Cognitive Model Research Articles

Navigating Supervision and Interprofessional Relationships in Health Psychology.

Previous literature has focused on either individual models of supervision, developing trainees' interprofessional competencies, or on developing and maintaining interprofessional relationships outside of training. For psychologists in medical settings, these concepts are inextricably linked, and supervision must combine these professional practices to successfully meet the needs of psychology trainees, patients, and interprofessional colleagues, in an increasingly integrated healthcare landscape. This paper presents a model for advancing interprofessional collaborative practice competencies in supervision as health psychology trainees progress through the developmental stages of clinical competency, while supervising psychologists also maintain interprofessional relationships. The Interprofessional Collaboration Supervision Model (IPCSM)for Psychology describes trainee, patient, and interprofessional team factors for supervising psychologists to consider in supervision, as well as various interventions to deploy when these interrelated dynamics impact training. Case examples are provided along with discussion on how to implement this model in supervision.

Effect of Cognitive Context-Based Modeling Instructional Strategies in Biology Concept Teaching: An Educational Action Study

This study uses educational action research to investigate the practical impact of cognitive context-based modeling instructional strategies in the teaching of biological concepts in high schools. The study recruited 57 high school seniors who were instructed in biology concepts in a traditional lecture-based classroom, using convenient sampling. Throughout the study, teachers used self-designed questionnaires and student classroom engagement observation scales to evaluate how often students previewed new concepts and their classroom engagement. The questionnaire results compared how often students previewed new biological concepts before and after implementing the modeling teaching strategy, revealing a significant increase from 20 to 38 students who consciously and frequently previewed new concepts. In addition, we performed a multivariate analysis of variance on the measured data obtained from the classroom participation observation scale under the two teaching strategies, which revealed significant differences in teachers effective coaching strategy, students response status and response quality. The key conclusion from this action study is that the use of cognitive context-based modeling teaching strategies in high school biology significantly enhances the frequency with which students preview new concepts and their classroom engagement. This research provides an empirical basis and valuable references to improve teaching approaches.

Recognition of facial emotion based on SOAR model

IntroductionExpressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human–machine interaction is the recognition of facial expressions and the creation of feedback, according to the perceived emotion.MethodsTo implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images, and considering the temporal information in dynamic images leads to more efficient and better classification. In addition, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased, and the working memory part of the SOAR model can be implemented.Results and discussionDue to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features, increasing the recognition rate. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. To compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models.

The effects of mobile phone dependence on athletic performance and its mechanisms

Mobile phone dependence (also known as internet dependence, MPD), defined as a problematic behavior characterized by excessive use or intermittent craving to use a mobile phone, results in various social, behavioral, and affective problems in daily life. In sports, MPD is directly related to the physical and mental health and sports performance of athletes. The individual and environmental factors, neurobiological mechanisms and theoretical models of MPD affecting athletic performance were analyzed by reviewing previous studies, aiming to construct effective training and development protocols to prevent and control the occurrence of MPD in athletes. At present, athletic performance can be affected by MPD through individual factors and environmental factors. The neurobiological mechanisms between the two are based on the brain reward system and microwave radiation from mobile phones, with athletic performance being restricted by alterations in the corresponding brain regions. Relevant theoretical models mainly include the social cognitive model of self-regulation and the integrative model of self-control, which explain the interrelationship between MPD and athletic performance from the perspectives of athletes’ self-regulation and self-control, respectively. As an emerging phenomenon, the influence pathways and mechanisms by which MPD affects athletic performance need to be further investigated. A longitudinal perspective should be adopted to trace the dynamic impact relationship between the two, and developing relevant theoretical frameworks from an interdisciplinary research perspective should be valuable for providing theoretical support for coaches and sports administrators to formulate scientific training protocols and thus improve the mental health of athletes.

Solving puzzles using knowledge-based automation: biomimicry of human solvers

AbstractThe human brain’s remarkable efficiency in solving puzzles through pictorial information processing serves as a valuable inspiration for computational puzzle solving. In this study, we present a nucleation algorithm for automated puzzle solving, developed based on statistical analysis of an empirical database. This algorithm effectively solves puzzles by choosing pieces with infrequent and iridescent edges as nucleation centers, followed by the identification of neighboring pieces with high resemblances from the remaining puzzle pieces. For the 8 different pictures examined in this study, both empirical data and computer simulations consistently demonstrate a power-law relationship between solving time and the number of puzzle pieces, with an exponent less than 2. We explain this relationship through the nucleation model and explore how the exponent is influenced by the color pattern of the puzzle picture. Moreover, our investigation of puzzle-solving processes reveals distinct principal pathways, akin to protein folding behavior. Our study contributes to the development of a cognitive model for human puzzle solving and color pattern recognition.

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.

Connecting process models to response times through Bayesian hierarchical regression analysis.

Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time data and obtain parameter estimates that have a clear psychological interpretation. A prerequisite for our analysis is a process model that generates a count of elementary information processing steps (EIP steps) for each trial of an experiment. We can estimate the duration of an EIP step by assuming that every EIP step is of random duration, modeled as draws from a gamma distribution. A natural effect of summing several random EIP steps is that the expected spread of the overall response time increases with a higher EIP step count. With modern probabilistic programming tools, it becomes relatively easy to fit Bayesian hierarchical models to data and thus estimate the duration of a step for each individual participant. We present two examples in this paper: The first example is children's performance on simple addition tasks, where the response time is often well predicted by the smaller of the two addends. The second example is response times in a Sudoku task. Here, the process model contains some random decisions and the EIP step count thus becomes latent. We show how our EIP regression model can be extended to such a case. We believe this approach can be used to bridge the gap between classical cognitive modeling and statistical inference and will be easily applicable to many use cases.

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease.

While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better.

In cognitive psychometrics, the parameters of cognitive models are used as measurements of the processes underlying observed behavior. In decision making, the diffusion decision model (DDM) is by far the most commonly used cognitive psychometric tool. One concern when using this model is that more recent theoretical accounts of decision-making place more emphasis on neural plausibility, and thus incorporate many assumptions not found in the DDM. One such model is the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we investigate whether the lack of such mechanisms in the DDM compromises its ability to measure the processes it does purport to measure. We cross-fit the DDM and IDM, and find that the conclusions of DDM would be mostly consistent with those from an analysis using a more neurally plausible model. We also show that the Ornstein-Uhlenbeck Model (OUM) model, a variant of the DDM that includes the potential for leakage (or self-excitation), reaches similar conclusions to the DDM regarding the assumptions they share, while also sharing an interpretation with the IDM in terms of self-excitation (but not leakage). Since the OUM is relatively easy to fit to data, while being able to capture more neurally plausible mechanisms, we propose that it be considered an alternative cognitive psychometric tool to the DDM.

Influence of Severity and Susceptibility Information Content on Users’ Psychological Response in Emergencies—The Moderating Effect of Regulatory Focus

Information content is crucial for public opinion governance, yet its psychological mechanisms remain unclear. This paper endeavors to bridge this knowledge gap by examining the influence of information content on users’ psychological responses. Drawing insights from the heuristic-systematic model (HSM) and regulatory focus theory, we constructed a novel cognitive and emotional response model. Regression analysis reveals that susceptibility strongly influences primary cognitive responses, while severity affects secondary ones. Susceptibility has a stronger impact on emotional responses than severity. Promotion-focus users are less influenced by severity and susceptibility than prevention-focus users. These results offer valuable insights for emergency management strategies and enhance individuals’ resilience in coping with the psychological toll of emergencies.

Clinical value of cognitive behavioral nursing model to patients with congenital glaucoma.

There have been studies on the use of cognitive behavioral nursing intervention (CBNI) but the main subjects were patients with secondary glaucoma and there are fewer studies on the care of congenital glaucoma. To explore the clinical value of cognitive behavioral nursing model in patients with congenital glaucoma. One hundred and fifty-three postoperative patients with congenital glaucoma treated in our hospital between January 2021 and December 2022 were prospectively selected for the study and randomly divided into a control group (n= 77) and an observation group (n= 76). The control group was given routine nursing, and the observation group was given cognitive behavioral nursing mode on the basis of the control group. Anxiety self-assessment scale (SAS), depression self-assessment scale (SDS), Connor-Davidson toughness scale, and treatment adherence evaluation scale were used to evaluate the psychological state, mental toughness, treatment adherence, treatment effect and nursing care satisfaction in the two groups before and after 2 weeks of intervention. The efficacy of the treatment was also assessed by determining the visual acuity (VA), intraocular pressure (IOP), and mean defective (MD) value of the visual field of the two groups of patients. After nursing, the SDS score (46.33 ± 6.16 versus 53.21 ± 5.94) and SAS score (44.41 ± 5.6 versus 52.82 ± 6.31) in the observation group were lower than those in the control group (P< 0.05). The scores of optimism (11.55 ± 1.90 versus 8.20 ± 1.95), self-improvement (22.05 ± 3.60 versus 17.60 ± 4.30), tenacity (37.45 ± 3.10 versus 28.90 ± 4.55) and total score (71.35 ± 8.00 versus 56.85 ± 8.50) in the observation group were higher than those in the control group (P< 0.05). After care, the VA of the observation group (0.95 ± 0.22) was greater than that of the control group (0.84 ± 0.16), and the IOP (14.25 ± 0.58 versus 15.89 ± 0.67) and the MD (5.42 ± 0.46 versus 6.68 ± 0.49) of the observation group were less than that of the control group. The difference between the two groups was statistically significant (P< 0.05). The compliance (96.05% versus 85.71%) and nursing satisfaction (96.10% versus 85.71%) of the observation group were higher than those of the control group (P< 0.05). Cognitive-behavioural nursing care for glaucoma patients can improve patients' mental toughness, improve visual acuity, reduce intraocular pressure and mean visual field defect values, and have a positive effect on enhancing patients' treatment adherence and nursing satisfaction.

Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach.

Study on the influencing factors of rural residents' willingness to classify domestic waste in the context of acquaintance society: An example from Jiangsu Province in Chin.

Waste classification helps to maximize the recovery of resources, reduce environmental pollution and promote sustainable economic development. To improve the willingness of rural residents to classify waste, based on the background of a rural acquaintance society, this thesis constructed a theoretical model of waste classification cognition, willingness to classify, economic incentives, face consciousness and vernacular identity affecting the willingness of rural residents to classify waste, and put forward seven research hypotheses. With the help of 530 valid surveys of rural residents in Jiangsu Province, the seven hypotheses were verified using structural equation modeling, and the mediating effect of rural residents' willingness to classify waste was examined using the bootstrap method. The results showed that waste classification awareness, knowledge of waste classification, economic incentives, face-saving concepts and local identity all affected rural residents' willingness to classify waste, and all were significant at a 0.01 level. They also showed that face-saving concepts and local identity played partial mediating roles. Strategies to improve rural residents' willingness to classify waste have been proposed, such as multiple incentives to enhance subjective awareness, play the role of face culture in rural areas, strengthen the construction of public culture in rural areas, and improve rural residents' local identity.

An intelligent assessment method of criminal psychological attribution based on unbalance data

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.

COVID-19 vaccine hesitancy among adults in India: A primary study based on health behavior theories and 5C psychological antecedents model.

Despite the significant success of India's COVID-19 vaccination program, a sizeable proportion of the adult population remains unvaccinated or has received a single dose of the vaccine. Despite the recommendations of the Government of India for the two doses of the COVID-19 vaccine and the precautionary booster dose, many people were still hesitant towards the COVID-19 full vaccination. Hence, this study aimed to identify the primary behavioral and psychological factors contributing to vaccine hesitancy. Cross-sectional data was collected via a multi-stage sampling design by using a scheduled sample survey in the Gorakhpur district of Uttar Pradesh, India, between 15 July 2022 to 30 September 2022. This study has utilized three health behavior models-the Health Belief Model (HBM), the Theory of Planned Behavior (TPB), and the 5C Psychological Antecedents of vaccination, and employed bivariate and multivariable binary logistic regression model to assess the level of vaccine hesitancy and predictive health behavior of the respondents. Results indicate that among the constructs of the HBM and 5C Antecedents models, "perceived benefits", "confidence" and "collective responsibility" showed a lesser likelihood of COVID-19 vaccine hesitancy. However, in the TPB model constructs, a 'negative attitude towards the vaccine' showed a four times higher likelihood of COVID-19 vaccine hesitancy. From the future policy perspective, this study suggested that addressing the issue of 'negative attitudes towards the vaccine' and increasing the trust or confidence for the vaccine through increasing awareness about the benefits of the vaccination in India may reduce vaccine hesitancy.

Finding and Developing the 21st Century Scientific Pioneer

Research on the personality of creative scientists is revisited using the Neo-Socioanalytic model of personality psychology as an organizing framework. Past research shows that creative scientists, largely from the 20th century, were distinguished by high levels of openness to experience, dominance, ambition, intelligence, and low levels of agreeableness. Contradictory findings for environmental influences highlighted the potential importance of both supportive and adverse diversifying experiences. Developmental trends also run contrary to the likelihood of scientific creativity occurring later in life, which is consistent with the life span models of creativity. Based on this review, we highlight much needed updating to the basic science of scientific creativity, including the need to test these findings in populations other than older white men, and the testing of both environmental experiences and individual differences simultaneously. An agenda for identifying and developing the 21st century creative scientist is provided.

Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.

Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators’ intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose Intent-o-meter, a perceived human intent prediction model for multimodal (image and text) social media posts. Intent-o-meter models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. Intent-o-meter leverages Theory of Reasoned Action (TRA) factoring in (i) the creator’s attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator’s intention. We also introduce Intentgram, a dataset of 55K social media posts scraped from public Instagram profiles. We compare Intent-o-meter with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and Intentgram. We observe that leveraging TRA in addition to visual and textual features—as opposed to using only the latter–results in improved prediction accuracy by up to 7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.5\\%$$\\end{document} in Top-1 accuracy and 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} in AUC on Intentgram. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

The doctor–patient relationship has received widespread attention as a significant global issue affecting people’s livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such as uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics and morality. To address the problem of doctor–patient interaction differences arising from the doctor–patient diagnosis and treatment, we collected the textual content of doctor–patient dialogues in outpatient clinics of local first-class hospitals. We utilized case scenario analysis, starting from two specific cases: multi-patient visits with the same doctor and multi-doctor interaction differences with the same patient. By capturing the external interactions and the internal thought processes, we unify the external expressions and internal subjective cognition in doctor–patient interactions into interactions between data, information, knowledge, wisdom, and purpose (DIKWP) models. We propose a DIKWP semantic model for the doctor–patient interactions on both sides, including a DIKWP content model and a DIKWP cognitive model, to achieve transparency throughout the entire doctor–patient interaction process. We semantically–bidirectionally map the diagnostic discrepancy space to DIKWP uncertainty and utilize a purpose-driven DIKWP semantic fusion transformation technique to disambiguate the uncertainty problem. Finally, we select four traditional methods for qualitative and quantitative comparison with our proposed method. The results show that our method performs better in content and uncertainty handling. Overall, our proposed DIKWP semantic model for doctor–patient interaction processing breaks through the uncertainty limitations of natural language semantics in terms of interpretability, enhancing the transparency and interpretability of the medical process. It will help bridge the cognitive gap between doctors and patients, easing medical disputes.

Tight or Loose: Analysis of the Organization Cognition Process of Epidemic Risk and Policy Selection

In the context of Disease X risks, how governments and public health authorities make policy choices in response to potential epidemics has become a topic of increasing concern. The tightness of epidemic prevention policies is related to the effectiveness of the implementation of measures, while the organizational cognition of epidemic risks is related to the rationality of policy choices. During the three years of COVID-19, the Chinese government constantly adjusted the tightness of its prevention policies as awareness of the epidemic risk improved. Therefore, based on the epidemic risk organizational cognition model, the key nodes that affect the tightness of epidemic prevention policies can be explored to find the organizational behavior rules behind the selection of prevention policies. Firstly, through observing the adjustments made to the Chinese government’s prevention strategies during the epidemic, a time-series cross-case comparative analysis reveals how policy tightness shifted from stringent to lenient. This shift coincided with the organizational cognition of epidemic risk evolving from vague to clear. Secondly, by building the “knowledge-cognition” coordinate system to draw the organizational cognition spiral of epidemic risk, it is clear that the changes in the tightness of the prevention policies mainly came from the internalization and externalization of knowledge such as epidemic risk characteristics to promote the level of organizational cognition, which is manifested as expansion and deepening. Thirdly, the node changes in the interaction between organizational cognition development and policy choice proved that different stages of the epidemic had diverse environmental parameters. Moreover, as the epidemic nears its end, the focus of policy tightness is shifting from policy objectives to policy implementation around governance tools. The results indicate that organizational cognition of epidemic risk exhibits significant stages and periodicity. Additionally, epidemic risk characteristics, environmental coupling, and governance tools are crucial factors in determining the tightness of epidemic prevention policies.