Cognitive Model Research Articles

Assessing the influence of an android robot's persuasive behaviors and context of violation on compliance

This study investigates how the perception of persuasive behaviors (polite, logical, displeased, angry) of an android robot is affected by situations regarding the context of violation (affecting oneself or others), and by subject traits, such as compliance awareness (CA) and agreeableness (AG). We conducted a video-based experiment based on a mixed-subjects design with 98 participants from the US and conducted a three-way mixed analysis of variance to investigate the impact of persuasive types and situation types (as within-subject factors), and the subject trait groups (CA or AG, as between-subject factors), on the subjective impressions of the persuasive behaviors by the android robot. Results showed that more negative behaviors (anger and displeasure) are appraised as being more appropriate and effective to persuade a violator in situations where the violation affects others, while no clear preference was found in a situation where the violation affects only oneself. Regarding the subject traits, participants with higher CA and lower AG would be willing to adhere to any persuasive behaviors, while their counterparts would dislike being persuaded through negative behaviors by the robot. These findings can be considered in future studies to develop cognitive models for generating situation-aware behaviors in social robots.

How do people predict a random walk? Lessons for models of human cognition.

Repeated forecasts of changing values are common in many everyday tasks, from predicting the weather to financial markets. A particularly simple and informative instance of such fluctuating values are random walks: Sequences in which each point is a random movement from only its preceding value, unaffected by any previous points. Moreover, random walks often yield basic rational forecasting solutions in which predictions of new values should repeat the most recent value, and hence replicate the properties of the original series. In previous experiments, however, we have found that human forecasters do not adhere to this standard, showing systematic deviations from the properties of a random walk such as excessive volatility and extreme movements between subsequent predictions. We suggest that such deviations reflect general statistical signatures of cognition displayed across multiple tasks, offering a window into underlying mechanisms. Using these deviations as new criteria, we here explore several cognitive models of forecasting drawn from various approaches developed in the existing literature, including Bayesian, error-based learning, autoregressive, and sampling mechanisms. These models are contrasted with human data from two experiments to determine which best accounts for the particular statistical features displayed by participants. We find support for sampling models in both aggregate and individual fits, suggesting that these variations are attributable to the use of inherently stochastic prediction systems. We thus argue that variability in predictions is strongly influenced by computational noise within the decision making process, with less influence from "late" noise at the output stage. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Impaired belief revision yet intact information seeking in positive schizotypy: A modified task of bias against disconfirmatory evidence

Cognitive models of delusions emphasize the role of bias against disconfirmatory evidence (BADE) in maintaining false beliefs, but sources of this tendency remain elusive. While impaired information integration could be an explanation for this tendency, the lack of information seeking motive could also result in disregard for new evidence once a (false) belief is formed. The role of information seeking in the association between psychosis-proneness and belief inflexibility has not been investigated in the context of a social interpretation task. In this study, we modified the Interpretation Inflexibility Task (IIT), which assess bias against disconfirmatory evidence in interpersonal contexts, to permit assessment of information seeking by allowing participants to skip seeing increasingly disambiguating information (in the form of pictures at varying degrees of degradation). A robust regression analysis was conducted to examine whether increasing severity of positive schizotypy is associated with more frequent skipping of later trial stages, to examine information seeking. Controlling for the number of pictures seen by participants, a robust mixed effects analysis was conducted to investigate the associations of positive schizotypy, trait anxiety, and the emotional valence of the scenario with a measure of belief revision. Participants higher in positive schizotypy did not opt out of seeing disambiguating information more frequently, p = 0.65, ß = 0.04; despite this, they still exhibited heightened belief inflexibility by rating the lures and true explanations as equally plausible, p < 0.001, ß = -0.32. These results suggest that bias against disconfirmatory evidence in positive schizotypy is unlikely a result of reduced information seeking, leaving impaired information integration as a more likely source.

Emotion and cognition: on the cognitive processing model of nostalgia.

Nostalgia is a common emotional phenomenon, but its complexity has resulted in a limited understanding and interpretation. This study, grounded in emotional cognitive science, develops a cognitive processing model of nostalgia, encompassing its triggering mechanism, cognitive processes, and effects. The analysis indicates that nostalgia functions as an adaptive mechanism for individuals with deficiencies in belongingness and low-avoidance tendencies, helping them cope with disruptions in self-continuity by forging symbolic social bonds in a distant and idealized timespace. Essentially, nostalgia allows individuals to reconstruct alternate systems of meaning and value, which serve as references for defining self-worth and identity. This study advances the understanding of the complex cognitive processing involved in nostalgia and also provides an important reference for the study of complex emotions.

Why We Eat Calories: A Plurality Metaphor of Energy in Scientific Disciplines

AbstractIn the Next Generation Science Standards, energy is considered a “crosscutting concept” that bridges disciplinary boundaries and unites scientific disciplines. I examine how energy is represented in physics, biology, and chemistry contexts, using the reaction of molecular oxygen with sugar as an exemplar, and argue that disciplines disagree in how they represent the origin of energy that drives this process. In particular, while biology tends to locate energy as initially in the sugar molecule, chemistry locates the energy in molecular oxygen, and physics models energy as in the field between the molecules. That is to say, biology describes us as eating calories, chemistry as inhaling calories, and physics invents an abstract object (the field) as the container for energy. I then show how the conceptualizations made in each discipline stem from core disciplinary commitments, models, and concepts that structure what “counts” as an explanation. This conceptual plurality, then, is essential to disciplinary meaning. While such a pluralistic conceptualization appears to be contrary to scientific epistemology that prioritizes coherence and cognitive models that rely on unitary structures for transfer, I draw on recent research to argue that neither concern is fully founded. Finally, I suggest that building bridges between these contrasting conceptualizations may come later, in response to interdisciplinary questions and frameworks.

Conceptual Transformation of the South Kuril Islands’ Image in Japanese Media After 24 February 2022 (based on the materials of the English-language Japanese media)

The article is devoted to the study of the conceptual image of the South Kuril Islands in the leading English-language mass media of Japan after the beginning of the Russian Armed Forces’ special military operation in Ukraine. The relevance of the study is explained by the preservation of the hotbed of military and political tension near the Far Eastern borders of Russia, manifested in Japan’s territorial claims to the southern part of the Kuril Islands, which acquired a strongly pronounced verbalized ideological shift after February 24, 2022. The conceptual characteristics of the image of the islands (and closely related images of Russia/USSR) were identified using the methods of content analysis, cognitive-discursive analysis, cognitive interpretation, cognitive modeling, as well as elements of cognitive matrix analysis.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Exploring the role and mechanisms of environmental serious games in promoting pro-environmental decision-making: a focused literature review and future research agenda.

Environmental serious games aim to heighten players' awareness and comprehension of environmental issues, thus fostering pro-environmental decision-making. Research to date has affirmed these games' effectiveness in enhancing environmental knowledge and abilities, elevating consciousness regarding environmental matters, and promoting pro-environmental behavioral intentions and actions. Nonetheless, a detailed exploration into the precise mechanisms facilitating these impacts remains scarce. Leveraging theories of motivation, cognition, affect, and behavior, this paper outlines four hypothesized mechanisms of influence and introduces an Embodied-Enactive Cognition Model as a novel perspective. It suggests that future research should expand its inquiry into the multifaceted factors that influence pro-environmental decision-making, deepen the comprehension of the intrinsic mechanisms at play, pioneer novel research methodologies, and diversify the array of categories and contextual applications of environmental serious games.

서울시민 힐링프로젝트 마음온 치유활동가의 치유적 경험에 대한 사례연구

Objectives The purpose of this study is to examine the healing experiences of healing activists participating in the Seoul Citizens’ Healing Project Maeum-on Program and to examine the implications of these experiences to the counseling site. Methods Using qualitative case study methods, we selected five healing activists who were part of a team as research participants from June to August 2020. Participatory observation journal was prepared from the moment the activists first met until their disbandment. In addition, in-depth interviews were conducted 17 times befor, during, and after the program. Also, activity diaries, o-site sketches, and promotional materials written by activists were collected. The data Analysis was carried out using emic oding and etic coding metionds. To ensure the reliability of the study, expert meetings, member checks, and preliminary investigations were conducted. Results First, good participation experiences, interest in fields such as psychological counseling, and the belief that one’s activities make the world good became the context for healing activists to participate in healing projects. Second, the activists’s healing experience consists of receiving at the same time as giving, and was quite similar to he healing factors in counseling theory. Third, the moment of recognizing the need for conflict with colleagues, a lot of energy and time consumption, and counseling expertise was confirmed as a difficulty as a healing activist. Conclusions As the first study on the Maeum-on program, a new type of psychological program model that can spread social values was identified, and it was confirmed that a citizen-centered healing movement was possible. It can be used as basic data when developing group programs in the future.

PSXII-15 Theory of planned behavior-based evaluation of technology adoption in dairy farms

Abstract Studies on the adoption of farm innovations should include underlying psychological constructs that affect decisions and behavior of farmers such as intention, perceptions, and beliefs. Dairy farming technologies are continually evolving, making it necessary to review current farm practices frequently. Dairy farmers are actively exploring various alternative investment options to optimize production processes, minimize costs, and increase overall efficiency. However, careful evaluation is crucial to understanding the knowledge and attitude of farmers to these tools. We utilize psychological constructs from the theory of planned behavior approach to understand the technology adoption behavior of dairy farmers. The theory of planned behavior is a psychological model that explains how human behavior is influenced by attitudes, subjective norms, and perceived behavioral control. We add product marketing as another essential component based on the industry relevance. The objective was to understand the adoption decisions and the perceived usefulness of precision technologies by dairy farmers in Texas. Dairy farmers were delivered printed copies of a pretested survey instrument to assess technology use, on-farm decisions, and producers’ planned behavior related to technology adoption on farms. Thirty-two completed surveys were returned and analyzed for descriptive and Structural Equation Modeling using Microsoft Excel and SAS (ver. 9.4). When choosing a technology, respondents ranked (on a scale of 1 to 5; unimportant to very important) the availability of local tech support as the most important factor (score 4.16; range 3 to 5), followed by proven performance through research (4.0; 3 to 5), simplicity of use (3.82; 3 to 5), benefit to cost ratio (3.66; 2 to 5), and total cost of investment (3.5; 1 to 5). Regarding barriers to investment decisions, 26% ranked lack of access to capital as a primary issue, followed by environmental regulations (12.5%), political environment (12.5%), and sitting regulations (12.5%). The intention of farmers to utilize the technology was influenced by farmers’ evaluation of the use of technology (attitude), their perceptions about the social pressure and marketing (subjective norm), and their perceptions about their utility on farmers (perceived behavioral control). The three most influential behavioral beliefs as the drivers of attitude were an increase in farm production and performance, monitoring cows, and labor savings. The three most influential normative beliefs were drivers of the subjective norm: family and friends, neighbors, and marketing. The three most influential control beliefs were drivers of perceived behavioral control: availability of local tech support, benefit-to-cost ratio, and simplicity and ease of use. Respondents indicate that having a standard price, reduced cost, and providing a detailed cost analysis when marketing the technology increases confidence in purchasing and integration. The findings suggest developing better products, ensuring reasonable pricing, and performing proper validations to enhance the farmer’s trust helps to increase technology adoption in dairy farms.

A decision support framework for best-fitting blockchain platform selection in sustainable supply chains under uncertainty

Despite blockchain’s potential to enhance visibility and traceability in sustainable supply chains (SCs), its adoption is complex due to the various criteria (e.g., interoperability and cost) required for the best-fitting platform selection. This study aims to investigate conflicting criteria in the blockchain technology (BT) platform selection process for decision-making under uncertainty. We propose a three-phase decision support framework to study BT adoption considering technological, organizational, and environmental contexts. In the first phase, after exploring the evaluation criteria from multiple contexts, the developed framework incorporates uncertainty and reliability to deal with the BT platform evaluation problem. Then, fuzzy cognitive map modeling, advanced by a Z-number-based inference system, is introduced to model the causal relationships between criteria. This is followed by implementing a hybrid learning algorithm to assess the impact of each criterion on adoption decisions. Finally, the fuzzy combined compromise solution embedded in the framework prioritizes BT platforms to identify the most suitable ones for sustainable SC. The findings imply that performance efficiency, implementation costs, maintainability and operability can significantly affect the BT platform selection decisions. The outcomes offer more stable, reliable, and distinguishable solutions for the proposed problem compared to the traditional approaches. The results introduce Hyperledger and R3 Corda as the best-fitting platforms for adoption based on the identified criteria.

Explaining Crisis Situations via a Cognitive Model of Attention

Decision making in critical situations is a complex process. There are many processes to consider. This paper describes a theoretical approach to researching attentional processes and automatic unconscious processes in terms of metacognition. An application of the approach is presented to explain decision making and metacognition as a solution for ineffective cognitive biases during a crisis situation. Evidence is presented from studies on neuropsychology, cognitive control, and cognitive architectures. An application of the recently formulated semiotic methodology is implemented that allows the design of conceptual models of Attention as Action. The formulation of a general model of attentional processes is based on a set of rules. The crisis phenomenon, as the crisis situation trigger, is semiotically described and applied as insight for a crisis information system design that prompts its users toward self-aware internal decision making. The research conducted evidently shows how the approach can explain the design of several cognitive architectures. Pointing toward metacognition as a solution to a crisis phenomenon and cognitive biases, the paper shows that understanding human cognitive and behavioral processes can significantly improve management in a critical infrastructure crisis situation.

Which Communication Style Explains Happiness in Social Relationships among Young People?

This study examines which communication styles enhance happiness in social relationships among 454 students aged 18-24 from eight universities in Turkey. Data were collected through online and face-to-face surveys. Communication behaviors were categorized as expressiveness, emotionality, and manipulativeness using the Communication Styles Inventory (CSI). The Social Well-Being Scale, based on the PERMA model of positive psychology, was used to measure the quality of social relationships and life satisfaction. Correlation and regression analyses indicated that an expressive communication style is positively associated with higher social well-being, while emotional and manipulative communication styles do not significantly affect social well-being. The results highlight that promoting expressive communication behaviors can increase happiness and satisfaction in social interactions among young individuals.

Discovery of 6,7-Dihydropyrazolo[1,5-a]pyrazin-4(5H)-one Derivatives as mGluR2 Negative Allosteric Modulators with In Vivo Activity in a Rodent's Model of Cognition.

Allosteric modulators of the metabotropic group II receptors, mGluR2 and mGluR3, have been widely explored due to their ability to modulate cognitive and neurological functions in mood disorders, although none have been approved yet. In our search for new and selective mGluR2 negative allosteric modulators (NAMs), series of 6,7-dihydropyrazolo[1,5-a]pyrazin-4(5H)-one derivatives were identified from our published series of 1,3,5-trisubstituted pyrazoles. SAR evolution of the initial hit resulted in 100-fold improvement in the mGluR2 NAM potency and subsequent selection of compound 11 based on its overall profile, including selectivity and ADMET properties. Further pharmacokinetic-pharmacodynamic (PK-PD) relationship built showed that compound 11 occupied the mGluR2 receptor in a dose-dependent manner. Additionally, the compound revealed in vivo activity in V-maze as a model of cognition from a dose of 0.32 mg/kg. Compound 11 was selected to be evaluated further.

Interpretable neuro-cognitive diagnostic approach incorporating multidimensional features

Cognitive diagnostics is a pivotal area within educational data mining, focusing on deciphering students’ cognitive status via their academic performance. Traditionally, cognitive diagnostic models (CDMs) have evolved from manually designed probabilistic graphical models to sophisticated automated learning models employing neural networks. Despite their enhanced fitting capabilities, contemporary neuro-cognitive diagnostic models frequently overlook critical process information from students and suffer from reduced interpretability. To address these limitations, this paper introduces a neuro-cognitive diagnostic model that integrates multidimensional features (MFNCD) by incorporating students’ response time as process information. This approach facilitates the simultaneous modeling of students’ response accuracy and response speed using neural networks, thereby enhancing both the fitting capability and precision of the method. Furthermore, a multi-channel attention mechanism is employed to effectively capture the complex interactions between students and exercise characteristics, simulating the process of students answering questions and thereby improving the model's interpretability. Validated on four diverse datasets, MFNCD model demonstrates superior accuracy compared to other state-of-the-art (SOAT) baseline models. Additionally, our experiments confirm significant correlations between cognitive attributes, revealing interesting educational patterns, such as a positive correlation between speed and ability, and between ability and accuracy. These findings provide deeper insights into learning patterns that incorporate multidimensional features and suggest potential pathways for targeted educational interventions.

Dating in social anxiety disorder: A daily diary study

Romantic relationships are vital for health, well-being and quality of life, and an increasing percentage of romantic relations begin via use of dating apps and subsequently progress to dates. In the present study we examined dating application use and dates among individuals with SAD (n = 54) and without SAD (n = 54). Our sample included young adults (age range 18 to 33) who were not in a romantic relationship at the time of the study. For both individuals with SAD and without SAD, half of the participants were men, and half were women. We used a daily diary measurement in which participants reported on their dating application use and dates, as well as on concomitant emotions and perceptions for 21 consecutive days. We found that individuals with and without SAD did not differ in the frequency/duration of application use nor in the number of dates attended. However, individuals with SAD experienced dates as more negative compared to individuals without SAD (Cohen’s d = 0.65). Interestingly, individuals with SAD did not significantly differ in their experience of dates as positive compared to individuals without SAD. Moreover, use of dating applications/going on dates were associated with increases in shame (Cohen’s d = 0.59) and embarrassment (Cohen’s d = 0.45) and this was found to a greater extent among individuals with SAD compared to individuals without SAD (Cohen’s d = 0.50, 0.39 for shame and embarrassment respectively). Findings are discussed in the context of cognitive behavioral models of SAD and implications for treatment are considered.

Motivations, barriers, and social media volume usage influence on exercise

Exercise contributes to a healthy lifestyle. Nonetheless, many people are insufficiently active. This study investigated exercise motivation, barriers to exercise, and the impact of social media volume usage among 18-25 year olds from a Midwestern university (USA). Data was collected via survey. Overall, the sample noted the psychological and fitness sub models and the positive health and strength and endurance subscales to be most important. There was a significant difference between college athletes and nonathletes for two sub models and seven subscales. College athletes noted time constraints and nonathletes noted their lack of motivation and time constraints (equally) as top barriers. Overall, the primary barriers to exercise were rated as less impactful (p < .001) by the college athletes (45.3 ± 34.2) compared to nonathletes (69.2 ± 28.3). Social media usage was prevalent in our sample, and it might be differentially impacting users. Highest-volume users rated barriers to be more impactful than lowest-volume users (p = .035). Social media is a current-culture culprit that helps “not enough time” persist as a key barrier to exercise. The impacts of social media on fitness and activity need further investigation.

Conceptualization of Depression among Medical Students and Its Differences during Medical Education.

The definition of mental disorders has been traditionally a matter of discussion, and it has relevant implications in research and healthcare. Our aim was to explore the conceptualization medical students have of depression and to determine differences across academic years. The Maudsley Attitudes Questionnaire was adapted through a double translation, double back-translation and a preliminary validation, obtaining a Spanish edition. All students of the Faculty of Medicine of the University of Zaragoza and doctors who graduated from this University in 2020 were invited to answer the online questionnaire, and we received 222 answers (response rate: 15.2%). The results were compared by years and levels of education using an ANOVA. The social realist, behavioral, biological, cognitive and psychodynamic models were the most endorsed. The psychodynamic and nihilist models were less embraced by students in later educational years. These students also reported greater confidence in their understanding of depression and of its biological, cognitive and behavioral models. In conclusion, the conceptualization of depression among medical students is complex and multidimensional, and appears to be similar across different years of education. In later years, we found less support for the psychodynamic model, increased confidence in psychiatry and greater ease in handling the concepts of its leading models.

A cognitive bias awareness matrix for enhancing ERP Decision-Making in entrepreneurial firms

Purpose The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making. Design/methodology/approach The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency. Findings The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests. Research limitations/implications The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs. Practical implications The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making. Social implications The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem. Originality/value This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

Changes in Anxiety-Related Behaviors, Voiding Patterns, and Urinary Bladder Contractile Properties in Male Mice Exposed to Water Avoidance Stress for 1 Day and 28 Days.

Repeated water avoidance stress (WAS) for 10 days is a common rodent model to mimic the effect of chronic psychological stress on urinary bladder dysfunction. However, it remains obscure whether changes in the stress exposure period impact urinary bladder impairment differently. Therefore, this study aimed to investigate the effect of 1 (acute), 10 (chronic), and 28 (prolonged) days of WAS on anxiety-related behavior, voiding pattern, urinary bladder mast cells, and bladder contractility in C57BL/6J male mice. Mice exposed to 1 and 10 days of WAS showed decreased unsupported rearing. A decreased total void area after 1 and 10 days of the WAS was observed, which was reversed in the 28-day-WAS group. There was an increased number of degranulated mast cells in the bladder of the 10-day-WAS group. The 1-day WAS exposure enhanced tonic contractile response to a muscarinic agonist, carbachol, which was reversed by 5-HT3 receptor antagonist pre-incubation. Interestingly, the 28-day WAS group showed a similar tonic contractile response to the control group. Our findings provide more insightful information about using 1-day WAS as an acute psychological stress model, and stress exposure longer than 10 days did not produce anxiety-like behavior and urinary bladder impairment.