Computational Models of Decision Making

2008 ◽  
pp. 133-154 ◽  
Author(s):  
Jerome R. Busemeyer ◽  
Joseph G. Johnson
Keyword(s):  
Decision Making ◽  
Computational Models

scholarly journals Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alekhya Mandali ◽  
Arjun Sethi ◽  
Mara Cercignani ◽  
Neil A. Harrison ◽  
Valerie Voon
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Computational Models ◽  
Risky Choice ◽  
Risk Tolerance ◽  
Healthy Population ◽  
Hyperactivity Disorder ◽  
Risky Choices ◽  
Cardinal Symptom

AbstractRisk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift–diffusion model to extract parameters such as threshold (‘a’—amount of evidence accumulated before making a decision), drift rate (‘v’—information processing speed) and response bias (‘z’ apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

Computational basis of decision-making impairment in multiple sclerosis

2021 ◽  
pp. 135245852110593
Author(s):  
Rodrigo S Fernández ◽  
Lucia Crivelli ◽  
María E Pedreira ◽  
Ricardo F Allegri ◽  
Jorge Correale
Keyword(s):  
Multiple Sclerosis ◽  
Decision Making ◽  
Computational Models ◽  
Iowa Gambling Task ◽  
Memory Impairments ◽  
Hierarchical Bayesian Analysis ◽  
Control Subjects ◽  
Neurocognitive Impairments ◽  
Neurocognitive Evaluation ◽  
Computational Basis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Computational Models ◽  
Human Perception ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Affective States ◽  
Modeling And Analysis ◽  
Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

Pupil-Linked Arousal Biases Evidence Accumulation Toward Desirable Percepts During Perceptual Decision-Making

2021 ◽  
Vol 32 (9) ◽  
pp. 1494-1509
Author(s):  
Yuan Chang Leong ◽  
Roma Dziembaj ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Perceptual Decision Making ◽  
Categorization Task ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Perceptual Judgments ◽  
Significant Events ◽  
Arousal System ◽  
Objective Representation

People’s perceptual reports are biased toward percepts they are motivated to see. The arousal system coordinates the body’s response to motivationally significant events and is well positioned to regulate motivational effects on perceptual judgments. However, it remains unclear whether arousal would enhance or reduce motivational biases. Here, we measured pupil dilation as a measure of arousal while participants ( N = 38) performed a visual categorization task. We used monetary bonuses to motivate participants to perceive one category over another. Even though the reward-maximizing strategy was to perform the task accurately, participants were more likely to report seeing the desirable category. Furthermore, higher arousal levels were associated with making motivationally biased responses. Analyses using computational models suggested that arousal enhanced motivational effects by biasing evidence accumulation in favor of desirable percepts. These results suggest that heightened arousal biases people toward what they want to see and away from an objective representation of the environment.

Introduction

2019 ◽  
pp. 1-18
Author(s):  
William B. Rouse
Keyword(s):  
Decision Making ◽  
Competitive Advantage ◽  
Computational Models ◽  
Decision Makers ◽  
Evidence Based ◽  
Leading Indicators ◽  
Tipping Points ◽  
Decision Making Models

Chapter 1 provides the introduction to this book. Predictions can seldom specify what will happen, so, inevitably, one addresses what might happen. There are often many possible futures, with leading indicators and potential tipping points for each scenario. Computational models can be used to explore designs of systems and policies to determine whether these designs will likely be effective and to aid in decision-making. Models are means to ends rather then ends in themselves. Decision-makers seldom crave models. They want their questions answered in an evidence-based manner. Decision-makers want insights that provide them with a competitive advantage. They want to understand possible futures to formulate robust and resilient strategies for addressing these futures.

scholarly journals Reward sensitivity differs depending on global self-esteem in value-based decision-making

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Aya Ogasawara ◽  
Yoshiyuki Ohmura ◽  
Yasuo Kuniyoshi
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Learning Task ◽  
Self Esteem ◽  
Expected Value ◽  
Reward Sensitivity ◽  
Self Confidence ◽  
Maximum Value ◽  
Individual Personality ◽  
Social Decision Making

AbstractGlobal self-esteem is a component of individual personality that impacts decision-making. Many studies have discussed the different preferences for decision-making in response to threats to a person’s self-confidence, depending on global self-esteem. However, studies about global self-esteem and non-social decision-making have indicated that decisions differ due to reward sensitivity. Here, reward sensitivity refers to the extent to which rewards change decisions. We hypothesized that individuals with lower global self-esteem have lower reward sensitivity and investigated the relationship between self-esteem and reward sensitivity using a computational model. We first examined the effect of expected value and maximum value in learning under uncertainties because some studies have shown the possibility of saliency (e.g. maximum value) and relative value (e.g. expected value) affecting decisions, respectively. In our learning task, expected value affected decisions, but there was no significant effect of maximum value. Therefore, we modelled participants’ choices under the condition of different expected value without considering maximum value. We used the Q-learning model, which is one of the traditional computational models in explaining experiential learning decisions. Global self-esteem correlated positively with reward sensitivity. Our results suggest that individual reward sensitivity affects decision-making depending on one’s global self-esteem.

Once More with Feeling: Augmenting Recognition Primed Decision Making with Affective Factors

2002 ◽  
Vol 46 (3) ◽  
pp. 303-307 ◽  
Author(s):  
Eva Hudlicka ◽  
Jonathan Pfautz
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Personality Traits ◽  
Empirical Evidence ◽  
Computational Models ◽  
Cognitive Architecture ◽  
Human Perception ◽  
Cognitive Engineering ◽  
Affective States ◽  
Affective Factors

Although quintessentially human, emotions have, until recently, been largely ignored in the human factors cognitive engineering / decision-making area. This is surprising, as extensive empirical evidence indicates that emotions, and personality traits, influence human perception and decision-making. This is particularly the case in crisis situations, when extreme affective states may arise (e.g., anxiety). The development of more complete and realistic theories of human perception and decision-making, and associated computational models, will require the inclusion of personality and affective considerations. In this paper, we propose an augmented version of the recognition-primed decision-making theory, which takes into consideration trait and state effects on decision-making. We describe a cognitive architecture that implements this theory, and a generic methodology for modeling trait and state effects within this architecture. Following an initial prototype demonstration, the full architecture is currently being implemented in the context of a military peacekeeping scenario.

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