scholarly journals Stronger Prejudices Are Associated With Decreased Model-Based Control

2022 ◽  
Vol 12 ◽  
Author(s):  
Miriam Sebold ◽  
Hao Chen ◽  
Aleyna Önal ◽  
Sören Kuitunen-Paul ◽  
Negin Mojtahedzadeh ◽  
...  
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Computational Modeling ◽  
Reaction Times ◽  
Model Based Control ◽  
Model Based ◽  
Relative Dominance ◽  
Model Free ◽  
Decision Making Processes

Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or “automatic” reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making.Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.

scholarly journals Metacontrol of decision-making strategies in human aging

2019 ◽  
Author(s):  
Florian Bolenz ◽  
Wouter Kool ◽  
Andrea M.F. Reiter ◽  
Ben Eppinger
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Cognitive Abilities ◽  
Reduced Model ◽  
Task Structure ◽  
Costs And Benefits ◽  
Sequential Decision ◽  
Model Based Control ◽  
Model Based ◽  
Model Free

When making decisions, humans employ different strategies which are commonly formalized as model-free and model-based reinforcement learning. While previous research has reported reduced model-based control with aging, it remains unclear whether this is due to limited cognitive capacities or a reduced willingness to engage in an effortful strategy. Moreover, it is not clear how aging affects the metacontrol of decision making, i.e. the dynamic adaptation of decision-making strategies to varying situational demands. To this end, we tested younger and older adults in a sequential decision-making task that dissociates model-free and model-based control. In contrast to previous research, in this study we applied a task in which model-based control led to higher payoffs in terms of monetary reward. Moreover, we manipulated the costs and benefits associated with model-based control by varying reward magnitude as well as the stability of the task structure. Compared to younger adults, older adults showed reduced reliance on model-based decision making and less adaptation of decision-making strategies to varying costs and benefits of model-based control. Our findings suggest that aging affects the dynamic metacontrol of decision-making strategies and that reduced model-based control in older adults is due to limited cognitive abilities to represent the structure of the task.

scholarly journals Reduced model-based decision-making in gambling disorder

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Florent Wyckmans ◽  
A. Ross Otto ◽  
Miriam Sebold ◽  
Nathaniel Daw ◽  
Antoine Bechara ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Gambling Disorder ◽  
Reaction Times ◽  
Learning System ◽  
Behavioral Learning ◽  
Model Based ◽  
Model Free ◽  
Effective Interventions ◽  
Compulsive Behaviors

AbstractCompulsive behaviors (e.g., addiction) can be viewed as an aberrant decision process where inflexible reactions automatically evoked by stimuli (habit) take control over decision making to the detriment of a more flexible (goal-oriented) behavioral learning system. These behaviors are thought to arise from learning algorithms known as “model-based” and “model-free” reinforcement learning. Gambling disorder, a form of addiction without the confound of neurotoxic effects of drugs, showed impaired goal-directed control but the way in which problem gamblers (PG) orchestrate model-based and model-free strategies has not been evaluated. Forty-nine PG and 33 healthy participants (CP) completed a two-step sequential choice task for which model-based and model-free learning have distinct and identifiable trial-by-trial learning signatures. The influence of common psychopathological comorbidities on those two forms of learning were investigated. PG showed impaired model-based learning, particularly after unrewarded outcomes. In addition, PG exhibited faster reaction times than CP following unrewarded decisions. Troubled mood, higher impulsivity (i.e., positive and negative urgency) and current and chronic stress reported via questionnaires did not account for those results. These findings demonstrate specific reinforcement learning and decision-making deficits in behavioral addiction that advances our understanding and may be important dimensions for designing effective interventions.

scholarly journals Boosting model-based control in older adults

2019 ◽  
Author(s):  
Edward Patzelt ◽  
Wouter Kool ◽  
Samuel J. Gershman
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Developmental Trajectory ◽  
Incentive Structure ◽  
Extensive Experience ◽  
Older Adulthood ◽  
Model Based Control ◽  
Model Based ◽  
Model Free ◽  
Model Free Control

The tension between habits and plans is reflected in everyday decision-making. Habits are computationally cheap, but fail to flexibly adapt to changes in the environment. Planning is a flexible decision-making strategy, but requires greater resources. Arbitration between habits and plans has been formalized using reinforcement learning algorithms that distinguish between model-free control (habits) and model-based control (plans). Evidence about these two decision-making approaches suggests model-based control follows a developmental trajectory, emerging during adolescence, strengthening during young adulthood, and declining in older adulthood. The normative decline in planning (model-based control) presents the opportunity to develop interventions to increase flexible decision-making. Therefore, we asked if incentives could be used to increase model-based control in older adults. We expected older adults would fail to increase model-based control in response to incentives. This prediction was based upon prior research suggesting older adulthood is associated with deficits in representing and updating the expected value of rewards. Contrary to our expectations, in Experiment 1 we found that incentives could be used to boost model-based control in older adults sampled from an online population. We hypothesized this may be due to previous experience with the task (or with similar tasks). In Experiment 2, a naïve sample of older adults did not boost model-based control in response to incentives. These results suggest that incentives may be a useful intervention to increase model-based planning in older adulthood, but this may require extensive experience with the incentive structure.

scholarly journals Hand interception of occluded motion in humans: a test of model-based vs. on-line control

2015 ◽  
Vol 114 (3) ◽  
pp. 1577-1592 ◽  
Author(s):  
Barbara La Scaleia ◽  
Myrka Zago ◽  
Francesco Lacquaniti
Keyword(s):  
Visual Information ◽  
Target Motion ◽  
Physical Models ◽  
Model Based Control ◽  
Projectile Motion ◽  
Starting Position ◽  
Model Based ◽  
Model Free ◽  
Ball Velocity ◽  
On Line

Two control schemes have been hypothesized for the manual interception of fast visual targets. In the model-free on-line control, extrapolation of target motion is based on continuous visual information, without resorting to physical models. In the model-based control, instead, a prior model of target motion predicts the future spatiotemporal trajectory. To distinguish between the two hypotheses in the case of projectile motion, we asked participants to hit a ball that rolled down an incline at 0.2 g and then fell in air at 1 g along a parabola. By varying starting position, ball velocity and trajectory differed between trials. Motion on the incline was always visible, whereas parabolic motion was either visible or occluded. We found that participants were equally successful at hitting the falling ball in both visible and occluded conditions. Moreover, in different trials the intersection points were distributed along the parabolic trajectories of the ball, indicating that subjects were able to extrapolate an extended segment of the target trajectory. Remarkably, this trend was observed even at the very first repetition of movements. These results are consistent with the hypothesis of model-based control, but not with on-line control. Indeed, ball path and speed during the occlusion could not be extrapolated solely from the kinematic information obtained during the preceding visible phase. The only way to extrapolate ball motion correctly during the occlusion was to assume that the ball would fall under gravity and air drag when hidden from view. Such an assumption had to be derived from prior experience.

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

What if …..? Designing tools to help the public make difficult decisions

2009 ◽  
Vol 17 (3) ◽  
pp. 202-210
Author(s):  
Patricia Wright
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Information Overload ◽  
Decision Aids ◽  
Digital Tools ◽  
Behavior Changes ◽  
Psychological Processes ◽  
The Public ◽  
Decision Making Processes ◽  
Diverse Audience

Information overload results from having plenty of data but not enough time to organize it so that it assists decision making. This paper argues that although digital tools can help people make decisions, their development could benefit from an appreciation of how people’s behavior changes as the display features of the tools change. Therefore advantages could come from greater collaboration between designers and researchers who explore the psychological processes that enable decision making (processes such as search, understanding, inference and memory). Evidence is provided of individual differences in the way decision aids are used, and the value of multimodality information to accommodate diverse audience needs.

scholarly journals No substantial change in the balance between model-free and model-based control via training on the two-step task

2019 ◽  
Vol 15 (11) ◽  
pp. e1007443
Author(s):  
Elmar D. Grosskurth ◽  
Dominik R. Bach ◽  
Marcos Economides ◽  
Quentin J. M. Huys ◽  
Lisa Holper
Keyword(s):  
Model Based Control ◽  
Model Based ◽  
Model Free ◽  
Substantial Change

Individual Differences in Pilot Performance

1976 ◽  
Vol 20 (17) ◽  
pp. 403-409
Author(s):  
Miles R. Murphy
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Pilot Performance ◽  
Research Areas ◽  
Decision Making Processes ◽  
Research Decision ◽  
Glide Slope ◽  
Pilot Behavior

Selected literature on individual differences in pilot performance is reviewed in order to indicate a possible direction for research. Decision-making performance in contingency situations is seen as a potentially fruitful area for study of individual differences, although information on the relative roles of experience and cognitive abilities, styles, and strategies are needed in all research areas. The role of cognitive styles in pilot performance is essentially unexplored; however, the identification of individual pilot behavior differences that have been attributed to style differences and the results of automobile driver behavior research suggest considerable potential. Approaches to studying pilot decision-making processes are discussed, with emphasis given to the wrong-model approach in which accident and incident data, or “process tracing” provide experimental computational structures. Analysis of data from a simulator experiment on V/STOL zero-visibility landing performance suggests that the order of ranking of individual pilot's effectiveness varies with particular situations defined by combinations of tracking requirements (e.g., glide slope, localizer) and glide-slope segment, or speed requirements; the data are being further analyzed.

scholarly journals Arbitration between Action Strategies in Obsessive-Compulsive Disorder

2015 ◽  
Vol 22 (2) ◽  
pp. 188-198 ◽  
Author(s):  
Patricia Gruner ◽  
Alan Anticevic ◽  
Daeyeol Lee ◽  
Christopher Pittenger
Keyword(s):  
Decision Making ◽  
Obsessive Compulsive Disorder ◽  
Behavioral Outcomes ◽  
Cognitive Remediation ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Model Based ◽  
Model Free ◽  
Action Strategies ◽  
Evaluative Processes

Decision making in a complex world, characterized both by predictable regularities and by frequent departures from the norm, requires dynamic switching between rapid habit-like, automatic processes and slower, more flexible evaluative processes. These strategies, formalized as “model-free” and “model-based” reinforcement learning algorithms, respectively, can lead to divergent behavioral outcomes, requiring a mechanism to arbitrate between them in a context-appropriate manner. Recent data suggest that individuals with obsessive-compulsive disorder (OCD) rely excessively on inflexible habit-like decision making during reinforcement-driven learning. We propose that inflexible reliance on habit in OCD may reflect a functional weakness in the mechanism for context-appropriate dynamic arbitration between model-free and model-based decision making. Support for this hypothesis derives from emerging functional imaging findings. A deficit in arbitration in OCD may help reconcile evidence for excessive reliance on habit in rewarded learning tasks with an older literature suggesting inappropriate recruitment of circuitry associated with model-based decision making in unreinforced procedural learning. The hypothesized deficit and corresponding circuitry may be a particularly fruitful target for interventions, including cognitive remediation.

scholarly journals Higher Meta-cognitive Ability Predicts Less Reliance on Over Confident Habitual Learning System

2019 ◽  
Author(s):  
Sara Ershadmanesh ◽  
Mostafa Miandari ◽  
Abdol-hossein Vahabie ◽  
Majid Nili Ahmadabadi
Keyword(s):  
Decision Making ◽  
Cognitive Ability ◽  
Learning Strategies ◽  
Learning Style ◽  
Learning Systems ◽  
Learning System ◽  
Variable Environment ◽  
Free System ◽  
Model Based ◽  
Model Free

AbstractMany studies on human and animals have provided evidence for the contribution of goal-directed and habitual valuation systems in learning and decision-making. These two systems can be modeled using model-based (MB) and model-free (MF) algorithms in Reinforcement Learning (RL) framework. Here, we study the link between the contribution of these two learning systems to behavior and meta-cognitive capabilities. Using computational modeling we showed that in a highly variable environment, where both learning strategies have chance level performances, model-free learning predicts higher confidence in decisions compared to model-based strategy. Our experimental results showed that the subjects’ meta-cognitive ability is negatively correlated with the contribution of model-free system to their behavior while having no correlation with the contribution of model-based system. Over-confidence of the model-free system justifies this counter-intuitive result. This is a new explanation for individual difference in learning style.

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