Cognitive Model Research Articles

Prediction models for cognitive frailty in community-dwelling older adults: A scoping review

ObjectivesThis review investigates the current status of cognitive frailty risk prediction models for community-dwelling older adults, aiming to explore the shortcomings and provide insights for model optimisation. MethodsWe adhered to the PRISMA guidelines for scoping review and followed the Joanna Briggs Institute Manual for Evidence Synthesis. ResultsThis article includes a total of 10 studies, revealing a prevalence of cognitive frailty ranging from 4.8 % to 39.6 %. The methods used for model construction included both logistic regression and machine learning. The predictors varied across the models, with age, education level, gender, and physical activity level being the most frequently cited factors. ConclusionsWhile most models showed good applicability, all models displayed a high risk of bias. Future endeavors should concentrate on leveraging existing tools to ensure standardization in development and conducting rigorous evaluations of prediction models for cognitive frailty in community-dwelling older adults.

Comparison of Malondialdehyde, Acetylcholinesterase, and Apoptosis-Related Markers in the Cortex and Hippocampus of Cognitively Dysfunctional Mice Induced by Scopolamine

Objectives: Until now, many researchers have conducted evaluations on hippocampi for analyses of cognitive dysfunction models using scopolamine. However, depending on the purposes of these analyses, there are differences in the experimental results for the hippocampi and cortexes. Therefore, this study intends to compare various analyses of cognitive dysfunction after scopolamine administration with each other in hippocampi and cortexes. Methods: Scopolamine was administered at three dosages in mice: 0.5, 1, and 3 mg/kg. And this study evaluates the differences in cognitive function and the expression of malondialdehyde (MDA), acetylcholinesterase (AChE), and brain-derived neurotrophic factor (BDNF) in mice’s hippocampi and cortexes based on scopolamine dosages. Results: The Morris water maze test was conducted between 1 and 3 h after scopolamine injection to assess its duration. A significant decrease in behavioral ability was evaluated at 1 h, and we observed a similar recovery to the normal group at 3 h. And the Morris water maze escape latency showed differences depending on scopolamine concentration. While the escape waiting time in the control group and scop 0.5 administration group remained similar to that seen before administration, the administration of scop 1 and 3 increased it. In the experimental group administered scop 1 and 3, cerebral MDA levels in the cerebral cortex significantly increased. In the hippocampus, the MDA level in the scopolamine-administered groups slightly increased compared to the cortex. A Western blotting assay shows that Bax and Bcl-xl showed a tendency to increase or decrease depending on the concentration, but BDNF increased in scop 0.5, and scop 1 and 3 did not show a significant decrease compared to the control at the cerebral cortex. In the hippocampus, BDNF showed a concentration-dependent decrease in expression. Conclusions: This study’s findings indicate that chemical analyses for MDA and AChE can be performed in the cerebral cortex, while the hippocampus is better suited for protein analysis of apoptosis and BDNF.

Reducing Consumer–Brand Incongruity Through Corporate Social Responsibility and Brand Trust: Exploring Negative Word‐of‐Mouth (NWOM)

ABSTRACTDrawing upon consumer–brand disidentification theory and balance theory, this study examines symbolic and ideological incongruity in consumer–brand relationships through an original conceptual model shaped by negative past experiences, brand trust, perceived corporate social responsibility (CSR), and negative word‐of‐mouth (NWOM). A preliminary study was conducted to explore the dimensions of consumers' negative past experiences by topic detection. Latent Dirichlet allocation (LDA) topic modeling was undertaken to analyze online consumer reviews (n = 6095) about a coffee chain brand. The dimensions detected in this preliminary study were included in the research model and further analyzed in the main study. The main study, a cross‐sectional consumer survey (n = 522), tested the original research model by way of partial least squares structural equation modeling (PLS‐SEM) on SmartPLS. The findings showed that negative past experiences consisted of product‐related, service‐related, and technology‐related problems and negatively influenced brand trust. It was found that brand trust and perceived CSR negatively affected symbolic and ideological incongruity, while symbolic and ideological incongruity positively influenced NWOM. The findings provide empirical evidence for balance theory by showing that the three critical domains of consumer–brand relationships (ideological, symbolic, and experiential) provide a complex cognitive model that covers personal‐symbolic and moral‐societal aspects of consumer–brand disidentification and consequent NWOM intentions. In line with consumer–brand disidentification theory, the results contribute to the literature by demonstrating the direct negative impacts of brand trust and perceived CSR on symbolic and ideological incongruity, as well as the direct positive impacts of symbolic and ideological incongruity on NWOM.

Decoding the influence of emotional and attentional states on self-control using facial analysis

Self-control plays a pivotal role in pursuing long-term goals related to health and financial well-being. While ample evidence suggests that humans are prone to occasional self-control lapses, little is known about how changes in emotional and attentional states affect the ability to maintain self-control. In two studies (N1 = 109 and N2 = 90), we used emotion recognition software to decode participants’ facial expressions while manipulating their attentional and emotional states during a Psychomotor Vigilance Task (PVT) before exerting self-control in a subsequent task. Our findings reveal dissociable roles of attention and valence in maintaining self-control, depending on the distinct demands of the task. Specifically, performance in a subsequent cognitive task was predominantly associated with changes in attentional states during the PVT rather than valence. Conversely, preferences in a subsequent social task were associated with changes in valence states during the PVT, while attention showed no effect. This dissociation between attention-invoked and emotion-invoked lapses in self-control holds significant implications for psychological and economic models of self-control, ultimately contributing to the development of policies to mitigate the detrimental consequences of individual self-control lapses.

Cognitive processing biases of social anxiety in adults who do and do not stutter

PurposeAdults who stutter are at risk of developing high levels of social anxiety, leading to negative outcomes and contributing towards stuttering relapse post treatment. To ensure that psychological treatments for social anxiety in stuttering adults are relevant and effective, a broader empirical understanding of the mechanisms of social anxiety in stuttering populations is required. Four key cognitive processing biases identified as maintenance factors in cognitive behavioral models of social anxiety were examined: self-focused attention, safety behavior use, negative self-imagery, and interpretation bias. MethodsAdults who stutter and non-stuttering adults (N = 186) were assessed via an online survey. Participants were categorised into two groups based on stuttering: formally diagnosed stuttering and non-stuttering. Within those groups, participants were further categorised as having low or high levels of social anxiety. Cognitive processing bias was assessed in response to two hypothetical social scenarios (i.e., social interaction and social performance). ResultsAs predicted, in both adults who stutter and who are non-stuttering, high social anxiety was related to greater self-focused attention, safety behavior use and negative self-imagery compared to low social anxiety. No significant effect of social anxiety or stuttering was found in relation to interpretation bias. A significant effect of social anxiety upon negative self-imagery was observed in formally-diagnosed adults who stutter compared to non-stuttering adults. ConclusionThe results support the use of cognitive behavioral models of social anxiety as a framework for research and treatment development within stuttering populations.

The Roles of Neural Networks in Language Acquisition

ABSTRACTHow can modern neural networks like language models be useful to the field of language acquisition, and more broadly cognitive science, if they are not a priori designed to be cognitive models? As developments towards natural language understanding and generation have improved leaps and bounds, with models like GPT‐4, the question of how they can inform our understanding of human language acquisition has re‐emerged. As such, it is critical to examine how in practice linking hypotheses between models and human learners can be safely established. To address these questions, we propose a model taxonomy, including four modelling approaches, each having differing goals, from exploratory hypothesis generation to hypothesis differentiation and testing. We show how the goals of these approaches align with the overarching goals of science and linguistics by connecting our taxonomy to the realist versus instrumentalist approaches in philosophy of science. We survey recent work having adopted each of our modelling approaches and address the importance of computational modelling in language acquisition studies.

Analyzing early childhood allergy prevention motivation of mothers of infants and its predictors using latent class analysis and structural equation modelling

BackgroundAllergic diseases are among the most common chronic diseases in childhood. Early childhood allergy prevention (ECAP) behaviors of those caring for the infant during pregnancy and the first months of life may influence the risk of allergy development over the life course. Motivation and intention to use appropriate primary ECAP measures are thus of critical importance.AimsTo characterize parental ECAP motivation, (a) valid indicators will be developed and (b) typical parental characteristics will be identified. (c) According to socio-cognitive models, the predictive value of parental risk perception, control belief and self-efficacy for parental ECAP motivation shall be determined.MethodA sample of N = 343 (expectant) mothers of infants completed a questionnaire on self-reported ECAP motivation, risk perception, control belief, and self-efficacy. The cross-sectional data were analyzed using latent class analysis and structural equation modelling including nominal regression models.ResultsFour typical maternal response profiles (motivated to a customary degree, 70%; motivated to use primary preventive measures, 17.8%; reluctant towards new prevention measures, 6.4%; highly motivated to apply preventive measures in case of an existing allergy, 5.8%) could be identified for the items on ECAP motivation. After splitting the model variables “risk perception” (allergy vs. allergy-associated general health problems) and “self-efficacy” (trust vs. insecurity) a satisfactory model-fit was achieved (CFI = .939; RMSEA = .064). Particularly, increased “risk perception-allergy” (OR = 1.655) and “self-efficacy-insecurity” (OR = 2.013) as well as lower “risk perception-general health” (OR = 0.555) and “control belief” (OR = 0.217), respectively, are associated with higher ECAP motivation.ConclusionThe use of ECAP-measures by parents to protect their newborns from allergies is important, but there are deficiencies in their implementation. Based on a social cognitive model approach, predictive characteristics could be identified, which are associated with increased motivation to implement ECAP-measures. For public health our findings provide a promising basis for conception of behavioral and environmental ECAP prevention measures and their motivated implementation by parents.

Development of a Cognitive Assessment Checklist for First-Grade Mathematics: Utilizing Hierarchical Cognitive Diagnostic Modeling in Elementary Education

This study aimed to address the need for a comprehensive assessment tool to evaluate the mathematical abilities of first-grade students through cognitive diagnostic assessment (CDA). The primary challenge involved in this endeavor was to delineate the specific cognitive skills and sub-skills pertinent to first-grade mathematics (FG-M) and to determine the most suitable model for assessment. Employing a mixed-methods approach, the research identified nine cognitive attributes essential for FG-M and developed a Q-matrix delineating the relationship between these attributes. A preliminary version of the FG-M checklist comprising 74 items was formulated. Subsequently, the assessment tool was administered to 1018 first-grade students, with their teachers utilizing the FG-M checklist for evaluation. Results indicated a commendable accuracy in classifying the nine cognitive attributes, ranging from 0.68 to 0.92, with an average accuracy of 0.76. Furthermore, the validity of the scores was corroborated by a substantial correlation (r = 0.78) between scores from the FG-M checklist and the arithmetic subtest of the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). Overall, the study concluded that the scores from the FG-M checklist are reliable for assessing the mathematical skills of first-grade students. The findings underscored the students’ proficiency in fundamental mathematical concepts such as cardinality, addition, subtraction, weight, and statistics. However, areas of weakness were identified in concepts related to multiplication, time, symmetry, and geometry. This research contributes significantly to the advancement of CDA in educational contexts, providing educators with a valuable tool for identifying students’ strengths and weaknesses in mathematical cognition.

НЕЙРОСЕТЕВОЙ ПОДХОД К КОГНИТИВНОМУ МОДЕЛИРОВАНИЮ

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

Verification and Validation of Cognitive Workload Models for Adaptive Automation Tasks

Technological advances that seek to address future operational challenges abound. While advanced capabilities are being developed, there is an important space for human design considerations, including cognitive workload. One proposed solution to improve cognitive workload management is adaptive automation (AA). This research used a novel, model-based approach to assess the impacts of AA on cognitive workload. This assessment modeled the tasks in NASA’s Multi-attribute Task Battery-II (MATB-II) using the Improved Performance Research Integration Tool (IMPRINT). The effort sought to investigate the relationship of cognitive workload, situation awareness, and performance through three human-in-the-loop studies with 120 participants using MATB-II. The research also attempted to validate cognitive workload models from IMPRINT. The IMPRINT models were representative of the MATB tasks with statistically significant differences between workload conditions, which mirrored the models’ predictions. The results demonstrated that AA system task models can be developed using IMPRINT to provide design recommendations.

How to Bridge Cognitive and Cultural Models of Religious Evolution

The scientific study of religion has roots in at least two traditions: a cognitive tradition which typically uses experimental studies to examine the proximal psychological processes underlying religious belief and ritual, and an anthropological tradition which uses historical and ethnographic data to examine how religious systems culturally evolve in social groups. In Religion Evolving, Purzycki and Sosis bridge both traditions and encourage a more integrated evolutionary science of religion. In this essay, I present three suggestions for how to make this science as ambitious and collaborative as possible. These suggestions are: (1) to move beyond straw man debates that pit byproduct models against functional models of religious evolution and acknowledge that these models are compatible from a dual inheritance perspective, (2) to evaluate functionalist claims with caution, recognizing how and when religion is adaptive vs. maladaptive for secular society, and (3) to avoid reductive definitions of religion and embrace religious pluralism. With these lessons in mind, we can usher in a bolder evolutionary science of religion that draws from both cognitive and cultural traditions.

Bridging the gap in talent: A framework for interdisciplinary research on autism spectrum disorder persons in accounting and information systems

The accounting profession continues to face a talent shortage. In addition, the skills and demands of accountants continue to evolve as technology changes. At the same time, many autism spectrum disorder (ASD) persons remain unemployed or underemployed. Companies, including the Big 4, have begun hiring programs looking at a diverse set of individuals, including ASD persons. General anecdotes suggest ASD persons are thriving in terms of innovation, efficiency, and productivity. While neurotypical (NT) persons have been shown in multiple studies to rely on simple heuristics, which reduce judgment quality, we argue that there are neuropsychological differences in visual perception and attention that play a vital role in why ASD persons may be less susceptible to heuristics and biases; hence, increasing judgment quality. This paper presents an interdisciplinary research framework that adapts the human information processor model of cognition to bring together the judgment and decision-making, autism, and neuropsychological literatures to focus on areas where ASD persons may have judgment-related advantages over NT persons. We discuss the framework and present example research questions for two areas where ASD accountants are performing tasks: cybersecurity and software development. Our framework presents a guide for future empirical research where accounting academics can lead an interdisciplinary effort to support company inclusion programs of ASD persons and, more broadly, of neurodivergent persons.

Discrimination of mnemonic similarity is associated with short-term and long-term memory precision.

Remembering specific memories with precision relies on the differentiation of similar memory contents - a process commonly referred to as pattern separation and behaviorally operationalized as lure discrimination in the mnemonic similarity task. Although pattern separation is typically investigated in the context of long-term memory (LTM), recent research extends these findings to short-term memory (STM) within a mixture model framework, emphasizing the distinction between memory quality and quantity. According to this framework, pattern separation is associated with memory precision across STM and LTM, regardless of the overall memory likelihood. However, these associations among memory quality measures may persist without the mixture model assumption. In an alternative model, a unitary memory strength measure quantified as a discrimination score (d') may also capture the association between pattern separation and memory quality, as pattern separation has been previously linked with strength-based memory performance. We tested these possibilities based on individual differences among 132 participants who underwent tasks measuring mnemonic pattern separation and STM/LTM quality. We found that behavioral estimates of pattern separation are significantly correlated with STM and LTM precision, irrespective of the likelihood of STM/LTM recall success. However, these associations are absent when considering the correlation between pattern separation and memory strength under a unitary model framework. By leveraging individual differences to constrain our understanding of cognitive models, our data unravel the intricate relationship between pattern separation and memory quality across timescales. These findings may therefore contribute to identifying sensitive behavioral measures for detecting subtle memory deficits in older adults or clinical populations.

Iconic models in science and psychology

Scientists use a wide variety of models to formally represent their theories, including computational, quantitative, and mathematical models. A type of model often overlooked in discussions of modeling is an iconic model, which is a visual or physical representation of the structures and processes comprising some natural phenomenon. In the current paper, we present a brief history of iconic models as they have been employed in other sciences such as astronomy, physics, biology, and chemistry. We then present several iconic models that are well known to psychologists and show how they played important roles in theory construction and evaluation. Modern methods of iconic modeling are next presented and demonstrated with several examples. Finally, we close with a discussion of how iconic models can be used in tandem with computational, quantitative, or mathematical modeling procedures in the quest for rigorous theories of the human psyche.

Using Theory and Practice to Develop Lottery Ticket Warning Messages as a Means of Promoting More Responsible Gambling

Background Playing the lottery is considered a public health concern in many countries due to financial losses associated with it, but also due to other harmful health and social problems. Focus of the article Informed by both theory and practice, this article focuses on developing lottery ticket warning messages targeting players harmed by excessive spending to promote sustainable behavior change. Research Question What should be communicated in lottery ticket warning messages targeting people at risk to facilitate behavior change and maintain it? Approach We bridge the gap between theory and practice and illustrate how theoretical models and current communications can shape messages. Specifically, we identify and examine lottery ticket warning messages and analyze their relative congruence to prevailing theories to assess the potential effectiveness of the messages and to recommend warning labels for lottery tickets. In addition, we incorporate knowledge used by cognitive psychologists in counselling to create warnings more likely to motivate and sustain behavior change. Importance to the Social Marketing Field Merging academic theory with existent communications ensures better knowledge translation and potentially better outcomes. Introducing verified techniques from Cognitive Behavior Therapy into social marketing initiatives could be a step forward into triggering behavior change. Methods A literature review was conducted to identify and select theoretical models appropriate to guide communications for lottery tickets harmful consumption. Searches were conducted to identify initiatives aiming at persuading and helping at-risk individuals to change their behavior in a healthy manner. Social marketing campaign messages were then compared with theoretical paradigms to identify ways in which campaign messages could be improved. Results Results show campaign messages were relatively consistent with some aspects of the theoretical models. Warning messages provide information that is relevant to the target audience and describe the severity and vulnerability of irresponsible consumption. They also attempt to decrease perceived costs and increase perceived self-efficacy in a variety of ways. Some also attempt to modify cognitive distortions the audience has in respect to chances of winning. However, initiatives should also increase consumers’ perceptions of the response-efficacy by emphasizing their agency, and further enhance individuals’ perception of self-efficacy by incorporating strategies from the Cognitive Model. Recommendations for Research and Practice Academics and practitioners can build on each other’s knowledge on an ongoing basis. By making it default to always consider both theory and practice, initiatives are likely to become more effective. Managers responsible for developing warning messages and implementing social marketing campaigns can experiment with the various theoretical approaches to determine which are comparatively more effective for their situations. Limitations Future research is needed to test the comparative effectiveness of these theories as a basis of creating persuasive warning messages.

Self-Regulated Multimedia Cognitive Learning Model: Enhancing Vocabulary Acquisition in Adult English Learners

Self-Regulated Multimedia Cognitive Learning Model: Enhancing Vocabulary Acquisition in Adult English Learners

Prospective Relations Between Inferential Styles and Depressive Symptoms Among Children of Mothers with Major Depression

ABSTRACT Objective Current models of depression risk in children include both family history and cognitive models of risk; however, these models are rarely integrated. This study aimed to address this gap by examining how cognitive vulnerabilities featured in the hopelessness theory of depression – negative inferential styles for the causes, consequences, and self-characteristic implications of negative events – may increase risk for the intergenerational transmission of depression. Specifically, we examined whether children of mothers with a history of major depressive disorder (MDD), compared to children of never-depressed mothers, exhibit more negative inferential styles and whether maternal history of MDD moderates prospective relations between children’s inferential styles and depressive symptoms. Method Participants were 251 children (ages 8–14 at baseline; 51% girls; 81% Non-Hispanic White) of mothers with (n = 129) or without (n = 122) a history of MDD. Children’s inferential styles and depressive symptoms were assessed at baseline and then every 6 months for 2 years. Results Using random intercept cross-lagged panel models (RI-CLPMs), we found that children of mothers with a history of MDD, compared to children of never-depressed mothers, had more negative inferential styles for the causes and consequences of negative events, but not for self-characteristics, and higher depressive symptom levels, across the follow-up. In addition, there were reciprocal, transactional relations between children’s inferential styles for causes and their depressive symptoms across the follow-up, with no evidence for moderation by maternal MDD. Conclusions Children’s inferential styles for the causes of negative events may be a useful marker of risk for the intergenerational transmission of depression, which could be targeted to reduce risk for depression.

Responding to location-based triggers of cravings to return to substance use: A qualitative study.

Individuals with substance use disorders face many challenges in establishing and maintaining recovery, most notably from contextual factors such as people, locations, events, emotions, and other triggers of cravings that could spark return to use. We sought to understand how individuals experience and cope with locations as triggers. We conducted 31 semi-structured in-depth interviews with individuals who had recently been in substance use treatment and reported problem levels of alcohol and opioid use according to screening instruments. Interviews were designed to explore how individuals experience location-based triggers. We coded these interviews according to concepts outlined in Marlatt and Gordon's cognitive behavioral model and used thematic analysis to generate themes in participant experiences. We found that participants described triggers as interrelated and associated with multiple people, locations, events, or emotions. Participants commonly identified 'home' as a trigger, one that is particularly difficult to cope with. Participants most commonly employed avoidance as a coping strategy. Finally, participants described an expectation that substance use was associated with more time spent outside of 'home' while recovery was associated with limited travel and more time spent at home. Location-based triggers represent a particularly challenging barrier to sustained substance use disorder recovery, partly due to the amorphous and interrelated nature of such triggers. The identification of 'home' as a primary trigger and common use of avoidance as a coping strategy suggest the need for additional recovery support and interventions. We plan to use these findings to develop and test an intervention promoting coping strategies.

Cognitive Model Based on The Dl Random Forest Method for Profi t Forecasting and Fuzzy Algorithm for Assessing Company Sustainability in Conditions of Uncertainty

The article is devoted to the problem of supporting management decision-making on choosing a strategic partner whose activities would be effective and sustainable. During the study, based on the results of the work of enterprises in the domestic confectionery industry, a dataset was generated, which was subsequently used for the deep learning model DL-model "Random Forest" in order to calculate the predicted values of the net profi t of enterprises in the industry. Assessing companies for the purpose of selecting a strategic partner, using models such as: deep learning model "Random Forest" (DL Random Forest), VaR, Z-Altman, Hurwitz matrix, Fuzzy algorithm in modern conditions has great practical significance. The relevance of the study lies in the fact that in conditions of increasing market uncertainty, approaches to ensuring the sustainable development of an organization based on AI systems are increasingly being used. The scientific novelty lies in the fact that the study used a set of models that made it possible to assess the actual sustainability of companies and, based on the calculated forecast values of net profi t, as well as business efficiency indicators ROE and ROS, to make a decision on the choice of potential business partners. During the study the following were developed: a VaR model, which made it possible to obtain an assessment of fi nancial risk, a Z-Altman model for assessing the risk of bankruptcy of an enterprise. In addition, based on the calculated parameters of the regression equation, a Hurwitz matrix was formed, which made it possible to draw a conclusion about the sustainability of each enterprise as a system. The use of the Fuzzy algorithm made it possible to obtain a decision on choosing a partner enterprise.

Personalized Model-Driven Interventions for Decisions From Experience.

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