scholarly journals Hierarchies of Motivation Predict Individuals’ Attitudes and Values: A Neuropsychological Operationalization of the Five Factor Model

2021 ◽  
Author(s):  
Cameron Berg
Keyword(s):  
Decision Making ◽  
Factor Model ◽  
Five Factor Model ◽  
Machine Learning Algorithms ◽  
Trade Off ◽  
Trade Offs ◽  
Human Decision ◽  
Individual Decisions ◽  
And Behavior ◽  
Recurrent Patterns

Scientific investigations of human personality are concerned with uncovering recurrent patterns of behavior, valuation, and cognition across time. The Five Factor Model (FFM), commonly known as the “Big 5,” is considered the most scientifically rigorous consolidation of the components of human decision-making and behavior. This research presents a novel hypothesis for systematizing the factors of the FFM into a series of emotional, motivational, and intellectual trade-offs. 193 adult participants completed an online decision-making battery composed of scenarios generated in accordance with each superordinate trade-off framework. Machine learning algorithms were subsequently implemented to assess whether a participant’s individual “score” was able to predict their independently-reported attitudes related to political affiliation, gender identity, career preferences, economic beliefs, political correctness, spirituality, and others. Across every attitude probed, the trade-off framework presented in this research was able to more strongly predict a participant’s response than any other model or scale that could be found in the literature. This research strongly supports the utility of conceptualizing individual decisions, preferences, values, and motivations through the lens of the FFM-based trade-off frameworks outlined in this work.

scholarly journals Hierarchies of Motivation Predict Individuals’ Attitudes and Values: A Neuropsychological Operationalization of the Five Factor Model

2021 ◽  
Author(s):  
Cameron Berg
Keyword(s):  
Decision Making ◽  
Factor Model ◽  
Five Factor Model ◽  
Machine Learning Algorithms ◽  
Trade Off ◽  
Trade Offs ◽  
Human Decision ◽  
Individual Decisions ◽  
And Behavior ◽  
Recurrent Patterns

Scientific investigations of human personality are concerned with uncovering recurrent patterns of behavior, valuation, and cognition across time. The Five Factor Model (FFM), commonly known as the “Big 5,” is considered the most scientifically rigorous consolidation of the components of human decision-making and behavior. This research presents a novel hypothesis for systematizing the factors of the FFM into a series of emotional, motivational, and intellectual trade-offs. 193 adult participants completed an online decision-making battery composed of scenarios generated in accordance with each superordinate trade-off framework. Machine learning algorithms were subsequently implemented to assess whether a participant’s individual “score” was able to predict their independently-reported attitudes related to political affiliation, gender identity, career preferences, economic beliefs, political correctness, spirituality, and others. Across every attitude probed, the trade-off framework presented in this research was able to more strongly predict a participant’s response than any other model or scale that could be found in the literature. This research strongly supports the utility of conceptualizing individual decisions, preferences, values, and motivations through the lens of the FFM-based trade-off frameworks outlined in this work.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

scholarly journals Neural signatures of value comparison in human cingulate cortex during decisions requiring an effort-reward trade-off

2016 ◽  
Author(s):  
Miriam C Klein-Flügge ◽  
Steven W Kennerley ◽  
Karl Friston ◽  
Sven Bestmann
Keyword(s):  
Decision Making ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Optimal Decision ◽  
Trade Off ◽  
Trade Offs ◽  
Caudal Portion ◽  
Neural Signature ◽  
The Difference ◽  
Neural Signatures

AbstractIntegrating costs and benefits is crucial for optimal decision-making. While much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human functional magnetic resonance imaging during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area (SMA) and the caudal portion of dorsal anterior cingulate cortex (dACC) encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modelled effort-discounted subjective values using a novel behavioural model. This revealed that the same network of regions involving dACC and SMA encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in SMA and ventro-medial PFC (vmPFC) correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not vmPFC as typically reported for outcome-based choice. Furthermore, distinct frontal circuits ‘drive’ behaviour towards reward-maximization and effort-minimization.Significance StatementThe neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behaviour, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioural characterization of how humans trade-off reward-maximization with effort-minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits ‘drive’ behaviour towards reward-maximization or effort-minimization.

Big Five personality traits in simulated negotiation settings

2018 ◽  
Vol 13 (2) ◽  
pp. 201-213 ◽  
Author(s):  
Pedro Fontes Falcão ◽  
Manuel Saraiva ◽  
Eduardo Santos ◽  
Miguel Pina e Cunha
Keyword(s):  
Personality Traits ◽  
Factor Model ◽  
Five Factor Model ◽  
Big Five Personality ◽  
Content Type ◽  
Situational Variables ◽  
Integrative Negotiation ◽  
Negotiation Behavior ◽  
New Findings ◽  
And Behavior

Purpose After a hiatus in the research on individual differences in negotiation, there has been a surge of renewed interest in recent years followed by several new findings. The purpose of this paper is to explore the effects that personality, as structured by the five-factor model, have over negotiation behavior and decision making in order to create new knowledge and prescribe advice to negotiators. Design/methodology/approach This study replicates observations from earlier studies but with the innovation of using a different methodology, as data from a sample of volunteer participants were collected in regard to their personality and behavior during two computerized negotiation simulations, one with the potential for joint gains and the other following a more traditional bargaining scenario. Findings Significant results for both settings were found, with the personality dimensions of agreeableness, conscientiousness, and extraversion systematically reoccurring as the most statistically relevant, although expressing different roles according to the type of negotiation and measure being registered. The findings thus suggest a multidimensional relationship between personality and situational variables in which specific traits can either become liabilities or assets depending upon whether the potential for value creation is present or not. Originality/value The new findings on the impacts of personality traits on both distributive and integrative negotiations allow negotiators to improve their performance and to adapt to specific distributive or integrative negotiation situations.

scholarly journals Fuzzy Multicriteria Decision-Making Model for Time-Cost-Risk Trade-Off Optimization in Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
M. Ammar Alzarrad ◽  
Gary P. Moynihan ◽  
Muhammad T. Hatamleh ◽  
Siyuan Song
Keyword(s):  
Decision Making ◽  
Construction Projects ◽  
Multicriteria Decision Making ◽  
Attractive Alternative ◽  
Time Cost ◽  
Analytic Hierarchy ◽  
Trade Off ◽  
Multicriteria Decision ◽  
Trade Offs ◽  
Cost Risk

As is often the case in project scheduling, when the project duration is shortened to decrease total cost, the total float is lost resulting in added critical or nearly critical activities. This, in turn, results in decreasing the probability of completing the project on time and increases the risk of schedule delays. To solve this problem, this research developed a fuzzy multicriteria decision-making (FMCDM) model. The objective of this model is to help project managers improve their decisions regarding time-cost-risk trade-offs (TCRTO) in construction projects. In this model, an optimization algorithm based on fuzzy logic and analytic hierarchy process (AHP) has been used to analyze the time-cost-risk trade-off alternatives and select the best one based on selected criteria. The algorithm was implemented in the MATLAB software and applied to two case studies to verify and validate the presented model. The presented FMCDM model could help produce a more reliable schedule and mitigate the risk of projects running overbudget or behind schedule. Further, this model is a powerful decision-making instrument to help managers reduce uncertainties and improve the accuracy of time-cost-risk trade-offs. The presented FMCDM model employed fuzzy linguistic terms, which provide decision-makers with the opportunity to give their judgments as intervals comparing to fixed value judgments. In conclusion, the presented FMCDM model has high robustness, and it is an attractive alternative to the traditional methods to solve the time-cost-risk trade-off problem in construction.

scholarly journals Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies

2017 ◽  
Author(s):  
Michal Kosinski
Keyword(s):  
Impression Management ◽  
Satisfaction With Life ◽  
Factor Model ◽  
Five Factor Model ◽  
Self Report ◽  
Small Samples ◽  
Height Ratio ◽  
Self Monitoring ◽  
Behavioral Tendencies ◽  
And Behavior

A growing number of studies have linked facial width-to-height ratio (fWHR) with various antisocial or violent behavioral tendencies. However, those studies have predominantly been laboratory based and low powered. This work reexamined the links between fWHR and behavioral tendencies in a large sample of 137,163 participants. Behavioral tendencies were measured using 55 well-established psychometric scales, including self-report scales measuring intelligence, domains and facets of the five-factor model of personality, impulsiveness, sense of fairness, sensational interests, self-monitoring, impression management, and satisfaction with life. The findings revealed that fWHR is not substantially linked with any of these self-reported measures of behavioral tendencies, calling into question whether the links between fWHR and behavior generalize beyond the small samples and specific experimental settings that have been used in past fWHR research.

scholarly journals Human behavior inside and outside bureaucracy: Lessons from psychology

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Asbjørn Sonne Nørgaard
Keyword(s):  
Decision Making ◽  
Psychological Research ◽  
Psychological Needs ◽  
Human Beings ◽  
Public Officials ◽  
Personality Psychology ◽  
Intuitive Thinking ◽  
Human Decision ◽  
Key Issues ◽  
And Behavior

Both Herbert A. Simon and Anthony Downs borrowed heavily from psychology to develop more accurate theories of Administrative Behavior outside and Inside Bureaucracy: Simon, to explicate the cognitive shortcomings in human rationality and its implications; and Downs, to argue that public officials, like other human beings, vary in their psychological needs and motivations and, therefore, behave differently in similar situations. I examine how recent psychological research adds important nuances to the psychology of human decision-making and behavior and points in somewhat other directions than those taken by Simon and Downs. Cue-taking, fast and intuitive thinking, and emotions play a larger role in human judgment and decision-making than what Simon suggested with his notion of bounded rationality. Personality trait theory provides a more general and solid underpinning for understanding individual differences in behavior, both inside and outside bureaucracy, than the 'types of officials' that Downs discussed. I present an agenda for a behavioral public administration that takes key issues in cognitive psychology and personality psychology into account.

The Role of Executive Function in Shaping Reinforcement Learning

2020 ◽  
Author(s):  
Milena Rmus ◽  
Samuel McDougle ◽  
Anne Collins
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Instrumental Behavior ◽  
Complex Environments ◽  
Neural Computations ◽  
Human Decision ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of instrumental behavior are assumed to be supported by the brain’s executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

COSIMO: A cognitive simulation model of human decision making and behavior in accident management of complex plants

1992 ◽  
Vol 22 (5) ◽  
pp. 1058-1074 ◽  
Author(s):  
P.C. Cacciabue ◽  
F. Decortis ◽  
B. Drozdowicz ◽  
M. Masson ◽  
J.-P. Nordvik
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Cognitive Simulation ◽  
Accident Management ◽  
Human Decision ◽  
And Behavior
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