Cognitive Model Research Articles

Mapping the basal temporal language network: a SEEG functional connectivity study

The Basal Temporal Language Area (BTLA) is recognized in epilepsy surgery setting when cortical electrical stimulation (CES) of the ventral temporal cortex (VTC) trigger anomia or paraphasia during naming tasks. Despite acknowledging a ventral language stream, current cognitive language models fail to properly integrate this entity. In this SEEG study we used cortico-cortical evoked potentials in nine epileptic patients to assess and compare the effective connectivity of 73 sites in the left VTC of which 26 were deemed eloquent for naming after CES (BTLA). Eloquent sites connectivity supports the existence of a basal temporal language network (BTLN) structured around posterior projectors while the fusiform gyrus behaved as an integrator. BTLN was strongly connected to the amygdala and hippocampus unlike the non-eloquent sites, except for the anterior fusiform gyrus (FG). These observations support the FG as a multimodal functional hub and add to our understanding of ventral temporal language processing.

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach

Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a greater exposure to general risk factors for poor cognition. To determine the extent that impairments in cognition in psychosis reflect risk factor exposures. This cross-sectional study examined the relationship between exposures and cognitive function using data from the Bipolar-Schizophrenia Network on Intermediate Phenotypes studies 1 and 2 across 6 sites. Participants included healthy controls; patients with schizophrenia, schizoaffective disorder, or bipolar I disorder with psychosis; and relatives of patients. Predictive modeling was performed using extreme gradient boosting regression to train a composite cognitive score prediction model with nested cross-validation. Shapley additive explanations values were used to examine the relationship between exposures and cognitive function. Exposures were chosen based on associations with cognition previously identified: age, sex, race and ethnicity, childhood adversity, education, parental education, parental socioeconomic status, parental age at birth, substance use, antipsychotic dose, and diagnosis. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. A total of 3370 participants were included: 840 healthy controls, 709 patients with schizophrenia, 541 with schizoaffective disorder, 457 with bipolar I disorder with psychosis, and 823 relatives of patients. The mean (SD) age was 37.9 (13.3) years; 1887 were female (56%) and 1483 male (44%). The model predicted cognitive scores with high accuracy: out-of-sample Pearson correlation between predicted and observed cognitive composite score was r = 0.72 (SD = 0.03). Individuals with schizophrenia (z = -1.4), schizoaffective disorder (z = -1.2), and bipolar I disorder with psychosis (z = -0.5) all had significantly worse cognitive composite scores than controls. Factors other than diagnosis and medication accounted for much of this impairment (schizophrenia z = -0.73, schizoaffective disorder z = -0.64, bipolar I disorder with psychosis z = -0.13). Diagnosis accounted for a lesser proportion of this deficit (schizophrenia z = -0.29, schizoaffective disorder z = -0.15, bipolar I disorder with psychosis z = -0.13), and antipsychotic use accounted for a similar deficit across diagnostic groups (schizophrenia z = -0.37, schizoaffective disorder z = -0.33, bipolar I disorder with psychosis z = -0.26). This study found that transdiagnostic factors accounted for a meaningful share of the variance in cognitive functioning in psychosis. A significant proportion of the cognitive impairment in psychosis may reflect factors relevant to cognitive functioning in the general population. When considering interventions, a diagnosis-agnostic, symptom-targeted approach may therefore be appropriate.

Exploring cognitive behavioral mechanisms related to dissociation among patients suffering from schizophrenia: A pilot study

IntroductionDissociation is a challenging syndrome present in multiple psychiatric disorders for which no gold standard treatment exists. Recently, some authors have developed a Cognitive Behavioral Model of dissociation, offering new insights for psychotherapy. However, this model has primarily been assessed in patients suffering from PostTraumatic Stress Disorder (PTSD). Therefore, the aim of this study is to explore the relevance of this conceptualization in patients suffering from schizophrenia. MethodWe recruited 20 patients (15 men) suffering from schizophrenia. After providing consent, they completed self-reported questionnaires assessing dissociation, PTSD, beliefs about emotion, beliefs about dissociation, mindfulness abilities, and emotion regulation. Then, they responded to three open-ended questions exploring the triggers of dissociation and strategies used to counteract it. ResultsWe found a low prevalence of the different cognitive-behavioral mechanisms related to dissociation in our sample. However, we found significant relationships between these mechanisms and dissociative symptoms. This relationship was also identified in the qualitative analysis. ConclusionThe present results offer primary data supporting the relevance of the cognitive-behavioral conceptualization of dissociation in patients suffering from schizophrenia. Further studies should be conducted with larger samples and include additional variables to enhance the understanding of dissociation in this population.

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

AbstractCognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

Profiling the skill mastery of introductory programming students: A cognitive diagnostic modeling approach

Profiling the skill mastery of introductory programming students: A cognitive diagnostic modeling approach

Models of Variability in Probabilistic Causal Judgments

AbstractMost theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating ‘non-reasoning’ processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.

Model of Comparative Analysis of the Structure and Semantics of Causal Constructions in Multi-Structural Languages

This article explores the application of constructive grammar, a linguistic approach that integrates cognitive, syntactic, and semantic frameworks to analyse causative constructions in English and Russian languages. Constructive grammar interprets not only the form and content of linguistic components but also the structure itself, which influences and imposes constraints on these components. The study considers the methodological evolution from traditional syntactic theories to cognitive models, particularly the influence of Ch. Fillmore’s work. By focusing on causative constructions, specifically analytical causatives like "The police made him confess," the research draws upon the theories of V.P. Nedyalkov, G.G. Silnitsky, and A.A. Kholodovich, among others, to provide a comparative analysis of causative structures in different languages. Cognitive and generative grammar perspectives, including R. King's spatial metaphor and L. Talmy’s force dynamics, are integrated to establish a comprehensive framework for understanding the universal and language-specific properties of causative expressions. The findings highlight the importance of the cognitive interpretation of syntactic structures, the role of energy transfer between agents and patients, and the diverse semantic types of causative situations. The study concludes by suggesting future research directions in the syntactic classification and formal expression of causative components.

Autistic traits linked to anxiety and dichotomous thinking: sensory sensitivity and intolerance of uncertainty as mediators in non-clinical population

A recent theoretical cognitive model posits that three cognitive constructs—intolerance of uncertainty, lack of predictive processing, and dichotomous thinking—may interact with anxiety in autistic individuals. However, the interrelationships among these constructs remain unclear. We investigated this relationship in a non-clinical population using five questionnaires (Autism Spectrum Quotient, Glasgow Sensory Questionnaire, Short Intolerance of Uncertainty Scale, Penn State Worry Questionnaire, and Dichotomous Thinking Inventory) and conducted a path analysis. Data were collected from 405 adults (aged 20–22 years) in Experiment 1 and 628 (aged 20–49 years) in Experiment 2. In both experiments, autistic traits were significantly associated with both anxiety and dichotomous thinking, mediated by intolerance of uncertainty. Autistic traits were linked to intolerance of uncertainty, mediated by atypical sensory processing. Furthermore, autistic traits were directly associated with anxiety. No direct association was observed between anxiety and dichotomous thinking. Our results confirm the validity of Stark’s cognitive model in a non-clinical population. These findings provide new insights into anxiety and dichotomous thinking and shed light on the cognitive styles of autistic individuals.

Exploring the Relationship Between Challenge and Reward for Game Based Learning

The aim of this research is to investigate how problem-solving challenges affect young people’s motivation for learning computer programming. It explores the relationship between difficulty and reward as part of a game-based learning application and evaluates the effectiveness of the ARCS learning model when teaching primary school children. Research in game-based learning is based, in part, on the study of the impact of games on students' learning motivation, engagement and outcomes. The ARCS model, developed by John Keller in 1987, is a widely recognized model for designing effective instruction that motivates and engages learners. The model is a cognitive psychology-based learning model that consists of four main elements: Attention, Relevance, Confidence, and Satisfaction. A pilot study comprising primary school teachers was conducted to evaluate the effectiveness of the ARCS model. Two experiments were conducted: the first an experimental group based on the design of the game focussing on the player’s experience and evaluation of the difficulty and reward mechanisms. For this group, the game dynamically changes in terms of difficulty and rewards. This is achieved by adjusting the difficulty and rewards through a dynamic player score to maintain player attention, confidence, and satisfaction. The second experiment involved a control group, where the game adopts a traditional linear learning model where difficulty and reward are fixed. For both experiments, data was collected from a feedback questionnaire. The questionnaire was designed to be closely aligned and mapped to the ARCS learning model’s four elements. The scores of the results were based on the Likert scale and t-test to determine the significance of the difference in scores between the experimental and control groups. Results based on data analysis of the user study show that the overall mean score of the experimental group is higher (= 4/5) compared to the control group (= 2.2/5) on all four ARCS elements. Further analysis shows that the most prominent teaching impact is on players’ satisfaction.

Age-Dependent Relationship between Self-Esteem and Depressive Symptoms in Korean Adolescents: a Meta-Analysis of Longitudinal Studies.

Self-esteem is a powerful protective factor against depressive symptoms, with cognitive vulnerability theory suggesting that early self-esteem more strongly predicts later depressive symptoms than vice versa. While some meta-analyses have examined these relationships, limited knowledge exists on the prospective relationship between self-esteem and depressive symptoms, particularly how it changes with age. This study conducted a longitudinal meta-analysis of nine studies (20,733 participants) on the prospective relationship between self-esteem and depressive symptoms in Korean children and adolescents. Relationships were analyzed by integrating the effect sizes of 22 zero-order correlations and 17 partial correlations and examining age-related changes. The results indicated significant negative correlations in both zero-order and partial correlations. Age-related changes showed a reverse U-shape trend in partial correlations, with the strongest negative association during childhood, weakening until age 14, and then strengthening again. Moderator analysis revealed no significant effect size variation based on publication type or gender proportion, but more recent birth cohorts exhibited a stronger negative relationship in zero-order correlations. Significant differences were also found based on the measurement instrument of depressive symptoms. These findings highlight the importance of tailored interventions to enhance self-esteem and mitigate depressive symptoms, extending the cognitive vulnerability model within Korea's cultural context and providing insights into Korean youth mental health.

A cognitive load assessment method for fighter cockpit human-machine interface based on integrated multi-criteria decision making

The primary interface for communication between pilots and aircraft systems is the fighter cockpit Human-Machine Interface (HMI). Since reduced cognitive load ensures that pilots will operate the aircraft safely and effectively, it is critical to evaluate pilot cognitive load during HMI. A cognitive load assessment method, based on multi-criteria decision-making, is proposed to accurately quantify the relationship between the HMI and the cognitive load in the fighter’s cockpit. Firstly, based on the integrated Multi-Criteria Decision-Making (MCDM) method, the Step-wise Weight Assessment Ratio Analysis (SWARA) and MEthod based on the Removal Effects of Criteria (MEREC) methods are used to assign subjective and objective weights, respectively. Moreover, the Combined Compromise Solution (CoCoSo) method is applied to rank the scenarios to establish a cognitive load assessment model for the cockpit HMI of the fighter jet. Secondly, an evaluation standard system of fighter cockpit HMI is proposed, drawn upon multiple sets of eye-movement criteria and subjective assessment criteria. Moreover, cognitive load experiments of fighter cockpit HMI are conducted using eye-tracking technology to get the objective physiological cognitive data as well as the subjective assessment data of the subjects. Consequently, the parameter data sets of the eye-movement criteria and the subjective criteria for the evaluation of cognitive load are obtained. The proposed method is applied to analyze the cognitive load assessment of a fighter jet cockpit HMI layout. This application aims to verify the effectiveness of the assessment method in evaluating the cognitive load of the HMI layout. Through sensitivity and comparison analyses, the model was further verified to have excellent robustness and applicability for cognitive load assessment. The advantages of this method can be seen through the comparison, which is that it has higher discriminability when assessing the degree of cognitive load. At the same time, it has higher flexibility in dealing with complex and ambiguous cognitive load assessment information.

Empowering Unvaccinated Youth: Feasibility, Acceptability, and Efficacy of a COVID-19 Serious Game-Based Intervention.

Objective: To evaluate a serious game-based intervention's feasibility, acceptability, and efficacy in encouraging coronavirus disease (COVID-19) vaccination among 11-14-year-olds in the United States and assess participant experiences. Materials and Methods: The study, grounded in social cognitive theory and health belief model, recruited and engaged 32 English-speaking parent-child dyads with unvaccinated youths via snowball sampling and social media outreach. These dyads were randomly assigned to either the COVID-19 serious game-based intervention group (n = 16) or a usual caregroup (n = 16). The study measured beliefs, self-efficacy, and intentions regarding vaccination using surveys before and after the intervention, with follow-up at 2 months to evaluate vaccine uptake. Semistructured interviews provided insights into the gaming experience and study process. Results: All 32 eligible parent-youth dyads (16 in each group) completed the study with full participation. Most participants were Black or African American fathers, making up 75% and 81.3% of the intervention and control groups, respectively. The intervention impacted vaccine uptake, with 75% of the intervention group vaccinating their children compared with 37.5% of the control group [χ2(1, n = 32) = 4.57, P = 0.033]. Key factors for vaccine uptake included parents' motivation and youths' perceived susceptibility, with correlations of r(30) = 0.66, P = 0.006, and r(30) = 0.55, P = 0.029, respectively. The intervention reshaped youth perceptions about self and community protection. Participants expressed overall satisfaction with both the intervention and the study process. Conclusions: The intervention was found to be feasible and acceptable, with potential for integration into youth vaccination strategies to support COVID-19 vaccine decision-making.

Factors associated with physical and psychological wellness of American adults with asthma

Objective: The study objective was to investigate the factors associated with the physical and psychological wellness of United States (US) adults with asthma.Methods: This cross-sectional analysis used a sample of 2329 US adults with asthma in the 2021 Medical Expenditure Panel Survey data. A logistic regression model investigated the association of the following factors and the dependent variables (physical wellness and psychological wellness): age, sex, race, ethnicity, education, employment, healthcare provision, marriage, income, regular physical activity, current smoker, pain, and limitations. Nationally representative estimates were produced through a weighted analysis. The data structure was maintained using cluster and strata variables. The alpha limit was 0.05.Results: Factors associated with higher odds of reporting good physical wellness included: private (versus no) healthcare provision (odds ratio [OR] = 2.63, 95% confidence interval [CI] = 1.10–6.26), good (versus poor) psychological wellness (OR = 6.83, 95% CI = 4.35–10.72), regular (versus no regular) physical activity (OR = 2.18, 95% CI = 1.42–3.34), little/moderate (versus quite a bit/extreme) pain (OR = 3.51, 95% CI = 2.38–5.15) and no (versus any) limitation (OR = 3.73, 95% CI = 2.30–6.06). In the psychological wellness model, those aged ≥70 (OR = 6.18, 95% CI = 2.72–14.07), 60–69 (OR = 4.64, 95% CI = 2.13–10.10), and 50–59 (OR = 4.96, 95% CI = 2.24–11.02) versus those aged 18–29, and good (versus poor) physical wellness (OR = 6.89, 95% CI = 4.34–10.94) were associated with higher odds of reporting good versus poor psychological wellness.Conclusion: These results may be helpful at targeting resources to optimize the wellness of US adults with asthma. Additional studies are needed to determine any temporal associations between these findings.

The Bilingual Dual-path model

Abstract Experimental research has yielded many important psycholinguistic findings in bi-/multilingualism. However, cognitive computational models of sentence processing were limited to the single-language case until recently. In this methodological review, we discuss cognitive modelling work that uses the Bilingual Dual-path model to simulate experimental research on bilingual sentence production, processing, and development. As a detailed example of such work, we then report on new simulations conducted with the model. Finally, we suggest directions for future cognitive modelling research using this model.

Caregiving, caring intensity, and allostatic load: A comparison of caring to others inside and outside the home

ObjectiveCaregiving to a sick or disabled relative is a key chronic stress model in health psychology. However, caregiving is not uniformly stressful, and this study tested whether caregiving effects on life satisfaction and allostatic load varies by caring intensity, i.e., caring within the home, outside the own home, or both (e.g. Dual caring). MethodUsing data from the UK Understanding Society Wave 2, a sample of non-caregivers (N = 3149) and caregivers (N = 562) met inclusion criteria and completed measures of life satisfaction and provided blood samples for capturing endocrine, cardiovascular and immune parameters for assessment of allostatic load. ResultsDual caregivers had lower life satisfaction compared to non-carers, and other caregiver groups. Further, dual caregivers had higher levels of allostatic load compared to non-carers and those caring within the home and those caring outside the home. These group differences withstood adjustment for several co-variates including gender, education, income and lifestyle factors. ConclusionsThese results confirm that caregiving is not uniformly stressful with dual caring, an index of caring intensity being more damaging for health. The findings are also discussed in relation to the caregiver-control model of chronic stress.

Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts.

Over several years, the evaluation of polytomous attributes in small-sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for estimating polytomous attribution, given its proven feasibility for dichotomous cases. Two simulation studies and an empirical study assessed the impact of various factors on SVM classification performance, including training sample size, attribute structures, guessing/slipping levels, number of attributes, number of attribute levels, and number of items. The results indicated that SVM outperformed the pG-DINA model in classification accuracy under dependent attribute structures and small sample sizes. SVM performance improved with an increased number of items but declined with higher guessing/slipping levels, more attributes, and more attribute levels. Empirical data further validated the application and advantages of SVMs.

Cognitive, affective, and normative factors affecting digital insurance adoption among persons with disabilities: A two-stage SEM-ANN analysis

This study evaluates the determinants of digital insurance adoption among persons with disabilities (PWDs) using the cognitive, affective, and normative (CAN) model. The study considers (i) cognitive factors such as perceived credibility, perceived knowledge, perceived usefulness, perceived complexity, and facilitating conditions; (ii) affective factors including technology anxiety and technology pleasure; and (iii) normative factors encompassing social influence. Moreover, it explores the relationship between perceived complexity and behavioral intention (BI) to adopt digital insurance among PWDs, as mediated by perceived knowledge. This study employs a two-stage hybrid structural equation modeling–artificial neural network (SEM-ANN) approach to test the hypothesis, and data from 323 physically challenged participants were collected. Empirical results show that all factors, except for perceived complexity and technological anxiety, significantly predict BI adoption of digital insurance among PWDs, whereas perceived usefulness was found to have the highest impact on BI. Although perceived complexity affects perceived knowledge, it does not significantly mediate the relationship between complexity and BI. This study expands on the CAN model and provides practical insights for PWDs in adopting digital insurance.

Impact on ambulatory palliative appointment lead times by standardizing scheduling protocols based on cognitive computing models.

62 Background: Although palliative care is available across the United States, access continues to remain variable. In Texas, only 52% of hospital systems have palliative services. As a result, optimizing existing systems is critical to providing timely services to this vulnerable population. The Supportive Care Services Clinic at University Health in San Antonio, TX provides ambulatory palliative services to patients including physical and psychosocial symptom management and care coordination. For the period April 1, 2023 to June 30, 2023 average lead times for new palliative patient visits (NPPV) and hospital consult discharge visits (HCDV) was 53 days and 39 days respectively. The aim of this project was to decrease the lead times for NPPV and HCDV through standardization of scheduling criteria. We hypothesized that by applying a scheduling protocol utilizing cognitive computing models (CCM) calculating readmission risk scores to prioritize referrals, we could reduce appointment lead times. Methods: During the implementation phase, we developed referral triage criteria to prioritize patients on a scheduling wait list, including patients who were at higher risk of hospitalization or rapid decline based on CCM readmission risk scores calculated within the electronic medical record. Patients were assigned to next available in template. If a cancelation occurred, patients were pulled forward and vacancy was filled based on priority score. Results: For the initial post-implementation period between September 1, 2023 and November 30, 2023 NPPV average lead time was 47 days which was a reduction of 11% from baseline. The average lead time for HCDV was 33 days which was a reduction of 15% from baseline. In a second follow-up period from February 1, 2024 to April 30, 2024, the average lead time of NPPV was 22 days, a 58% decrease from baseline. The average lead time for HCSV was 24 days which was a significant reduction (p-value = 0.011) of 38% from the pre-implementation baseline. Conclusions: Utilization of CCM risk scores to triage NPPV and HCDV significantly reduced the lead times and resulted in more timely services for critical patients. We also noted increased template utilization. Future projects aim to study the effects of scheduling protocols on template utilization, cancellation rates, no-show rates, readmission rates and other health care utilization. Palliative appointment lead times. April-June 2023 Sept-Nov 2023 Feb-Apr 2024 Total Pt Seen 292 364 534 Avg Lead Time (d) 41 38 29 New Patient New Patient New Patient Completed 66 82 74 Avg Lead Time (d) 53 47 22 Hospital Discharge Hospital Discharge Hospital Discharge Completed 27 28 25 Avg Lead Time (d) 39 33 24

A Diffusion Model Account of Cognitive Variability in Healthy Aging and Mild Cognitive Impairment

ABSTRACT Within-person variation in cognitive performance is linked to pathological aging. Cognitive fluctuations have not been analyzed using cognitive process models, such as the diffusion model, to characterize which cognitive processes contribute to variability in cognition. We collected 21 daily assessments of attention and personality in younger adults, healthy older adults, and those with mild cognitive impairment. We employed mixed-effects location scale models to analyze group differences on mean diffusion parameters and daily variability. Discussion focuses on how these methods extend our understanding of how cognitive deficits might appear in aging and disease and the moderating influence of daily personality.

Cohesion Model and Scenario Simulation for Risk Prevention of “Blockchain + Agriculture Crowdfunding” from the Synergistic Perspective

Aiming at the problems such as scattered subjects, static mode and single function, this paper explores a dynamic synergistic risk prevention mode of agricultural crowdfunding that adapts to the age of data intelligence. Based on multiple prevention subjects, the blockchain technology and cohesion principle are introduced into agricultural crowdfunding prevention, and then a risk prevention dynamic model of “blockchain + agricultural crowdfunding” is constructed from the synergistic perspective. Fuzzy cognitive map model and its reasoning mechanism are used to simulate four scenarios of 1-subject, 2-subjects, 3-subjects, and 4-subjects respectively in the dynamic model and the static model. The results show that: as the number of risk prevention subjects increases, so does the cohesion, and the cohesion of multi-subjects reaches a higher state in less time and the steady-state faster, even though the fluctuation range reduces. The risk value for the multi-agent reduces rapidly and reaches a lower risk value in a shorter period of time. Overall, cohesion increases while risk reduces. In the same scenario, dynamic mode cohesion is stronger than static mode cohesion, and it takes less time to attain the steady-state value. Under various scenarios, the dynamic model’s cohesiveness steady-state value increases with the number of subjects, which is stronger than the static model’s. This study not only enriches and develops the theory of risk management of agricultural crowdfunding, but also provides a decision-making basis for the enterprises and the government, etc., to formulate, adjust, and implement risk prevention measures.