Population Modelling in Affective Disorders

Abstract Purpose of Review The prevalence of affective disorders is on the rise. This upward trajectory leads to a substantial personal and societal cost. There is growing body of literature demonstrating decision-making impairments associated with affective disorders, and more studies are using computational modelling methods to infer underlying mechanisms of these impairments from participant choice behaviour. However, lack of population modelling suggests that data resources may still be underutilised. Recent Findings A number of recent studies associated major depression with abnormal risky decision-making as well as impairments in temporal discounting and social decision-making. These domains capture relevant aspects of real-life decision-making. Consequently, data from these studies can be used to define behavioural phenotypes for major depression. Summary The manuscript describes a detailed proposal for population modelling to capture changes in the prevalence rate of major depression. The population modelling approach can also identify which decision-making domains can account for a larger part of impairments in psychosocial functioning and how behavioural interventions built on computational principles can target these to improve real-life psychosocial functioning in patient groups.

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Cognitive functioning in ADHD

10.1093/med/9780198739258.003.0010 ◽

2018 ◽

Author(s):

David Coghill ◽

Maggie Toplak ◽

Sinead Rhodes ◽

Nicoletta Adamo

Keyword(s):

Decision Making ◽

Temporal Discounting ◽

Duration Discrimination ◽

Motor Timing ◽

Motor Inhibition ◽

Clinical Perspective ◽

Neurocognitive Deficits ◽

Risky Decision ◽

Neuropsychological Measures ◽

Cognitive Domains

Inhibition, memory, temporal discounting, decision-making, timing, and intraindividual variability in reaction time have emerged as key cognitive domains for understanding neurocognitive deficits in individuals with ADHD. In the domain of inhibition, motor inhibition has been studied most extensively, with deficits demonstrated in both restraint and cancellation. Working memory difficulties have been identified using a broad range of tasks. Decision-making has been less well studied, but risky decision-making and temporal discounting have displayed relatively consistent effects. Motor timing, duration discrimination, duration reproduction, and variability on all of these tasks have also been implicated in ADHD. From a clinical perspective, whilst ADHD is clearly associated with a broad range of neuropsychological deficits, there is considerable heterogeneity and none of these deficits is required or necessary for a diagnosis. However, neuropsychological measures may help define cognitive subgroups within ADHD and these may in turn be useful in predicting course, outcome, and treatment response.

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Do adolescents take more risks? Not when facing a novel uncertain situation

10.1101/206128 ◽

2017 ◽

Author(s):

Éltető Noémi ◽

Janacsek Karolina ◽

Kóbor Andrea ◽

Takács Ádám ◽

Tóth-Fáber Eszter ◽

...

Keyword(s):

Decision Making ◽

Risk Taking ◽

Road Traffic ◽

Age Groups ◽

Real Life ◽

Risky Choice ◽

Conclusive Evidence ◽

Developmental Trajectory ◽

Risky Decision ◽

Age Related

AbstractIn real-life decision-making, sub-optimal risk-taking seems characteristic of adolescents. Such behavior increases the chance of serious negative, and at times, irreversible outcomes for this population (e.g., road traffic accidents, addictions). We are still lacking conclusive evidence, however, for an inverted U-shaped developmental trajectory for risk-taking. This raises the question whether adolescents are really more risk-prone or when facing a novel risky situation, they behave just as children and adults do. To answer this question, we used the Balloon Analogue Risk Task (BART) to assess the risky decision making of 188 individuals ranging in age from 7 to 30. The BART provided useful data for characterizing multiple aspects of risk-taking. Surprisingly, we found that adolescents were not more inclined to take risks than children or young adults. Participants in all age groups were able to adapt their learning processes to the probabilistic environment and improve their performance during the sequential risky choice. There were no age-related differences in risk-taking at any stage of the task. Likewise, neither negative feedback reactivity nor overall task performance distinguished adolescents from the younger and older age groups. Our findings prompt 1) methodological considerations about the validity of the BART and 2) theoretical debate whether the amount of experience on its own may account for age-related changes in real-life risk-taking, since risk-taking in a novel and uncertain situation was invariant across developmental stages.

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Measuring the Zero-Risk Bias

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000284 ◽

2017 ◽

Vol 225 (1) ◽

pp. 31-44 ◽

Cited By ~ 6

Author(s):

Elisabeth Schneider ◽

Bernhard Streicher ◽

Eva Lermer ◽

Rainer Sachs ◽

Dieter Frey

Keyword(s):

Decision Making ◽

Resource Allocation ◽

Psychological Factors ◽

Situational Factors ◽

Dynamic State ◽

Future Research ◽

Gambling Task ◽

Risky Decision ◽

Highly Sensitive ◽

Underlying Mechanisms

Abstract. Uncertainty is a dynamic state that is perceived as discomforting and individuals are highly motivated to reduce these feelings. With regard to risky decision making, people tend to overweigh the value of certainty and opt for zero-risk solutions, even if this results in a less favorable outcome. This phenomenon is referred to as the zero-risk bias and it has been demonstrated in varying contexts and with different methods. However, there is a high variance in the emergence of the bias reported by the existing literature, leaving it unclear to what extent the bias was evoked by the method or whether other psychological factors influenced people’s decision making. Four studies were conducted in order to investigate methodological and situational factors on the bias, comparing its emergence within different task formats (questionnaires vs. behavioral tasks), decision types (forced choice vs. free resource allocation), and different decision domains. Results indicate that the zero-risk bias is persistent over different methods but highly sensitive to contextual factors: abstractness of the task, decision domain, and appropriateness of the zero-risk option. First, its emergence varied between the task formats, in that it was shown more often in abstract than in concrete tasks. Second, participants’ choice of zero-risk did not correlate between different tasks, indicating effects of decision domain. Third, a zero-risk strategy seemed to be appropriate for dividing risks on objects (lottery urns in a gambling task) but not on persons (in a health scenario). In the latter situation, aspects like fairness influenced choice. Future research is needed to explore the relation between these factors and identify their underlying mechanisms.

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Decision-Making

10.1093/oso/9780197512494.003.0008 ◽

2021 ◽

pp. 104-117

Author(s):

Itay Basevitch ◽

Gershon Tenenbaum

Keyword(s):

Decision Making ◽

Coping Strategies ◽

Real World ◽

Self Regulation ◽

Real Life ◽

Neurophysiological Mechanisms ◽

Underlying Mechanisms ◽

And Coping ◽

World Environment

Decision-making (DM) has been studied from two main perspectives: cognitive and ecological. Findings indicate that experts have advanced DM skills that enhance performance. The underlying mechanisms of DM skills relate to the attention and anticipation capacities to function without interruption under pressure of time and to counter various sources of stress (e.g., self-regulation and coping strategies). There are still many questions that must be addressed to fully account for the DM process and apply the findings in a real-world environment. The most urgent questions relate to the neurophysiological mechanisms underlying DM, team DM processes, training and measuring DM, making creative decisions, and comprehending the process of coaches’ DM during competitive conditions and other real-life situations.

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Risk-Taking Unmasked: Using Risky Choice and Temporal Discounting to Explain COVID-19 Preventative Behaviors

10.31234/osf.io/uaqc2 ◽

2020 ◽

Author(s):

Kaileigh A. Byrne ◽

Stephanie Gabrielle Six ◽

Reza Ghaiumy Anaraky ◽

Maggie W. Harris ◽

Emma L. Winterlind

Keyword(s):

Decision Making ◽

Individual Differences ◽

Temporal Discounting ◽

The United States ◽

Risky Choice ◽

Risky Decision Making ◽

Risky Decision ◽

Social Distancing ◽

Prevention Guidelines ◽

Decision Making Behavior

To reduce the spread of COVID-19 transmission, government agencies in the United States (US) have recommended COVID prevention guidelines, including wearing masks and social distancing. However, compliance with these guidelines have been inconsistent. This study examined whether individual differences in decision-making and motivational propensities predicted compliance with COVID-19 preventative behaviors in a representative sample of US adults (N=225). Participants completed an online study in September 2020 that included a risky choice decision-making task, temporal discounting task, and measures of appropriate mask wearing, social distancing, and perceived risk of engaging in public activities. Linear regression results indicated that greater risky decision-making behavior and temporal discounting were associated with less appropriate mask-wearing behavior and social distancing. Additionally, demographic factors, including political affiliation and income level, were also associated with differences in COVID-19 preventative behaviors. Path analysis results showed that risky decision-making behavior, temporal discounting, and risk perception collectively predicted 61% of the variance in appropriate mask-wearing behavior. Individual differences in general decision-making patterns are therefore highly predictive of who complies with COVID-19 prevention guidelines.

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Assessing a Perceptual Model of Risky Real-Time Decision Making

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129604000417 ◽

1996 ◽

Vol 40 (4) ◽

pp. 223-227 ◽

Cited By ~ 1

Author(s):

Joshua B. Hurwitz

Keyword(s):

Decision Making ◽

Real Time ◽

Risk Taking ◽

Real Life ◽

Decision Making Under Risk ◽

Risky Decision ◽

Automobile Accidents ◽

Perceptual Model ◽

Objective Risk ◽

Risk Acceptance

A new model of real-time risky decision making is introduced that predicts tradeoffs between processing and risk taking during driving. This model, called Decision-Making under Risk in a Vehicle, or DRIVE, was fitted to data from a task in which subjects decided when to cross an intersection as a car approached from the cross street. Results showed that subjects attempted to cross less often before the oncoming car when it started closer to the intersection, even though objective risk was the same regardless of starting distance. Also, when the car started closer, subjects who reported having more real-life automobile accidents were less likely to take advantage of a longer opportunity to cross first. These results, along with results from fitting DRIVE to the data, suggest that risk-taking effects can be accounted for by a model of risk perception, and not by a model of risk acceptance.

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Risk-taking unmasked: Using risky choice and temporal discounting to explain COVID-19 preventative behaviors

PLoS ONE ◽

10.1371/journal.pone.0251073 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251073

Author(s):

Kaileigh A. Byrne ◽

Stephanie G. Six ◽

Reza Ghaiumy Anaraky ◽

Maggie W. Harris ◽

Emma L. Winterlind

Keyword(s):

Decision Making ◽

Individual Differences ◽

Perceived Risk ◽

Temporal Discounting ◽

The United States ◽

Risky Choice ◽

Risky Decision Making ◽

Risky Decision ◽

Social Distancing ◽

Online Study

To reduce the spread of COVID-19 transmission, government agencies in the United States (US) recommended precautionary guidelines, including wearing masks and social distancing to encourage the prevention of the disease. However, compliance with these guidelines has been inconsistent. This correlational study examined whether individual differences in risky decision-making and motivational propensities predicted compliance with COVID-19 preventative behaviors in a sample of US adults (N = 404). Participants completed an online study from September through December 2020 that included a risky choice decision-making task, temporal discounting task, and measures of appropriate mask-wearing, social distancing, and perceived risk of engaging in public activities. Linear regression results indicated that greater temporal discounting and risky decision-making were associated with less appropriate mask-wearing behavior and social distancing. Additionally, demographic factors, including personal experience with COVID-19 and financial difficulties due to COVID-19, were also associated with differences in COVID-19 preventative behaviors. Path analysis results showed that risky decision-making behavior, temporal discounting, and risk perception collectively predicted 55% of the variance in appropriate mask-wearing behavior. Individual differences in general decision-making patterns are therefore highly predictive of who complies with COVID-19 prevention guidelines.

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Risk taking for potential losses but not gains increases with time of day

10.31234/osf.io/3qdnx ◽

2020 ◽

Author(s):

Rachel Bedder ◽

Matilde M. Vaghi ◽

Raymond J Dolan ◽

Robb Rutledge

Keyword(s):

Decision Making ◽

Risk Taking ◽

Real Life ◽

Computational Modelling ◽

Time Of Day ◽

Medical Decision ◽

Fine Grained ◽

Potential Losses ◽

And Gender ◽

Gains And Losses

Humans exhibit distinct risk preferences when facing choices involving potential gains and losses. These preferences are subject to neuromodulatory influence, particularly from dopamine and serotonin. As these neuromodulators manifest distinct circadian rhythms, this suggests decision making under risk might be affected by time of day. Here, in a large subject sample (N = 26,899), we tested the hypothesis of a diurnal modulation in risk taking for gains and losses. We found that risky options with potential losses were increasingly chosen over the course of the day, but observed no such change for how often risky options with only potential gains were chosen. Using a computational modelling approach to obtain a more fine-grained account, we show this diurnal change in risk preference reflects a decrease in sensitivity to increasing losses, but no change in the relative impacts of gains and losses on choice. This diurnal sensitivity, present across two different task designs, was robust to between- and within-subject analysis, to country (i.e., UK and US samples), age, and gender. Thus, our findings reveal a striking diurnal modulation in human decision making, a pattern with potential importance for real-life preferences that include voting, medical decision making, and global stock market investments.

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