Self-paced Manner Research Articles

How does musical rhythm influence grammatical processing at the neurophysiological level?

Numerous behavioral studies have demonstrated a rhythmic priming effect (RPE) on grammatical processing using grammaticality judgment tasks (GJT), where participants performed better following regular rhythmic sequences compared to baseline conditions or irregular rhythmic sequences (i.e. auditory rhythmic sequences with violated metrical structure). Only a few studies, however, have explored neurophysiological RPE in grammatical processing. Such neurophysiological investigations have been limited to GJT presented auditorily, have been primarily focused on the French- and German-speaking adult participants, and have rarely used baseline nonpriming conditions. The objective of the present study was to investigate neurophysiological correlates of the RPE in the GJT presented in visual modality. In the current study, we registered a 128-channel electroencephalogram while Russian-speaking adolescents performed a visual GJT, where each sentence was presented word by word in a self-paced manner. Before each experimental block, participants listened to regular rhythmic sequences, irregular rhythmic sequences, or silence. We observed that the late negativity in the event-related potential was larger for the ungrammatical condition compared to the grammatical condition only after the presentation of irregular rhythmic sequences. This effect, referred to as the N600 component in previous research, has been associated with increased cognitive complexity. In conclusion, results suggest that exposure to irregular rhythmic stimulation may lead to increased cognitive demand. This is attributed to the complexity associated with concurrently executing the GJT and managing rhythmic disruption, consequently increasing the strain on working memory resources.

Unifying Multi-Modal Uncertainty Modeling and Semantic Alignment for Text-to-Image Person Re-identification

Text-to-Image person re-identification (TI-ReID) aims to retrieve the images of target identity according to the given textual description. The existing methods in TI-ReID focus on aligning the visual and textual modalities through contrastive feature alignment or reconstructive masked language modeling (MLM). However, these methods parameterize the image/text instances as deterministic embeddings and do not explicitly consider the inherent uncertainty in pedestrian images and their textual descriptions, leading to limited image-text relationship expression and semantic alignment. To address the above problem, in this paper, we propose a novel method that unifies multi-modal uncertainty modeling and semantic alignment for TI-ReID. Specifically, we model the image and textual feature vectors of pedestrian as Gaussian distributions, where the multi-granularity uncertainty of the distribution is estimated by incorporating batch-level and identity-level feature variances for each modality. The multi-modal uncertainty modeling acts as a feature augmentation and provides richer image-text semantic relationship. Then we present a bi-directional cross-modal circle loss to more effectively align the probabilistic features between image and text in a self-paced manner. To further promote more comprehensive image-text semantic alignment, we design a task that complements the masked language modeling, focusing on the cross-modality semantic recovery of global masked token after cross-modal interaction. Extensive experiments conducted on three TI-ReID datasets highlight the effectiveness and superiority of our method over state-of-the-arts.

Multimodal aspects of sentence comprehension: Do facial and color cues interact with processing negated and affirmative sentences?

Negation is usually considered as a linguistic operator reversing the truth value of a proposition. However, there are various ways to express negation in a multimodal manner. It still remains an unresolved issue whether nonverbal expressions of negation can influence linguistic negation comprehension. Based on extensive evidence demonstrating that language comprehenders are able to instantly integrate extralinguistic information such as a speaker's identity, we expected that nonverbal cues of negation and affirmation might similarly affect sentence comprehension. In three preregistered experiments, we examined how far nonverbal markers of negation and affirmation-specifically, the so-called "not face" (see Benitez-Quiroz et al., 2016) and red or green color (see Dudschig et al., 2023)-interact with comprehending negation and affirmation at the sentential level. Participants were presented with photos ("not face" vs. positive control; Experiments 1 and 2) or color patches (red vs. green; Experiment 3). They then read negated and affirmative sentences in a self-paced manner or judged the sensibility of negated and affirmative sentences (e.g., "No, I do not want to sing" vs. "Yes, I would like to buy a sofa"). Both frequentist statistics and Bayes factors resulting from linear mixed-effects analyses showed that processing times for negated and affirmative sentences were not significantly modulated by the nonverbal features under investigation. This indicates that their influence might not extend to sentential negation or affirmation comprehension. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multi-muscle synergies in preparation for gait initiation in Parkinson’s disease

ObjectiveWe investigated changes in indices of muscle synergies prior to gait initiation and the effects of gaze shift in patients with Parkinson’s disease (PD). A long-term objective of the study is to develop a method for quantitative assessment of gait-initiation problems in PD. MethodsPD patients without clinical signs of postural instability and two control groups (age-matched and young) performed a gait initiation task in a self-paced manner, with and without a quick prior gaze shift produced by turning the head. Muscle groups with parallel scaling of activation levels (muscle modes) were identified as factors in the muscle activation space. Synergy index stabilizing center of pressure trajectory in the anterior-posterior and medio-lateral directions (indices of stability) was quantified in the muscle mode space. A drop in the synergy index in preparation to gait initiation (anticipatory synergy adjustment, ASA) was quantified. ResultsCompared to the control groups, PD patients showed significantly smaller synergy indices and ASA for both directions of the center of pressure shift. Both PD and age-matched controls, but not younger controls, showed detrimental effects of the prior gaze shift on the ASA indices. ConclusionsPD patients without clinically significant posture or gait disorders show impaired stability of the center of pressure and its diminished adjustment during gait initiation. SignificanceThe indices of stability and ASA may be useful to monitor pre-clinical gait disorders, and lower ASA may be relevant to emergence of freezing of gait in PD.

Effect of Anticipatory Shooting Strategy on Performance Consistency in Skilled Elite Archer

PURPOSE: This study examined the effect of anticipatory control strategies on stable upright posture and consistency in archery performance among skilled elite archers.METHODS: Nine skilled archery players participated in this study and performed repeated shooting trials under two different shooting conditions: clicker and non-clicker. In the clicker condition, archers shot in response to clicker signals, whereas in the non-clicker condition, they used an anticipatory strategy to determine shooting time in a self-paced manner without using the clicker. A motion capture system with six infrared cameras was used to measure the coordinates of the bow and archers’ hands, which were then used to calculate the aiming precision index and draw-related variables. Electromyography of the lower leg muscles and the center of pressure (COP) were also analyzed for a short period immediately before release to determine the differences in anticipatory postural adjustments (APAs) between the two shooting conditions.RESULTS: The non-clicker condition resulted in a relatively short drawing duration and better precision index. The COP speed rapidly increased immediately before the release (i.e., APAs), and the rate of increase was lower in the non-clicker condition than in the clicker shooting condition. Furthermore, smaller APAs were significantly correlated with better-aiming precision in the non-clicker condition.CONCLUSION: These findings suggest that using an anticipatory strategy rather than reacting to a clicker can improve archery performance consistency by reducing APA and ensuring a stable shooting posture. This strategy can be used in archery training to predict clicker signals during the aim-release stage.

Instructional Competencies of Laboratory School Teachers in Remote Blended Learning

The teaching-learning process depends heavily on teachers. As a result, they are expected to be knowledgeable in a wide range of competencies and to continue to develop their knowledge, abilities, and experiences, especially in terms of mastery of the subject matter, teaching, classroom management, and evaluation now that institutions have adopted a remote blended learning approach. The participants were eighteen teachers from Bukidnon State University, specifically the Elementary Laboratory School, and they were purposively chosen. A descriptive method of research using quantitative data was employed. The data the researcher wanted to collect needed to come directly from people, in this case, the teachers. The mean and standard deviation were used to explore the teachers’ instructional competencies, while frequency and percentage were used to determine the factors affecting the acquisition of teachers’ instructional competencies. Evaluation skills emerged as the topmost instructional competence practiced by teachers. From the instructional competencies of teachers, the following were the topmost indicators: teach the subject matter in a clear and simple manner; organize and present the subject matter clearly and coherently utilizing various learning materials such as PPT, learning kits, modules, worksheets, video clips, and others; share the link for the virtual class in advance and start the learning activities on time; and evaluate the performance of the learners on the basis of the learning objectives through quizzes and major examinations done in a self-paced manner.

Home-Enclosure-Based Behavioral and Wireless Neural Recording Setup for Unrestrained Rhesus Macaques.

Electrophysiological studies with behaving nonhuman primates often require the separation of animals from their social group as well as partial movement restraint to perform well-controlled experiments. When the research goal per se does not mandate constraining the animals' movements, there are often still experimental needs imposed by tethered data acquisition. Recent technological advances meanwhile allow wireless neurophysiological recordings at high band-width in limited-size enclosures. Here, we demonstrate wireless neural recordings at single unit resolution from unrestrained rhesus macaques while they performed self-paced, structured visuomotor tasks on our custom-built, stand-alone touchscreen system [eXperimental Behavioral Instrument (XBI)] in their home environment. We were able to successfully characterize neural tuning to task parameters, such as visuo-spatial selectivity during movement planning and execution, as expected from existing findings obtained via setup-based neurophysiology recordings. We conclude that when movement restraint and/or a highly controlled, insulated environment are not necessary for scientific reasons, cage-based wireless neural recordings are a viable option. We propose an approach that allows the animals to engage in a self-paced manner with our XBI device, both for fully automatized training and cognitive testing, as well as neural data acquisition in their familiar environment, maintaining auditory and sometimes visual contact with their conspecifics.

EEG source derived salience network coupling supports real-world attention switching

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.

Does implicit theory of intelligence moderate judgment of learning-based study time allocation?

Prior research demonstrated that the theory of intelligence (TOI), which included incremental theory (intelligence is a malleable trait and develops incrementally) and entity theory (intelligence is a fixed and stable trait), affected metacognitive control processes. We focused on metacognitive control processes, such as study time allocation, and examined whether TOI moderated the relationship between judgments of learning (JOLs) and study time allocation (JOL-based study time allocation). In the experiments, participants were asked to remember word pairs and make JOLs during the initial study phase. Subsequently, they restudied these in a self-paced manner. Our results suggest that the TOI did not moderate JOL-based study time allocation. Experiment 1 showed that participants allocated more study time to lower JOLs items in a laboratory setting. Experiment 2 obtained similar results in an online setting. These results suggest that individuals devote more study time to poorly learned (lower JOLs) items, regardless of the TOI.

Prefrontal Dopaminergic Regulation of Cue-Guided Risky Decision-Making Performance in Rats.

Risky decision-making is the decision made by individuals when they know the probability of each outcome. In order to survive in unpredictable environments, it is necessary for individuals to assess the probability of events occurring to an make appropriate decisions. There are few studies on the neural basis of risky decision-making behavior guided by external cues, which is related to the relative paucity of animal behavioral paradigms. Previous studies have shown that the prefrontal cortex (PFC) plays a key role in risk-based decision-making. The PFC receives projections from the dopamine (DA) system from the ventral tegmental area of the midbrain. The mesocorticolimbic DA system regulates the judgments of reward and value in decision-making. However, the specific receptor mechanism for prefrontal DA regulation of cue-guided risky decision-making behavior remains unclear. Here we established a cue-guided risky decision-making behavioral paradigm (RDM task) to detect the behavior of rats making decisions between a small certain reward and a large uncertain reward in a self-paced manner. The D1 receptor antagonist SCH-23390 (5 mM) or agonist SKF-82958 (5 mM), and the D2 receptor antagonist thioridazine hydrochloride (5 mM) or agonist MLS-1547 (5 mM) was injected into the mPFC, respectively, to investigate how the behavior in the RDM task was changed. The results showed that: (1) rats were able to master the operation of the cue-guided RDM task in a self-paced way; (2) a majority of rats were inclined to choose risk rather than a safe option when the reward expectations were equal; and (3) risk selection was reduced upon inhibition of D1 receptors or stimulation of D2 receptors, but increased upon stimulation of D1 receptors or inhibition of D2 receptors, suggesting that the RDM performance is regulated by D1 and D2 receptors in the mPFC. The present results suggest that DA receptors in the mPFC of rats are involved in regulating cue-guided RDM behavior, with differential involvement of D1 and D2 receptors in the regulation.

Motor and visual influences on auditory neural processing during speaking and listening

During speaking or listening, endogenous motor or exogenous visual processes have been shown to fine-tune the auditory neural processing of incoming acoustic speech signal. To compare the impact of these cross-modal effects on auditory evoked responses, two sets of speech production and perception tasks were contrasted using EEG. In a first set, participants produced vowels in a self-paced manner while listening to their auditory feedback. Following the production task, they passively listened to the entire recorded speech sequence. In a second set, the procedure was identical except that participants also watched online their own articulatory movements. While both endogenous motor and exogenous visual processes fine-tuned auditory neural processing, these cross-modal effects were found to act differentially on the amplitude and latency of auditory evoked responses. A reduced amplitude was observed on auditory evoked responses during speaking compared to listening, irrespective of the auditory or audiovisual feedback. Adding orofacial visual movements to the acoustic speech signal also speeded up the latency of auditory evoked responses, irrespective of the perception or production task. Taken together, these results suggest distinct motor and visual influences on auditory neural processing, possibly through different neural gating and predictive mechanisms.

SPAN: A self-paced association augmentation and node embedding-based model for software bug classification and assignment

Effective bug classification and assignment to relevant developers improves the efficiency of software management. However, textually dependent approaches produce inconsistent results on varying datasets, while approaches that depend upon multi-source data can produce dataset conflicts and inaccuracy. Accordingly, we introduce a model based on Self-Paced Association augmentation and Node embedding (SPAN), which uses an effective combination of textually dependent and independent bug categorization to produce consistent results, followed by a bug assignment mechanism to prevent conflicts. To this end, we present a novel unified classifier and assignment model that exploits the connections between nodes in the Software Bug Report Network (SBRNet) to identify the target features. The model is capable of accurately categorizing bugs in a self-paced manner with association augmentation. Finally, we present an approach that assigns the most appropriate developer for bug resolution through SBRNet node information embedding. Our deep two-step self-paced solution is capable of categorizing software bugs with improved accuracy, while still utilizing fewer features. Results reveal that our model is more effective (up to 98% classification accuracy and 96% for bug assignment) when compared to its counterparts.

Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma

Model overfitting and data imbalance are two main challenges in radiomics studies. In this study, we develop a high-generalizable classifier MERGE (Multi-kErnelRegression withGraphEmbedding) and an imbalance framework SUS (Sensitivity-basedUnder-Sampling) to address these two challenges. First, we integrate the class compactness graph into multi-kernel regression to keep the samples from the same class close together when they are transformed to the label space. In the class compactness graph, each pair of samples from the same class are linked by an undirected weighted edge to capture the relationship between two samples. In such a way, samples from the same class can be kept as close as possible so that the model overfitting problem can be weakened to a great extent. Secondly, to utilize potentially informative data, we propose a sensitivity-based under-sampling imbalance ensemble framework SUS. In each ensemble procedure, the majority class is organized into different blocks through clustering according to the sensitivity of each sample computed by MERGE and then each block is randomly under-sampled in a concordant & self-paced manner. We collect 100 advanced nasopharyngeal carcinoma patients from Hong Kong Queen Elizabeth Hospital and use the proposed SUS-MERGE framework to predict distant metastasis using radiomics features extracted from tumor subregions of different image modalities. Experimental results show promising performance as compared with benchmarking methods.

What Makes an Image Interesting and How Can We Explain It.

Here, we explore the question: What makes a photograph interesting? Answering this question deepens our understanding of human visual cognition and knowledge gained can be leveraged to reliably and widely disseminate information. Observers viewed images belonging to different categories, which covered a wide, representative spectrum of real-world scenes, in a self-paced manner and, at trial’s end, rated each image’s interestingness. Our studies revealed the following: landscapes were the most interesting of all categories tested, followed by scenes with people and cityscapes, followed still by aerial scenes, with indoor scenes of homes and offices being least interesting. Judgments of relative interestingness of pairs of images, setting a fixed viewing duration, or changing viewing history – all of the above manipulations failed to alter the hierarchy of image category interestingness, indicating that interestingness is an intrinsic property of an image unaffected by external manipulation or agent. Contrary to popular belief, low-level accounts based on computational image complexity, color, or viewing time failed to explain image interestingness: more interesting images were not viewed for longer and were not more complex or colorful. On the other hand, a single higher-order variable, namely image uprightness, significantly improved models of average interest. Observers’ eye movements partially predicted overall average interest: a regression model with number of fixations, mean fixation duration, and a custom measure of novel fixations explained >40% of variance. Our research revealed a clear category-based hierarchy of image interestingness, which appears to be a different dimension altogether from memorability or awe and is as yet unexplained by the dual appraisal hypothesis.

Gather.town: An opportunity for self-paced learning in a synchronous, distance-learning environment

The COVID-19 pandemic has forced Higher Education to adopt distance-learning approaches in traditionally face-to-face and practical-based fields such as the Health and Life sciences. Such an abrupt change to distance-learning contexts brings a variety of challenges to student learning communities, and ensuring key skills are effectively transferred. Chief among these is the limited opportunity students have to discuss their individual needs with their educators and peers in a synchronous manner. Proximity-based video-conferencing platforms such as gather.town can offer a unique opportunity for learners to interact with educators as well as pre-developed materials in a self-paced manner to tailor the teaching experience, and develop these relationships in a distance-learning context. In this case study the concepts of statistical analysis and the use of the data analysis software R is introduced to 38 University students using the online platform gather.town. With the use of private spaces, pre-recorded videos, and demonstrators, students are trained in both the concepts and practical skills to undertake data analysis in a self-paced manner. Both students and demonstrators provide their opinions on the effectiveness of the platform, and identify its benefits, preferring it to alternative online systems such as MS Teams for their educational sessions.

SAGE Campus

The SAGE Campus platform provides 18 different courses with roughly 220 hours of online learning modules. The author reviewed the service from the perspective of a college student to see if it was an appropriate learning environment. The primary audience for the courses are graduate students in the social sciences, but undergraduate and graduate students of all disciplines may find courses that are worthwhile to investigate. At the time of the review, the course topics covered content such as information literacy, data management and other data science skills, research design, and how to get published. Many librarians and teaching faculty may recommend students take these courses to supplement their education. Students can learn through these courses in a self-paced manner, and there are no scores or grades associated with completion of a course. Overall, the SAGE Campus platform provides a low-stress way for students to enhance their understanding of many topics relevant to research in the social sciences.

Curious Objects: How Visual Complexity Guides Attention and Engagement.

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience-and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons-essentially quantifying the "amount of information" within each shape-and then used this approach to ask new questions about the perception of complexity. Experiments 1-3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors-a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4-5 asked subjects to study serially presented objects in a self-paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects-even when object shape was completely task-irrelevant-suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of "perceptual curiosity" about objects that encourages subsequent attentional engagement.

Dual self-paced multi-view clustering

By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made in past few years, most existing multi-view clustering methods still suffer the following shortcomings: (1) most MVC methods are non-convex and thus are easily stuck into suboptimal local minima; (2) the effectiveness of these methods is sensitive to the existence of noises or outliers; and (3) the qualities of different features and views are usually ignored, which can also influence the clustering result. To address these issues, we propose dual self-paced multi-view clustering (DSMVC) in this paper. Specifically, DSMVC takes advantage of self-paced learning to tackle the non-convex issue. By applying a soft-weighting scheme of self-paced learning for instances, the negative impact caused by noises and outliers can be significantly reduced. Moreover, to alleviate the feature and view quality issues, we develop a novel feature selection approach in a self-paced manner and a weighting term for views. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.

Rapid and Accumulated Modulation of Action-Effects on Action.

Auditory feedback to a keypress is used in many devices to facilitate the motor output. The timing of auditory feedback is known to have an impact on the motor output, yet it is not known if a keypress action can be modulated on-line by an auditory feedback or how quick an auditory feedback can influence an ongoing keypress. Furthermore, it is not clear if the prediction of auditory feedback already changes the early phase of a keypress action independent of sensory feedback, which would suggest that such prediction changes the motor plan. In the current study, participants pressed a touch-sensitive device with auditory feedback in a self-paced manner. The auditory feedback was given either after a short (60 msec) or long (160 msec) delay, and the delay was either predictable or not. Our results showed that the keypress peak force was modulated by the amount of auditory feedback delay even when the delay was unpredictable, thus demonstrating an on-line modulation effect. The latency of the on-line modulation was suggested to be as low as 70 msec, indicating a very fast sensory to motor mapping circuit in the brain. When the auditory feedback delay was predictable, a change in the very early phase of keypress motor output was found, suggesting that the prediction of sensory feedback is crucial to motor control. Therefore, even a simple keypress action contains rich motor dynamics, which depend on expected as well as on-line perceived sensory feedback.

How laypersons consider differences in sources’ trustworthiness and expertise in their regulation and resolution of scientific conflicts

ABSTRACT When reading scientific information on the Internet laypersons frequently encounter conflicting claims. However, they usually lack the ability to resolve these scientific conflicts based on their own prior knowledge. This study aims to investigate how differences in the trustworthiness and/or expertise of the sources putting forward the conflicting claims affect laypersons’ explanation and resolution of the scientific conflict. We sequentially presented 144 participants with two conflicting scientific claims regarding the safety of nanoparticles in sunscreen and manipulated whether the scientists putting forward the claims differed in their trustworthiness and/or expertise. After having read the claims on a computer in a self-paced manner, participants rated their subjective explanations for the conflicting claims, assessed their personal claim agreement, and completed a source memory task. We examined how differences in source trustworthiness and source expertise affected these measures. Results showed that trustworthiness differences resulted in higher attribution of the conflict to motivational explanations, and expertise differences in higher attribution of the conflict to competence explanations, than without respective differences. Furthermore, main effects of trustworthiness differences and of expertise differences on readers’ claim agreement were shown, with participants agreeing more with claims from sources of higher trustworthiness or expertise.