Cognitive Model Research Articles

Factors influencing consumers' purchase behavior toward indigenous chicken: insights from a cognitive affect behavior model

PurposeThis study addresses a critical gap in understanding consumer behavior toward indigenous chicken, investigating the interaction between cognitive factors and purchase decisions. The current research offers a valuable contribution to the field of sustainable food marketing by shedding light on these dynamics.Design/methodology/approachThe research employed a quantitative survey method to gather data from consumers of indigenous chicken in Malaysia and analyzed it using structural equation modeling.FindingsHealth and price consciousness, along with effort expectancy, significantly influence consumer attitudes toward indigenous chicken. However, environmental consciousness and availability do not directly impact attitude. Similarly, attitude mediates the relationship between health consciousness, price consciousness, effort expectancy and purchase behavior, while attitude does not mediate the relationship between environmental consciousness, availability and purchase behavior of indigenous chicken.Originality/valueThis study is one of the pioneering works to apply the cognitive affect behavior (CAB) model to examine the factors influencing consumer attitudes and purchase behavior toward indigenous chicken. It investigates how constructs such as health consciousness, environmental consciousness, price consciousness, effort expectancy and availability affect these attitudes and behaviors, offering novel insights into the purchase intentions of younger and educated individuals.

Game Theory and Reinforcement Learning in Cognitive Radar Game Modeling and Algorithm Research: A Review

Game Theory and Reinforcement Learning in Cognitive Radar Game Modeling and Algorithm Research: A Review

Применение функционального подхода к разработке модели психологической службы в системе общего и среднего профессионального образования

<p style="text-align: justify;"><span lang="EN-US">The article presents a theoretical overview of approaches to the functioning of psychological service models in the education system. The experience of organizing models of psychological and pedagogical assistance to participants in educational relations in three subjects of the Russian Federation (Perm Krai, Sverdlovsk Region, Yaroslavl Region), which in 2023 became the winners of the All-Russian competition of regional models of psychological service in the system of general education and secondary vocational education, is considered. The concept of a functional model is revealed, and the possibilities of its application in the organization of psychological services in the education system are presented. As part of the development of a draft functional model of psychological services in the system of general education and secondary vocational education, three functional blocks are considered, which involve the implementation of certain functions at the federal, regional, municipal and institutional levels.</span></p>

Avoiding siloed approaches: Integrating psychological insights into sustainable farming.

This study enhances our understanding of the psychological factors influencing farmers' adoption of sustainable farming practices, specifically those related to achieving NetZero emissions. It achieves this by integrating various psychological theories with practical farming methods within the context of Behavioral-Adoption and Purpose-Driven contexts. The research consisted of two studies. Study 1 employed Exploratory Factor Analysis (EFA) to analyze responses from 438 UK farmers regarding their attitudes toward a series of Net Zero policy commitments, drawing on psychological theories including the Unified Theory of Acceptance and Use of Technology, the Theory of Planned Behavior, the Prototype Willingness Model, Implementation Intentions, Self-Determination Theory, Eudaimonia, and the Integrated Model of Health Literacy. The findings revealed a new Integrated Motivation Model for Sustainable Farming that comprises seven factors: Agricultural Commitment and Stewardship, Sustainable Farming Readiness and Confidence, Sustainable Incentive Engagement and Acceptance, Climate Adaptation Competence and Confidence, Net Zero Accountability and Reporting Commitment, Community Influence and Commitment in Sustainable Farming, and Innovation and Technological Competence. Study 2 validated these factors through the development of a 21-item Integrated Motivation Model for Sustainable Farming scale and use of Confirmatory Factor Analysis (CFA) to confirm the 7-factor structure using a subsample of 418 UK farmers from Study 1 and an additional 230 US farmers. Furthermore, Study 2 tested the concurrent validity of the new scale by demonstrating significant associations with reported sustainable farming behaviors. These findings underscore the complex interplay of motivational, cognitive, and social processes influencing sustainable farming practices. The integrated psychological model developed through this research provides parsimonious and valuable insights for policymakers to foster sustainable practices in farming effectively. The confirmation of this model across farming populations enhances its generalizability and potential to guide targeted interventions aimed at achieving behavioral change in pursuit of Net Zero targets in agriculture.

Diagnosing middle school students’ proficiency in constructing scientific explanations with the integration of chemical reactions and patterns: a cognitive diagnostic modeling approach

ABSTRACT Scientific explanation has been widely recognised as one of the primary proficiencies in global science education. From research and practical perspectives, investigating the cognitive process of constructing scientific explanations by integrating core ideas is essential to make sense of phenomena or solve problems. This study applied the cognitive diagnosis modeling (CDM) approach to diagnose students’ cognitive patterns of constructing scientific explanations by integrating chemical reactions and patterns. We localised three well-designed assessment tasks and correspondingly developed five cognitive attributes. Responses from 244 Grade 9 students in two middle schools were collected, scored, and analyzed. Results show that the five developed cognitive attributes are reliable and effective in identifying students’ cognitive challenges in constructing scientific explanations in a content-specific domain. In addition, we articulated students’ potential cognitive process of constructing scientific explanations. We identified a primary cognitive pathway that students may follow: making correct claims, finding sufficient evidence based on data patterns, and providing scientific principles for the reasoning process. The cognitive diagnosis results could be used to guide teachers in selecting teaching materials and strategies and arranging lesson sequences to support the development of students’ scientific explanations by integrating chemical reactions and patterns.

Genetically stratified Parkinson's disease with freezing of gait is related to specific pattern of cognitive impairment and non-motor dominant endophenotype.

Freezing of gait (FOG) is an important milestone in the individual disease trajectory of people with Parkinson's disease (PD). Based on the cognitive model of FOG etiology, the mechanism behind FOG implies higher executive dysfunction in PDFOG+. To test this model, we investigated the FOG-related phenotype and cognitive subdomains in idiopathic PD (iPD) patients without genetic variants linked to PD from the Luxembourg Parkinson's study. A cross-sectional analysis comparing iPDFOG+ (n = 118) and iPDFOG- (n = 378) individuals was performed, followed by the application of logistic regression models. Consequently, regression models were fitted for a subset of iPDFOG+ (n = 35) vs. iPDFOG- (n = 126), utilizing a detailed neuropsychological battery to assess the association between FOG and cognitive subdomains. Both regression models were adjusted for sociodemographic confounders and disease severity. iPDFOG+ individuals presented with more motor complications (MDS-UPDRS IV) compared to iPDFOG- individuals. Moreover, iPDFOG+ individuals exhibited a higher non-motor burden, including a higher frequency of hallucinations, higher MDS-UPDRS I scores, and more pronounced autonomic dysfunction as measured by the SCOPA-AUT. In addition, iPDFOG+ individuals showed lower sleep quality along with lower quality of life (measured by PDSS and PDQ-39, respectively). The cognitive subdomain analysis in iPDFOG+ vs. iPDFOG- indicated lower scores in Benton's Judgment of Line Orientation test and CERAD word recognition, reflecting higher impairment in visuospatial, executive function, and memory encoding. We determined a significant association between FOG and a clinical endophenotype of PD with higher non-motor burden. While our results supported the cognitive model of FOG, our findings point to a more widespread cortical impairment across cognitive subdomains beyond the executive domain in PDFOG+ with additional higher impairment in visuospatial function and memory encoding.

The belief that being high is a natural part of your personality predicts an increase in manic symptoms over time in bipolar disorder.

Several psychological models of bipolar disorder propose that certain types of appraisals can lead to increases in manic symptoms. We tested whether the belief that being 'high' is a natural part of one's personality and correlates with manic symptoms 4 months later when controlling for manic symptoms at baseline. This was a prospective 4-month follow-up design using self-report measures. Forty people with a diagnosis of bipolar disorder completed a measure of manic symptoms, a measure of appraisals associated with bipolar disorder, and a single-item measure, 'To what extent do you feel like being "high" is a natural part of your personality?', at baseline and follow-up. The single-item measure showed modest stability over time and construct validity in its correlation with a standardised measure of appraisals in bipolar disorder. As predicted, the single-item measure correlated with manic symptoms at follow-up when controlling for manic symptoms at baseline. The belief that being 'high' is a natural part of one's personality is a potential predictor of manic symptoms. Further research needs to study the potential mediating mechanisms such as activating behaviours, and control for indicators of the bipolar endophenotype.

The Cognitive Restoration Effects of Resveratrol: Insight Molecular through Behavioral Studies in Various Cognitive Impairment Models

The Cognitive Restoration Effects of Resveratrol: Insight Molecular through Behavioral Studies in Various Cognitive Impairment Models

A unified EM framework for estimation and inference of normal ogive item response models.

Normal ogive (NO) models have contributed substantially to the advancement of item response theory (IRT) and have become popular educational and psychological measurement models. However, estimating NO models remains computationally challenging. The purpose of this paper is to propose an efficient and reliable computational method for fitting NO models. Specifically, we introduce a novel and unified expectation-maximization (EM) algorithm for estimating NO models, including two-parameter, three-parameter, and four-parameter NO models. A key improvement in our EM algorithm lies in augmenting the NO model to be a complete data model within the exponential family, thereby substantially streamlining the implementation of the EM iteration and avoiding the numerical optimization computation in the M-step. Additionally, we propose a two-step expectation procedure for implementing the E-step, which reduces the dimensionality of the integration and effectively enables numerical integration. Moreover, we develop a computing procedure for estimating the standard errors (SEs) of the estimated parameters. Simulation results demonstrate the superior performance of our algorithm in terms of its recovery accuracy, robustness, and computational efficiency. To further validate our methods, we apply them to real data from the Programme for International Student Assessment (PISA). The results affirm the reliability of the parameter estimates obtained using our method.

A cognitive modelling of translation

Abstract This paper attempts to usher in a cognitive linguistic theory into (cognitive) translation studies and offers a theoretical model of translation from the construal perspective. In this construal-based theory, translators are modelled as construers which are featured by subjectivity, and meaning decoding and encoding in translation are equated with construal which is manifested at two levels. At the cognitive level, translators construe the source text whereas at the linguistic level the construal established by the translators is packaged in the target language. Translation process involves translators’ construal operations such as perspective, selection, prominence and dynamicity, and imagination. Since construal is situated in context, it is impossible to recreate the construals of the source text author but optimal construal can be envisioned if the situated contexts for the source text author(s) as well as that for the target text are best accommodated.

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.