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Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data. [ABSTRACT FROM AUTHOR]

Continuous sign language recognition (CSLR) is an intricate task aimed at transcribing sign language sequences from continuous video streams into sentences. Typically, deep learning-based CSLR systems are composed of a visual input encoder for feature extraction and a sequence learning model for the corresponding relationship between the input sequence and output sentence-level labels. The complex nature of sign language, characterized by an extensive vocabulary and many similar gestures and motions, renders CSLR particularly challenging. Additionally, the unsupervised nature of CSLR due to the unavailability of signing glosses for alignment necessitates detailed labeling of each word in a sentence, thereby limiting the amount of training data available. In this paper, we proposes a CSLR framework named KSRB-Net to address these critical problems. The proposed method incorporates a practical module that efficiently captures frame-wise motion information and spatio-temporal context information, which can be embedded into existing feature extraction modules. Additionally, a keyframe extraction algorithm based on the characteristics of the sign language dataset is designed to significantly accelerate the model training and reduce the risk of overfitting. Finally, connectionist temporal classification is employed as the objective function to capture the alignment proposal. The proposed method is validated on three datasets, namely the Chinese TJUT-SLRT, the Chinese USTC-CSL, and the German RWTH-Phoenix-Weather-2014. Experiment results demonstrate that the KSRB-Net achieves 98.40% accuracy and outperforms state-of-the-art methods in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Procedural memory is involved in the acquisition and control of skills and habits that underlie rule and procedural learning, including the acquisition of grammar and phonology. The serial reaction time task (SRTT), commonly used to assess procedural learning, has been shown to have poor stability (test–retest reliability). We investigated factors that may affect the stability of the SRTT in adults. Experiment 1 examined whether the similarity of sequences learned in two sessions would impact stability: test–retest correlations were low regardless of sequence similarity (r <.31). Experiment 2 added a third session to examine whether individual differences in learning would stabilise with further training. There was a small (but nonsignificant) improvement in stability for later sessions (Sessions 1 and 2: r =.42; Sessions 2 and 3: r =.60). Stability of procedural learning on the SRTT remained suboptimal in all conditions, posing a serious obstacle to the use of this task as a sensitive predictor of individual differences and ultimately theoretical advance. [ABSTRACT FROM AUTHOR]

Bonobos appear to show little evidence of learning to make one response (R1) to an AB sequence and a different response (R2) to sequences BB, AA, and BA (Lind et al. PLoS ONE 18(9):e0290546, 2023), yet under different conditions, pigeons can learn this (Weisman et al. Exp Psychol Anim Behav Process 6(4):312, 1980). Aspects of the bonobo procedure may have contributed to this failure. Most important, no response was required in the presence of the stimuli to encourage attention to them. Furthermore, learning to make one response to the target sequence and another to the other sequences involves a bias that allows for better than chance responding. With the two-alternative forced-choice procedure used with the bonobos, the R1 response is correct for one sequence, whereas the R2 response is correct for three sequences. To correct for this, there are three times as many AB trials as each of the other sequences. However, this correction allows a bias to develop in which reinforcement often can be obtained by using only the last stimulus seen as the basis of choice (e.g., when the last stimulus is B respond R1 when the last stimulus is A respond R2). This solution yields reinforcement on five out of six, or 83%, of the trials. In the present experiment with pigeons, using this two-alternative forced choice procedure, most subjects tended to base their choice on the last-seen stimulus. This design allowed subjects to use a suboptimal but relatively effective choice strategy. [ABSTRACT FROM AUTHOR]

Our repertoire of motor skills is filled with sequential movements that need to be performed in a specific order. Here, we used functional magnetic resonance imaging to investigate whether the human hippocampus, a region known to support temporal order in non-motor memory, represents information about the order of sequential motor actions in human participants (both sexes). We also examined such representations in other regions of the motor network (i.e., the premotor cortex, supplementary motor area, anterior superior parietal lobule, and striatum) already known for their critical role in motor sequence learning. Results showed that the hippocampus represents information about movements in their learned temporal position in the sequence, but not about movements or temporal positions in random movement patterns. Other regions of the motor network coded for movements in their learned temporal position, as well as movements and positions in random movement patterns. Importantly, movement coding contributed to sequence learning patterns in primary, supplementary, and premotor cortices but not in striatal and parietal regions. Our findings deepen our understanding of how striatal and cortical regions contribute to motor sequence learning and point to the capacity of the hippocampus to represent movements in their temporal context, an ability possibly explaining its contribution to motor learning. [ABSTRACT FROM AUTHOR]

During visuomotor learning, improvements in motor performance accompany changes in how people use vision. However, the dependencies between altered visual reliance and improvements in motor skill is unclear. The present studies used an online sequence learning task to quantify how changing the availability of visual information affected motor skill learning (study 1) and how changing motor skill affected visual reliance (study 2). Participants used their keyboard to respond to targets falling vertically down a game screen. In study 1 (n = 49), the availability of visual information was altered by manipulating where the targets were visible on the screen. Three experimental groups practiced the task during full or limited vision conditions (when the targets were only visible in specific areas). We hypothesized that limiting visual information would reduce motor learning (i.e., the rate of improvement during training trial blocks). Instead, although participants performed worse during limited vision trials (P < 0.001), there was no difference in learning rate (P = 0.87). In study 2 (n = 119), all participants practiced the task with full vision and their visual reliance (i.e., their performance change between full and limited vision conditions) was quantified before and after training. We hypothesized that with motor learning, visual reliance on future targets would increase, whereas visual reliance on the current targets would decrease. The results of study 2 partially support our hypotheses with visual reliance decreasing for all visual areas (P < 0.001). Together, the results suggest changing motor skill alters how people use vision, but changing visual availability does not affect motor learning. NEW & NOTEWORTHY: Previous research has established how people use visual information changes with motor learning. However, the dependencies of these two processes on each other are unclear. We find that limiting the availability of visual information degrades motor performance but not motor learning. We also find that motor learning reduces the impact of limiting the availability of visual information on motor performance. Together, these results suggest that how people use visual information depends on their motor skill. [ABSTRACT FROM AUTHOR]

In the Serial Reaction Time Task, participants respond to several stimuli usually being unaware that the stimuli follow a predefined sequence while still learning the sequence. In the present study, we aimed to clearly separate explicit intentional learning from implicit incidental learning by either informing participants about all details of the sequence or not informing participants about the existence of the sequence. Further, we explored the influence of anticipatory cues during practice while anticipatory cues were either presented (extrinsically triggered anticipation) or not presented (self-reliant intrinsic anticipation). Participants were tested before and after practice in the Practice Sequence and a Control Sequence. To test automatization, tests were performed in Single-Task and Dual-Task Blocks. Results showed that after learning with explicit instructions, participants memorized the sequence more deeply and executed the sequence faster than after learning without explicit instructions. Further, by learning with anticipatory cues, participants memorized the sequence less deeply and executed the sequence slower than by learning without anticipatory cues. Unexpectedly, automatization was sequence-unspecific and independent of the practice conditions. In conclusion, detailed explicit prior information about the sequence facilitates sequence learning while anticipatory online cues during practice hamper sequence learning. [ABSTRACT FROM AUTHOR]

Human-centered artificial intelligence (HCAI) needs to be able to adapt to anticipated user behavior. We argue that the anticipation capabilities required for HCAI adaptation can be modeled best with the help of a cognitive architecture. This paper introduces an ACT-R cognitive model that uses instance-based learning to observe and learn situations and actions in the form of mental models. These mental models enable the anticipation of the behavior of individual users. The model is applied to a use case of automation surprise in commercial aviation to test how anticipation can best be modeled for cockpit applications. Empirical data from a flight simulator study including behavioral, neurophysiological and eye-tracking measures from 13 pilots were used to evaluate the model. Results show that the accuracy of the model is significantly higher than chance, demonstrating that combining context information, user state data and a cognitive model can enable HCAI adaptation based on anticipated user behavior. [ABSTRACT FROM AUTHOR]

Recent evidence shows that observers are able to learn across-trial regularities as indicated by faster responses to targets whose location was predicted by the target's location on the preceding trial. The present study investigated whether responding to both targets of the pair, as was the case in studies thus far, was needed for learning to occur. Participants searched for a shape singleton target and responded to the line inside. There were two across-trial predicting-predicted regularities regarding target locations: if the target appeared at one specific location on a given trial, it would appear at another specific location on the next trial. Unlike previous experiments, for one of these regularity pairs a response was only needed on either the first or the second target in the pair. Experiment 1 showed that across-trial learning only occurred when responding was required to both targets of a pair. If the response to one target of a pair had to be withheld, no learning occurred. Experiment 2 showed that the absence of learning cannot be attributed to carry-over inhibition resulting from not having to respond. After learning across-trial contingencies, learning remained in place even when the response to the first target of the pair had to be withheld. Our findings show that the execution of the (arbitrary) simple key-press response for both trials of the pair was needed for across-trial statistical learning to occur, whereas solely attending target locations did not result in any learning. [ABSTRACT FROM AUTHOR]

This study examined the attentional demands of movement sequence representations at different temporal points after single- or dual-task practice. The visual-spatial representation encodes the movement based on visual-spatial coordinates such as the target locations. The motor representation encodes the movement in motor coordinates including joint angles and muscle activation patterns. Participants were randomly assigned to a single-task or dual-task practice group. Following acquisition, participants performed two retention tests and inter-manual transfer tests, both under dual-task and single-task. The transfer tests consisted of a mirror and non-mirror test and examined motor and visual-spatial representation development. The main finding is that attentional demands of the sequence representations were not affected by the practice condition. However, movement initiation requires more attention than the end of the movement in both representations. [ABSTRACT FROM AUTHOR]

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S‐R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non‐adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule‐based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto‐parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper‐learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions. [ABSTRACT FROM AUTHOR]

Auditory processing of speech and non-speech stimuli oftentimes involves the analysis and acquisition of non-adjacent sound patterns. Previous studies using speech material have demonstrated (i) children's early emerging ability to extract non-adjacent dependencies (NADs) and (ii) a relation between basic auditory perception and this ability. Yet, it is currently unclear whether children show similar sensitivities and similar perceptual influences for NADs in the non-linguistic domain. We conducted an event-related potential study with 3-year-old children using a sine-tone-based oddball task, which simultaneously tested for NAD learning and auditory perception by means of varying sound intensity. Standard stimuli were A x B sine-tone sequences, in which specific A elements predicted specific B elements after variable x elements. NAD deviants violated the dependency between A and B and intensity deviants were reduced in amplitude. Both elicited similar frontally distributed positivities, suggesting successful deviant detection. Crucially, there was a predictive relationship between the amplitude of the sound intensity discrimination effect and the amplitude of the NAD learning effect. These results are taken as evidence that NAD learning in the non-linguistic domain is functional in 3-year-olds and that basic auditory processes are related to the learning of higher-order auditory regularities also outside the linguistic domain. [ABSTRACT FROM AUTHOR]

Psychomotor slowing has consistently been observed in schizophrenia, however research on motor learning in schizophrenia is limited. Additionally, motor learning in schizophrenia has never been compared with the waning of motor learning abilities in the elderly. Therefore, in an extensive study, 30 individuals with schizophrenia, 30 healthy age-matched controls and 30 elderly participants were compared on sensorimotor learning tasks including sequence learning and adaptation (both explicit and implicit), as well as tracking and aiming. This paper presents new findings on an explicit motor sequence learning task, an explicit verbal learning task and a simple aiming task and summarizes all previously published findings of this large investigation. Individuals with schizophrenia and elderly had slower Movement Time (MT)s compared with controls in all tasks, however both groups improved over time. Elderly participants learned slower on tracking and explicit sequence learning while individuals with schizophrenia adapted slower and to a lesser extent to movement perturbations in adaptation tasks and performed less well on cognitive tests including the verbal learning task. Results suggest that motor slowing is present in schizophrenia and the elderly, however both groups show significant but different motor skill learning. Cognitive deficits seem to interfere with motor learning and performance in schizophrenia while task complexity and decreased movement precision interferes with motor learning in the elderly, reflecting different underlying patterns of decline in these conditions. In addition, evidence for motor slowing together with impaired implicit adaptation supports the influence of cerebellum and the cerebello-thalamo-cortical-cerebellar (CTCC) circuits in schizophrenia, important for further understanding the pathophysiology of the disorder. [ABSTRACT FROM AUTHOR]

Learning regularities in the environment is a fundament of human cognition, which is supported by a network of brain regions that include the hippocampus. In two experiments, we assessed the effects of selective bilateral damage to human hippocampal subregion CA3, which was associated with autobiographical episodic amnesia extending ~50 years prior to the damage, on the ability to recognize complex, deterministic event sequences presented either in a spatial or a non-spatial configuration. In contrast to findings from related paradigms, modalities, and homologue species, hippocampal damage did not preclude recognition memory for an event sequence studied and tested at four spatial locations, whereas recognition memory for an event sequence presented at a single location was at chance. In two additional experiments, recognition memory for novel single-items was intact, whereas the ability to recognize novel single-items in a different location from that presented at study was at chance. The results are at variance with a general role of the hippocampus in the learning and recognition of complex event sequences based on non-adjacent spatial and temporal dependencies. We discuss the impact of the results on established theoretical accounts of the hippocampal contributions to implicit sequence learning and episodic memory. [ABSTRACT FROM AUTHOR]

Prior research investigating whether and how multisensory information facilitates skill learning is quite mixed; whereas some research points to congruent information improving learning, other work suggests that people become reliant on the redundant information, such that its removal ultimately detracts from the ability to perform a unisensory task. We examined this question using the Serial Interception Sequence Learning (SISL) task, a visuo-motor paradigm in which participants implicitly learn a sequence embedded in noise. We investigated whether adding auditory information in different ways would enhance real time sequence learning and whether any benefits of multisensory learning would persist with visual-only testing. Auditory information was used either as feedback on the visuo-motor task (Experiments 1 and 2) or was presented synchronously with visual information during learning (Experiment 3). Robust sequence-specific performance advantages occurred across conditions and experiments; however, auditory information enhanced real-time performance only when it was synchronized with visual information. Participants were significantly more accurate, faster, and more precise with stimulus-locked auditory information during training. Notably, these benefits did not generalize to the visual-only context, suggesting that the benefits of stimulus-locked auditory information are primarily useful only when the perceptual information is present. [ABSTRACT FROM AUTHOR]

The adoption of weighted regularized extreme learning machines (WR-ELMs) has been recognized as an effective approach to addressing class imbalance by differentially weighting sample classes. Traditional batch learning methodologies, however, falter due to their inefficiency in adapting to network restructuring and their inability to process streaming data. By introducing incremental and sequential learning, the incremental weighted regularized extreme learning machine (IWR-ELM) and the online weighted regularized extreme learning machine (OWR-ELM) are proposed in this paper to enhance WR-ELM's flexibility and responsiveness. Specifically, the IWR-ELM facilitates optimal hidden layer node selection, thereby enhancing model adaptability without necessitating full retraining. Conversely, the OWR-ELM is engineered for real-time data stream processing, enabling continuous learning from new data segments without retaining outdated information. We also address the concurrent challenges of concept drift and class imbalance by presenting an enhanced online weighted regularized extreme learning machine, which incorporates enhancement factors to elevate the significance of recent data. Finally, the competitiveness of our proposed algorithm is demonstrated in terms of its training time and performance through extensive experiments conducted on class-imbalanced datasets. Our comprehensive evaluations on diverse class-imbalanced datasets affirm the superior efficiency and performance of our proposed solutions in terms of training speed and accuracy. [ABSTRACT FROM AUTHOR]

Sensitivity to the fixed ordering of actions and events, or deterministic sequence learning, is an important skill throughout adulthood. Yet, it remains unclear whether age deficits in sequencing exist, and we lack a firm understanding of which factors might contribute to age-related impairments when they arise. Though debated, executive functioning, governed by the frontal lobe, may underlie age-related sequence learning deficits in older adults. The present study asked if age predicts errors in deterministic sequence learning across the older adult lifespan (ages 55–89), and whether executive functioning accounts for any age-related declines. Healthy older adults completed a comprehensive measure of frontal-based executive abilities as well as a deterministic sequence learning task that required the step-by-step acquisition of associations through trial-and-error feedback. Among those who met a performance-based criterion, increasing age was positively correlated with higher sequencing errors; however, this relationship was no longer significant after controlling for executive functioning. Moreover, frontal-based executive abilities mediated the relationship between age and sequence learning performance. These findings suggest that executive or frontal functioning may underlie age deficits in learning judgment-based, deterministic serial operations. [ABSTRACT FROM AUTHOR]

Identifying wordlike units in language is typically done by applying a battery of criteria, though how to weight these criteria with respect to one another is currently unknown. We address this question by investigating whether certain criteria are also used as cues for learning an artificial language—if they are, then perhaps they can be relied on more as trustworthy top‐down diagnostics. The two criteria for grammatical wordhood that we consider are a unit's free mobility and its internal immutability. These criteria also map to two cognitive mechanisms that could underlie successful statistical learning: learners might orient themselves around the low transitional probabilities at unit boundaries, or they might seek chunks with high internal transitional probabilities. We find that each criterion has its own facilitatory effect, and learning is best where they both align. This supports the battery‐of‐criteria approach to diagnosing wordhood, and also suggests that the mechanism behind statistical learning may not be a question of either/or; perhaps the two mechanisms do not compete, but mutually reinforce one another. [ABSTRACT FROM AUTHOR]

We studied object-location binding in pigeons using a sequence learning procedure. A sequence of four objects was presented, one at a time at one of four locations on a touchscreen. A single peck at the object ended the trial, and food reinforcement was delivered intermittently. In Experiment 1, a between-subjects design was used to present objects, locations, or both in a regular sequence or randomly. Response time costs on nonreinforced probe tests on which object order, location order, or both were disrupted revealed sequence learning effects. Pigeons encoded location order when it was consistent, but not object order when it alone was consistent. When both were consistent, pigeons encoded both, and showed evidence of object-location binding. In Experiment 2, two groups of pigeons received training on sequences where the same object always appeared at the same location. For some pigeons a consistent sequence was used while for others sequence order was randomized. Only when sequence order was consistent was object-location binding found. These experiments are the first demonstrations of strong and lasting feature binding in pigeons and are consistent with a functional account of learning.

Cartoon movies are a primary source of entertainment for children. However, concerns arise when these movies inadvertently expose children to violent scenes. This paper addresses the challenge of detecting subtle instances of violence within cartoon movies. The main difficulty in this task is the sequential nature of movies, where a sequence of frames must be considered in their given order. Existing methods have not effectively addressed the issue. In this study, we tackled this challenge by employing a sequential model. The research comprises three key steps. Initially, a histogram technique was implemented to select key frames from the video sequences. Subsequently, a Convolutional Neural Network (CNN) was utilized to extract prominent features from these selected key frames. In the third phase, the acquired features were utilized to train a sequential model using sequence-based learning. The model was then refined through transfer learning, using a dataset containing scenes devoid of violence, as well as scenes depicting varying forms of violence, including bloodshed, fights, gunshots, and explosions. A significant contribution of this study is the meticulous categorization of violent scenes into four distinct types, allowing for further investigation into the diverse effects of different violence categories. Furthermore, the study introduces an innovative approach by integrating a dense layer into the sequential model to enhance final classification. The trained model’s performance was comprehensively evaluated using metrics such as F1 score, precision, accuracy, and recall. To validate the effectiveness of the proposed model, it was benchmarked against state-of-the-art methods. This study presents an innovative deep-learning methodology for the identification of violent scenes in cartoon movies. Its potential applications encompass a wide range, including safeguarding children from inappropriate content.

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

Flexible process planning involves selecting and sequencing the requisite operations, and assigning the right machine, tool and access direction to each selected operation for minimising the production cost or the completion time. It is one of the challenging combinatorial optimisation problems due to sequencing flexibility, processing flexibility and operation flexibility. A sequence learning harmony search algorithm is accordingly proposed. Distinctively, the well-designed algorithm searches for the optimal process plan by intelligently finding the proper immediate successor for each selected operation in turn rather than resorting to the common shifting and swapping operators in sequencing. The innovative algorithm does not also require extra efforts to plot the operational precedence graph or the AND/OR-network graph. The experimental results indicate that the proposed algorithm significantly outperforms other heuristics in terms of the quality of solution found and the convergence rate of the algorithm. For the large-scale complicated instances, the proposed algorithm establishes a challenging flag. [ABSTRACT FROM AUTHOR]

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for "chunking" of these letters into "words." The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error‐driven learning. Experiments 1 and 2 showed that error‐driven learning was a better predictor of response latencies than either n‐gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error. [ABSTRACT FROM AUTHOR]

Despite the fact that reliability estimation is crucial for robust inference, it is underutilized in neuroscience and cognitive psychology. Appreciating reliability can help researchers increase statistical power, effect sizes, and reproducibility, decrease the impact of measurement error, and inform methodological choices. However, accurately calculating reliability for many experimental learning tasks is challenging. In this study, we highlight a number of these issues, and estimate multiple metrics of internal consistency and split-half reliability of a widely used learning task on a large sample of 180 subjects. We show how pre-processing choices, task length, and sample size can affect reliability and its estimation. Our results show that the Alternating Serial Reaction Time Task has respectable reliability, especially when learning scores are calculated based on reaction times and two-stage averaging. We also show that a task length of 25 blocks can be sufficient to meet the usual thresholds for minimally acceptable reliability. We further illustrate how relying on a single point estimate of reliability can be misleading, and the calculation of multiple metrics, along with their uncertainties, can lead to a more complete characterization of the psychometric properties of tasks. [ABSTRACT FROM AUTHOR]

The present study investigated whether incidental learning of spatial and temporal associations in hybrid visual and memory search enable observers to predict targets in space and time. In three experiments, observers looked for four, previously memorized, target items across many trials. We examined effects of learning target item sequences (e.g., the butterfly always follows the paint-box), target item-location associations (the butterfly is always in the right corner), and target item-location sequences (the butterfly in the right corner always follows the paint-box in the lower middle-left). Learning effects for the sequences of target items alone were weak. By contrast, we found good learning of target item-location associations and a reliable effect of sequence learning for target item-location associations. These findings suggest that spatiotemporal learning in hybrid search is hierarchical: Only if spatial and non-spatial target features are bound, temporal associations bias attention dynamically to the target expected to occur next. [ABSTRACT FROM AUTHOR]

When human and non-human animals learn sequences, they manage to implicitly extract statistical regularities through associative learning mechanisms. In two experiments conducted with a non-human primate species (Guinea baboons, Papio papio), we addressed simple questions on the learning of simple AB associations appearing in longer noisy sequences. Using a serial reaction time task, we manipulated the position of AB within the sequence, such that it could be either fixed (by appearing always at the beginning, middle, or end of a four-element sequence; Experiment 1) or variable (Experiment 2). We also tested the effect of sequence length in Experiment 2 by comparing the performance on AB when it was presented at a variable position within a sequence of four or five elements. The slope of RTs from A to B was taken for each condition as a measurement of learning rate. While all conditions differed significantly from a no-regularity baseline, we found strong evidence that the learning rate did not differ between the conditions. These results indicate that regularity extraction is not impacted by the position of the regularity within a sequence and by the length of the sequence. These data provide novel general empirical constraints for modeling associative mechanisms in sequence learning. [ABSTRACT FROM AUTHOR]

Auditory processing of speech and non-speech stimuli oftentimes involves the analysis and acquisition of non-adjacent sound patterns. Previous studies using speech material have demonstrated (i) children’s early emerging ability to extract non-adjacent dependencies (NADs) and (ii) a relation between basic auditory perception and this ability. Yet, it is currently unclear whether children show similar sensitivities and similar perceptual influences for NADs in the non-linguistic domain. We conducted an event-related potential study with 3-year-old children using a sine-tone-based oddball task, which simultaneously tested for NAD learning and auditory perception by means of varying sound intensity. Standard stimuli were A × B sine-tone sequences, in which specific A elements predicted specific B elements after variable × elements. NAD deviants violated the dependency between A and B and intensity deviants were reduced in amplitude. Both elicited similar frontally distributed positivities, suggesting successful deviant detection. Crucially, there was a predictive relationship between the amplitude of the sound intensity discrimination effect and the amplitude of the NAD learning effect. These results are taken as evidence that NAD learning in the non-linguistic domain is functional in 3-year-olds and that basic auditory processes are related to the learning of higher-order auditory regularities also outside the linguistic domain.

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing.

Based on the characteristics of digital literature, this paper proposes a new trend in the development of autonomy and human-computer interaction in digital literary creation. In view of the changes in the path of literary dissemination, it describes the mechanism of interaction between the digitization of literature and the audience, as well as the content. Taking the integration of literary creation and technology as an entry point, the Skip-Gram model is used to realize the parameter training of Chinese vocabulary, and combined with the binary language model, the joint distribution probability of each group of words is found and the optimal word splitting scheme with the highest probability is obtained. The attention mechanism of the improved channel effectively enhances the performance of the network model for the sequence learning task and predicts the currently occurring words according to the contextual features, realizing the automatic generation of literary creation content. To assess the effectiveness of the new media technology application, quality assessment indicators are introduced. Literary creation content on the two platforms has a mean value of 3.4 and 3.425, which are both of good quality. The overall user activity was above 20 and reached a peak of 45.265 in August, with a year-on-year increase ranging from 0.648 to 1.589, a significant increase in activity, and the integration of new media technology significantly enhanced the enthusiasm for literary creation.

Chunking mechanisms, the processes of grouping several items together into a single processing unit, are central to several cognitive processes in human and non-human primates and notably to the acquisition of visuomotor sequences. Individuals segment sequences into chunks to perform visuomotor tasks more fluidly, rapidly, and accurately. Using an operant conditioning device, we previously studied the precise mechanisms by which chunks are formed and reorganized during sequence learning. Eighteen Guinea baboons (Papio papio) repeatedly produced the same fixed sequence of nine movements during 1,000 trials by pointing to a moving target on a touch screen. We found that chunking patterns are reorganized during the course of learning, with chunks becoming progressively fewer and longer. We also identified two forms of reorganization of the chunking pattern: the recombination of preexisting chunks and the concatenation of two distinct chunks into a single one. To understand the conditions under which these reorganizations occur, we study here how the stability of a chunk and the stability of chunk boundaries are related to these reorganizations. Our analyses showed that less stable chunks and less stable boundaries are more likely to produce reorganizations. These results provide new evidence about the fine-grained dynamics of chunking mechanisms during sequence learning.

We investigated the procedural learning deficit hypothesis in Developmental Coordination Disorder (DCD) while controlling for global performance such as slower reaction times (RTs) and variability. Procedural (sequence) learning was assessed in 31 children with DCD and 31 age-matched typically developing (TD) children through a serial reaction time task (SRTT). Sequential and random trial conditions were intermixed within five training epochs. Two repeated measures ANOVAs were conducted on a Sequence-Specific Learning Index (SSLI) and a Global Performance Index (GPI, speed/accuracy measure) with Epoch (for SSLI and GPI) and Condition (for GPI) as within-subjects factors, and Group as between-subjects factor. Controlling for RTs differences through normalized RTs, revealed a global reduction of SSLI in children with DCD compared with TD peers suggesting reduced sequence learning skills in DCD. Still, a significant Group x Condition interaction observed on GPI indicated that children from both groups were able to discriminate between sequential and random trials. DCD presented reduced procedural learning skills after controlling for global performance. This finding highlights the importance of considering the general functioning of the child while assessing learning skills in patients. [ABSTRACT FROM AUTHOR]

Unconscious acquisition of sequence structure from experienced events can lead to explicit awareness of the pattern through extended practice. Although the implicit-to-explicit transition has been extensively studied in humans using the serial reaction time (SRT) task, the subtle neural activity supporting this transition remains unclear. Here, we investigated whether frequency-specific neural signal transfer contributes to this transition. A total of 208 participants (107 females) learned a sequence pattern through a multisession SRT task, allowing us to observe the transitions. Session-by-session measures of participants' awareness for sequence knowledge were conducted during the SRT task to identify the session when the transition occurred. By analyzing time course RT data using switchpoint modeling, we identified an increase in learning benefit specifically at the transition session. Electroencephalogram (EEG)/magnetoencephalogram (MEG) recordings revealed increased theta power in parietal (precuneus) regions one session before the transition (pretransition) and a prefrontal (superior frontal gyrus; SFG) one at the transition session. Phase transfer entropy (PTE) analysis confirmed that directional theta transfer from precuneus fi SFG occurred at the pretransition session and its strength positively predicted learning improvement at the subsequent transition session. Furthermore, repetitive transcranial magnetic stimulation (TMS) modulated precuneus theta power and altered transfer strength from precuneus to SFG, resulting in changes in both transition rate and learning benefit at that specific point of transition. Our brain-stimulation evidence supports a role for parietal fi prefrontal theta signal transfer in igniting conscious awareness of implicitly acquired knowledge. [ABSTRACT FROM AUTHOR]

Predictive processes and numerous cognitive, motor, and social skills depend heavily on sequence learning. The visuomotor Serial Reaction Time Task (SRTT) can measure this fundamental cognitive process. To comprehend the neural underpinnings of the SRTT, non-invasive brain stimulation stands out as one of the most effective methodologies. Nevertheless, a systematic list of considerations for the design of such interventional studies is currently lacking. To address this gap, this review aimed to investigate whether repetitive transcranial magnetic stimulation (rTMS) is a viable method of modulating visuomotor sequence learning and to identify the factors that mediate its efficacy. We systematically analyzed the eligible records (n = 17) that attempted to modulate the performance of the SRTT with rTMS. The purpose of the analysis was to determine how the following factors affected SRTT performance: (1) stimulated brain regions, (2) rTMS protocols, (3) stimulated hemisphere, (4) timing of the stimulation, (5) SRTT sequence properties, and (6) other methodological features. The primary motor cortex (M1) and the dorsolateral prefrontal cortex (DLPFC) were found to be the most promising stimulation targets. Low-frequency protocols over M1 usually weaken performance, but the results are less consistent for the DLPFC. This review provides a comprehensive discussion about the behavioral effects of six factors that are crucial in designing future studies to modulate sequence learning with rTMS. Future studies may preferentially and synergistically combine functional neuroimaging with rTMS to adequately link the rTMS-induced network effects with behavioral findings, which are crucial to develop a unified cognitive model of visuomotor sequence learning. [ABSTRACT FROM AUTHOR]

Gamma oscillations may coordinate pre- and postsynaptic neuronal firing to enhance plasticity within the hippocampus. Cross-regional gamma synchronisation may communicate sensory information to the hippocampus during memory formation, and hippocampal representations to the cortex during retrieval. Gamma oscillations nested within ongoing theta oscillations may encode and recall sequences of stimuli. Multiple, distinct oscillations may exist within the canonical gamma band (30–80 Hz), each with complementary roles in episodic memory. Enhanced gamma oscillatory activity (30–80 Hz) accompanies the successful formation and retrieval of episodic memories. While this co-occurrence is well documented, the mechanistic contributions of gamma oscillatory activity to episodic memory remain unclear. Here, we review how gamma oscillatory activity may facilitate spike timing-dependent plasticity, neural communication, and sequence encoding/retrieval, thereby ensuring the successful formation and/or retrieval of an episodic memory. Based on the evidence reviewed, we propose that multiple, distinct forms of gamma oscillation can be found within the canonical gamma band, each of which has a complementary role in the neural processes listed above. Further exploration of these theories using causal manipulations may be key to elucidating the relevance of gamma oscillatory activity to episodic memory. [ABSTRACT FROM AUTHOR]

Introduction: Our previous studies identified a paradoxical implicit motor learning curve in schizophrenia (SZ) and bipolar disorder (BD) patients. This study aimed to verify whether those previously observed deficits may be captured by a new version of the ambidextrous serial reaction time task (SRTT), prepared for use in the MRI. Methods: This study involved 186 participants. A total of 97 participants (33 BD, 33 SZ, and 31 healthy controls, HCs) completed the original, unlimited time response variant of SRTT. A total of 90 individuals (30 BD, 30 SZ, and 30 HCs) underwent a newer, limited response time version of this procedure. Results: There was no significant difference in terms of implicit motor learning indices between both limited and unlimited response time SRTT. Compared to HCs, SZ, and BD patients presented decreased indices of implicit motor learning. Both clinical groups showed a paradoxical learning pattern that differed significantly from the HCs. Moreover, in the SZ group, the pattern depended on the hand performing SRTT. Discussion: The limited response time SRTT variant allowed us to replicate the findings of disrupted implicit motor learning in SZ and BD. The use of this paradigm in further neuroimaging studies may help to determine the neuronal underpinnings of this cognitive dysfunction in the abovementioned clinical groups. [ABSTRACT FROM AUTHOR]

Most animals engage in complex activities that are the combination of simpler actions expressed over a period of time. The mechanisms organizing such sequential behavior have been of long-standing biological and psychological interest. Previously, we observed pigeons' anticipatory behavior with a within-session sequence involving four choice alternatives suggestive of a potential understanding of the overall order and sequence of the items within a session. In that task, each colored alternative was correct for 24 consecutive trials as presented in a predictable sequence (i.e., A first, then B, then C, then D). To test whether these four already-trained pigeons possessed a sequential and linked representation of the ABCD items, we added a second four-item sequence involving new and distinct colored choice alternatives (i.e., E first for 24 trials, then F, then G, then H) and then alternated these ABCD and EFGH sequences over successive sessions. Over three manipulations, we tested and trained trials composed of combinations of elements from both sequences. We determined that pigeons did not learn any within-sequence associations among the elements. Despite the availability and explicit utility of such sequence cues, the data suggest instead that pigeons learned the discrimination tasks as a series of temporal associations among independent elements. This absence of any sequential linkage is consistent with the hypothesis that such representations are difficult to form in pigeons. This pattern of data suggests that for repeated sequential activities in birds, and potentially other animals including humans, there are highly effective, but underappreciated, clock-like mechanisms that control the ordering of behaviors. [ABSTRACT FROM AUTHOR]

Solar irradiance forecasting plays an essential role in efficient solar energy systems and managing power demand sustainably. In present work, a new residual ensemble learning approach, which consists of two advanced base models, namely Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs), is proposed for solar irradiance forecasting. A model performance depends on data utilized for modeling and the modeling approach employed on the data. This paper focuses on both these aspects of the forecast model by proposing a three module approach. Firstly, a mechanism is proposed for the collection and analysis of multiple-site data surrounding the target location. A hexagon gridding system based algorithm is proposed for selection of multiple sites neighboring the target location. Then, correlation and feature importance scores are utilized as measures for feature selection to choose the most relevant data for forecasting target solar irradiance. In the second module, a residual ensemble learning model is proposed to forecast solar irradiance. The proposed framework is inspired by the hybrid forecast mechanism that considers the linear and non-linear characteristics for modeling. Advanced DNN models of Recurrent Neural Networks are also exploited for developing an accurate and robust model. The last module performs the integration of the deep neural network information and predicts the future values of solar irradiance. For a reliable and comprehensive assessment, the proposed framework is validated with data from four different solar power sites obtained from NASA's POWER repository. The residual ensemble model is trained on past 36 years of data as input for forecasting one day ahead, four days ahead and ten days ahead values of solar irradiance. Performance evaluation is carried out by comparing the prediction results with other models, including benchmark persistence, deep neural networks, and recurrent neural network approaches on performance indexes of MSE and RMSE. The proposed model shows an improvement in forecast performance by approximately 2.5 percent in prediction error. The predictive performance and stability make the proposed residual ensemble learning approach a reliable solar irradiance prediction model. [ABSTRACT FROM AUTHOR]