Decision making as related to varying certain cognitive variables

1966 ◽  
Author(s):  
Francis R. J. Fields
2018 ◽  
Vol 35 (1) ◽  
pp. 101-127 ◽  
Author(s):  
Adam Morris ◽  
Fiery Cushman

Abstract:Humans often comply with social norms, but the reasons why are disputed. Here, we unify a variety of influential explanations in a common decision framework, and identify the precise cognitive variables that norms might alter to induce compliance. Specifically, we situate current theories of norm compliance within the reinforcement learning framework, which is widely used to study value-guided learning and decision-making. This framework offers an appealingly precise language to distinguish between theories, highlights the various points of convergence and divergence, and suggests novel ways in which norms might penetrate our psychology.


2011 ◽  
Author(s):  
Shu Hao Teo ◽  
Christopher M. Nguyen ◽  
Torricia H. Yamada ◽  
Michael Koenigs ◽  
Daniel Tranel ◽  
...  

2020 ◽  
Vol 6 (2) ◽  
pp. 75-89
Author(s):  
Delia Vîrgă

This study is aimed to show the influence of cognitive and non-cognitive factors on decisional efficiency through the design of a theoretical-explicative model and by testing it against reality. This model reflects the link between cognitive variables, personality variables and decisional performance. The participants in this study (N=88) are managers in a IT&C company and have an average age of 32.3 years and a average working seniority of 8.6 years, 74.9% being males and 25.1 % being females. The instruments used were California Psychological Inventory (CPI 260 items form), a questionnaire for assessing the decisional style, a decision making questionnaire, decisional skills test (BTPAC), and Raven standard test, Plus form, a questionnaire for assessing cognitive complexity and Melbourne decision making questionnaire. In order to evaluate decisional performance I developed an behaviorally anchored scale. The evaluation of cognitive competencies, defined in behavioral terms like decision making performance and cognitive complexity, together with the personality dimensions, help us to select managers with an increased adaptive orientation to organizational change and a better decisional performance


2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i18-i18
Author(s):  
Adam Gerstenecker ◽  
Kristen Triebel

Abstract BACKGROUND: Medical decision-making capacity refers to the ability to make informed decisions about medical treatment. Understanding is the most cognitively demanding aspect of medical decision-making and requires the ability to comprehend medically-related information and then use that information to make decisions about diagnosis, prognosis, and treatment options. In previous papers, we have shown that knowledge about specific cognitive abilities that affect understanding in brain cancer could be used to construct actuarial equations designed to help clinicians identify persons with brain cancer or brain metastases at risk of understanding impairment. METHODS: In total, 184 participants (67 with brain metastasis, 41 with non-brain metastasis, 29 with malignant glioma, and 47 healthy controls) were recruited. All participants were administered a neuropsychological battery that included a performance-based measure of medical decision-making capacity. Impairment cutoffs were calculated from control group performance. Using the cognitive scores that were most highly associated with understanding, logistic and linear regression models were used to construct actuarial equations designed to predict intact/impaired understanding and understanding scores, respectively. RESULTS: As expected, both brain cancer groups had poorer understanding than controls and approximately 50% of both brain cancer groups exhibited impaired understanding. Over 24% of the non-brain metastasis group exhibited impaired understanding. Significant associations were found between understanding and all administered cognitive variables, with the strongest correlations noted as between understanding and measures of executive function, verbal memory, and verbal fluency. Using these cognitive variables, we were able to construct predictive equations that showed strong psychometric properties. CONCLUSIONS: These data demonstrate how cognitive measures can estimate medical understanding in persons with cancer. Clinically, these findings suggest that poor verbal memory, executive function, and/or phonemic fluency function could serve as “red flags” for reduced consent capacity in this patient population, and thus signal that a more comprehensive medical decision-making capacity evaluation is warranted.


2020 ◽  
Vol 6 ◽  
pp. 205520762098024
Author(s):  
Thomas Gültzow ◽  
Eline Suzanne Smit ◽  
Raesita Hudales ◽  
Carmen D Dirksen ◽  
Ciska Hoving

Objectives Evidence-based smoking cessation support tools (EBSTs) can double the quitting chances, but uptake among smokers is low. A digital decision aid (DA) could help smokers choose an EBST in concordance with their values and preferences, but it is unclear which type of smokers are interested in a digital DA. We hypothesized that smokers’ general decision-making style (GDMS) could be used to identify early adopters. This study therefore aimed to identify smoker profiles based on smokers’ GDMS and investigate these profiles’ association with intention to use a digital DA. Design A cross-sectional dataset (N = 200 smokers intending to quit) was used to perform a hierarchical cluster analysis based on smokers’ GDMS scores. Methods Clusters were compared on demographic and socio-cognitive variables. Mediation analyses were conducted to see if the relationship between cluster membership and intention was mediated through socio-cognitive variables (e.g., attitude). Results Two clusters were identified; “ Avoidant Regretters ” (n = 134) were more avoidant, more regretful and tended to depend more on others in their decision making, while “ Intuitive Non-regretters ” (n = 66) were more spontaneous and intuitive in their decision making. Cluster membership was significantly related to intention to use a DA, with “ Avoidant Regretters ” being more interested. Yet, this association ceased to be significant when corrected for socio-cognitive variables (e.g., attitude). This indicates that cluster membership affected intention via socio-cognitive variables. Conclusions The GDMS can be used to identify smokers who are interested in a digital DA early on. As such, the GDMS can be used to tailor recruitment and DA content.


2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.


2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.


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