Probability Learning Research Articles

Natural gas leakage detection from offshore platform by OGI camera and unsupervised deep learning

Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability p=0.1 are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.

Mystery Box Game: A Game-Based Learning to Improve the Academic Achievement of Grade 10 Learners in Probability

This action research investigated the effectiveness of integrating the Mystery Box game to improve the academic achievement of grade 10 learners in Probability at a public school in Ozamiz City. The study, conducted in the 2024 school year, employed a classroom-based action research design with 41 purposively sampled participants. Pre and posttests assessed probability understanding, while a comprehensive lesson plan and a questionnaire gathered information on improvements after a game intervention. Statistical analysis included frequency, Percentage, mean, standard deviation, T-test, and thematic analysis. The findings revealed that integrating the Mystery Box game significantly increased learners' academic achievement in Probability. In conclusion, the post-intervention outcomes decisively confirmed the game's effectiveness in enhancing probability learning, fostering a love for learning, and promoting academic performance and social connections. The research recommended consistently integrating game-based learning in Probability, teacher training in gamification, monitoring and feedback on student progress, and applying the Mystery Box Game to enhance engagement and comprehension across multiple subjects.

Intelligent sequential multi-impulse collision avoidance method for non-cooperative spacecraft based on an improved search tree algorithm

The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making collision avoidance significantly more challenging than that for space debris. Much existing research focuses on the continuous thrust model, whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions. Additionally, it is important to minimize the impact on the original mission while avoiding non-cooperative targets. On the other hand, the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer, posing challenges for practical engineering applications. To conquer these difficulties, this paper makes the following key contributions: (A) a turn-based (sequential decision-making) limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time; (B) a novel Selection Probability Learning Adaptive Search-depth Search Tree (SPL-ASST) algorithm is proposed for non-cooperative target avoidance, which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search (MCTS). Numerical simulations confirm the effectiveness and efficiency of the proposed method.

Predicting nodal metastasis progression of oral tongue cancer using a hidden Markov model in MRI.

The presence of occult nodal metastases in patients with oral tongue squamous cell carcinomas (OTSCCs) has implications for treatment. More than 30% of patients will have occult nodal metastases, yet a considerable number of patients undergo unnecessary invasive neck dissection to confirm nodal status. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement at the lymph node level (LNL) given the location of macroscopic metastases and the tumor stage using the MRI method. A total of 108 patients of OTSCCs were included in the study. A hidden Markov model (HMM) was used to compute the probabilities of transitions between states over time based on MRI. Learning of the transition probabilities was performed via Markov chain Monte Carlo sampling and was based on a dataset of OTSCC patients for whom involvement of individual LNLs was reported. Our model found that the most common involvement was that of level I and level II, corresponding to a high probability of 𝑝b1 = 0.39 ± 0.05, 𝑝b2 = 0.53 ± 0.09; lymph node level I had metastasis, and the probability of metastasis in lymph node II was high (93.79%); lymph node level II had metastasis, and the probability of metastasis in lymph node III was small (7.88%). Lymph nodes progress faster in the early stage and slower in the late stage. An HMM can produce an algorithm that is able to predict nodal metastasis evolution in patients with OTSCCs by analyzing the macroscopic metastases observed in the upstream levels, and tumor category.

Peripheral vision contributes to implicit attentional learning: Findings from the "mouse-eye" paradigm.

The central visual field is essential for activities like reading and face recognition. However, the impact of peripheral vision loss on daily activities is profound. While the importance of central vision is well established, the contribution of peripheral vision to spatial attention is less clear. In this study, we introduced a "mouse-eye" method as an alternative to traditional gaze-contingent eye tracking. We found that even in tasks requiring central vision, peripheral vision contributes to implicit attentional learning. Participants searched for a T among Ls, with the T appearing more often in one visual quadrant. Earlier studies showed that participants' awareness of the T location probability was not essential for their ability to learn. When we limited the visible area around the mouse cursor, only participants aware of the target's location probability showed learning; those unaware did not. Adding placeholders in the periphery did not restore implicit attentional learning. A control experiment showed that when participants were allowed to see all items while searching and moving the mouse to reveal the target's color, both aware and unaware participants acquired location probability learning. Our results underscore the importance of peripheral vision in implicitly guided attention. Without peripheral vision, only explicit, but not implicit, attentional learning prevails.

Intermixed levels of visual search difficulty produce asymmetric probability learning.

When performing novel tasks, we often apply the rules we have learned from previous, similar tasks. Knowing when to generalize previous knowledge, however, is a complex challenge. In this study, we investigated the properties of learning generalization in a visual search task, focusing on the role of search difficulty. We used a spatial probability learning paradigm in which individuals learn to prioritize their search toward the locations where a target appears more often (i.e., high-probable location) than others (i.e., low-probable location) in a search display. In the first experiment, during a training phase, we intermixed the easy and difficult search trials within blocks, and each was respectively paired with a distinct high-probable location. Then, during a testing phase, we removed the probability manipulation and assessed any generalization of spatial biases to a novel, intermediate difficulty task. Results showed that, as training progressed, the easy search evoked a stronger spatial bias to its high-probable location than the difficult search. Moreover, there was greater generalization of the easy search learning than difficult search learning at test, revealed by a stronger bias toward the former's high-probable location. Two additional experiments ruled out alternatives that learning during difficult search itself is weak and learning during easy search specifically weakens learning of the difficult search. Overall, the results demonstrate that easy search interferes with difficult search learning and generalizability when the two levels of search difficulty are intermixed.

Attenuation of spatial bias with target template variation

This study investigated the impact of target template variation or consistency on attentional bias in location probability learning. Participants conducted a visual search task to find a heterogeneous shape among a homogeneous set of distractors. The target and distractor shapes were either fixed throughout the experiment (target-consistent group) or unpredictably varied on each trial (target-variant group). The target was often presented in one possible search region, unbeknownst to the participants. When the target template was consistent throughout the biased visual search, spatial attention was persistently biased toward the frequent target location. However, when the target template was inconsistent and varied during the biased search, the spatial bias was attenuated so that attention was less prioritized to a frequent target location. The results suggest that the alternative use of target templates may interfere with the emergence of a persistent spatial bias. The regularity-based spatial bias depends on the number of attentional shifts to the frequent target location, but also on search-relevant contexts.

One at a Time: Progressive Multi-Step Volumetric Probability Learning for Reliable 3D Scene Perception

Numerous studies have investigated the pivotal role of reliable 3D volume representation in scene perception tasks, such as multi-view stereo (MVS) and semantic scene completion (SSC). They typically construct 3D probability volumes directly with geometric correspondence, attempting to fully address the scene perception tasks in a single forward pass. However, such a single-step solution makes it hard to learn accurate and convincing volumetric probability, especially in challenging regions like unexpected occlusions and complicated light reflections. Therefore, this paper proposes to decompose the complicated 3D volume representation learning into a sequence of generative steps to facilitate fine and reliable scene perception. Considering the recent advances achieved by strong generative diffusion models, we introduce a multi-step learning framework, dubbed as VPD, dedicated to progressively refining the Volumetric Probability in a Diffusion process. Specifically, we first build a coarse probability volume from input images with the off-the-shelf scene perception baselines, which is then conditioned as the basic geometry prior before being fed into a 3D diffusion UNet, to progressively achieve accurate probability distribution modeling. To handle the corner cases in challenging areas, a Confidence-Aware Contextual Collaboration (CACC) module is developed to correct the uncertain regions for reliable volumetric learning based on multi-scale contextual contents. Moreover, an Online Filtering (OF) strategy is designed to maintain representation consistency for stable diffusion sampling. Extensive experiments are conducted on scene perception tasks including multi-view stereo (MVS) and semantic scene completion (SSC), to validate the efficacy of our method in learning reliable volumetric representations. Notably, for the SSC task, our work stands out as the first to surpass LiDAR-based methods on the SemanticKITTI dataset.

The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning.

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 predictionerror.

The effect of emotional faces on reward-related probability learning in depressed patients

BackgroundExisting research indicates that individuals with Major Depressive Disorder (MDD) exhibit a bias toward salient negative stimuli. However, the impact of such biased stimuli on concurrent cognitive and affective processes in individuals with depression remains inadequately understood. This study aimed to investigate the effects of salient environmental stimuli, specifically emotional faces, on reward-associated processes in MDD. MethodsThirty-three patients with recurrent MDD and thirty-two healthy controls (HC) matched for age, sex, and education were included in the study. We used a reward-related associative learning (RRAL) task primed with emotional (happy, sad, neutral) faces to investigate the effect of salient stimuli on reward-related learning and decision-making in functional magnetic resonance imaging (fMRI). Participants were instructed to ignore emotional faces during the task. The fMRI data were analyzed using a full-factorial general linear model (GLM) in Statistical Parametric Mapping (SPM12). ResultsIn depressed patients, cues primed with sad faces were associated with reduced amygdala activation. However, both HC and MDD group exhibited reduced ventral striatal activity while learning reward-related cues and receiving rewards. LimitationsThe patients'medication usage was not standardized. ConclusionsThis study underscores the functional alteration of the amygdala in response to cognitive tasks presented with negative emotionally salient stimuli in the environment of MDD patients. The observed alterations in amygdala activity suggest potential interconnected effects with other regions of the prefrontal cortex. Understanding the intricate neural connections and their disruptions in depression is crucial for unraveling the complex pathophysiology of the disorder.

Time-Frequency Bins Selection for Direction of Arrival Estimation Based on Speech Presence Probability Learning

Time-Frequency Bins Selection for Direction of Arrival Estimation Based on Speech Presence Probability Learning

Real-time spatiotemporal forecast of natural gas jet fire from offshore platform by using deep probability learning

Blow-outs occurred on offshore platform and associated fires have been recurrent during the previous few decades, and poses a potential safety hazard to humans, property and the surrounding environment. Although the real-time forecast based on deep learning have shown promise in the fields of fire modelling and hazardous area evaluations, jet fire spatio-temporal modelling has not yet undergone sufficient investigation in complex ocean engineering cases like offshore platforms. This research therefore proposes a deep learning-based framework for jet fire spatio-temporal probabilistic real-time forecast by developing the Hybrid-VB-ConvSTnn model, which integratesConvGRU and variational Bayesian inference. And the significant hyperparameters were locally optimized through sensitivity analysis and finally identified as Monte Carlo (MC) sampling number m = 100 and dropout probability p = 0.1. By performance comparison with different models, the Hybrid-VB-ConvSTnn model shows competitive spatio-temporal forecasting capabilities in terms of both real-time (Inference time = 0.83s) and accuracy (R2 = 0.982). Moreover, the Hybrid-VB-ConvSTnn model could provide the additional uncertainty inferences based on the probability density of the Bernoulli distribution, which avoids the inherent shortcomings of “overconfidence” for traditional point-estimate models and lends credibility to flame boundary identification. The proposed framework could support the digital twin-based fire emergency management on offshore platforms by more comprehensive and robust risk evaluation.

Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities.

Humans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each observation. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual observations are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects' learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities.

A classroom experience about the application of research-based learning for the teaching of probability in engineering

<abstract> <p>We explored the implementation of a Research-Based Learning (RBL) for the teaching and learning of probability in engineering education. The approach provided a student-centered learning environment, integrating practical activities within an active learning conception. A detailed RBL task in connection with weather forecasting was described, highlighting a methodical 10-day learning process that considers research, practical application, and reflection. The effectiveness of this RBL task was evaluated through thematic analysis of student reflective essays, focusing on their learning experiences and challenges. The analysis revealed enhanced understanding of probability, varied student engagement, and the value of practical learning. It also identified challenges in conceptualizing abstract probability concepts. The findings highlighted the RBL's potential in improving students' conceptual grasp and application of probability in real-world scenarios, providing key insights for future educational strategies in engineering. This study contributes to the broader discourse on innovative teaching methodologies in engineering education, demonstrating the impact of active, inquiry-based learning approaches.</p> </abstract>

Contribution of peripheral vision to attentional learning.

Attention is tuned towards locations that frequently contain a visual search target (location probability learning; LPL). Peripheral vision, covering a larger field than the fovea, often receives information about the target. Yet what is the role of peripheral vision in attentional learning? Using gaze-contingent eye tracking, we examined the impact of simulated peripheral vision loss on location probability learning. Participants searched for a target T among distractor Ls. Unbeknownst to them, the T appeared disproportionately often in one quadrant. Participants searched with either intact vision or "tunnel vision," restricting the visible search items to the central 6.7º (in diameter) of the current gaze. When trained with tunnel vision, participants in Experiment 1 acquired LPL, but only if they became explicitly aware of the target's location probability. The unaware participants were not faster finding the target in high-probability than in low-probability locations. When trained with intact vision, participants in Experiment 2 successfully acquired LPL, regardless of whether they were aware of the target's location probability. Thus, whereas explicit learning may proceed with central vision alone, implicit LPL is strengthened by peripheral vision. Consistent with Guided Search (Wolfe, 2021), peripheral vision supports a nonselective pathway to guide visual search.

The effects of goal-driven attention on the acquisition of location probability learning.

Increasing evidence has shown that implicit learning shapes visuospatial attention, yet how such learning interacts with top-down, goal-driven attention remains unclear. This study investigated the relationship between task goals and selection history using a location probability learning (LPL) paradigm. We tested whether a top-down spatial cue facilitates or interferes with the acquisition of implicit LPL. In a visual search task, participants were asked to give precedence to one of four, spatially cued, quadrants of the screen. Unbeknownst to them, there was an underlying uneven spatial probability in which the target appeared disproportionately often in the cued quadrant (37.5%) and a second, uncued quadrant (37.5%). To assess what participants had learned, neutral, uncued testing trials with an equal target location probability (25%) were used. Results revealed faster search times in the cued and the uncued high-probability quadrants compared to the two low-probability quadrants and these fast search times remained prevalent in the neutral testing blocks. Importantly, LPL was comparable between the cued and uncued locations in the testing blocks, suggesting that the spatial cue neither facilitated nor interfered with LPL. These results support the dual-system view of attention, revealing parallel systems supporting both goal-driven and experience-guided attention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Learning obstacle of probability learning based on the probabilistic thinking level

This study aims to determine students learning obstacles in probability material based on their probabilistic thinking (PT) level using the theory of didactical situation (TDS) perspective. This is qualitative research with the case study method. The subject consisted of 23 grade 9 students in junior high school who had studied the material and had taken the test. The test results were used to classify students depending on their PT level using the framework developed by Graha A. Jones. Furthermore, interviews were performed with three representative students from each PT level. The interviews indicated that students in each PT level continue to face learning obstacles, which include instrumental ontogenic and epistemological obstacles. The lowest PT level demonstrated a more complex and comprehensive learning obstacle in all constructs of PT. From the TDS perspective, all students who are at various levels of PT have reached an action situation. Only 33% of the students at the subjective level and 83% of the students at the transition level reached the formulation situation. Still, students at that level needed help to reach the other two situations. Unlike the two previous levels, students at the level of quantitative informal thinking can achieve situations of validation and institutionalization.

Further perceptions of probability: In defence of associative models.

Extensive research in the behavioral sciences has addressed people's ability to learn stationary probabilities, which stay constant over time, but only recently have there been attempts to model the cognitive processes whereby people learn-and track-nonstationary probabilities. In this context, the old debate on whether learning occurs by the gradual formation of associations or by occasional shifts between hypotheses representing beliefs about distal states of the world has resurfaced. Gallistel et al. (2014) pitched the two theories against each other in a nonstationary probability learning task. They concluded that various qualitative patterns in their data were incompatible with trial-by-trial associative learning and could only be explained by a hypothesis-testing model. Here, we contest that claim and demonstrate that it was premature. First, we argue that their experimental paradigm consisted of two distinct tasks: probability tracking (an estimation task) and change detection (a decision-making task). Next, we present a model that uses the (associative) delta learning rule for the probability tracking task and bounded evidence accumulation for the change detection task. We find that this combination of two highly established theories accounts well for all qualitative phenomena and outperforms the alternative model proposed by Gallistel et al. (2014) in a quantitative model comparison. In the spirit of cumulative science, we conclude that current experimental data on human learning of nonstationary probabilities can be explained as a combination of associative learning and bounded evidence accumulation and does not require a new model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Statistics education research at the school level in Australia and New Zealand: A 30-year journey

The introduction of statistical concepts into school curricula in Australia and New Zealand in the early 1990s initiated an ongoing research program into the learning and teaching of statistics and probability in both countries. This paper reviews the contribution of Australian and New Zealand researchers to building statistical literacy at school, alongside international developments. From recognising how students develop understanding of specific statistical and probabilistic concepts, through teacher knowledge and beliefs for teaching statistics, to intervention studies and targeted teaching, the field of statistics education has grown and changed. Statistics and probability are now well established as part of the mathematics curriculum. The importance of linking statistical literacy and statistical understanding across the curriculum, as well as in STEM, has also begun to receive attention as other subjects have recognised the importance of data in their fields. Following a comprehensive review of the field in Australia and New Zealand, this paper then considers emerging areas of interest, such as new approaches to data visualisation, and suggests future research.

Stability of individual differences in implicitly guided attention.

Daily activities often occur in familiar environments, affording us an opportunity to learn. Laboratory studies have shown that people readily acquire an implicit spatial preference for locations that frequently contained a search target in the past. These studies, however, have focused on group characteristics, downplaying the significance of individual differences. In a pre-registered study, we examined the stability of individual differences in two variants of an implicit location probability learning (LPL) task. We tested the possibility that individual differences were stable in variants that shared the same search process, but not in variants involving different search processes. In Experiment 1, participants performed alternating blocks of T-among-Ls and 5-among-2s search tasks. Unbeknownst to them, the search target appeared disproportionately often in one region of space; the high-probability regions differed between the two tasks. LPL transferred between the two tasks. In addition, individuals who showed greater LPL in the T-task also did so in the 5-task and vice versa. In Experiment 2, participants searched for either a camouflaged-T against background noise or a well-segmented T among well-segmented Ls. These two tasks produced task-specific learning that did not transfer between tasks. Moreover, individual differences in learning did not correlate between tasks. Thus, LPL is associated with stable individual differences across variants, but only when the variants share common search processes.