Learning Research Articles

Mediterranean diet score linked to cognitive functioning in women: evidence from the Czech Republic

Abstract Background Evidence suggests that adherence to the Mediterranean diet (MED) may be beneficial in preventing cognitive decline, although findings are inconsistent. We aimed to explore this association in the Czech population of older adults. Methods A total of 6,028 males and females from the Czech arm of the Health Alcohol and Psychosocial factors in Eastern Europe (HAPIEE) study were included in the analysis. Dietary data were collected using a food frequency questionnaire, and MED was calculated based on nine food groups. The MED score ranged from 0 to 16 points and was categorized into three groups: 0-7, 8-10, and 11-16 points. Cognitive function was measured using four tests assessing verbal memory and learning, verbal fluency, and attention, mental speed and concentration, from which single z-scores were computed. The composite score of cognitive function was computed as the mean of z-scores. The cross-sectional associations between MED and composite score of cognitive function were analyzed using multivariate linear regression. Results Females with a dietary score of 8-10 points (Beta=0.05, 95% CI: -0.002; 0.097), and those with a score of 11-16 points (Beta=0.08, 95% CI: 0.016; 0.140) had a higher composite cognitive score than women in lowest adherence group. Looking at specific domains, women in the highest adherence group had significantly better immediate verbal memory (Beta=0.12, 95% CI: 0.031; 0.205) and delayed recall (Beta=0.12, 95% CI: 0.027; 0.212), respectively, than those in the lowest adherence group. There were no associations in males. Conclusions Higher adherence to the MED was associated with better cognitive functioning in verbal memory and composite cognitive score in Czech females. The results suggest that the Mediterranean diet may help to improve cognitive functioning in older women. #NGEU Key messages • Findings suggest that Mediterranean diet may be beneficial in cognitive functioning in older women. • Higher adherence to the Mediterranean diet is connected to better cognitive function particularly in verbal memory domain.

Individual differences in information demand have a low dimensional structure predicted by some curiosity traits

To understand human learning and progress, it is crucial to understand curiosity. But how consistent is curiosity's conception and assessment across scientific research disciplines? We present the results of a large collaborative project assessing the correspondence between curiosity measures in personality psychology and cognitive science. A total of 820 participants completed 15 personality trait measures and 9 cognitive tasks that tested multiple aspects of information demand. We show that shared variance across the cognitive tasks was captured by a dimension reflecting directed (uncertainty-driven) versus random (stochasticity-driven) exploration and individual differences along this axis were significantly and consistently predicted by personality traits. However, the personality metrics that best predicted information demand were not the central curiosity traits of openness to experience, deprivation sensitivity, and joyous exploration, but instead included more peripheral curiosity traits (need for cognition, thrill seeking, and stress tolerance) and measures not traditionally associated with curiosity (extraversion and behavioral inhibition). The results suggest that the umbrella term "curiosity" reflects a constellation of cognitive and emotional processes, only some of which are shared between personality measures and cognitive tasks. The results reflect the distinct methods that are used in these fields, indicating a need for caution in comparing results across fields and for future interdisciplinary collaborations to strengthen our emerging understanding of curiosity.

Modulation of memory by prism adaptation in healthy subjects

Recent findings suggest that prism adaptation can extend its effects beyond spatial attention, modulating the performance of different cognitive tasks by acting on cerebellar, parietal and temporal-frontal networks. We tested groups of healthy subjects to investigate the effects of rightward vs. leftward prism adaptation vs. neutral lenses exposure in a series of memory tasks, probing either short-term (Digit span, Corsi span) or long-term memory (Supraspan verbal and spatial learning). In the short-term memory tasks, leftward prism adaptation selectively increased verbal span, while rightward prism adaptation increased spatial span. In the long-term memory tasks, leftward prism adaptation selectively increased verbal supraspan, i.e., increased the number of digits in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. On the other hand, rightward prism adaptation selectively increased spatial supraspan, i.e. it increased the number of spatial positions in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. Moreover, rightward, but not leftward, prism adaptation selectively increased supraspan recall after a delay interval, regardless of the stimulus material, i.e., it increased the number of digits or spatial positions recalled after a delay interval. Neutral lenses exposure did not influence any memory task. These findings suggest that prism adaptation can induce both modality/hemispheric-specific and process-specific effects on short-term and long-term explicit memory.

Sex differences in the association of overweight with cognitive performance in individuals with first-episode psychosis

Cognitive deficits and overweight are prominent challenges in the treatment of psychosis, which have a direct impact on patients’ quality of life. We aim to determine whether there is an association of overweight with cognitive performance and whether there are sex differences in this association. We included 170 individuals with first-episode psychosis (FEP) (mean age 23.08 years, 32.9% females) attending an early intervention service who underwent clinical, biometric, and cognitive assessments by the MATRICS Consensus Cognitive Battery. A set of two-way analyses of covariance (ANCOVAs) were conducted for each cognitive test. Sex, overweight, and their interaction were included as factors. Nearly 34% of the participants were overweight without differences between males and females. The excess of weight did not exert any main effect on cognition; however, overweight females performed significantly worse than non-overweight females in processing speed, verbal learning and memory, reasoning and problem-solving, and global cognitive function, whereas in males, there were no differences. Our findings highlight that sex matters in the study of metabolic and cognitive factors in FEP to develop targeted interventions based on sex perspectives.

Implementing the optimized hippo-avoidance prophylactic cranial irradiation for limited-stage small cell lung cancer by tomotherapy and volumetric modulated arc therapy.

Hippo-avoidance prophylactic cranial irradiation (HA-PCI) requires a hippocampal avoidance zone expanded from hippocampus to ensure dose fall-off and compensate for setup errors. Most studies recommend a 5-mm margin, while it could be optimized to a 2-mm expansion. Here, we showed the details of optimized HA-PCI for limited-stage small cell lung cancer (LS-SCLC). This cohort study reviewed patients with LS-SCLC receiving optimized HA-PCI from August 2014 to June 2020 in the National Cancer Center of China. The hippo-related dose parameters were summarized. The comparison of the Hopkins Verbal Learning Test-Revised (HVLT-R) scores in different time points was conducted. The Kaplan-Meier method was used to calculate the survival rates. A total of 112 patients were included. The average doses of hippocampus and hippocampal avoidance zone were 6.80 Gy (IQR: 6.40-7.44) and 7.63 Gy (IQR: 7.14-8.39). No differences were observed in the two radiation techniques (tomotherapy [TOMO] vs. volumetric-modulated arc therapy [VMAT]). The decline of HVLT-R score remained in a low level and not significant in assessable patients (p = 0.095). With a median follow-up of 52 months (95% CI: 47.2-56.7), the 2-year overall survival and progression-free survival were 74.1% and 50.0%, respectively. Two intracranial recurrence lesions (2.3%) located <2 mm from the hippocampus. Optimized HA-PCI could achieve similar dose limitation by TOMO and VMAT techniques with favorable efficacy and minor toxicity.

Biopsychosocial Impact of Multiple Sclerosis in Omani Patients: A Multicenter Comparative Study

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disorder characterized by various clinical presentations and manifestations that include biopsychosocial impediments. This study has three interrelated goals relevant to biopsychosocial functioning: (i) compare reasoning ability, neuropsychological functioning, affective range, and quality of life (QoL) between people with multiple sclerosis (PwMS) and healthy controls; (ii) explore gender differences in reasoning ability and neuropsychological functioning, affective symptoms, and QoL among PwMS; and (iii) examine the relationship between QoL and cognitive performance in PwMS, focusing on those with inadequate vs. adequate QoL. Methods: This multicenter study was carried out among clinically stable PwMS (no relapse in the last two months) at follow-up in two tertiary care units in urban Oman. Healthy controls, matched for age and sex, were also recruited as a comparison group. Data were collected using cognitive batteries sensitive to current reasoning ability and conventional neuropsychological batteries designed to measure verbal learning, visual-spatial ability, and processing speed. The affective range (anxiety and depressive symptoms) and quality of life (QoL) were also evaluated. Results: The PwMS group scored lower on current reasoning ability, verbal learning, visual-spatial ability, and processing speed compared to the control group. The incidence of anxiety was higher in the PwMS group, but there were no statistically significant differences in depressive symptoms. No significant differences were found in cognitive variables between the two sexes, except in visual-spatial ability, where women outperformed men. PwMS with low QoL scored lower on attention and concentration indices than those with adequate QoL. According to QoL, no significant differences were observed in reasoning, verbal learning, or visual-spatial ability. Conclusions: The present sentinel study suggests that the Omani cohort with MS tends to have lower indices of current reasoning ability, visual and spatial memory, and cognitive speed compared to control subjects. Gender differences are minimal, except for visual-spatial abilities, where women outperform men. Quality of life significantly affects cognitive functioning. In general, the biopsychosocial impediment appears to be significant, indicating the need for comprehensive evaluation and care in the management of MS.

Promises and challenges of generative artificial intelligence for human learning.

Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

Social and emotional learning in the cerebellum.

The posterior cerebellum has a critical role in human social and emotional learning. Three systems and related neural networks support this cerebellar function: a biological action observation system as part of an extended sensorimotor integration network, a mentalizing system for understanding a person's mental and emotional state subserved by a mentalizing network, and a limbic network supporting core emotional (dis)pleasure and arousal processes. In this Review, I describe how these systems and networks support social and emotional learning via functional reciprocal connections initiating and terminating in the posterior cerebellum and cerebral neocortex. It is hypothesized that a major function of the posterior cerebellum is to identify and encode temporal sequences of events, which might help to fine-tune and automatize social and emotional learning. I discuss research using neuroimaging and non-invasive stimulation that provides converging evidence for this hypothesized function of cerebellar sequencing, but also other potential functional accounts of the posterior cerebellum's role in these social and emotional processes.

Amyloid deposition and its association with depressive symptoms and cognitive functions in late-life depression: a longitudinal study using amyloid-β PET images and neuropsychological measurements

BackgroundAlthough depression is linked to an increased risk of dementia, the association between late-onset depression (LOD) and amyloid burden remains unclear. This study aimed to determine amyloid deposition in patients with LOD compared to healthy controls (HC) using amyloid-beta (Aβ) positron emission tomography (PET) images and neuropsychological assessments.MethodsForty patients first diagnosed with major depressive disorder after the age of 60 (LOD) and twenty-one healthy volunteers (HC) were enrolled. Depression and anxiety were evaluated using the 17-item Hamilton Depression Scale, Hamilton Anxiety Rating Scale, and Clinical Global Impression Scale. Cognitive function was assessed using the Korean versions of the Mini-Mental Status Examination, Montreal Cognitive Assessment, and Seoul Neuropsychological Screening Battery at baseline and 3-month follow-up. 18F-florbetapir PET images were co-registered with T1-weighted magnetic resonance images.ResultsThere was no significant difference in Aβ deposition between LOD and HC groups. No significant correlation between Aβ burden and depressive symptom severity was found in LOD patients. Higher somatic anxiety was correlated with lower Aβ burden in multiple brain regions, including the left inferior frontal lobe (p = 0.009), right anterior cingulate (p = 0.003), and right superior frontal lobe (p = 0.009). Despite cognitive recovery in areas such as attention (Digit Span Forward, p = 0.026), memory (Auditory Verbal Learning Test Recall Total, p = 0.010; Rey Complex Figure Test Delayed Recall, p = 0.039), and frontal executive function (Contrasting Program, p = 0.033) after three months of antidepressant treatment, cognitive improvement showed no association with amyloid deposition.ConclusionsThese findings suggest distinct mechanisms may underlie amyloid deposition in neurodegenerative changes associated with depression. While amyloid burden in specific brain regions negatively correlated with somatic anxiety, it showed no significant correlation with the severity of depression or overall cognitive function.

Le rôle de la mémoire de travail dans l’acquisition incidente du vocabulaire en langue seconde durant la compréhension orale chez les enfants : une revue de littérature

Developing vocabulary to understand oral and written language in first language (L1) and second language (L2) is a major challenge for both adults and young learners. The learning of words also depends on the working memory (WM), which is central to human cognition and language learning; several studies have established its important role in learning L1 (Gathercole &amp; Baddeley, 1989, 1990) and, even more so, in L2 (Baddeley &amp; al., 1998; Gathercole &amp; al., 1999; Service &amp; Kohonen, 1995). Despite previous studies on the role of WM in the incidental acquisition of second language vocabulary during listening comprehension, the need for clarification on this role, especially in children, remains to be explored. The aim of this article is to present a synthesis of the literature on the role of WM in the incidental acquisition of second language vocabulary during listening comprehension in children to highlight this need for the necessary clarifications.

Neuropsychological outcomes following endovascular clot retrieval and intravenous thrombolysis in ischemic stroke.

Cognitive impairment, anxiety, depression, fatigue, and dependence in instrumental activities of daily living (ADL) are common after stroke; however, little is known about how these outcomes may differ following treatment with endovascular clot retrieval (ECR), intravenous tissue plasminogen activator (t-PA), or conservative management. Patients were recruited after acute treatment and invited to participate in an outcome assessment 90-120 days post-stroke. The assessment included a cognitive test battery and several questionnaires. The COVID-19 pandemic led to significant disruptions in recruitment and data collection, and the t-PA and conservative management groups were combined into a standard medical care (SMC) group. Sixty-two participants were included in the study (ECR = 31, SMC = 31). Mean age was 66.5 (20-86) years, and 35 (56.5%) participants were male. Participants treated with ECR had significantly higher National Institutes of Health Stroke Scale scores at presentation and significantly lower education. After adjusting for stroke severity, premorbid intellectual ability, and age, treatment with ECR was associated with significantly better performances on measures of cognitive screening, visual working memory, and verbal learning and memory. Participants treated with ECR also experienced less fatigue and were more likely to achieve independence in basic and instrumental ADLs. Despite this, cognitive impairment and fatigue were still common among participants treated with ECR and anxiety and depression symptoms were experienced similarly by both groups. Cognitive impairment and fatigue were less common but still prevalent following treatment with ECR. This has important practical implications for stroke rehabilitation, and routine assessment of cognition, emotion, and fatigue is recommended for all stroke survivors regardless of stroke treatment and functional outcome.

Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual’s latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (− 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences.

Artificial General Intelligence for the Detection of Neurodegenerative Disorders

Parkinson’s disease and Alzheimer’s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.

Personalized Model-Driven Interventions for Decisions From Experience.

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.

Human motor learning dynamics in high-dimensional tasks.

Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.

Bones at Home

Remote teaching during the COVID-19 pandemic led to a range of pedagogical challenges for anthropology laboratory courses. In biological anthropology courses such as Human Osteology, hands-on experience is essential to achieving learning outcomes, including basic bone and feature (i.e., landmark) identification, identification from fragmentary remains, and age and sex estimation. To address the need for training that includes object-based, tactile (haptic) learning in fields such as biological anthropology and archaeology, all Human Osteology students at Mount Royal University and the University of Manitoba took home plastic model skeletons. The purpose of this study was to evaluate how well remotely educated undergraduates (REUs) met human osteology learning objectives when supported by plastic model skeletons at home. We present the results of a survey designed to test core osteological skills obtained by REUs in comparison with undergraduates educated with in-person laboratory components (IPUs) and experts in the field (zero to four and five or more years of experience). REU scores did not differ significantly from those of IPU or Junior Experts with less than five years of experience. Students performed well in bone identification but were limited in their ability to apply common sex and age estimation methods and to identify incomplete elements. Our findings reinforce the importance of haptic learning and years of experience in human osteological learning. They support the use of take-home models as valuable resources in both remote and in-person undergraduate teaching. This work is a step toward more inclusive universal instructional design that can be applied across various anthropology laboratory courses.

Towards accelerating discovery via physics-driven and interactive multi-fidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time-consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest towards active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge on the system in form of partially known physics laws and often varies exploration policies during the experiment. Here, we propose an interactive workflow building on multi-fidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) sMFBO, and finally extending it to allow human in the loop interactive iMFBO workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly non-smooth multi-fidelity simulation data generated from an Ising model, considering spin-spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real time human decisions for improved exploration with increased alignment to ground truth.

Exploring the effects of COVID-19 on verbal memory function in schizophrenia: Multiple case study and brief literature review

Individuals recovering from COVID-19 may experience persistent impairment in verbal memory performance, potentially due to illness-related hippocampal injury. Although verbal memory dysfunction is central to schizophrenia, the interactions between this vulnerability and COVID-19 remain unclear, with no imaging studies addressing the issue to-date. To explore this gap and generate hypotheses for future research, we adopted a multiple case study approach. Two pairs of individuals with an ICD-10 diagnosis of schizophrenia were selected, each consisting of one case with a positive COVID-19 anamnesis and one without. We calculated the Reliable Change Index to estimate the clinical significance of verbal memory performance changes, with annualized change rates in hippocampal volumes assessed against normative data. Compared to their matches, COVID-19 positive cases did not show mutually consistent changes in verbal memory performance: one case experienced a significant decline in verbal memory and learning, while the other showed a general normalization of test scores. Left hippocampal volumes showed a comparatively slowed increase, while the right hippocampi decreased in volume, although these atrophy rates did not exceed those expected in general population samples. Based on these findings, we hypothesize that COVID-19 alone does not lead to verbal memory decline in schizophrenia. Instead, the relationship between the diseases may depend on additional factors. Our case pairs differed in body mass index, systolic blood pressure, sex, phase of illness, and whole grey matter volume trajectories, leading us to hypothesize that these variables represent additional predictors or moderators of this relationship.

Toward comprehensive short utterances manipulations detection in videos

AbstractIn a landscape increasingly populated by convincing yet deceptive multimedia content generated through generative adversarial networks, there exists a significant challenge for both human interpretation and machine learning algorithms. This study introduces a shallow learning technique specifically tailored for analyzing visual and auditory components in videos, targeting the lower face region. Our method is optimized for ultra-short video segments (200-600 ms) and employs wavelet scattering transforms for audio and discrete cosine transforms for video. Unlike many approaches, our method excels at these short durations and scales efficiently to longer segments. Experimental results demonstrate high accuracy, achieving 96.83% for 600 ms audio segments and 99.87% for whole video sequences on the FakeAVCeleb and DeepfakeTIMIT datasets. This approach is computationally efficient, making it suitable for real-world applications with constrained resources. The paper also explores the unique challenges of detecting deepfakes in ultra-short sequences and proposes a targeted evaluation strategy for these conditions.

Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport

Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards.