-learning education methods on learning, retention, and interest in English language courses. Participants were EFL students studying at Islamic Azad University, for the academic year 2021-2022. A multiple-stage cluster-sampling method was used to select the target participants. Three hundred and twenty EFL learners participated in the study. Students were studying in different majors: accounting, economics, psychology, physical education, law, management, and sociology. Two English tests were applied, a teacher-made VTS (Vocabulary Size Test) and an achievement test (including reading comprehension and grammar questions). Also, a questionnaire was applied to measure the students' learning interest in F2F and online learning groups. The study found significant differences in learning outcomes related to students' English learning and vocabulary retention rates. It was seen that the E-learning group that participated in online sessions through the Learning Management Systems (LMS) platform outperformed the F2F group. Another critical finding revealed that learners' interest in learning English in E-learning classes was higher than in the F2F group. In addition, all constructs of interest (feeling happy, attention, interest, and participation) were higher in scores in the E-learning than in the F2F group. Language teachers, university instructors, educators, syllabus designers, school administrators, and policymakers might rethink their teaching approaches and incorporate E-learning into the curriculum to meet their students' needs.

Intelligent Detection of Hazardous Goods Vehicles and Determination of Risk Grade Based on Deep Learning.

Currently, deep learning has been widely applied in the field of object detection, and some relevant scholars have applied it to vehicle detection. In this paper, the deep learning EfficientDet model is analyzed, and the advantages of the model in the detection of hazardous good vehicles are determined. The adaptive training model is built based on the optimization of the training process, and the training model is used to detect hazardous goods vehicles. The detection results are compared with Cascade R-CNN and CenterNet, and the results show that the proposed method is superior to the other two methods in two aspects of computational complexity and detection accuracy. Simultaneously, the proposed method is suitable for the detection of hazardous goods vehicles in different scenarios. We make statistics on the number of detected hazardous goods vehicles at different times and places. The risk grade of different locations is determined according to the statistical results. Finally, the case study shows that the proposed method can be used to detect hazardous goods vehicles and determine the risk level of different places.

Multi-Feature Single Target Robust Tracking Fused with Particle Filter.

Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3-4, conv4-4 and conv5-4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance.

The Timescale of Control: A Meta-Control Property that Generalizes across Tasks but Varies between Types of Control.

Prominent models of control assume that conflict and the probability of conflict are signals used by control processes that regulate attention. For example, when conflict is frequent across preceding trials (i.e., high probability of conflict), control processes bias attention toward goal-relevant information on subsequent trials. An important but underspecified question regards the meta-control property of timescale-that is, how far back does the control system "look" to determine the probability of conflict? To address this question, Aben, Verguts, and Van den Bussche (2017) developed a statistical model quantifying the timescale of control. In a flanker task, they observed short timescales for lists with a low probability of conflict (which induce reactive control) and long timescales for lists with a high probability of conflict (which induce proactive control). To investigate the domain generality of these timescales, we applied their model to two additional conflict tasks that manipulated the list-wide probability of conflict. Our findings replicated Aben et al. suggesting meta-control may be task general with respect to timescales operating on the list level. We subsequently modified their model to examine timescale differences for items in the same list that differed in their probability of conflict but not the type of control engaged. We failed to detect a difference in timescales between items. Collectively, the findings demonstrate that differences in the timescale of control are task general and suggest that timescale differences are driven by the type of control engaged and not by the probability of conflict per se.

Wavelet decomposition facilitates training on small datasets for medical image classification by deep learning.

The adoption of low-dose computed tomography (LDCT) as the standard of care for lung cancer screening results in decreased mortality rates in high-risk population while increasing false-positive rate. Convolutional neural networks provide an ideal opportunity to improve malignant nodule detection; however, due to the lack of large adjudicated medical datasets these networks suffer from poor generalizability and overfitting. Using computed tomography images of the thorax from the National Lung Screening Trial (NLST), we compared discrete wavelet transforms (DWTs) against convolutional layers found in a CNN in order to evaluate their ability to classify suspicious lung nodules as either malignant or benign. We explored the use of the DWT as an alternative to the convolutional operations within CNNs in order to decrease the number of parameters to be estimated during training and reduce the risk of overfitting. We found that multi-level DWT performed better than convolutional layers when multiple kernel resolutions were utilized, yielding areas under the receiver-operating curve (AUC) of 94% and 92%, respectively. Furthermore, we found that multi-level DWT reduced the number of network parameters requiring evaluation when compared to a CNN and had a substantially faster convergence rate. We conclude that utilizing multi-level DWT composition in place of early convolutional layers within a DNN may improve for image classification in data-limited domains.

Are efficient learners of verbal stimuli also efficient and precise learners of visuospatial stimuli?

People differ in how quickly they learn information and how long they remember it, and these two variables are correlated such that people who learn more quickly tend to retain more of the newly learned information. Zerr and colleagues [2018. Learning efficiency: Identifying individual differences in learning rate and retention in healthy adults. Psychological Science, 29(9), 1436-1450] termed the relation between learning rate and retention as learning efficiency, with more efficient learners having both a faster acquisition rate and better memory performance after a delay. Zerr et al. also demonstrated in separate experiments that how efficiently someone learns is stable across a range of days and years with the same kind of stimuli. The current experiments (combined N = 231) replicate the finding that quicker learning coincides with better retention and demonstrate that the correlation extends to multiple types of materials. We also address the generalisability of learning efficiency: A person's efficiency with learning Lithuanian-English (verbal-verbal) pairs predicts their efficiency with Chinese-English (visuospatial-verbal) and (to a lesser extent) object-location (visuospatial-visuospatial) paired associates. Finally, we examine whether quicker learners also remember material more precisely by using a continuous measure of recall accuracy with object-location pairs.

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals.

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks.

This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach.

Sensorimotor priors are effector dependent.

During sensorimotor tasks, subjects use sensory feedback but also prior information. It is often assumed that the sensorimotor prior is just given by the experiment and that the details for acquiring this prior (e.g., the effector) are irrelevant. However, recent research has suggested that the construction of priors is nontrivial. To test if the sensorimotor details matter for the construction of a prior, we designed two tasks that differ only in the effectors that subjects use to indicate their estimate. For both a typical reaching setting and an atypical wrist rotation setting, prior and feedback uncertainty matter as quantitatively predicted by Bayesian statistics. However, in violation of simple Bayesian models, the importance of the prior differs across effectors. Subjects overly rely on their prior in the typical reaching case compared with the wrist case. The brain is not naively Bayesian with a single and veridical prior. NEW & NOTEWORTHY Traditional Bayesian models often assume that we learn statistics of movements and use the information as a prior to guide subsequent movements. The effector is merely a reporting modality for information processing. We asked subjects to perform a visuomotor learning task with different effectors (finger or wrist). Surprisingly, we found that prior information is used differently between the effectors, suggesting that learning of the prior is related to the movement context such as the effector involved or that naive models of Bayesian behavior need to be extended.

Cortical neural modulation by previous trial outcome differentiates fast- from slow-learning rats on a visuomotor directional choice task.

To better understand the neural cortical underpinnings that explain behavioral differences in learning rate, we recorded single-unit activity in primary motor (M1) and secondary motor (M2) areas while rats learned to perform a directional (left or right) operant visuomotor association task. Analysis of neural activity during the early portion of the cue period showed that neural modulation in the motor cortex was most strongly associated with two task factors: the previous trial outcome (success or error) and the current trial's directional choice (left or right). Furthermore, the fast learners, defined as those who had steeper learning curves and required fewer learning sessions to reach criterion performance, encoded the previous trial outcome factor more strongly than the directional choice factor. Conversely, the slow learners encoded directional choice more strongly than previous trial outcome. These differences in task factor encoding were observed in both the percentage of neurons and the neural modulation depth. These results suggest that fast learning is accompanied by a stronger component of previous trial outcome in the modulation representation present in motor cortex, which therefore may be a contributing factor to behavioral differences in learning rate. NEW & NOTEWORTHY We chronically recorded neural activity as rats learned a visuomotor directional choice task from a naive state. Learning rates varied. Single-unit neural modulation of two motor areas revealed that the fast learners encoded previous trial outcome more strongly than directional choice, whereas the reverse was true for slow learners. This finding provides novel evidence that rat learning rate is strongly correlated with the strength of neural modulation by previous trial outcome in motor cortex.

Disruption of the Right Temporoparietal Junction Impairs Probabilistic Belief Updating.

Generating and updating probabilistic models of the environment is a fundamental modus operandi of the human brain. Although crucial for various cognitive functions, the neural mechanisms of these inference processes remain to be elucidated. Here, we show the causal involvement of the right temporoparietal junction (rTPJ) in updating probabilistic beliefs and we provide new insights into the chronometry of the process by combining online transcranial magnetic stimulation (TMS) with computational modeling of behavioral responses. Female and male participants performed a modified location-cueing paradigm, where false information about the percentage of cue validity (%CV) was provided in half of the experimental blocks to prompt updating of prior expectations. Online double-pulse TMS over rTPJ 300 ms (but not 50 ms) after target appearance selectively decreased participants' updating of false prior beliefs concerning %CV, reflected in a decreased learning rate of a Rescorla-Wagner model. Online TMS over rTPJ also impacted on participants' explicit beliefs, causing them to overestimate %CV. These results confirm the involvement of rTPJ in updating of probabilistic beliefs, thereby advancing our understanding of this area's function during cognitive processing.SIGNIFICANCE STATEMENT Contemporary views propose that the brain maintains probabilistic models of the world to minimize surprise about sensory inputs. Here, we provide evidence that the right temporoparietal junction (rTPJ) is causally involved in this process. Because neuroimaging has suggested that rTPJ is implicated in divergent cognitive domains, the demonstration of an involvement in updating internal models provides a novel unifying explanation for these findings. We used computational modeling to characterize how participants change their beliefs after new observations. By interfering with rTPJ activity through online transcranial magnetic stimulation, we showed that participants were less able to update prior beliefs with TMS delivered at 300 ms after target onset.

Learning Efficiency: Identifying Individual Differences in Learning Rate and Retention in Healthy Adults.

People differ in how quickly they learn information and how long they remember it, yet individual differences in learning abilities within healthy adults have been relatively neglected. In two studies, we examined the relation between learning rate and subsequent retention using a new foreign-language paired-associates task (the learning-efficiency task), which was designed to eliminate ceiling effects that often accompany standardized tests of learning and memory in healthy adults. A key finding was that quicker learners were also more durable learners (i.e., exhibited better retention across a delay), despite studying the material for less time. Additionally, measures of learning and memory from this task were reliable in Study 1 ( N = 281) across 30 hr and Study 2 ( N = 92; follow-up n = 46) across 3 years. We conclude that people vary in how efficiently they learn, and we describe a reliable and valid method for assessing learning efficiency within healthy adults.

Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking.

Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to be a challenging problem due to the unpredictability of various real-time situations, such as illumination variations, occlusion, fast motion, deformation, and scale variation, even though we only know the initial target position. To address these matters, we used a kernelized-correlation-filter-based translation filter with the integration of multiple features such as histogram of oriented gradients (HOG) and color attributes. These powerful features are useful to differentiate the target from the surrounding background and are effective for motion blur and illumination variations. To minimize the scale variation problem, we designed a correlation-filter-based scale filter. The proposed adaptive model’s updating and dynamic learning rate strategies based on a peak-to-sidelobe ratio effectively reduce model-drifting problems by avoiding noisy appearance changes. The experiment results show that our method provides the best performance compared to other methods, with a distance precision score of 79.9%, overlap success score of 59.0%, and an average running speed of 74 frames per second on the object tracking benchmark (OTB-2015).

Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization.

I discuss top-down modulation of perception in terms of a variable Bayesian learning rate, revealing a wide range of prior hierarchical expectations that can modulate perception. I then switch to the prediction error minimization framework and seek to conceive cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate. This approach retains cognitive penetration as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability. It prevents rampant, relativistic cognitive penetration of perception and yet is consistent with the continuity of cognition and perception.

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters.

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.